The document discusses design for reliability (DFR) topics including the need for DFR, the DFR process, terminology, Weibull plotting, system reliability, DFR testing, and accelerated testing. It provides details on the DFR process, common reliability terminology such as reliability, failure rate, mean time to failure, and the bathtub curve. It also explains the exponential distribution and Weibull plotting, which are important reliability analysis tools.
Design for reliability (DFR) is an industry-wide practice and a philosophy of considering reliability in an early stage of product design and development, to achieve a highly-reliable product while with sustainable cost. Physical of Failure (PoF) is recognized as a key approach of implementing DFR in a product design and development process. The author will present a case study to illustrate predicting and identifying product failure early in the design phase with the help of a quantitative PoF model based analysis tool.
Accelerated life testing plans are designed under multiple objective consideration, with the resulting Pareto optimal solutions classified and reduced using neural network and data envelopement analysis, respectively.
The document discusses potential issues with using MTBF/MTTF as the primary reliability metric for the defense and aerospace industries. It argues that MTBF/MTTF provides an incomplete view of reliability across the entire product lifecycle and can result in overly optimistic assessments. The document proposes using an alternative metric called Bx/Lx, which specifies the life point where no more than a certain percentage (like 10%) of failures have occurred. This provides a more comprehensive view of reliability focused on early failures. Overall, the document advocates updating reliability metrics and practices to better reflect physical failure mechanisms.
Physics of Failure (also known as Reliability Physics) is a science-based approach for achieving Reliability by Design. The approach is based on research to identify and understand the processes that initiate and propagate mechanisms that ultimately results in failure. This knowledge when used in Computer Aided Engineering (CAE) durability simulations and reliability assessment can evaluate if a new design, under actual operating is susceptible to the root causes of failure such as fatigue, fracture, wear, and corrosion during the intended service life of the product.
The objective is to identify and eliminate potential failure mechanisms in order to prevent operational failures through stress-strength analysis to produce a robust design and aid in the selection of capable manufacturing practices. This is accomplished by modeling the material strength and architecture of the components and technologies a product is based upon to evaluating their ability to endure the life-cycle usage and environmental stress conditions the product is expected to encounter over its service life in the field or during durability or reliability qualification tests.
The ability to identify and quantify the specific hazard risks timeline of specifics failure risks in a new product while it is still on the drawing board (or CAD screen) enables a product team to design reliability into a product by revising the design to eliminate or mitigate failure risks. This capability results in a form of Virtual Validation and Virtual Reliability Growth during the a product’s design phase that can be implemented faster and at lower costs than the traditional Design-Build-Test-Fixed approach to Reliability Growth during a product’s development and test phase.
This webinar compares classical reliability concepts and relates them to the PoF approach as applied to Electrical/Electronic (E/E) System and technologies. This webinar is intended for E/E Product Engineers, Validation/Test Engineers, Quality, Reliability and Product Assurance Personnel, CAE Modeling Analysts, R&D Staff and their supervisor.
This document summarizes a training presentation on building reliability into designs. It discusses the business case for reliability, including asset growth through doing more with less, achieving operability targets, and reducing life cycle costs. It then covers strategies like assessing common equipment reliability, conducting a robust strategic phase for projects, and following the capital project process and gates. Finally, it outlines developing a reliability program plan to identify the reliability tools and methods that will be applied to ensure the design meets reliability requirements.
Achieving high product reliability has become increasingly vital for manufacturers in order to meet customer expectations amid the threat of strong global competition. Poor reliability can doom a product and jeopardize the reputation of a brand or company. Inadequate reliability also presents financial risks from warranty, product recalls, and potential litigation. When developing new products, it is imperative that manufacturers develop reliability specifications and utilize methods to predict and verify that those reliability specifications will be met. This 4-Hour course provides an overview of quantitative methods for predicting product reliability from data gathered from physical testing or from field data
This is a three parts lecture series. The parts will cover the basics and fundamentals of reliability engineering. Part 1 begins with introduction of reliability definition and other reliability characteristics and measurements. It will be followed by reliability calculation, estimation of failure rates and understanding of the implications of failure rates on system maintenance and replacements in Part 2. Then Part 3 will cover the most important and practical failure time distributions and how to obtain the parameters of the distributions and interpretations of these parameters. Hands-on computations of the failure rates and the estimation of the failure time distribution parameters will be conducted using standard Microsoft Excel.
Part 2. Reliability Calculations
1.Use of failure data
2.Density functions
3.Reliability function
4.Hazard and failure rates
This is a three parts lecture series. The parts will cover the basics and fundamentals of reliability engineering. Part 1 begins with introduction of reliability definition and other reliability characteristics and measurements. It will be followed by reliability calculation, estimation of failure rates and understanding of the implications of failure rates on system maintenance and replacements in Part 2. Then Part 3 will cover the most important and practical failure time distributions and how to obtain the parameters of the distributions and interpretations of these parameters. Hands-on computations of the failure rates and the estimation of the failure time distribution parameters will be conducted using standard Microsoft Excel.
Part 3. Failure Time Distributions
1.Constant failure rate distributions
2.Increasing failure rate distributions
3.Decreasing failure rate distributions
4.Weibull Analysis – Why use Weibull?
Design for reliability (DFR) is an industry-wide practice and a philosophy of considering reliability in an early stage of product design and development, to achieve a highly-reliable product while with sustainable cost. Physical of Failure (PoF) is recognized as a key approach of implementing DFR in a product design and development process. The author will present a case study to illustrate predicting and identifying product failure early in the design phase with the help of a quantitative PoF model based analysis tool.
Accelerated life testing plans are designed under multiple objective consideration, with the resulting Pareto optimal solutions classified and reduced using neural network and data envelopement analysis, respectively.
The document discusses potential issues with using MTBF/MTTF as the primary reliability metric for the defense and aerospace industries. It argues that MTBF/MTTF provides an incomplete view of reliability across the entire product lifecycle and can result in overly optimistic assessments. The document proposes using an alternative metric called Bx/Lx, which specifies the life point where no more than a certain percentage (like 10%) of failures have occurred. This provides a more comprehensive view of reliability focused on early failures. Overall, the document advocates updating reliability metrics and practices to better reflect physical failure mechanisms.
Physics of Failure (also known as Reliability Physics) is a science-based approach for achieving Reliability by Design. The approach is based on research to identify and understand the processes that initiate and propagate mechanisms that ultimately results in failure. This knowledge when used in Computer Aided Engineering (CAE) durability simulations and reliability assessment can evaluate if a new design, under actual operating is susceptible to the root causes of failure such as fatigue, fracture, wear, and corrosion during the intended service life of the product.
The objective is to identify and eliminate potential failure mechanisms in order to prevent operational failures through stress-strength analysis to produce a robust design and aid in the selection of capable manufacturing practices. This is accomplished by modeling the material strength and architecture of the components and technologies a product is based upon to evaluating their ability to endure the life-cycle usage and environmental stress conditions the product is expected to encounter over its service life in the field or during durability or reliability qualification tests.
The ability to identify and quantify the specific hazard risks timeline of specifics failure risks in a new product while it is still on the drawing board (or CAD screen) enables a product team to design reliability into a product by revising the design to eliminate or mitigate failure risks. This capability results in a form of Virtual Validation and Virtual Reliability Growth during the a product’s design phase that can be implemented faster and at lower costs than the traditional Design-Build-Test-Fixed approach to Reliability Growth during a product’s development and test phase.
This webinar compares classical reliability concepts and relates them to the PoF approach as applied to Electrical/Electronic (E/E) System and technologies. This webinar is intended for E/E Product Engineers, Validation/Test Engineers, Quality, Reliability and Product Assurance Personnel, CAE Modeling Analysts, R&D Staff and their supervisor.
This document summarizes a training presentation on building reliability into designs. It discusses the business case for reliability, including asset growth through doing more with less, achieving operability targets, and reducing life cycle costs. It then covers strategies like assessing common equipment reliability, conducting a robust strategic phase for projects, and following the capital project process and gates. Finally, it outlines developing a reliability program plan to identify the reliability tools and methods that will be applied to ensure the design meets reliability requirements.
Achieving high product reliability has become increasingly vital for manufacturers in order to meet customer expectations amid the threat of strong global competition. Poor reliability can doom a product and jeopardize the reputation of a brand or company. Inadequate reliability also presents financial risks from warranty, product recalls, and potential litigation. When developing new products, it is imperative that manufacturers develop reliability specifications and utilize methods to predict and verify that those reliability specifications will be met. This 4-Hour course provides an overview of quantitative methods for predicting product reliability from data gathered from physical testing or from field data
This is a three parts lecture series. The parts will cover the basics and fundamentals of reliability engineering. Part 1 begins with introduction of reliability definition and other reliability characteristics and measurements. It will be followed by reliability calculation, estimation of failure rates and understanding of the implications of failure rates on system maintenance and replacements in Part 2. Then Part 3 will cover the most important and practical failure time distributions and how to obtain the parameters of the distributions and interpretations of these parameters. Hands-on computations of the failure rates and the estimation of the failure time distribution parameters will be conducted using standard Microsoft Excel.
Part 2. Reliability Calculations
1.Use of failure data
2.Density functions
3.Reliability function
4.Hazard and failure rates
This is a three parts lecture series. The parts will cover the basics and fundamentals of reliability engineering. Part 1 begins with introduction of reliability definition and other reliability characteristics and measurements. It will be followed by reliability calculation, estimation of failure rates and understanding of the implications of failure rates on system maintenance and replacements in Part 2. Then Part 3 will cover the most important and practical failure time distributions and how to obtain the parameters of the distributions and interpretations of these parameters. Hands-on computations of the failure rates and the estimation of the failure time distribution parameters will be conducted using standard Microsoft Excel.
Part 3. Failure Time Distributions
1.Constant failure rate distributions
2.Increasing failure rate distributions
3.Decreasing failure rate distributions
4.Weibull Analysis – Why use Weibull?
Weibull Analysis is an important tool for Reliability Engineering. It can be used verifying the design life at component level, comparing two designs and warranty analysis.
Reliability is defined as the ability of a product to perform as expected over time, and is formally defined as the probability that a product performs its intended function for a stated period of time under specified operating conditions. Maintainability is the probability that a system or product can be retained in or restored to operating condition within a specified time. There are two types of failures - functional failures that occur early due to defects, and reliability failures that occur after some period of use. Reliability can be inherent in a product's design or achieved based on observed performance. Reliability is measured through metrics like failure rate, mean time to failure, and mean time between failures.
The document discusses various techniques for designing products for reliability, including derating components, accelerated life testing, and reliability estimation methods. It describes how reliability modeling should guide the design process from the beginning to design out potential failure mechanisms. The goal is to develop longer-lived products through an iterative approach of testing, analyzing failures, and redesigning to improve reliability. Key aspects of a reliability-focused design process include understanding failure mechanisms, developing reliability databases, and using super-accelerated life testing techniques.
This document discusses Weibull analysis, which is commonly used in reliability engineering. The Weibull distribution can take on many shapes depending on the value of the β parameter. Weibull analysis is useful for mechanical reliability due to its versatility. The document defines the Weibull probability density function and describes how it is used to derive reliability metrics like failure rate and mean time to failure. Examples are provided to demonstrate how Weibull analysis can be used to determine failure percentages and mean time to failure for products.
Reliability engineering involves understanding failure mechanisms and predicting when failures will occur using various analytical tools and testing. The field draws on multiple engineering disciplines to optimize system dependability cost-effectively. Key aspects of reliability engineering include determining what components will fail and when, using tools like failure mode and effects analysis. Reliability engineers also need strong interpersonal skills to work effectively with design teams and influence decisions while providing technical information.
This is a three parts lecture series. The parts will cover the basics and fundamentals of reliability engineering. Part 1 begins with introduction of reliability definition and other reliability characteristics and measurements. It will be followed by reliability calculation, estimation of failure rates and understanding of the implications of failure rates on system maintenance and replacements in Part 2. Then Part 3 will cover the most important and practical failure time distributions and how to obtain the parameters of the distributions and interpretations of these parameters. Hands-on computations of the failure rates and the estimation of the failure time distribution parameters will be conducted using standard Microsoft Excel.
Part 1. Reliability Definitions
1.Reliability---Time dependent characteristic
2.Failure rate
3.Mean Time to Failure
4.Availability
5.Mean residual life
This standard defines methods for calculating the early life failure rate of a product, using accelerated
testing, whose failure rate is constant or decreasing over time. For technologies where there is adequate
field failure data, alternative methods may be used to establish the early life failure rate.
The purpose of this standard is to define a procedure for performing measurement and calculation of early
life failure rates. Projections can be used to compare reliability performance with objectives, provide line
feedback, support service cost estimates, and set product test and screen strategies to ensure that the
Early life Failure Rate meets customers' requirements.
With the increase in global competition, more and more costumers consider reliability as one of their primary deciding factors, when purchasing new products. Several companies have invested in developing their own Design for Reliability (DFR) processes and roadmaps in order to be able to meet those requirements and compete in today’s market. This presentation will describe the DFR roadmap and how to effectively use it to ensure the success of the reliability program by focusing on the following DFR elements.
HALT is not just “shake and bake” but a test philosophy, we look at the stressors and the level of overstress used to obtain successful results in a wide variety of products. Modulated Excitation™ is offered as the key to intermittent failure detection; a true breakthrough for “no fault found” field returns. Finally latent failures from vibration are “developed” to where they are patent (visible to test) using moisture to complete the art failure detection.
Authors: (i) Prashanth Lakshmi Narasimhan,
(ii) Mukesh Ravichandran
Industry: Automobile -Auto Ancillary Equipment ( Turbocharger)
This was presented after the completion of our 2 months internship at Turbo Energy Limited during our 3rd Year Summer holidays (2013)
This document summarizes a presentation given by Fred Schenkelberg at the Applied Reliability Symposium in San Diego, California in 2007. The presentation discusses how MTBF (Mean Time Between Failure) is a poor metric for reliability and promotes using other metrics like MTTF (Mean Time To Failure) instead. It covers how MTBF is calculated, issues with only looking at the mean time, and how failure distributions and models and other factors like cost should be considered for a better understanding of reliability.
The document contains KPI data for various sites including E-MTBF (Electrical Mean Time Between Failure), MTBBF (Mean Time Between Board Failure), and uptime for Fusion and Synchro equipment. Charts are presented showing the data for each metric across sites. The PSK site combines data for Synchro AC and CPG equipment. Overall equipment reliability as measured by MTBF, MTBBF, and uptime is reported.
Weibull Analysis is an important tool for Reliability Engineering. It can be used verifying the design life at component level, comparing two designs and warranty analysis.
Reliability is defined as the ability of a product to perform as expected over time, and is formally defined as the probability that a product performs its intended function for a stated period of time under specified operating conditions. Maintainability is the probability that a system or product can be retained in or restored to operating condition within a specified time. There are two types of failures - functional failures that occur early due to defects, and reliability failures that occur after some period of use. Reliability can be inherent in a product's design or achieved based on observed performance. Reliability is measured through metrics like failure rate, mean time to failure, and mean time between failures.
The document discusses various techniques for designing products for reliability, including derating components, accelerated life testing, and reliability estimation methods. It describes how reliability modeling should guide the design process from the beginning to design out potential failure mechanisms. The goal is to develop longer-lived products through an iterative approach of testing, analyzing failures, and redesigning to improve reliability. Key aspects of a reliability-focused design process include understanding failure mechanisms, developing reliability databases, and using super-accelerated life testing techniques.
This document discusses Weibull analysis, which is commonly used in reliability engineering. The Weibull distribution can take on many shapes depending on the value of the β parameter. Weibull analysis is useful for mechanical reliability due to its versatility. The document defines the Weibull probability density function and describes how it is used to derive reliability metrics like failure rate and mean time to failure. Examples are provided to demonstrate how Weibull analysis can be used to determine failure percentages and mean time to failure for products.
Reliability engineering involves understanding failure mechanisms and predicting when failures will occur using various analytical tools and testing. The field draws on multiple engineering disciplines to optimize system dependability cost-effectively. Key aspects of reliability engineering include determining what components will fail and when, using tools like failure mode and effects analysis. Reliability engineers also need strong interpersonal skills to work effectively with design teams and influence decisions while providing technical information.
This is a three parts lecture series. The parts will cover the basics and fundamentals of reliability engineering. Part 1 begins with introduction of reliability definition and other reliability characteristics and measurements. It will be followed by reliability calculation, estimation of failure rates and understanding of the implications of failure rates on system maintenance and replacements in Part 2. Then Part 3 will cover the most important and practical failure time distributions and how to obtain the parameters of the distributions and interpretations of these parameters. Hands-on computations of the failure rates and the estimation of the failure time distribution parameters will be conducted using standard Microsoft Excel.
Part 1. Reliability Definitions
1.Reliability---Time dependent characteristic
2.Failure rate
3.Mean Time to Failure
4.Availability
5.Mean residual life
This standard defines methods for calculating the early life failure rate of a product, using accelerated
testing, whose failure rate is constant or decreasing over time. For technologies where there is adequate
field failure data, alternative methods may be used to establish the early life failure rate.
The purpose of this standard is to define a procedure for performing measurement and calculation of early
life failure rates. Projections can be used to compare reliability performance with objectives, provide line
feedback, support service cost estimates, and set product test and screen strategies to ensure that the
Early life Failure Rate meets customers' requirements.
With the increase in global competition, more and more costumers consider reliability as one of their primary deciding factors, when purchasing new products. Several companies have invested in developing their own Design for Reliability (DFR) processes and roadmaps in order to be able to meet those requirements and compete in today’s market. This presentation will describe the DFR roadmap and how to effectively use it to ensure the success of the reliability program by focusing on the following DFR elements.
HALT is not just “shake and bake” but a test philosophy, we look at the stressors and the level of overstress used to obtain successful results in a wide variety of products. Modulated Excitation™ is offered as the key to intermittent failure detection; a true breakthrough for “no fault found” field returns. Finally latent failures from vibration are “developed” to where they are patent (visible to test) using moisture to complete the art failure detection.
Authors: (i) Prashanth Lakshmi Narasimhan,
(ii) Mukesh Ravichandran
Industry: Automobile -Auto Ancillary Equipment ( Turbocharger)
This was presented after the completion of our 2 months internship at Turbo Energy Limited during our 3rd Year Summer holidays (2013)
This document summarizes a presentation given by Fred Schenkelberg at the Applied Reliability Symposium in San Diego, California in 2007. The presentation discusses how MTBF (Mean Time Between Failure) is a poor metric for reliability and promotes using other metrics like MTTF (Mean Time To Failure) instead. It covers how MTBF is calculated, issues with only looking at the mean time, and how failure distributions and models and other factors like cost should be considered for a better understanding of reliability.
The document contains KPI data for various sites including E-MTBF (Electrical Mean Time Between Failure), MTBBF (Mean Time Between Board Failure), and uptime for Fusion and Synchro equipment. Charts are presented showing the data for each metric across sites. The PSK site combines data for Synchro AC and CPG equipment. Overall equipment reliability as measured by MTBF, MTBBF, and uptime is reported.
MTBF is a common metric among practitioners and users of reliability prediction, safety assurance, and maintenance planning. However, there are a number of significant flaws and limitations with this approach. This presentation goes through those limitations and uses that information to suggest alternatives that may provide much greater insight into product performance.
امروزه ویدئو دیتا پروژکتور در موقعیتهای مختلفی ، به کمک کاربران شتافته و با بالا بردن بهره وری آموزش ، جلسات و سمینارها نقش بسزایی در ارتقاء کیفی اینگونه گردهمایی ها داشته است .ذیلا به پاره ای از مصارف دیتاپروژکتور اشاره می شود :
۱-کلاس های درس از دبستان تا دانشگاه (همیشه یک تصویر گویاتر و موثرتر از هزاران واژه و کلمه میباشد . بدیهی است که آموزش برپایه تصویر می تواند حتی در مقاطع پایین نظام آموزشی بسیار موثر واقع شود)
۲-آموزشگاههای خصوصی و نیمه خصوصی
۳- اتاق جلسات و کنفرانس مدیران (که در آن انواع جلسات دمو و پرزنت انجام می گیرد)
۴- نمایشگاهها و شوروم های شرکتهای خصوصی و صنعتی (به جهت پخش فایلهای تبلیغاتی در ابعاد بزرگ)
۵-بکارگیری از دیتا پروژکتور در سالنهای همایش و آمفی تاتر
۶- سینما ها
۷-مدیران و کارشناسان شرکت های مهندسی مشاور
۸-استفاده از ویدیو دیتا پروژکتور در سینمای خانگی
گستردگی جغرافیایی کشورها از یکسو، کمبود نیروی انسانی متخصص در علوم مختلف و افزایش هزینههای کاری از سوی دیگر، منجر به عدم دسترسی سازمانها و شرکتها به همة منابع مورد نیاز شده است.
ویدئوکنفرانس یک فناوری منحصر به فرد است که برقراری ارتباط صوتی و تصویری (به صورت زنده) افراد را در مکانهای مختلف با فواصل مختلف امکانپذیر مینماید.
هزینههای سرسامآور جابجایی اساتید، متخصصین و مدیران مجموعهها برای برگزاری نشستهای گوناگون به صورت هزینههای آشکار و نیز از دستدادن بخش قابل توجهی از زمان، نیرو و بازده کاری و فکری این افراد، به عنوان هزینههای پنهان، نیاز بسیاری را برای به کارگیری از فناوریهای مدرن ارتباطی به خصوص ویدئوکنفرانس ایجاد کرده است.
در کنار امکانات ارتباطی ویدئوکنفرانس، با بهرهگیری از این سیستم میتوانید در یک زمان واحد در چندین مکان حضور داشته باشید. امکانی که تنها با استفاده از این تکنولوژی میسر خواهد بود.
DISCUS DFM focuses on characteristic management at an earlier stage in the product lifecycle when a manufacturing engineer is analyzing the detailed design of the part. In fact, by helping to define the applicable specs and annotations to include on the design, DISCUS DFM can actually assist with the definition of the Technical Data Package (TDP).
DISCUS DFM picks up where today’s leading CAD tools leave off by empowering the product team to consider the key considerations for manufacturing the part. An overview of the flow:
You start DISCUS by opening the native 3D CAD model in the model/drawing panel.
DISCUS will automatically review the model and its associated PMI and add the balloons to the model and the rows in the Bill of Characteristics.
You select the appropriate part family and likely list of manufacturing processes to consider for fabricating the part.
At this point, DISCUS DFM enables you to evaluate the part DFM by applying rules associated with the part’s features and characteristics versus the likely manufacturing processes.
The evaluation of the part against the integrated manufacturing knowledgebase results in a list of pertinent DFM constraints, recommended annotations/PMI for the part, and more.
When you're completed the analysis of the model, you can export the DFM data for review with the DFM engineer or the entire Integrated Product Team.
With DISCUS DFM, you consistently and correctly add the vital details to the design, giving you the ability to manufacture the new part right the first time. DISCUS DFM is the tool to improve the quality and productivity of your engineers.
Application of Survival Data Analysis- Introduction and Discussion (存活数据分析及应用- 简介和讨论), will give an overview of survival data analysis, including parametric and non-parametric approaches and proportional hazard model, providing a real life example of survival data-based field return analysis. Several common issues in survival data analysis will also be discussed.
This PPT is a preview on my recent DFM Handbook-“ Taoist Directions for Design & Development “- targeted to Design Engineering Professionals ,Industries and Institutions .I am offering FREE on-line Consultancy on my ‘Tao of DFM’ .For on-line consultancy as well as detailed implementations please email to erramalingam.ks@gmail.com
Please visit www.dfmablog.com and www.dfmhandbook.com
Er Ramalingam DFM & Innovation Consultant
Chennai -90 INDIA
FRACAS: A method of analyzing the failure codes assigned to the individual work orders and identifying common themes and trends. The root cause of the high impact items are determined, with a corrective action identified and executed to prevent reoccurrence of the issue.
This document discusses maintenance and methods for tracking losses and overcoming losses through proper maintenance activities. It defines maintenance as actions to retain or restore equipment to its maximum useful life. The three main types of maintenance are preventive, breakdown, and corrective. Preventive maintenance includes periodic and predictive maintenance. Periodic maintenance involves spare part replacement on a predefined schedule, while predictive maintenance uses equipment like bearing meters to determine condition-based maintenance. Metrics like mean time between failure (MTBF) and mean time to repair (MTTR) are discussed to measure equipment reliability and maintainability. Uptime is also defined as a percentage measure of up-time without downtime.
The document discusses MTBF (mean time between failures), including how to calculate, predict, and test it. It addresses common misconceptions about MTBF and describes a two-day training plan that covers the basics of MTBF as well as how to analyze MTBF reports and predictions. The training provides answers to questions and considers reliability modeling techniques to estimate component and system-level MTBF.
This document provides an introduction to survival data analysis. It discusses key concepts like censoring, where the event of interest is not observed for some subjects due to other events. Right censoring is common, where the event was not observed by the end of the study. Conditioning is also important, where the risk of an event changes over time based on a subject's survival up to that point. Basic notation is introduced, including the survival function, hazard function, and density function for modeling event times. The document outlines topics like parametric and non-parametric models, regression methods, and complications in survival analysis.
MTBF / MTTR - Energized Work TekTalk, Mar 2012Energized Work
The document discusses metrics for measuring system availability and reliability such as MTBF, MTTR, and various "nines" of availability. It notes that while increasing availability is important, reducing recovery time after failures through measures like redundant systems, data replication, and disaster recovery plans is also crucial. The key metrics are recovery time objective (RTO) and recovery point objective (RPO) which specify how long a system can be down and how much data can be lost respectively. The document concludes that the right approach depends on each system's specific requirements and that failures will inevitably occur, so the focus should be on rapid recovery.
The document discusses key principles of design for manufacturing (DFM) including minimizing part count, using standard components and materials, designing for tolerances, collaborating with manufacturing, and understanding production processes and costs. It emphasizes reducing costs at each stage of production from components to assembly to overhead. Designs should be optimized through an iterative process of cost analysis and redesign while considering production volumes and other factors.
This seminar session provides an overview of major aspects of reliability engineering, including general introduction of reliability engineering (definition of reliability, function of reliability engineering, a brief history of reliability, etc.), reliability basics (metrics used in reliability, commonly-used probability distributions in reliability, bathtub curve, reliability demonstration test planning, confidence intervals, Bayesian statistics application in reliability, strength-stress interference theory, etc.), accelerated life testing (ALT) (types of ALT, Arrhenius model, inverse power law model, Eyring model, temperature-humidity model, etc.), reliability growth (reliability-based growth models, MTBF-based growth model, etc.), systems reliability & availability (reliability block diagram, non-repairable or repairable systems, reliability modeling of series systems, parallel systems, standby systems, and complex systems, load sharing reliability, reliability allocation, system availability, Monte Carlo simulation, etc.), and degradation-based reliability (introduction of degradation-based reliability, difference between traditional reliability and degradation-based reliability, etc.).
This document discusses metastability, mean time between failures (MTBF), synchronizers, and synchronizer failures. It begins with introductions to metastability and cases where it can occur. It then illustrates metastability with diagrams and graphs. It discusses how systems enter metastability and what occurs during metastability. The document derives the MTBF equation and provides an example calculation. It concludes by listing references for further information.
Fault tolerance refers to a system's ability to continue operating correctly even if some components fail. There are three categories of faults: transient, intermittent, and permanent. Fault tolerance is achieved through redundancy, including information, time, and physical redundancy. Reliability is the probability a system will function as intended for a given time. It depends on design, components, and environment. Reliability increases through quality control and redundancy. Maintainability is the probability a failed system can be repaired within a time limit. Availability is the probability a system will be operational when needed. Series systems fail if any component fails, while parallel systems fail only if all components fail.
This document provides an overview of survival analysis. It defines key terms like survival, censoring, and hazard functions. It describes the Kaplan-Meier method for estimating survival functions from censored data and comparing survival curves between groups using the log-rank test. Censoring occurs when subjects are lost to follow-up before the event of interest. The Kaplan-Meier method accounts for censoring to calculate the probability of surviving up to different time points.
Design for Assembly (DFA) is a vital component of concurrent engineering – the multidisciplinary approach to product development. You might think it strange to begin by thinking about the assembly before you have designed all the components, but you can often eliminate many parts at the conceptual stage, and save yourself a lot of trouble.
This slideshow provides an introduction to the rules that are used in industry to produce affordable, reliable products. It includes the in-depth analysis of two real-world products subjected to a "product autopsy", detailed in photographs, plus tutor notes and recommendations for additional activities including an assembly game.
+++
Thanks for all the interest shown in this presentation... visit Capacify and leave me a message if you have any questions or comments. Also let me know if you'd like to have me as a guest speaker: the in-class 'ease of assembly game' is always fun.
The document discusses Source2VALUE, a software solution called CARE that provides computer aided redocumentation and evaluation of source code. CARE analyzes source code to provide metrics, detect clones, check standards and guidelines, and generate documentation to help reduce software maintenance costs by 15-25%. CARE is meant for organizations that outsource development and have over 100,000 lines of code, providing transparency and insights into the software to help with cost control, quality and risk assessment. The solution supports various programming languages and provides functionality like merge and diff analyses, cross references, and filtering.
The document discusses Source2VALUE, a software solution called CARE that provides computer aided redocumentation and evaluation of source code. CARE analyzes source code to provide metrics, detect changes, clones, and violations of standards and guidelines. It generates documentation and reports to help reduce software maintenance costs, improve transparency, and support auditing. A demo of the CARE approach and Source2VALUE portal is then presented.
CMMI High Maturity Best Practices HMBP 2010: Demystifying High Maturity Imple...QAI
Demystifying High Maturity Implementation Using Statistical Tools & Techniques
-Sreenivasa M. Gangadhara
Ajay Simha
Archana V. Kumar
(Honewell Technology Solutions Lab)
.
presented at
1st International Colloquium on CMMI High Maturity Best Practices held on May 21, 2010, organized by QAI
The document discusses CAI's Vericenter, a center of excellence for software quality and testing. It provides full-service testing solutions to improve software quality through expertise in processes, tools, and project management. The goal is to optimize resources, reduce risks, and deliver results through comprehensive system, user, integration, and data testing. CAI's approach utilizes leading tools and techniques and leverages experienced staff to shorten the testing process, increase quality and performance, and decrease costs.
1. The document discusses integration and testing, including software quality assurance, integration approaches, and types of testing.
2. It provides an overview of roles in quality assurance and when quality assurance activities occur in the software development lifecycle.
3. Integration can be done using top-down or bottom-up approaches, progressively aggregating functionality while testing occurs in parallel with development.
World Class Manufacturing Asset Utilizationlksnyder
Woodard & Curran proposes a 3-phase program to help their beverage client improve manufacturing asset utilization and efficiency. Phase 1 involves measuring equipment performance data. Phase 2 is to analyze the data to identify improvement opportunities. Phase 3 implements solutions such as equipment upgrades, training, and process changes. The goal is to benchmark performance and work towards world-class OEE metrics through a collaborative change management approach.
Bayesian reliability demonstration test in a design for reliability processASQ Reliability Division
This document discusses Bayesian reliability demonstration tests (BRDT) in the design for reliability (DFR) process. It presents challenges with traditional reliability demonstration tests, and how BRDT can help address these challenges by incorporating prior knowledge of a product's reliability from DFR activities. The document outlines how BRDT uses Bayesian statistics with a prior reliability distribution, typically Beta, to calculate posterior reliability and determine confidence levels. It proposes a simplified BRDT algorithm for DFR that constructs the prior reliability distribution based on DFR inputs then performs trade-off studies to determine test parameters like sample size. BRDT allows testing with smaller sample sizes by leveraging reliability information from the DFR process.
Session #1: Development Practices And The Microsoft ApproachSteve Lange
This document discusses Microsoft's approach to development best practices, which focuses on collaboration, managing team workflow, driving predictability, ensuring quality early and often, and integrating work frequently. It describes how Microsoft's Visual Studio Team System provides tools to help with collaboration, work tracking, process guidance, testing, version control, and reporting to support development teams.
Level 1 PdM programs have spotty coverage and informal standards, while Level 2 programs are experimenting with basic certifications, alarms, and 2 or fewer PdM technologies. Level 3 programs have expanded coverage of equipment, use 3 or more PdM technologies, and have some basic standards and controls in place. Level 4 programs have good practices like higher certifications, integrated technologies, and formal workflows that are typically followed, while Level 5 programs represent best practices with comprehensive coverage, integration, and accountability across all elements.
1. The document discusses software quality and reliability in engineering. It defines quality as software being bug-free, on time, meeting requirements, and maintainable. Reliability is the probability of failure-free operation over time in a given environment.
2. Ensuring quality involves preventing and detecting faults during all phases of the software development life cycle from requirements to testing. The V-model helps achieve quality by involving testers early on.
3. Reliability focuses on avoiding faults during design and detecting problems during all phases through techniques like fault tolerance, forecasting, and measuring metrics like MTBF.
The document discusses integration and integration techniques. It defines integration as connecting different applications within an enterprise so they can exchange data and interoperate as needed. Integration can occur at the process, application, or data level. Common integration techniques include standard data definitions, databases, middleware, message-based integration using buses or brokers, and software-based integration using adapters or RPCs. The document also discusses common software architectures like layered systems, client-server, and service-oriented architecture and how they support integration.
This document provides an overview of Failure Mode and Effects Analysis (FMEA). FMEA is a systematic method used to evaluate potential failure modes in a design, process or service and their causes and effects. It involves analyzing potential failures, their likelihood and severity, and identifying actions to address potential failures with high risk priority numbers. The document defines key terms in FMEA like severity, occurrence, detection and risk priority number. It also outlines the FMEA process, including steps to identify potential failure modes, effects, causes, current controls and priority actions.
The document discusses software quality testing services provided by Independent Testing Service including software testing, localization and maintenance support. It outlines their technical expertise in areas like programming languages, databases, web servers and testing tools. The document also provides examples of their software testing process and a case study of projects they have worked on.
The document describes Dunwoody Group's Eclipse ARM Analytics product. It provides proven executive experience in collections, specialized products for the credit and collections market, and a dynamic scoring model called the Eclipse Model. The Eclipse Model provides operational intelligence through predictive scoring capabilities that maximize recoveries and reduce costs. It is customized for each client portfolio and designed to evolve with the client's strategies.
This document provides an overview of the FedRAMP process for obtaining security authorization for cloud systems. It describes the objectives of FedRAMP, including establishing a standardized approach to assessing and authorizing cloud systems. The document then outlines the key stages of the FedRAMP process from the perspective of a cloud service provider, including initiation, security assessment, and continuous monitoring. It provides examples of documents involved in each stage, such as the system security plan, security assessment plan, and continuous monitoring materials. The overall goal of FedRAMP is to increase security and oversight of cloud systems supporting government agencies.
Zend Solutions For Operational Maturity 01 21 2010phptechtalk
The document discusses enhancing the operational maturity of PHP applications and infrastructure. It outlines key priorities for CTOs, CIOs and engineering VPs around maintaining quality, managing applications at scale, increasing deployment success rates, and securing applications. It then analyzes typical challenges in ensuring predictability across the development, staging and production environments. The document proposes that automation and best practices can help create predictability by mastering the basics, proactive planning, achieving stability and continuous monitoring. Zend's solutions leverage automation to help clients increase their operational maturity level.
The document discusses requirements management and its importance. It notes that requirements management is a large process that encompasses the entire system, including platforms, mechanics, software and hardware. Effective requirements management is key to product success. The document recommends that companies focus on strengthening their requirements management process by improving individual areas over time rather than trying to overhaul the whole process at once. Tailoring requirements management specifically to a company's needs will help ensure value and eliminate costs associated with rework.
The document discusses implementing Lean principles in product development to reduce costs and cycle times. It outlines traditional development problems like long cycles, high costs, and changes to requirements. Lean product development focuses on understanding customer value, front-loading the process, and visual project alignment. Workshops are used to capture new information, focus on value-adding activities, and create action plans to streamline development through techniques like QFD, prototyping, and integrated cross-functional teams.
Parasoft Concerto A complete ALM platform that ensures quality software can b...Engineering Software Lab
Parasoft Concerto is a complete software development management platform that ensures quality software can be produced consistently and efficiently–in any language.
By integrating policy-driven project management with Parasoft Test's quality lifecycle management as well as Parasoft Virtualize's dev/test environment management, Parasoft Concerto ensures predictable project outcomes while driving unprecedented levels of productivity and application quality.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help boost feelings of calmness, happiness and focus.
The document discusses the results of a study on the impact of COVID-19 lockdowns on air pollution. Researchers found that lockdowns led to significant short-term reductions in nitrogen dioxide and fine particulate matter pollution globally as transportation and industrial activities declined substantially. However, the document notes that the improvements in air quality were temporary and pollution levels rose back to pre-pandemic levels as restrictions eased and activity increased again.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise boosts blood flow, releases endorphins, and promotes changes in the brain which help regulate emotions and stress levels.
This document provides a summary from Charles Holley, EVP of Finance & Treasurer at Wal-Mart Stores, Inc., at the Citigroup Retail Conference on February 14, 2007. It discusses Wal-Mart's January 2007 comp store sales results, expansion plans for fiscal year 2008, strategies to build upon their small business foundation and broaden their appeal to customers, and efforts to improve business operations and efficiency.
This document discusses best practices for measuring web analytics and marketing for Web 2.0 applications. It provides tips on measuring engagement, usage of features, and community and commerce metrics to better understand customers and improve applications. Event tagging is recommended over page views. Overall the document advocates using web analytics as an insight machine to optimize applications and personalize marketing based on customer behavior and value.
This document provides a summary from Charles Holley, EVP of Finance & Treasurer at Wal-Mart Stores, Inc., at the Citigroup Retail Conference on February 14, 2007. It discusses Wal-Mart's January 2007 comp store sales results, expansion plans for fiscal year 2008, strategies to build upon their small business foundation and broaden their appeal to customers, and efforts to improve business operations and efficiency.
This document discusses challenges with integrating design into agile projects. It proposes that agile design requires embracing change, communal ownership of design problems rather than pre-defined solutions, and evolving designs through play and frequent iteration. Successful agile design processes focus on communication, flexibility and exposing problems rather than fixating on solutions. Designers and developers must work closely together, with designers involved in coding and frequent demonstrations of work in progress.
The document discusses how web applications are becoming more complex and integrated. It notes that Bungee Labs will be demonstrating how to connect to the future of web applications and services through increased complexity, integration of services, and fluid interactivity. The discussion will also explore questions around these topics and where the technologies are headed.
The document defines two terms:
Mobile Web refers to websites designed to be viewed on mobile devices and accessed via the internet like regular websites.
Location-based Services (LBS) refers to the ability of mobile devices to provide location-relevant information to users via GPS.
The document then predicts that mobile will revolutionize how people gather and interact with information in the next three years through the convergence of mobile and web services, termed "Mobile 2.0".
The document discusses the size and growth of the mobile web. It notes that there are currently over 2 billion mobile subscribers globally, with projections that the number of mobile web users will reach 1.3 billion by 2010. The document also discusses how mobile access can revolutionize how people gather and share information in the next few years by reaching anyone through any medium.
Sterling Homes Australia is proud to announce the availability of Form-A-Wall in Australia. Form-A-Wall is a formulated concrete construction system that is cost effective, quick to install, and structurally superior to conventional building methods. It provides benefits such as termite resistance, high wind ratings, fire resistance, and predictable construction timelines. Form-A-Wall can be used for a variety of residential and commercial building applications including houses, townhouses, high-rise buildings, fencing, and more.
Momentum Infocare Pvt. Ltd. is a leading provider of IT solutions focusing on the corporate market. It has been providing IT infrastructure services since 1993 and is ISO 9001:2000 certified. It offers a range of data center, storage, security and infrastructure management solutions and has over 100 satisfied customers, with 70% being repeat clients. It has technology alliances with leading providers and case studies depicting successful projects for customers across industries.
The presentation to the McKinney City Council on June 18, 2007 covered library planning issues including site plans, floor plans, and two exterior design schemes. Scheme 1 and Scheme 2 provided different elevation and perspective views of the potential library building from Eldorado Parkway and its entryway.
The document outlines Murray D. Martin's formula for profitable growth at Pitney Bowes which is achieved through organic revenue growth and enhanced productivity. It provides details on Pitney Bowes' global mailstream operations, products, services, markets, and drivers to expand customer relationships and revenue growth. The summary also discusses initiatives to improve productivity through sales channel management, process reengineering, and aligning work, skills, and locations.
This document is the Mobile Web Developer's Guide published by mobile Top Level Domain (mTLD), Ltd. It provides an introduction to creating simple mobile sites for common handsets. The guide covers mobile web strategy, information architecture, design, standards, best practices and techniques for getting started with XHTML mobile content. It aims to give developers and site owners enough knowledge to start creating web content for mobile users.
The document discusses the rise of the "Widgetsphere" and how it allows for new ways of reaching audiences through portable web applications called widgets. It outlines how widgets can be used to aggregate, create and share content across various platforms like social networks, blogs, and startpages. The document provides guidance on how to develop a syndication strategy using widgets, including planning content for an audience, designing the widget, creating and promoting it, distributing it across various sites, measuring engagement, and adapting the widget based on feedback.
This document discusses mobility in e-business and outlines strategies for successful online ventures. It begins with an e-commerce case study of a flower delivery company that achieved 45% annual growth without a physical office presence. It then discusses concepts like humanizing the web through virtual offices that allow businesses to be accessible to customers without geographic constraints. The document also outlines top strategies for e-commerce including prioritizing web real estate, search engine optimization, affiliate programs, providing valuable content, and implementing customer loyalty programs. It emphasizes using techniques like video to create personal connections with customers.
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdfTechgropse Pvt.Ltd.
In this blog post, we'll delve into the intersection of AI and app development in Saudi Arabia, focusing on the food delivery sector. We'll explore how AI is revolutionizing the way Saudi consumers order food, how restaurants manage their operations, and how delivery partners navigate the bustling streets of cities like Riyadh, Jeddah, and Dammam. Through real-world case studies, we'll showcase how leading Saudi food delivery apps are leveraging AI to redefine convenience, personalization, and efficiency.
CAKE: Sharing Slices of Confidential Data on BlockchainClaudio Di Ciccio
Presented at the CAiSE 2024 Forum, Intelligent Information Systems, June 6th, Limassol, Cyprus.
Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Programming Foundation Models with DSPy - Meetup Slides
Dfr Presentation
1. RAL
VEM DFR – Design for Reliability
DFR – Fundamentals for Engineers
Reliability Audit Lab
2. RAL
VEM
Topics that will be covered:
1. Need for DFR
2. DFR Process
3. Terminology
4. Weibull Plotting
5. System Reliability
6. DFR Testing
7. Accelerated Testing
Reliability Audit Lab
4. RAL
VEM
What Customers Care about:
1. Product Life…. i.e., useful life before wear-out.
2. Minimum Downtime…. i.e., Maximum MTBF.
3. Endurance…. i.e., # operations, robust to
environmental changes.
4.Stable Performance…. i.e., no degradation in CTQs.
5. ON time Startup…. i.e., ease of system startup
Reliability Audit Lab
6. RAL
VEM Reliable Product Vision
Failure Mode
Failure Rate Resources/Costs
Identification
(Pre-Launch)
Release Release
Resources/costs
# Failure Modes
DFR
Failure Rate
50%
No DFR
No DFR
No DFR
DFR Goal DFR
5%
Time
Time Time
Identify & “eliminate” Start with lower “running Reduce overall costs by
inherent failure modes rate”, then aggressively employing DFR from the
beginning.
before launch. (Minimize “grow” reliability. (Reduce
Excursions!) Warranty Costs)
Take control of our product quality and aggressively drive to our goals
Reliability Audit Lab
8. RAL
VEM
NPI Process
• Field data analysis
• CTQ Identification
DP1 DP3
• Customer Metrics DP0 Specify Design DP2 Implement
Rel. Goal Setting Production / Field
• Assess Customer needs • Establish audit program
• Develop Reliability metrics • FRACAS system using ‘Clarify’
• Establish Reliability goals • Correlate field data & test results
System Model Verification
• Execute Reliability Test strategy
Design
• Construct functional block diagrams • Continue Growth Testing
• Define Reliability model • Accelerated Tests
• Apply robust design tools
• ID critical comps. & failure potential • Demonstration Testing
• DFSS tools
• Allocate reliability targets • Agency / Compliance Testing
• Generate life predictions
• Begin Growth Testing
Reliability Audit Lab
9. RAL
VEM
Legacy Product DFR Process . . .
Review Historical Data
• Review historical reliability & field failure data
1 • Review field RMA’s
• Review customer environments & applications
Analyze Field & In-house Endurance Test Data
• Develop product Fault Tree Analysis
2 • Identify and pareto observed failure modes
Develop Reliability Profile & Goals
• Develop P-Diagrams & System Block Diagram
• Generate Reliability Weibull plots for operational endurance
3 • Allocate reliability goals to key subsystems
• Identify reliability gaps between existing product & goals for each subsystem
Develop & Execute Reliability Growth Plan
• Determine root cause for all identified failures
4 • Redesign process or parts to address failure mode pareto
• Validate reliability improvement through accelerated life testing & field betas
Institute Reliability Validation Program
• Implement process firewalls & sensors to hold design robustness
5 • Develop and implement long-term reliability validation audit
Reliability Audit Lab
10. RAL
VEM Design For Reliability Program Summary
Keys to DFR:
• Customer reliability expectations & needs must be fully understood
• Reliability must be viewed from a “systems engineering” perspective
• Product must be designed for the intended use environment
• Reliability must be statistically verified (or risk must be accepted)
• Field data collection is imperative (environment, usage, failures)
• Manufacturing & supplier reliability “X’s” must be actively managed
DFR needs to be part of the entire product development cycle
Reliability Audit Lab
12. RAL
VEM
What do we mean by
1. Reliability
2. Failure
3. Failure Rate
4. Hazard Rate
5. MTTF / MTBF
Reliability Audit Lab
13. RAL
VEM
1. Reliability R(t): The probability that an item will perform its intended
function without failure under stated conditions for a
specified period of time
2. Failure: The termination of the ability of the product to perform its
intended function
3. Failure Rate [F(t)]: The ratio of no. of failures within a sample to the
cumulative operating time.
4. Hazard Rate [h(t)]: The instantaneous probability of failure of an item
given that it has survived until that time, sometimes
called as instantaneous failure rate.
Reliability Audit Lab
14. RAL
VEM Failure Rate Calculation Example
EXAMPLE: A sample of 1000 meters is tested for a week,
and two of them fail. (assume they fail at the end of the
week). What is the Failure Rate?
2
2 failures
Failure Rate = = failures /hour
1000 * 24 * 7 hours 168 , 000
= 1.19E-5 failures/hr
Reliability Audit Lab
15. RAL
VEM
Probability Distribution Function (PDF):
The Probability Distribution Function (PDF) is the distribution f(t) of times to
failure. The value of f(t) is the probability of the product failing precisely at
time t.
f (t)
Probability Distribution Function
time
t
Reliability Audit Lab
16. RAL
VEM
Common Distributions
Probability Density Variate,
Probability
Distribution Function, f(t) Range, t
−λt
f t =λe 0≤t∞
Exponential
t
− β
β t β−1 0≤t∞
f t = ⋅ ⋅e β
Weibull
ηη
2
− t− μ
1 2
2σ
f t = ⋅e
Normal −∞t ∞
σ 2π
ln t −μ 2
1
Log 2
2σ
0≤t∞
f t = ⋅e
Normal
σt 2π
Reliability Audit Lab
17. RAL
VEM
Cumulative Distribution Function (CDF) :
The Cumulative Distribution Function (CDF) represents the probability that the product
fails at some time prior to t. It is the integral of the PDF evaluated from 0 to t.
t
CDF =F t =∫ f t dt
0
f (t)
Probability Distribution Function
time
t1
Cumulative
Distribution Function
Reliability Audit Lab
18. RAL
VEM
Reliability Function R(t)
The reliability of a product is the probability that it does not fail before time t. It is therefore
the complement of the CDF:
t
Typical characteristics:
R t =1−F t =1−∫ f t dt
• when t=0, R(t)=1
0
or
• when t→∞, R(t) →0
∞
R t =∫ f t dt
t
f (t)
Probability Density Function
R(t) = 1-F(t)
time
t
Reliability Audit Lab
19. RAL
VEM
Hazard Function h(t)
The hazard function is defined as the limit of the failure rate as Δt
approaches zero.
In other words, the hazard function or the instantaneous failure rate is
obtained as
h(t) = lim [R(t) – R(t+Δt)] / [Δt * R(t)]
Δt -> 0
The hazard function or hazard rate h(t) is the conditional probability of failure
in the interval t to (t + Δt), given that there was no failure at t. It is expressed
as
h(t) = f(t) / R(t).
Reliability Audit Lab
20. RAL
VEM
Hazard Functions
As shown the hazard rate is a function of time.
What type of function does hazard rate exhibit with time?
The general answer is the bathtub-shaped function.
The sample will experience a high failure rate at the beginning of the
operation time due to weak or substandard components, manufacturing
imperfections, design errors and installation defects. This period of
decreasing failure rate is referred to as the “infant mortality region”
This is an undesirable region for both the manufacturer and consumer
viewpoints as it causes an unnecessary repair cost for the manufacturer
and an interruption of product usage for the consumer.
The early failures can be minimized by improving the burn-in period of
systems or components before shipments are made, by improving the
manufacturing process and by improving the quality control of the products.
Reliability Audit Lab
21. RAL
VEM
At the end of the early failure-rate region, the failure rate will eventually
reach a constant value. During this constant failure-rate region the failures
do not follow a predictable pattern but occur at random due to the changes
in the applied load.
The randomness of material flaws or manufacturing flaws will also lead to
failures during the constant failure rate region.
The third and final region of the failure-rate curve is the wear-out region.
The beginning of the wear out region is noticed when the failure rate starts
to increase significantly more than the constant failure rate value and the
failures are no longer attributed to randomness but are due to the age and
wear of the components.
To minimize the effect of the wear-out region, one must use periodic
preventive maintenance or consider replacement of the product.
Reliability Audit Lab
22. Product's Hazard Rate Vs. Time : RAL
VEM
“The Bathtub Curve”
Random Failure
Infant Mortality Wear out
(Useful Life)
h(t) decreasing
h(t) increasing
Hazard Rate, h(t)
h(t) constant
Wear out
Manufacturing
Failures
Defects
Random
Failures
Time
Reliability Audit Lab
23. RAL
VEM
Mean Time To Failures [MTTF] -
One of the measures of the system's reliability is the mean time to
failure (MTTF). It should not be confused with the mean time between
failure (MTBF). We refer to the expected time between two successive
failures as the MTTF when the system is non-repairable.
When the system is repairable we refer to it as the MTBF
Now let us consider n identical non-repairable systems and observe the
time to failure for them. Assume that the observed times to failure are
t1, t2, .........,tn. The estimated mean time to failure, MTTF is
MTTF = (1/n)Σ ti
Reliability Audit Lab
24. Useful Life Metrics: Mean Time
RAL
VEM
Between Failures (MTBF)
Mean Time Between Failures [MTBF] - For a repairable
item, the ratio of the cumulative operating time to the
number of failures for that item.
(also Mean Cycles Between Failures, MCBF, etc.)
EXAMPLE: A motor is repaired and returned to service
six times during its life and provides 45,000
hours of service. Calculate MTBF.
Total operating time 45 ,000
MTBF = = = 7,500 hours
¿ of failures 6
MTBF or MTTF is a widely-used metric during the
Useful Life period, when the hazard rate is constant
Reliability Audit Lab
25. RAL
VEM
The Exponential Distribution
If the hazard rate is constant over time, then the product follows the exponential
distribution. This is often used for electronic components.
ht = λ=constant
1
MTBF mean time between failures =
λ
−λt
f t =λe
−λt
F t =1−e
Rt =e−λt
1
−λ
At MTBF: R t =e−λt =e =e−1 =36. 8
λ
Appropriate tool if failure rate is known to be constant
Reliability Audit Lab
27. RAL
VEM
Useful Life Metrics: Reliability
Reliability can be described by the single parameter exponential distribution when
the Hazard Rate, λ, is constant (i.e. the “Useful Life” portion of the bathtub curve),
=e
t
−
MTBF − FR t Where: t = Mission length
R=e (uptime or cycles
in question)
EXAMPLE: If MTBF for a motor is 7,500 hours, the probability
of operating for 30 days without failure is ...
= 0 .908 = 90 . 8
30 ∗ 24 hours
−
7500 hours
R=e
A mathematical model for reliability during Useful Life
Reliability Audit Lab
29. RAL
VEM
Weibull Probability Distribution
• Originally proposed by the Swedish
engineer Waloddi Weibull in the early 1950’s
• Statistically represented fatigue failures
• Weibull probability density function (PDF,
distribution of values):
β
β -1 − t
t
β η
f t = e
β
η
Equation valid for minimum life = 0
t = Mission length (time, cycles, etc.)
β = Weibull Shape Parameter, “Slope”
Waloddi Weibull 1887-1979
η = Weibull Scale Parameter, “Characteristic Life”
Reliability Audit Lab
30. RAL
VEM The Weibull Distribution
This powerful and versatile reliability function is capable of modeling
most real-life systems because the time dependency of the failure rate
can be adjusted.
β
h t = β t β -1
η
β
β−1 − t
βt η
t = β e
f
η
β
−t
η
R t =1−F t =e
Reliability Audit Lab
31. RAL
VEM
Weibull PDF
β
β−1 − t
βt
Exponential when β = 1.0
• η
t = β e
f
Approximately normal when β = 3.44
• η
• Time dependent hazard rate
0 .0 0 5
β=0.5
0 .0 0 4
η=1000
β=3.44
0 .0 0 3
η=1000
β=1.0
0 .0 0 2
η=1000
0 .0 0 1
500 1000 1500 2000
Reliability Audit Lab
32. RAL
VEM
β > 1: Highest failure rate later-
Weibull Hazard Function “Wear-Out”
f t f t
ht = =
1 - F t R t 0.006
β=3.44
β=0.5
[ ]
η=1000
β−1 β
η=1000
β t t
exp − 0.004
h η η
ht =
{ [ ]}
h(t)
β
β=1.0
t
1 - 1 - exp −
η=1000
η 0.002
β
t β -1
ht = β 0 500 1000 1500 2000 2500
η
Time
β < 1: Highest failure rate early-
β = 1: Constant failure rate
“Infant Mortality”
Reliability Audit Lab
33. Weibull Reliability Function RAL
VEM
Reliability is the probability that the part survives to time t.
1
β
−t β=3.44
η
R t =1−F t =e η=1000
0.8
β=1.0
0.6
η=1000
R(t) β=0.5
0.4
η=1000
0.2
0
0 500 1000 1500 2000 2500
Time
Reliability Audit Lab
34. RAL
VEM
Summary of Useful Definitions - Weibull Analysis
Beta (β): The slope of the Weibull CDF when printed on Weibull paper
B-life: A common way to express values of the cumulative density function - B10
refers to the time at which 10% of the parts are expected to have failed.
CDF: Cumulative Density Function expresses the time-dependent probability that a
failure occurs at some time before time t.
Eta (η): The characteristic life, or time at which 63.2% of the parts are expected to
have failed. Also expressed as the B63.2 life. This is the y-intercept of the
CDF function when plotted on Weibull paper.
PDF: Probability Density Function expresses the expected distribution of failures
over time.
Weibull plot: A plot where the x-axis is scaled as ln(time) and the y-axis is scaled as
ln(ln(1 / (1-CDF(t))). The Weibull CDF plotted on Weibull paper will be a
straight line of slope β and y intercept = ln(ln(1 / (1-CDF(0))) = η.
Reliability Audit Lab
35. RAL
VEM Weibull Analysis
What is a Weibull Plot ?
Log-log plot of probability of
•
failure versus age for a product
or component Weibull Best Fit
Nominal “best-fit” line, plus
•
Observed
confidence intervals Failures
Easily generated, easily
•
interpreted graphical read-out
Confidence on Fit
Comparison: test results for a
•
redesigned product can be
plotted against original product
or against goals
Reliability Audit Lab
36. Weibull Shape Parameter (β ) and RAL
VEM
Scale Parameter (η ) Defined
β is called the SLOPE
For the Weibull distribution, the slope describes the
steepness of the Weibull best-fit line (see following
slides for more details). β also has a relationship
with the trend of the hazard rate, as shown on the
“bathtub curves” on a subsequent slide.
η is called the CHARACTERISTIC LIFE
For the Weibull distribution, the characteristic life is
equal to the scale parameter, η. This is the time at
which 63.2% of the product will have failed.
Scale and Shape are the Key Weibull Parameters
Reliability Audit Lab
37. RAL
VEM β and the Bathtub Curve
β<1 β=1
• Implies “infant mortality” • Implies failures are “random”, individually
unpredictable
• If this occurs:
Failed products “not to print” • An old part is as good as a new part (burn-
Manufacturing or assembly defects in not appropriate)
Burn-in can be helpful
• If this occurs:
• If a component survives infant mortality Failures due to external stress,
phase, likelihood of failure decreases with maintenance or human errors.
age. Possible mixture of failure modes
β>4
1<β<4
• Implies rapid wearout
• Implies mild wearout
• If this occurs, suspect:
• If this occurs
Material properties
Low cycle fatigue
Brittle materials like ceramics
Corrosion or Erosion
Scheduled replacement may be cost
• Not a bad thing if it happens after mission
effective
life has been exceeded.
Reliability Audit Lab
39. RAL
VEM
System Reliability Evaluation
A system (or a product) is a collection of components arranged according
to a specific design in order to achieve desired functions with acceptable
performance and reliability measures.
Clearly, th type of components used, their qualities, and the design
configuration in which they are arranged have a direct effect on the
system performance an its reliability. For example, a designer may use a
smaller number of high-quality components and configure them in a such
a way to result in a highly reliable system, or a designer may use larger
number of lower-quality components and configure them differently in
order to achieve the same level of reliability.
Once the system is configured, its reliability must be evaluated and
compared with an acceptable reliability level. If it does not meet the
required level, the system should be redesigned and its reliability should
be re-evaluated.
Reliability Audit Lab
40. RAL
VEM
Reliability Block Diagram (RBD) Technique
The first step in evaluating a system's reliability is to construct a reliability
block diagram which is a graphical representation of the components of the
system and how they are connected.
The purpose of RBD technique is to represent failure and success criteria
pictorially and to use the resulting diagram to evaluate System Reliability.
Benefits
The pictorial representation means that models are easily understood and
therefore readily checked.
Block diagrams are used to identify the relationship between elements in the
system. The overall system reliability can then be calculated from the
reliabilities of the blocks using the laws of probability.
Block diagrams can be used for the evaluation of system availability
provided that both the repair of blocks and failures are independent
events, i.e. provided the time taken to repair a block is dependent only on
the block concerned and is independent of repair to any other block
Reliability Audit Lab
41. RAL
VEM
Elementary models
Before beginning the model construction, consideration should be given to
the best way of dividing the system into blocks. It is particularly
important that each block should be statistically independent of all
other blocks (i.e. no unit or component should be common to a number
of blocks).
The most elementary models are the following
Series
Active parallel
m-out-of-n
Standby models
Reliability Audit Lab
42. RAL
VEM Typical RBD configurations and related formulae
Simple Series and Parallel System
Figure a shows the units A,B,C,….Z constituting a system. The interpretation can be stated as
‘any unit failing causes the system as a whole to fail’, and the system is referred to as active series system.
Under these conditions, the reliability R(s) of the system is given by
R(s) = Ra * Rb * Rc * ………Rz
O
A B C Z
I
a) Series System
Figure b shows the units X and Y that are operating in such a way that the system will survive as long as
At lest one of the unit survives. This type of system is referred to as an active parallel system.
R(s) = 1 – (1 – Rx)(1 – Ry)
X
O
I
Y
b) Parallel System
Reliability Audit Lab
43. RAL
VEM
A Series / Parallel System
When blocks such as X and Y themselves comprise sub-blocks in series, block diagrams of the
type are illustrated in figure c.
Rx = Ra1 * Rb1 * Rc1 *……..Rz1;
Ry = Ra2 * Rb2 * Rc2 *……..Rz2
Rs = 1 – (1 – Rx)(1 – Ry)
A1 B1 C1 Z1
O
I
A2 B2 C2 Z2
c) Series / ParallelSystem
Reliability Audit Lab
44. RAL
VEM
m-out-of-n units
The figure represents instances where system success is assured whenever at least m of
n identical units are in an operational state. Here m = 2, n = 3.
Rs = (Rx)^3 + 3*(Rx)^2*Fx, where Fx = 1 – Rx.
X
X 2/3
I O
X
d) m-out-of-n System
Reliability Audit Lab
46. RAL
VEM Reliability Testing - Why?
Reliability Testing allows us to:
• Determine if a product’s design is capable of performing its intended
function for the desired period of time.
• Have confidence that our sample-based prediction will accurately
reflect the performance of the entire population.
• Provide a path to “grow” a product’s reliability by identifying weak
points in the design.
• Confirm the product’s performance in the field.
• Identify failures caused by severe applications that exceed the ratings,
and recognize opportunities for the product to safely perform under
more diverse applications.
Reliability Audit Lab
47. RAL
VEM Reliability Testing - Measures
Reliability Testing answers questions like …
• What is my product’s Failure Rate?
• What is the expected life?
.
. ..
• Which distribution does my data follow?
..
• What does my hazard function look like?
• What failure modes are present?
• How “mature” is my product’s reliability?
These metrics and more can be obtained with the right reliability test
Reliability Audit Lab
48. RAL
VEM
Four Major Categories of Reliability Testing
• Reliability Growth Tests (RGT)
- Normal Testing
- Accelerated Testing
• Reliability Demonstration Tests (RDT)
• Production Reliability Acceptance Tests (PRAT)
• Reliability Validation (RV)
Reliability Audit Lab
49. RAL
VEM
Reliability Testing - Growth Testing
Scope: To determine a product’s physical limitations, functional
capabilities and inherent failure mechanisms.
• Emphasis is on discovering & “eliminating” failure modes
• Failures are welcome. . . represent data sources
• Failures in development = less failures in field
• Used with a changing design to drive reliability growth
• Sample size is typically small
• Test Types: Normal or Accelerated Testing
• Can be very helpful early in process when done on competitor
products which are sufficiently similar to the new design.
Used early & throughout the design process
Reliability Audit Lab
50. RAL
VEM
Reliability Testing … Demonstration Testing
Scope: To demonstrate the product’s ability to fulfill reliability,
availability & design requirements under realistic conditions.
• Failures are no longer hoped for, because they jeopardize compliance (though
it’s still better to catch a problem before rather than after launch!)
• Management tool . . . provides means for verifying compliance
• Provide reliability measurement, typically performed on a static design
(subsequent design changes may invalidate the demonstrated reliability results)
• Sample size is typically larger, due to need for degree of confidence in results
and increased availability of samples.
Used at end of design stages to demonstrate compliance to specification
Reliability Audit Lab
51. Reliability Testing … Production Reliability RAL
VEM
Acceptance Testing (PRAT)
Scope: To ensure that variation in materials, parts, &
processes related to move from prototypes to full production
does not affect product reliability
• Performed during full production, verifies that predictions based on
prototype results are valid in full production
• Provides feedback for continuous improvement in sourcing/manufacturing
• Sample size ranges from full(screen) to partial (audit)
• Test Types: Highly Accelerated Stress Screens/Audits (HASS/A),
Environmental Stress Screening (ESS), Burn in
Screens and Audits precipitate and detect hidden defects
Reliability Audit Lab
52. RAL
VEM Reliability Testing … Validation
Scope: To ensure that the product is performing reliably in the
actual customer environment/application.
• “Testing results” based on actual field data sources
• Provides field feedback on the success of the design
• Helps to improve future design / redesign & prediction methods
• Requires effective data collection & corrective action process
• Sample size depends on the customer & product type
Reliability Validation tracks field data on Customer Dashboards
Reliability Audit Lab
53. RAL
VEM
Reliability Testing … The Path
NPI (New Products):
Set Reliability Goals Implement Production Establish service schedule
Develop Models Reliability Demonstration Keep updated dashboards
NPI Pilot Readiness
Initial Design Audit Programs Ensure Data Collection
Mature Design
Accelerated Testing Improve future design
Pilot Testing
Initial Design Implementation Post-Sales Service
Demonstration Testing Acceptance Testing Validation Testing
Growth Testing
Legacy Products:
Implement changes
Complaint generated Revise goals
Reproduce Failure
Create case Clarify Redefine models Reliability Demonstration
Reliability Verification
Product redesign Audit Programs
Field Data Verification Product Redesign Implementation
Acquisition Growth Testing Demonstration Testing Acceptance Testing
Validation Testing
Reliability Tests are critical at all stages!
Reliability Audit Lab
55. RAL
VEM Accelerated Testing
Scope : Accelerated testing allows designers to make predictions about the
life of a product by developing a model that correlates reliability under
accelerated conditions to reliability under normal conditions.
Model:
BASIC CONCEPT The model is how we extrapolate back
to normal stress levels.
Time to Failure
.
.
.
. Common Models:
.
. • Arrhenius: Thermal
• Inverse Power Law: Non-Thermal
Stress
}
}
• Eyring: Combined
To predict here, we test here
(Elevated stress level)
(Normal stress level)
Results @ high stress + stress-life relationship = Results @ normal stress
Reliability Audit Lab
56. RAL
VEM
Accelerated Testing
Key steps in planning an accelerated test:
• Choose a stress to elevate: requires an understanding of the anticipated
failure mechanism(s) - must be relevant (temp. & vibration usually apply)
• Determine the accelerating model: requires knowledge of the nature of
the acceleration of this failure mechanism, as a function of the accelerating
stress.
• Select elevated stress levels: requires a previous study of the product’s
operating & destructive limits to ensure that the elevated stress level does
not introduce new failure modes which would not occur at normal
operating stress levels.
Applicability of technique depends on careful planning and execution
Reliability Audit Lab
57. RAL
VEM
Parametric Reliability Models
One of the most important factors that influence the design process of a
product or a system is the reliability values of its components.
In order to estimate the reliability of the individual components or the entire
system, we may follow one or more of the following approaches.
Historical Data
➢
➢Operational Life Testing
➢Burn-In Testing
➢Accelerated Life Testing
Reliability Audit Lab
58. RAL
VEM
Approach 1 : Historical Data
The failure data for the components can be found in data banks such as
GIDEP (Government-Industry Data Exchange Program),
➢
MIL-HDBK-217 (which includes failure data for components as well as
➢
procedures for reliability prediction),
AT&T Reliability Manual and
➢
Bell Communications Research Reliability Manual.
➢
In such data banks and manuals, the failure data are collected from
different manufacturers and presented with a set of multiplying factors
that relate to different manufacturer's quality levels and environmental
conditions
Reliability Audit Lab