Objectives
To understand Weibull distribution
To be able to use Weibull plot for failure time analysis and
diagnosis
To be able to use software to do data analysis
Organization
Distribution model
Parameter estimation
Regression analysis
The document discusses using Weibull probability plots to analyze light bulb lifespan data. Engineers tested bulbs by stressing them beyond normal conditions to simulate long-term use and recorded failure times. A Weibull plot of the failure percentage against time shows the characteristic life (63.2% failure point) and shape factor. Conclusions note that to guarantee bulbs for 10 years, the characteristic life must be much longer than 10 years to keep failure rates acceptably low.
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.
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 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.
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.).
You’ve heard about Weibull Analysis, and want to know what it can be used for, OR you’ve used Weibull Analysis in the past, but have forgotten some of the background and uses….
This webinar looks at giving you the background of Weibull Analysis, and its use in analyzing failure modes. Starting from basics and giving examples of its uses in answering the questions:
• How many do I test, for how long?
• Is our design system wrong?
• How many more failures will I have in the next month, year, 5 years?
Sit in and listen and ask your questions … not detailed “How to” but “When & Why to”!
Accelerated life testing (ALT) is widely used to expedite failures of a product in a short time period for predicting the product’s reliability under normal operating conditions. The resulting ALT data are often characterized by a probability distribution, such as Weibull, Lognormal, Gamma distribution, along with a life-stress relationship. However, if the selected failure time distribution is not adequate in describing the ALT data, the resulting reliability prediction would be misleading. In this talk, we provide a generic method for modeling ALT data which will assist engineers in dealing with a variety of failure time distributions. The method uses Erlang-Coxian (EC) distributions, which belong to a particular subset of phase-type (PH) distributions, to approximate the underlying failure time distributions arbitrarily closely. To estimate the parameters of such an EC-based ALT model, two statistical inference approaches are proposed. First, a mathematical programming approach is formulated to simultaneously match the moments of the EC-based ALT model to the ALT data collected at all test stress levels. This approach resolves the feasibility issue of the method of moments. In addition, the maximum likelihood estimation (MLE) approach is proposed to handle ALT data with type-I censoring. Numerical examples are provided to illustrate the capability of the generic method in modeling ALT data.
Objectives
To provide an introduction to the statistical analysis of
failure time data
To discuss the impact of data censoring on data analysis
To demonstrate software tools for reliability data analysis
Organization
Reliability definition
Characteristics of reliability data
Statistical analysis of censored reliability data
The document discusses using Weibull probability plots to analyze light bulb lifespan data. Engineers tested bulbs by stressing them beyond normal conditions to simulate long-term use and recorded failure times. A Weibull plot of the failure percentage against time shows the characteristic life (63.2% failure point) and shape factor. Conclusions note that to guarantee bulbs for 10 years, the characteristic life must be much longer than 10 years to keep failure rates acceptably low.
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.
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 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.
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.).
You’ve heard about Weibull Analysis, and want to know what it can be used for, OR you’ve used Weibull Analysis in the past, but have forgotten some of the background and uses….
This webinar looks at giving you the background of Weibull Analysis, and its use in analyzing failure modes. Starting from basics and giving examples of its uses in answering the questions:
• How many do I test, for how long?
• Is our design system wrong?
• How many more failures will I have in the next month, year, 5 years?
Sit in and listen and ask your questions … not detailed “How to” but “When & Why to”!
Accelerated life testing (ALT) is widely used to expedite failures of a product in a short time period for predicting the product’s reliability under normal operating conditions. The resulting ALT data are often characterized by a probability distribution, such as Weibull, Lognormal, Gamma distribution, along with a life-stress relationship. However, if the selected failure time distribution is not adequate in describing the ALT data, the resulting reliability prediction would be misleading. In this talk, we provide a generic method for modeling ALT data which will assist engineers in dealing with a variety of failure time distributions. The method uses Erlang-Coxian (EC) distributions, which belong to a particular subset of phase-type (PH) distributions, to approximate the underlying failure time distributions arbitrarily closely. To estimate the parameters of such an EC-based ALT model, two statistical inference approaches are proposed. First, a mathematical programming approach is formulated to simultaneously match the moments of the EC-based ALT model to the ALT data collected at all test stress levels. This approach resolves the feasibility issue of the method of moments. In addition, the maximum likelihood estimation (MLE) approach is proposed to handle ALT data with type-I censoring. Numerical examples are provided to illustrate the capability of the generic method in modeling ALT data.
Objectives
To provide an introduction to the statistical analysis of
failure time data
To discuss the impact of data censoring on data analysis
To demonstrate software tools for reliability data analysis
Organization
Reliability definition
Characteristics of reliability data
Statistical analysis of censored reliability data
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.
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 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?
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
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.
How do you use the Weibull Distribution? It’s just one of many useful statistical distribution we have to master as reliability engineers. Let’s explore an array of distribution and the problems they can help solve in our day to day work.
Detailed Information: When confronted with a set of time to failure data, what is your goto analysis approach. For me it’s a Weibull plot. It’s quick, often provides some insight to ask better questions, and easy to explain to others. A histogram is another great starting point. If we know a little about the source of the data, we may favor the normal or lognormal distributions. If discreet data, then binomial is the first choice, yet Poisson or hypergeometric have uses, too. A basic understanding of statistical distributions provides you a way to summarize data providing insights to identify or solve problems. In this webinar we’ll explore a few distributions useful for reliability engineering work and talk about how to select a distribution, basics on interpreting distributions and just touch on judging if you have selected the right distribution.
This Accendo Reliability webinar originally broadcast on 14 April 2015.
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.
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.
This document provides an overview of a Design for Reliability (DFR) seminar presented by Mike Silverman of Ops A La Carte LLC. The seminar covers DFR concepts and tools over two days, with sessions on topics like planning for reliability, failure mode analysis, accelerated testing techniques, and root cause analysis. The document includes biographical information about Mike Silverman, the seminar schedule and objectives, an overview of the consulting company Ops A La Carte, and a high-level discussion distinguishing DFR from a "toolbox" approach and outlining the key activities in a structured DFR process.
The document discusses reliability and reliability testing. It defines reliability as the probability that a product will perform as expected over a stated period of time under specified operating conditions. Reliability is affected by factors like numerical value, intended function, life, and environmental conditions. Methods to achieve reliability include proper design, production processes, and transportation. Reliability testing involves constructing reliability curves and calculating failure rates using distributions like exponential, normal, and Weibull. Different types of reliability tests are discussed including failure-terminated, time-terminated, and sequential tests.
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.
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 is a presentation to the top management as to why reliability is important and what is the difference between a maintenance engineer and a reliability engineer.
Forecasting warranty returns with Wiebull FitTonda MacLeod
Analyze Wise provides a statistical analysis of warranty return data to forecast future returns using a Weibull distribution model. The analysis involves obtaining time-to-failure data from historical warranty returns, performing a regression to identify the best fitting distribution model and associated parameters, and using the model to predict return counts by time period. The forecasts can help companies plan repair resources, manage customer relationships, and evaluate warranty expenses and product performance.
This document discusses process capability analysis and process analytical technology. It begins with an introduction to capability, including histograms and the normal distribution. It then covers capability indices like Cp, Cpk, Pp and Ppk and how to calculate sigma. It discusses using capability analysis with attribute data by calculating defects per million opportunities (DPMO). It concludes with a brief overview of process analytical technology (PAT).
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)
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.
Statistical Process Control,Control Chart and Process Capabilityvaidehishah25
This document provides an overview of statistical process control (SPC). It discusses the key concepts of SPC including the 5M's (man, machine, material, method, milieu), control chart basics, process variability, common SPC tools like control charts, histograms, Pareto charts, and their purposes. Control charts are described as the most important SPC tool for distinguishing common from special cause variation to monitor if a process is in control. The document also covers variable and attribute control charts and considerations for chart selection based on data type.
Using microsoft excel for weibull analysisMelvin Carter
A simple introduction to reliability analysis of components. Though this lacks explanations of the calculated steps it shows how simple analysis can be. Note that it only addresses the Weibull distribution. It does share how to look elsewhere if the Weibull shape parameter is not near the ideal three(3).
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.
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 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?
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
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.
How do you use the Weibull Distribution? It’s just one of many useful statistical distribution we have to master as reliability engineers. Let’s explore an array of distribution and the problems they can help solve in our day to day work.
Detailed Information: When confronted with a set of time to failure data, what is your goto analysis approach. For me it’s a Weibull plot. It’s quick, often provides some insight to ask better questions, and easy to explain to others. A histogram is another great starting point. If we know a little about the source of the data, we may favor the normal or lognormal distributions. If discreet data, then binomial is the first choice, yet Poisson or hypergeometric have uses, too. A basic understanding of statistical distributions provides you a way to summarize data providing insights to identify or solve problems. In this webinar we’ll explore a few distributions useful for reliability engineering work and talk about how to select a distribution, basics on interpreting distributions and just touch on judging if you have selected the right distribution.
This Accendo Reliability webinar originally broadcast on 14 April 2015.
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.
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.
This document provides an overview of a Design for Reliability (DFR) seminar presented by Mike Silverman of Ops A La Carte LLC. The seminar covers DFR concepts and tools over two days, with sessions on topics like planning for reliability, failure mode analysis, accelerated testing techniques, and root cause analysis. The document includes biographical information about Mike Silverman, the seminar schedule and objectives, an overview of the consulting company Ops A La Carte, and a high-level discussion distinguishing DFR from a "toolbox" approach and outlining the key activities in a structured DFR process.
The document discusses reliability and reliability testing. It defines reliability as the probability that a product will perform as expected over a stated period of time under specified operating conditions. Reliability is affected by factors like numerical value, intended function, life, and environmental conditions. Methods to achieve reliability include proper design, production processes, and transportation. Reliability testing involves constructing reliability curves and calculating failure rates using distributions like exponential, normal, and Weibull. Different types of reliability tests are discussed including failure-terminated, time-terminated, and sequential tests.
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.
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 is a presentation to the top management as to why reliability is important and what is the difference between a maintenance engineer and a reliability engineer.
Forecasting warranty returns with Wiebull FitTonda MacLeod
Analyze Wise provides a statistical analysis of warranty return data to forecast future returns using a Weibull distribution model. The analysis involves obtaining time-to-failure data from historical warranty returns, performing a regression to identify the best fitting distribution model and associated parameters, and using the model to predict return counts by time period. The forecasts can help companies plan repair resources, manage customer relationships, and evaluate warranty expenses and product performance.
This document discusses process capability analysis and process analytical technology. It begins with an introduction to capability, including histograms and the normal distribution. It then covers capability indices like Cp, Cpk, Pp and Ppk and how to calculate sigma. It discusses using capability analysis with attribute data by calculating defects per million opportunities (DPMO). It concludes with a brief overview of process analytical technology (PAT).
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)
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.
Statistical Process Control,Control Chart and Process Capabilityvaidehishah25
This document provides an overview of statistical process control (SPC). It discusses the key concepts of SPC including the 5M's (man, machine, material, method, milieu), control chart basics, process variability, common SPC tools like control charts, histograms, Pareto charts, and their purposes. Control charts are described as the most important SPC tool for distinguishing common from special cause variation to monitor if a process is in control. The document also covers variable and attribute control charts and considerations for chart selection based on data type.
Using microsoft excel for weibull analysisMelvin Carter
A simple introduction to reliability analysis of components. Though this lacks explanations of the calculated steps it shows how simple analysis can be. Note that it only addresses the Weibull distribution. It does share how to look elsewhere if the Weibull shape parameter is not near the ideal three(3).
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.
The document discusses the end of an asset's useful life. It explains that when an asset is disposed of at the end of its useful life, it is either sold for cash or traded in for credit towards a new asset. The amount the asset is sold or traded for should be equal to its carrying value on the balance sheet. The carrying value represents the portion of the asset's historical cost that remains unused or unexpired and therefore the future economic benefits still available to a new owner.
The document discusses wind speed prediction using the Weibull distribution and a hybrid Weibull-ANN technique. It presents the motivation for improved wind speed prediction due to the increasing use of wind energy. The Weibull distribution is described as a common statistical model used to analyze wind speed data. An artificial neural network model with backpropagation is also introduced for prediction. The document then analyzes wind speed data from Bhubaneswar using Weibull distributions and histograms to model the data distributions. Finally, it evaluates the hybrid Weibull-ANN technique for wind speed prediction performance.
This document provides examples and explanations of statistical concepts related to probability and statistics. It includes 13 examples of applying statistics to solve problems involving hypothesis testing, confidence intervals, variance, and analyzing sample data. The examples cover topics such as comparing sample means to claimed values, determining sample sizes needed to estimate population parameters, and drawing statistical conclusions from clinical trial or survey data. Step-by-step solutions and explanations are provided for each example problem.
Failure Diagnostic and Performance Monitoring, it is a part of CM program for airlines, it is addressing the condition of components of the aircraft, either it is initial , random or wear failures.
This document outlines a reliability engineering management course. The course covers topics such as evaluating reliability approaches, engineering approaches to product reliability, setting reliability goals, identifying risks, using reliability testing methods like HALT and ALT, using reliability models, measurement considerations, and managing supplier reliability programs. The course emphasizes understanding reliability's value in the product lifecycle. It includes case studies of organizations and considers key factors for improving an organization's ability to produce reliable products. The course is offered in person and online in summer 2016, with assignments including homework, a midterm, project, and final exam.
Design of Experiment (DOE) has been widely applied on improving product performance. It is an important part of Design for Six Sigma (DFSS). However, due to its limitation on data requirement and model assumptions, it is not popularly used in life test. In this presentation, a method combining regular DOE technique with proper life data analysis method is presented. This method can be used to identify factors that affect product life and also can be used to optimize design variables to improve product reliability.
The document is an introduction to probability and statistics that covers topics like mathematical expectations, special probability distributions, functions of random variables, variance, standardized random variables, moments, moment generating functions, characteristic functions, covariance, correlation coefficients, conditional expectations, and other statistical concepts in the fifth week of the course.
This document provides an overview of the Poisson distribution and other special probability distributions. It discusses:
1) The Poisson distribution and its properties, including how it can model rare, independent events over time periods. Examples of how to calculate probabilities using the Poisson are provided.
2) Other discrete distributions like the binomial, negative binomial, and hypergeometric.
3) Continuous distributions like the uniform, exponential, gamma, chi-square, and Weibull distributions. Applications and properties of each are summarized.
4) Comparisons between distributions like how the Poisson approximates the binomial for large values. Overall, the document introduces several important probability distributions used in statistics.
Introduction to probability distributions-Statistics and probability analysis Vijay Hemmadi
The document provides an introduction to probability distributions. It defines random variables as variables that can take on a set of values with different probabilities. Random variables can be discrete or continuous. Probability functions map the possible values of a random variable to their respective probabilities. For discrete random variables, the probability mass function gives the probability of each possible value. For continuous variables, the probability density function is used. The cumulative distribution function gives the probability that a random variable is less than or equal to a particular value. Examples of discrete and continuous probability distributions and their associated functions are provided. Expected value and variance are introduced as key characteristics of probability distributions.
This document discusses duty cycle concepts in reliability engineering. It begins with definitions of time-based and stress-condition-based duty cycles. Time-based duty cycle is the proportion of time a system is active, while stress-condition-based duty cycle considers the level of stress applied. The document then discusses how duty cycle manifests differently across various industries and how it is used to calculate reliability, with duty cycle affecting mission time, failure mechanisms, and characteristic life. Examples are provided for hard disk drives to illustrate the effects of duty cycle on acceleration factors and mean time to failure.
The document discusses DNV's software tools for analyzing the ultimate and fatigue strength of floating production storage and offloading (FPSO) vessels. It introduces direct load and strength calculation methods, and compares deterministic versus spectral analysis approaches. It also highlights challenges for FPSO new builds and conversions, and describes DNV's integrated software package for modeling the hydrodynamics, loads, structural response, and fatigue life of these complex floating structures.
an analysis of wind energy potential using weibull distributionWorking as a Lecturer
This document analyzes wind energy potential using the Weibull distribution. It discusses two studies that used the Weibull distribution to model wind speed and calculate parameters to estimate wind power potential. One study used a simulation model to describe wind turbine characteristics and power generated at a Sahara site in Algeria. The other calculated Weibull shape and scale factors using four methods and compared theoretical and observed probability density functions to determine the best fit. Both found the Weibull distribution directly influences estimates of wind power potential at a given location.
The beginning of a checklist version of the CMMI guidelines. If you would like the original Excel version let me know, and let SlideShare know they need to support Excel files.
This document discusses survival analysis and its application to analyzing the departure dynamics of Wikipedia editors. It begins by defining survival analysis and its goal of modeling time-to-event data using techniques that account for censoring. A case study is presented on analyzing data from 110,000 Wikipedia editors to determine who is likely to stop editing, how long they will continue editing, and why they stop. Statistical techniques like the Kaplan-Meier estimator, Cox proportional hazards models, and adjusted survival curves are used to analyze editing durations and identify covariates that impact the hazard rate of editors stopping contributions.
Complete mapping with CMMI v1.3 and Agile Scrum practices. Easy interpretation of cmmi practices and how to apply in agile scrum lifecycles. CMMI Development maturity level 3 practices are mapped with agile scrum. Simpler and quick reference guide for practioners.
This document discusses various quality control tools and techniques used in total quality management (TQM). It describes the seven traditional quality control tools: flowchart, check sheet, histogram, Pareto chart, cause-and-effect diagram, scatter diagram, and control chart. It then relates these tools to the PDCA cycle. Additionally, it introduces seven new management tools: affinity diagram, relationship diagram, tree diagram, matrix diagram, matrix data analysis, decision tree, and arrow diagram. Finally, it briefly discusses Six Sigma, benchmarking, and failure mode and effects analysis (FMEA).
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W2 Simple Variance Ana...J. García - Verdugo
The document provides an overview of simple variance analysis (ANOVA). It describes how ANOVA can be used to determine the effect of one factor on a result (Y) and explain the proportion of variance caused by the factor. The document outlines the statistical model for one-way ANOVA and how to conduct ANOVA, interpret results, and evaluate statistical assumptions. Examples are provided on using Minitab to analyze real data using ANOVA.
This document provides an introduction and overview of the seven basic quality control tools: 1) check sheet, 2) histogram, 3) Pareto diagram, 4) cause-and-effect diagram, 5) scatter diagram, 6) stratification, and 7) graphs and control charts. Each tool is described in one to three sentences. The check sheet is used to simplify data collection. The histogram displays variation within a process using bars. The Pareto diagram indicates which problems should be solved first by prioritizing frequent defects.
The document discusses applying machine learning techniques to identify compiler optimizations that impact program performance. It used classification trees to analyze a dataset containing runtime measurements for 19 programs compiled with different combinations of 45 LLVM optimizations. The trees identified optimizations like SROA and inlining that generally improved performance across programs. Analysis of individual programs found some variations, but also common optimizations like SROA and simplifying the control flow graph. Precision, accuracy, and AUC metrics were used to evaluate the trees' ability to classify optimizations for best runtime.
The document provides an overview of reliability engineering concepts including definitions of reliability, mean time to failure, hazard rate, and the Weibull distribution model. It discusses the importance of reliability for products and businesses. Examples are provided on how to calculate reliability metrics like reliability and failure rate from failure data using the exponential and Weibull distribution models. The versatility of the Weibull model in modeling early failure, constant, and wear-out failure regions is also highlighted.
In this study, we have to project the airline travel for the next 12 months .The dataset used here is SASHELP.AIR which is Airline data and contains two variables – DATE and AIR( labeled as International Airline Travel).It contains the data from JAN 1949 to DEC 1960.
This document summarizes a research paper that proposes using a two-step sequential probability ratio test (SPRT) approach to analyze software reliability growth model (SRGM) data. Specifically, it applies the approach to the Half Logistic Software Reliability Growth Model (HLSRGM). The SPRT approach allows drawing conclusions about software reliability from sequential or continuous monitoring of failure data, potentially reaching conclusions more quickly than traditional hypothesis testing. Equations are provided for determining acceptance, rejection, and continuation regions based on comparing observed failure counts to lines derived from the HLSRGM mean value function. The approach is applied to five sets of existing software failure data to analyze results.
Logistic Regression in Case-Control StudySatish Gupta
This document provides an introduction to using logistic regression in R to analyze case-control studies. It explains how to download and install R, perform basic operations and calculations, handle data, load libraries, and conduct both conditional and unconditional logistic regression. Conditional logistic regression is recommended for matched case-control studies as it provides unbiased results. The document demonstrates how to perform logistic regression on a lung cancer dataset to analyze the association between disease status and genetic and environmental factors.
This document discusses various statistical methods used in engineering. It covers topics like sample plans, capability studies, gauge R&R studies, comparative analysis, design of experiments (DOE), correlation, regression, reliability, and the DMAIC process in Six Sigma. DOE techniques like full factorial designs, fractional factorial designs, custom designs, evaluation of designs, response surface methods, and residuals are explained. The document provides examples and outlines the applications of these various statistical analysis methods.
This document discusses various forecasting techniques. It covers qualitative and quantitative methods as well as different time horizons for forecasting. Specific quantitative techniques discussed include moving averages, exponential smoothing, regression analysis, and double exponential smoothing. Moving averages and exponential smoothing are described as methods for forecasting stationary time series. Exponential smoothing provides a weighted average of past observations with more weight given to recent observations. Double exponential smoothing accounts for trends by smoothing changes in the intercept and slope over time.
This document provides an introduction and overview of using the Eviews software platform. It discusses how to create and open work files, generate random and transformed time series data, perform descriptive statistics and correlation analysis, and check for autocorrelation using correlograms. Key aspects of time series properties like stationarity and white noise are also covered. The document demonstrates various commands and functions in Eviews for working with time series data, from importing datasets to generating statistics and exploring the characteristics of the series.
DETECTION OF RELIABLE SOFTWARE USING SPRT ON TIME DOMAIN DATAIJCSEA Journal
In Classical Hypothesis testing volumes of data is to be collected and then the conclusions are drawn which may take more time. But, Sequential Analysis of statistical science could be adopted in order to decide upon the reliable / unreliable of the developed software very quickly. The procedure adopted for this is, Sequential Probability Ratio Test (SPRT). In the present paper we proposed the performance of SPRT on Time domain data using Weibull model and analyzed the results by applying on 5 data sets. The parameters are estimated using Maximum Likelihood Estimation.
The document discusses the Seven Basic Tools of Quality, which are graphical techniques used to troubleshoot quality issues. The seven tools are: cause and effect diagram, check sheet, control chart, histogram, Pareto chart, scattered diagram, and stratification. Each tool is briefly described. For example, a cause and effect diagram displays potential causes for a quality issue, a check sheet collects quantitative or qualitative data, and a control chart determines if a process is in statistical control. The tools can help identify factors affecting quality and determine appropriate corrective actions.
This document summarizes the work done by an intern during their summer internship in the Medical Physics Department of Radiology. The intern conducted research to predict cancer outcomes based on breast lesion features. Key work included feature extraction from mammograms, analyzing features to differentiate malignant and benign lesions using ROC analysis and LDA, and exploring features to predict invasive vs. non-invasive cancer. Top predictive features were FWHM ROI, diameter, and margin sharpness. The intern gained skills in medical image analysis, statistical analysis, and evaluating results to identify trends.
This document provides an overview of quantitative data analysis techniques including descriptive statistics, reliability analysis, factor analysis, and various statistical tests. Descriptive statistics involve calculating frequencies, percentages, means, and cross-tabulations to summarize demographic and other variables. Reliability analysis using Cronbach's alpha is described to measure the internal consistency of scales. The steps for conducting an exploratory factor analysis are outlined. Finally, guidance is provided on selecting appropriate statistical tests such as t-tests, ANOVA, regression, chi-square, and Mann-Whitney U based on the variables' levels of measurement and number of groups being compared.
The document discusses the steps for conducting a response surface methodology (RSM) experiment using central composite design (CCD). It involves determining independent and dependent variables, selecting an appropriate CCD, conducting the experiment runs according to the design, analyzing the data using statistical methods to develop a mathematical model and check its adequacy, and using the model to optimize responses. Key aspects of RSM and CCD covered include developing the design, analyzing results through ANOVA and regression, and checking model validity.
A Study on Performance Analysis of Different Prediction Techniques in Predict...IJRES Journal
Time series data is a series of statistical data that is related to a specific instant or a specific time period. Here, the measurements are recorded on a regular basis such as monthly, quarterly and yearly. Most of the researchers have used one of the prediction techniques in prediction of time series data. But, they have not tested all prediction techniques on same data set. They have not even compared the performance of different prediction techniques on the same data set. In this research work, some well known prediction techniques have been applied in the same time series data set. The average error and residual analysis have been done for each and every applied technique. One technique has been selected based on the minimum average error and residual analysis among the all applied techniques. The residual analysis comprises of absolute residual, maximum residual, median of absolute residual, mean of absolute residual and standard deviation. To finalize the algorithm, same procedure has been applied on different time series data sets. Finally, one technique has been selected which has been given minimum error and minimum value of residual analysis in most cases.
This document discusses standard and hierarchical multiple regression. It provides examples using data on academic achievement (GPA) predicted from minutes spent studying, motivation, and anxiety. Standard multiple regression is used to assess how much variance in GPA is explained collectively by the three predictors. Specifically, it finds the predictors explain 65% of variance in GPA. It also describes interpreting individual predictor importance through coefficients like beta weights. Hierarchical regression is mentioned but not demonstrated.
1) The document discusses various techniques for detecting and correcting serial correlation in regression models, including plotting residuals, estimating the serial correlation coefficient ρ, and using the Durbin-Watson statistic.
2) It provides step-by-step instructions for implementing these techniques in EViews, including estimating models with generalized least squares using the AR(1) and Cochrane-Orcutt methods.
3) As an exercise, readers are asked to repeat the Cochrane-Orcutt estimation using a different dependent variable.
Integrate fault tree analysis and fuzzy sets in quantitative risk assessmentIAEME Publication
This document discusses integrating fault tree analysis and fuzzy sets for quantitative risk assessment. It presents a case study of applying fuzzy fault tree analysis to assess the risk of overpressure rupture in a flammable liquid storage tank. Fault tree analysis is used to model the relationships between failures that could lead to the top event. Boolean algebra is typically used to calculate failure probabilities but this introduces uncertainty. The document proposes using fuzzy sets to make the probabilities more precise by modeling vagueness and uncertainty. A fuzzy inference system is incorporated into the fault tree analysis. The results demonstrate that the fuzzy fault tree analysis model is better able to handle uncertainty in quantitative risk assessment compared to traditional fault tree analysis alone.
Similar to An introduction to weibull analysis (20)
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.
This document provides an overview of a talk on thermodynamic reliability given by Dr. Alec Feinberg. The talk covers using thermodynamics and non-equilibrium thermodynamics to assess damage in systems and components. It discusses how the second law of thermodynamics can be applied to describe aging damage. Examples are provided to show calculating entropy damage and aging ratios for simple resistor aging and complex systems. The talk also discusses measuring entropy damage over time and modeling degradation paths. Overall, the document introduces the concept of using thermodynamics to assess reliability and aging in engineered systems.
This document outlines key elements for establishing a sustainable root cause analysis program. It discusses the importance of having an involved sponsor, a clear resourcing plan with defined roles and responsibilities, formal triggers for when analyses should be conducted, protocols for collecting and preserving evidence, standardized reporting, and a system for tracking action items to completion. It also emphasizes tracking the financial value of the program and conducting audits to ensure the program's sustainability over the long term (minimum of 3 years). The overall message is that root cause analysis requires a formal, long-term commitment and cultural change, not just a one-time effort, to truly solve problems and prevent their recurrence.
Dynamic vs. Traditional Probabilistic Risk Assessment Methodologies - by Huai...ASQ Reliability Division
The document compares dynamic and traditional probabilistic risk assessment methodologies. Traditional methodologies like fault trees, event sequence diagrams, and FMECA require analysts to assess possible system failures. Dynamic methodologies like Monte Carlo simulation use executable models to simulate system behavior probabilistically over time and automatically generate event sequences. Dynamic methods can address limitations of traditional approaches that rely heavily on analyst judgment.
This document discusses efficient reliability demonstration tests that can reduce sample sizes and test times compared to conventional methods. It presents principles for test time reduction using degradation measurements during testing. Methods are provided for calculating optimal test plans that minimize costs while meeting reliability requirements and risk constraints. Decision rules are given for terminating tests early based on degradation measurements and risk estimates. An example application demonstrates how the approach can significantly reduce testing costs.
This document discusses using degradation data to model reliability and predict failure times. It begins by explaining how failures can be caused by degradation over time in mechanical components and integrated circuits. Examples of degradation mechanisms like creep, fatigue, and corrosion are provided. The document then discusses using non-destructive and destructive inspection of degradation parameters to build models and predict reliability. Accelerated degradation testing is also covered as a way to quickly generate degradation data under elevated stress conditions. Overall, the document provides an overview of modeling reliability using degradation data and predicting failure times based on degradation paths.
The webinar discusses innovation and the innovation process. It defines innovation as the successful conversion of new concepts and knowledge into new products and processes that deliver new customer value. The innovation process involves 4 steps: 1) finding opportunities, 2) connecting to conceptual solutions, 3) making solutions user-friendly, and 4) getting to market. Different personality types play different roles in innovation, including creators, connectors, developers, and doers. Reliability is also an important consideration in innovation to ensure solutions work well for customers. The webinar encourages participants to get involved in their company's innovation efforts or help establish an innovation process.
This document summarizes an ASQ webinar on reliably solving intractable problems. It outlines 8 principles for producing breakthroughs: 1) use divergent problem solving, 2) generate paradigm shifts, 3) agree on success criteria, 4) start with a strong commitment, 5) separate creative and analytical thinking, 6) involve stakeholders, 7) use consensus decision making, and 8) anticipate issues. It then describes a 13-step conversation process to resolve obstacles following these principles in 4 phases: establishing foundations, envisioning the future, establishing solutions, and ensuring support. The document provides tips for facilitating each step of the process.
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.
Improved QFN Reliability Process by John Ganjei. John will talk about the improvements in the reliability process in this webinar.
It is free to attend - see www.reliabilitycalendar.org/webinars/ to register for upcoming events.
Data Acquisition: A Key Challenge for Quality and Reliability ImprovementASQ Reliability Division
The document discusses challenges with data acquisition for quality and reliability analysis. It presents a 5-step process called DEUPM for targeted data acquisition: 1) Define the problem, 2) Evaluate existing data, 3) Understand data acquisition opportunities and limitations, 4) Plan data acquisition and analysis, 5) Monitor, clean data, analyze and validate. An example of using this process to validate the reliability of a new washing machine design within 6 months is provided to illustrate the steps. The process aims to ensure data acquisition is disciplined and sufficient to answer reliability questions.
The document discusses applying Failure Mode and Effects Criticality Analysis (FMECA) to software engineering. It describes FMECA as a structured method to anticipate failures and their causes. The document outlines how FMECA was originally used in industries like aerospace and nuclear engineering but has expanded to other domains. It then discusses applying FMECA at different levels of a software project, from requirements to architecture to design to code. The document advocates an "enlightened approach" to using FMECA across all representations and abstractions of software.
Astr2013 tutorial by mike silverman of ops a la carte 40 years of halt, wha...ASQ Reliability Division
This document summarizes a presentation titled "40 Years of HALT: What Have We Learned?" by Mike Silverman. The presentation discusses the evolution of Highly Accelerated Life Testing (HALT) over the past 40 years, including what HALT is and is not, basic HALT methodology, links between HALT and design for reliability, new advances in HALT, current adoption rates of HALT, and the future of HALT. The presentation aims to share lessons learned from thousands of engineers who have used HALT techniques over the past 40 years to improve product design and reliability.
Comparing Individual Reliability to Population Reliability for Aging SystemsASQ Reliability Division
This document discusses the differences between individual reliability (IndRel) and population reliability (PopRel) for aging systems. IndRel provides the reliability of a single system at a given age, while PopRel provides the probability that a randomly selected system from a population will work at a given time, taking into account the age distribution of systems in the population. The document outlines methods to estimate both IndRel and PopRel, including using Weibull and probit models on failure data. Examples are provided to demonstrate estimating IndRel and PopRel for projects using different statistical models and failure data.
This document summarizes a webinar on cost-optimized reliability test planning and decision-making through Bayesian methods. The webinar covered:
1. A brief review of Bayesian statistics and how it allows incorporating prior knowledge to optimize test planning.
2. Examples of how Bayesian methods can reduce required sample sizes for reliability testing compared to classical methods.
3. How Bayesian analysis allows improved comparative reliability decision-making between systems by properly accounting for relative failure rates.
The webinar provided specific examples of applying Bayesian priors and posteriors to reliability testing problems to reduce testing time and costs while maintaining or improving reliability assessment.
This document discusses planning effective reliability demonstration tests. It introduces stress-strength inference (SSI) as a concept that can help design reliability tests that are more efficient. SSI considers the relationship between stress applied during testing versus actual use conditions. It can help shorten test duration and reduce sample size needed while still demonstrating the required reliability. The document provides examples of how accelerated life testing, composite overload success runs, and fatigue testing can incorporate SSI to make reliability testing more practical and informative.
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.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
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.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
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.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
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
2. RONG PA N
ASSOCIATE PROFESSOR
A RIZONA ST A T E U NIVERSIT Y
EM A IL: RONG.PA N@ A SU .EDU
An Introduction to Weibull
Analysis
3. Outlines
4/12/2014Webinar for ASQ Reliability Division
3
Objectives
To understand Weibull distribution
To be able to use Weibull plot for failure time analysis and
diagnosis
To be able to use software to do data analysis
Organization
Distribution model
Parameter estimation
Regression analysis
4. A Little Bit of History
4/12/2014Webinar for ASQ Reliability Division
4
Waloddi Weibull (1887-1979)
Invented Weibull distribution in 1937
Publication in 1951
A statistical distribution function of wide
applicability, Journal of Mechanics, ASME,
September 1951, pp. 293-297.
Was professor at the Royal Institute of
Technology, Sweden
Research funded by U.S. Air Force
5. Weibull Distribution
4/12/2014Webinar for ASQ Reliability Division
5
A typical Weibull distribution function has two
parameters
Scale parameter (characteristic life)
Shape parameter
A different parameterization
Intrinsic failure rate
Common in survival analysis
3-parameter Weibull distribution
Mean time to failure
Percentile of a distribution
“B” life or “L” life
t
e
t
tf
1
)(
.0,,0,1)(
tetF
t
t
etF
1)(
t
etF 1)(
)/11( MTTF
6. Functions Related to Reliability
4/12/2014Webinar for ASQ Reliability Division
6
Define reliability
Is the probability of life time longer than t
Hazard function and Cumulative hazard
function
Bathtub curve
)(1)(1)()( tFtTPtTPtR
)(
)(
)(
tR
tf
th
t
dxxhtH
0
)()( )(
)( tH
etR
Time
Hazard
7. Understanding Hazard Function
4/12/2014Webinar for ASQ Reliability Division
7
Instantaneous failure
Is a function of time
Weibull hazard could be
either increasing function of
time or decreasing function
of time
Depending on shape
parameter
Shape parameter <1 implies
infant mortality
=1 implies random failures
Between 1 and 4, early wear
out
>4, rapid wear out
8. Connection to Other Distributions
4/12/2014Webinar for ASQ Reliability Division
8
When shape parameter = 1
Exponential distribution
When shape parameter is known
Let , then Y has an exponential distribution
Extreme value distribution
Concerns with the largest or smallest of a set of random
variables
Let , then Y has a smallest extreme value
distribution
Good for modeling “the weakest link in a system”
TY
TY log
9. Weibull Plot
4/12/2014Webinar for ASQ Reliability Division
9
Rectification of Weibull distribution
If we plot the right hand side vs. log failure time, then we
have a straight line
The slope is the shape parameter
The intercept at t=1 is
Characteristic life
When the right hand side equals to 0, t=characteristic
life
F(t)=1-1/e=0.63
At the characteristic life, the failure probability does not
depend on the shape parameter
loglog))(1log(log ttF
log
10. Weibull Plot Example
4/12/2014Webinar for ASQ Reliability Division
10
A complementary
log-log vs log plot
paper
Estimate failure
probability (Y) by
median rank
method
Regress X on Y
Find
characteristic life
and “B” life on the
plot
11. Complete Data
4/12/2014Webinar for ASQ Reliability Division
11
Order failure times from smallest to largest
Check median rank table for Y
Calculation of rank table uses binomial distribution
Y is found by setting the cumulative binomial function
equal to 0.5 for each value of sequence number
Can be generated in Excel by BETAINV(0.5,J,N-J+1)
J is the rank order
N is sample size
By Bernard’s approximation
Order
number
Failure
time
Median rank %
(Y)
1 30 12.94
2 49 31.38
3 82 50.00
4 90 68.62
5 96 87.06
)4.0/()3.0( NJY
13. Diagnosis using Weibull Plot
4/12/2014Webinar for ASQ Reliability Division
13
Small sample uncertainty
14. Diagnosis using Weibull Plot
4/12/2014Webinar for ASQ Reliability Division
14
Low failure times
15. Diagnosis using Weibull Plot
4/12/2014Webinar for ASQ Reliability Division
15
Effect of suspensions
16. Diagnosis using Weibull Plot
4/12/2014Webinar for ASQ Reliability Division
16
Effect of outlier
17. Diagnosis using Weibull Plot
4/12/2014Webinar for ASQ Reliability Division
17
Initial time correction
18. Diagnosis using Weibull Plot
4/12/2014Webinar for ASQ Reliability Division
18
Multiple failure modes
19. Maximum Likelihood Estimation
4/12/2014Webinar for ASQ Reliability Division
19
Maximum likelihood estimation (MLE)
Likelihood function
Find the parameter estimate such that the chance of having such failure
time data is maximized
Contribution from each observation to likelihood function
Exact failure time
Failure density function
Right censored observation
Reliability function
Left censored observation
Failure function
Interval censored observation
Difference of failure functions
)(tR
)(tF
)()(
tFtF
)(tf
20. Plot by Software
4/12/2014Webinar for ASQ Reliability Division
20
Minitab
Stat Reliability/Survival Distribution analysis Parametric
distribution analysis
JMP
Analyze Reliability and Survival Life distribution
R
Needs R codes such as
data <- c(….)
n <- length(data)
plot(data, log(-log(1-ppoints(n,a=0.5))), log=“x”, axes=FALSE,
frame.plot=TRUE, xlab=“time”, ylab=“probability”)
Estimation of scale and shape parameters can also be found by
res <- survreg(Surv(data) ~1, dist=“weibull”)
theta <- exp(res$coefficient)
alpha <- 1/res$scale
21. Compare to Other Distributions
4/12/2014Webinar for ASQ Reliability Division
21
Choose a distribution model
Fit multiple distribution models
Criteria (smaller the better)
Negative log-likelihood values
AICc (corrected Akaike’s information criterion)
BIC (Baysian information criterion)
22. Weibull Regression
4/12/2014Webinar for ASQ Reliability Division
22
When there is an explanatory variable
(regressor)
Stress variable in the accelerated life testing (ALT)
model
Shape parameter of Weibull distribution is often
assumed fixed
Scale parameter is changed by regressor
Typically a log-linear function is assumed
Implementation in Software
23. Final Remarks
4/12/2014Webinar for ASQ Reliability Division
23
Weibull distribution
2 parameters
3 parameters
Shape of hazard function
Different stages of bathtub curve
Weibull plot
Find the parameter estimation
Interpretation