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.
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.
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.
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.
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.
Engineered Resilient Systems, overview and status, 31 october 2011RNeches
The document discusses the need for Engineered Resilient Systems (ERS) to address challenges in developing systems that are affordable, effective, and adaptable. It outlines three key goals for ERS: (1) enabling affordable engineering via less rework; (2) ensuring effectiveness via better informed design decisions; and (3) facilitating adaptability through design and testing for a wider range of missions. It also identifies technical gaps in areas like system representation and modeling, characterizing changing operational environments, and enabling cross-domain coupling between different models. The overall aim is to develop technologies that allow systems to be engineered, analyzed, and adapted more quickly across their lifecycles.
Reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time. This document discusses key reliability concepts including the bathtub curve, reliability applications in different industries, reliability improvement strategies, and reliability-centered maintenance. It also defines important reliability terms and explains how reliability is important for reducing costs and improving customer satisfaction.
This document discusses model-based systems engineering (MBSE) and the use of system modeling languages. It motivates MBSE by describing how system models can integrate requirements, design, analysis and other engineering artifacts. It then provides an overview of the SysML modeling language and how it supports structural, behavioral, requirements and parametric modeling of systems. Finally, it describes how a system architecture model can act as an integrating framework to link various engineering analysis models across the lifecycle.
Estimating the principal of Technical Debt - Dr. Bill Curtis - WTD '12OnTechnicalDebt
This study summarizes results of a study of Technical Debt across 745 business applications comprising 365 million lines of code collected from 160 companies in 10 industry segments. These applications were submitted to a static analysis that evaluates quality within and across application layers that may be coded in different languages. The analysis consists of evaluating the application against a repository of over 1200 rules of good architectural and coding practice. A formula for estimating Technical Debt with adjustable parameters is presented. Results are presented for Technical Debt across the entire sample as well as for different programming languages and quality factors.
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.
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.
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.
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.
Engineered Resilient Systems, overview and status, 31 october 2011RNeches
The document discusses the need for Engineered Resilient Systems (ERS) to address challenges in developing systems that are affordable, effective, and adaptable. It outlines three key goals for ERS: (1) enabling affordable engineering via less rework; (2) ensuring effectiveness via better informed design decisions; and (3) facilitating adaptability through design and testing for a wider range of missions. It also identifies technical gaps in areas like system representation and modeling, characterizing changing operational environments, and enabling cross-domain coupling between different models. The overall aim is to develop technologies that allow systems to be engineered, analyzed, and adapted more quickly across their lifecycles.
Reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time. This document discusses key reliability concepts including the bathtub curve, reliability applications in different industries, reliability improvement strategies, and reliability-centered maintenance. It also defines important reliability terms and explains how reliability is important for reducing costs and improving customer satisfaction.
This document discusses model-based systems engineering (MBSE) and the use of system modeling languages. It motivates MBSE by describing how system models can integrate requirements, design, analysis and other engineering artifacts. It then provides an overview of the SysML modeling language and how it supports structural, behavioral, requirements and parametric modeling of systems. Finally, it describes how a system architecture model can act as an integrating framework to link various engineering analysis models across the lifecycle.
Estimating the principal of Technical Debt - Dr. Bill Curtis - WTD '12OnTechnicalDebt
This study summarizes results of a study of Technical Debt across 745 business applications comprising 365 million lines of code collected from 160 companies in 10 industry segments. These applications were submitted to a static analysis that evaluates quality within and across application layers that may be coded in different languages. The analysis consists of evaluating the application against a repository of over 1200 rules of good architectural and coding practice. A formula for estimating Technical Debt with adjustable parameters is presented. Results are presented for Technical Debt across the entire sample as well as for different programming languages and quality factors.
Proteus Venture Partners is a regenerative medicine fund focused on cell therapies, regenerative compounds, tissue engineering, and enabling technologies. It has a world-class team with complementary skills in science/technology, regulatory, operations, and finance. The team includes senior partners with decades of venture capital and operational experience, as well as scientific advisors who are leaders in the field with deep knowledge, experience, and networks in regenerative medicine.
Reliability growth planning (RGP) is emerging as a promising technique to address the reliability challenges arising from the distributed manufacturing environment. Unlike RGT (reliability growth testing), RGP drives the reliability growth of new products by spanning the product’s lifecycle from design, prototyping, manufacturing, to field use. It is a lifetime commitment to the product reliability via systematic failure analysis, rigorous corrective actions, and cost-effective financial investment. RGP has shown to be very effective, particularly in new product introductions under the fast time-to-market requirement.
The RGP process will be introduced based on the three-phase product lifecycle: 1) design for reliability during early product development; 2) accelerated lifetime testing and corrective actions in pilot line stage; and 3) continuous reliability improvement following the volume shipment. Trade-offs among reliability investment, warranty cost reduction, and customer satisfactions will be investigated from the perspective of the manufacturer and the customer. Reliability growth tools such as Crow/AMSAA, Pareto graphs, failure mode run chart, FIT (failure-in-time), and FMECA will be reviewed and their roles in the GRP process will be discussed and demonstrated. Case studies drawn from electronics equipment industry will be used to demonstrate the RGP applications and justify its benefits as well.
In parallel with the RGP, efforts have been devoted to developing optimal preventative maintenance programs, either time-based or usage-based strategies. Recently, CBM (condition based maintenance) is showing a great potential to achieve just-in-time maintenance or zero-downtime equipment. RGP and maintenance strategies share a common objective, i.e. achieving high system reliability and availability. In this presentation, optimal maintenance policies will be devised in the context of system reliability growth.
Environmental Stress Screening (ESS) is a test designed to uncover weak parts and workmanship defects. It subjects components, subassemblies, or full systems to environmental stresses like thermal cycling and vibration to induce early failures during manufacturing rather than in the field. This improves reliability and maintainability. The adaptive ESS process dynamically adjusts stress levels and times based on failure data to efficiently screen parts at minimum cost. ESS is generally applied during full-scale development and production and selectively during validation to improve outgoing quality and reliability.
Avoiding Soot Formation at Fuel Injector TipMetis Partners
This document is a request for proposals to develop surface treatments to eliminate carbon deposits forming on fuel injector tips. It provides background on the need to reduce particulate emissions from gasoline engines and details the opportunity. Proposals are sought for coatings or other treatments that can prevent carbon buildup during testing and meet automotive standards. The project may involve applying or testing coatings in Phase 1 and incorporating the technology into production methods in Phase 2. Responses will be evaluated based on technical merit, approach, proprietary position, economic potential, and respondent capabilities.
Reliability-Centered Maintenance. An introduction to by JBMmartinjib
Reliability is of a great interest for me because I studied it during my MSc. of Eng. and because I do believe in it: "a reliable asset is a safe asset"...
One of the many ways to improve the reliability of an asset is to implement a Reliability-Centered Maintainance.
1) The document summarizes responses from 7 individuals on their companies' awareness and implementation of IEC 61508.
2) Most respondents were from safety system manufacturers (4) and represented companies in the chemicals, oil & gas, and automotive sectors (1 each).
3) Key challenges for companies included understanding requirements, determining safety integrity levels, and a lack of expertise, guidance and reliability data. Most companies had partly or were planning to implement the standard.
Convert Italia offers PV performance services to maximize returns on PV plant investments over their operational lifetimes. Their services include plant monitoring and management, maintenance, and yield forecasting using proprietary tools and 30 years of experience. The goal is to increase revenues and availability while reducing costs through specialized expertise and centralized control. Key elements of their proposal include contracts based on performance ratios, dedicated monitoring systems, rapid on-site response, and auditing to ensure goals are met.
Leveraging Reusability and Traceability in Medical Device DevelopmentSeapine Software
Learn best practices for creating verifiable, traceable requirements. The presentation also includes information about how Seapine's TestTrack supports streamlining better processes, data capture, reusability, and traceability in the requirements phase and a Q&A session.
“Clinical Grade" Requirements to Enable a Mobile Health and Advanced Workflow Environment by Laurence Beaulieu; Chief Architect, Healthcare Solutions
Nortel Business Solutions
ATI's Systems Engineering - Requirements technical training course samplerJim Jenkins
This ATI professional
development course, Systems Engineering - Requirements, provides system engineers, team leaders, and managers with a clear understanding about how to develop good specifications affordably using modeling methods that encourage identification of the essential characteristics that must be respected in the subsequent design process.
This document discusses challenges with software scheduling and provides recommendations to improve software schedule estimation and tracking. It notes that software schedules often slip despite experience and process improvements. Common causes of scheduling issues include poor estimates due to undefined requirements, changing requirements, or inexperience. The document recommends that software schedules align with system schedules and allow time for requirements, design, implementation, and testing cycles. It presents techniques like Evidence-Based Scheduling using past performance data to generate realistic schedules and functional progress metrics rather than lines of code to improve schedule tracking.
Progress Software is a leading provider of software solutions that enable enterprises to be operationally responsive. The document discusses Progress' Responsive Process Management (RPM) suite, which provides real-time visibility into business processes and events, as well as the agility to change processes in response to situations. RPM exploits the hidden relationship between service-oriented architecture and business event processing. The document analyzes how RPM can help customers in various industries increase efficiencies and manage complex operations.
The document discusses determining requirements compliance during the design phase for a system of systems. It outlines the methodology used, which involves identifying and resolving non-compliant design aspects early through objective evidence and assessments. Requirements traceability and stakeholder involvement are important. The process connects requirements to verification and provides periodic assessments of design health. Making it work for complex systems requires collaboration, clear communication, and a simple approach.
This document discusses managing integrated project work across geographically dispersed NASA teams. It provides a case study of the Orion project, which involved collaboration between 10 NASA centers. Key challenges of geographic dispersion include different organizational cultures, time zones, and the need to be part of a larger distributed team. Suggested paths for success include frequent communication, building trust, establishing common goals and processes, and travel to facilitate in-person interactions. Geographic dispersion will continue as NASA relies more on distributed teams, but success requires focus on open communication and shared objectives.
This document summarizes key insights from a presentation on viewing project management through the lens of complexity theory. It discusses how complexity theory originated in the study of natural systems and how its concepts like emergence and non-linearity are relevant to project management. It also notes that while general systems theory promised to connect different fields, project management, cybernetics, and systems thinking ultimately diverged. The document reviews different perspectives on categorizing project complexity and shares insights from interviews where project managers discussed experiencing uncertainty, renegotiating plans, and maintaining progress despite radical uncertainty.
NASA is working to improve its cost estimating practices by emphasizing cost-risk identification, quantification and management. This includes developing range estimates rather than single point estimates to account for uncertainty. Cost-risk assessments involve analyzing risk from cost models, input parameters, and key project characteristics. Risks are quantified and combined to create a probabilistic 'S-curve' estimate. Earned Value Management data on high-risk project elements is proposed to help connect cost estimating and risk management throughout the project lifecycle. Regularly updating estimates and tracking high-risks will improve cost projections and risk-adjusted budgets.
This document summarizes best practices for software development for human-rated spacecraft. It discusses approaches to increasing software reliability through defect prevention and fault tolerance. It also outlines key aspects of an ideal software development process including requirements analysis, architectural design, detailed design, coding, testing and integration. Finally, it discusses considerations for requirements validation and verification, requirements management, and software architectural trades.
This document describes the career journey and experiences of Petros Maragkoudakis. It outlines his educational background in engineering and various roles he has held related to software engineering, testing, and project management. It provides details on the locations he has worked, technologies used, and certifications obtained throughout his career.
Professional engineering talent and innovative workforce solutions are critical for organizations to achieve agility, productivity and competitive advantage. Experis Engineering leverages their expertise, processes and technology to quickly source the right engineering professionals for both contract and permanent roles. They offer a suite of project solutions including project management, product development, testing and quality assurance to help clients accelerate goals. Experis has delivered over 3 million hours of engineering talent annually in the US.
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.
This webinar discusses robust design and reliability engineering. It is presented by Lou LaVallee, a senior reliability consultant from Ops A La Carte. The webinar will provide a 45-minute presentation on robust design principles followed by a 10-minute question and answer session. Registration demographics show over 200 attendees from 17 countries and 28 US states registered. The webinar is part of a monthly series hosted by Ops A La Carte, ASTR, and ASQ Reliability Division to discuss reliability topics.
Proteus Venture Partners is a regenerative medicine fund focused on cell therapies, regenerative compounds, tissue engineering, and enabling technologies. It has a world-class team with complementary skills in science/technology, regulatory, operations, and finance. The team includes senior partners with decades of venture capital and operational experience, as well as scientific advisors who are leaders in the field with deep knowledge, experience, and networks in regenerative medicine.
Reliability growth planning (RGP) is emerging as a promising technique to address the reliability challenges arising from the distributed manufacturing environment. Unlike RGT (reliability growth testing), RGP drives the reliability growth of new products by spanning the product’s lifecycle from design, prototyping, manufacturing, to field use. It is a lifetime commitment to the product reliability via systematic failure analysis, rigorous corrective actions, and cost-effective financial investment. RGP has shown to be very effective, particularly in new product introductions under the fast time-to-market requirement.
The RGP process will be introduced based on the three-phase product lifecycle: 1) design for reliability during early product development; 2) accelerated lifetime testing and corrective actions in pilot line stage; and 3) continuous reliability improvement following the volume shipment. Trade-offs among reliability investment, warranty cost reduction, and customer satisfactions will be investigated from the perspective of the manufacturer and the customer. Reliability growth tools such as Crow/AMSAA, Pareto graphs, failure mode run chart, FIT (failure-in-time), and FMECA will be reviewed and their roles in the GRP process will be discussed and demonstrated. Case studies drawn from electronics equipment industry will be used to demonstrate the RGP applications and justify its benefits as well.
In parallel with the RGP, efforts have been devoted to developing optimal preventative maintenance programs, either time-based or usage-based strategies. Recently, CBM (condition based maintenance) is showing a great potential to achieve just-in-time maintenance or zero-downtime equipment. RGP and maintenance strategies share a common objective, i.e. achieving high system reliability and availability. In this presentation, optimal maintenance policies will be devised in the context of system reliability growth.
Environmental Stress Screening (ESS) is a test designed to uncover weak parts and workmanship defects. It subjects components, subassemblies, or full systems to environmental stresses like thermal cycling and vibration to induce early failures during manufacturing rather than in the field. This improves reliability and maintainability. The adaptive ESS process dynamically adjusts stress levels and times based on failure data to efficiently screen parts at minimum cost. ESS is generally applied during full-scale development and production and selectively during validation to improve outgoing quality and reliability.
Avoiding Soot Formation at Fuel Injector TipMetis Partners
This document is a request for proposals to develop surface treatments to eliminate carbon deposits forming on fuel injector tips. It provides background on the need to reduce particulate emissions from gasoline engines and details the opportunity. Proposals are sought for coatings or other treatments that can prevent carbon buildup during testing and meet automotive standards. The project may involve applying or testing coatings in Phase 1 and incorporating the technology into production methods in Phase 2. Responses will be evaluated based on technical merit, approach, proprietary position, economic potential, and respondent capabilities.
Reliability-Centered Maintenance. An introduction to by JBMmartinjib
Reliability is of a great interest for me because I studied it during my MSc. of Eng. and because I do believe in it: "a reliable asset is a safe asset"...
One of the many ways to improve the reliability of an asset is to implement a Reliability-Centered Maintainance.
1) The document summarizes responses from 7 individuals on their companies' awareness and implementation of IEC 61508.
2) Most respondents were from safety system manufacturers (4) and represented companies in the chemicals, oil & gas, and automotive sectors (1 each).
3) Key challenges for companies included understanding requirements, determining safety integrity levels, and a lack of expertise, guidance and reliability data. Most companies had partly or were planning to implement the standard.
Convert Italia offers PV performance services to maximize returns on PV plant investments over their operational lifetimes. Their services include plant monitoring and management, maintenance, and yield forecasting using proprietary tools and 30 years of experience. The goal is to increase revenues and availability while reducing costs through specialized expertise and centralized control. Key elements of their proposal include contracts based on performance ratios, dedicated monitoring systems, rapid on-site response, and auditing to ensure goals are met.
Leveraging Reusability and Traceability in Medical Device DevelopmentSeapine Software
Learn best practices for creating verifiable, traceable requirements. The presentation also includes information about how Seapine's TestTrack supports streamlining better processes, data capture, reusability, and traceability in the requirements phase and a Q&A session.
“Clinical Grade" Requirements to Enable a Mobile Health and Advanced Workflow Environment by Laurence Beaulieu; Chief Architect, Healthcare Solutions
Nortel Business Solutions
ATI's Systems Engineering - Requirements technical training course samplerJim Jenkins
This ATI professional
development course, Systems Engineering - Requirements, provides system engineers, team leaders, and managers with a clear understanding about how to develop good specifications affordably using modeling methods that encourage identification of the essential characteristics that must be respected in the subsequent design process.
This document discusses challenges with software scheduling and provides recommendations to improve software schedule estimation and tracking. It notes that software schedules often slip despite experience and process improvements. Common causes of scheduling issues include poor estimates due to undefined requirements, changing requirements, or inexperience. The document recommends that software schedules align with system schedules and allow time for requirements, design, implementation, and testing cycles. It presents techniques like Evidence-Based Scheduling using past performance data to generate realistic schedules and functional progress metrics rather than lines of code to improve schedule tracking.
Progress Software is a leading provider of software solutions that enable enterprises to be operationally responsive. The document discusses Progress' Responsive Process Management (RPM) suite, which provides real-time visibility into business processes and events, as well as the agility to change processes in response to situations. RPM exploits the hidden relationship between service-oriented architecture and business event processing. The document analyzes how RPM can help customers in various industries increase efficiencies and manage complex operations.
The document discusses determining requirements compliance during the design phase for a system of systems. It outlines the methodology used, which involves identifying and resolving non-compliant design aspects early through objective evidence and assessments. Requirements traceability and stakeholder involvement are important. The process connects requirements to verification and provides periodic assessments of design health. Making it work for complex systems requires collaboration, clear communication, and a simple approach.
This document discusses managing integrated project work across geographically dispersed NASA teams. It provides a case study of the Orion project, which involved collaboration between 10 NASA centers. Key challenges of geographic dispersion include different organizational cultures, time zones, and the need to be part of a larger distributed team. Suggested paths for success include frequent communication, building trust, establishing common goals and processes, and travel to facilitate in-person interactions. Geographic dispersion will continue as NASA relies more on distributed teams, but success requires focus on open communication and shared objectives.
This document summarizes key insights from a presentation on viewing project management through the lens of complexity theory. It discusses how complexity theory originated in the study of natural systems and how its concepts like emergence and non-linearity are relevant to project management. It also notes that while general systems theory promised to connect different fields, project management, cybernetics, and systems thinking ultimately diverged. The document reviews different perspectives on categorizing project complexity and shares insights from interviews where project managers discussed experiencing uncertainty, renegotiating plans, and maintaining progress despite radical uncertainty.
NASA is working to improve its cost estimating practices by emphasizing cost-risk identification, quantification and management. This includes developing range estimates rather than single point estimates to account for uncertainty. Cost-risk assessments involve analyzing risk from cost models, input parameters, and key project characteristics. Risks are quantified and combined to create a probabilistic 'S-curve' estimate. Earned Value Management data on high-risk project elements is proposed to help connect cost estimating and risk management throughout the project lifecycle. Regularly updating estimates and tracking high-risks will improve cost projections and risk-adjusted budgets.
This document summarizes best practices for software development for human-rated spacecraft. It discusses approaches to increasing software reliability through defect prevention and fault tolerance. It also outlines key aspects of an ideal software development process including requirements analysis, architectural design, detailed design, coding, testing and integration. Finally, it discusses considerations for requirements validation and verification, requirements management, and software architectural trades.
This document describes the career journey and experiences of Petros Maragkoudakis. It outlines his educational background in engineering and various roles he has held related to software engineering, testing, and project management. It provides details on the locations he has worked, technologies used, and certifications obtained throughout his career.
Professional engineering talent and innovative workforce solutions are critical for organizations to achieve agility, productivity and competitive advantage. Experis Engineering leverages their expertise, processes and technology to quickly source the right engineering professionals for both contract and permanent roles. They offer a suite of project solutions including project management, product development, testing and quality assurance to help clients accelerate goals. Experis has delivered over 3 million hours of engineering talent annually in the US.
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.
This webinar discusses robust design and reliability engineering. It is presented by Lou LaVallee, a senior reliability consultant from Ops A La Carte. The webinar will provide a 45-minute presentation on robust design principles followed by a 10-minute question and answer session. Registration demographics show over 200 attendees from 17 countries and 28 US states registered. The webinar is part of a monthly series hosted by Ops A La Carte, ASTR, and ASQ Reliability Division to discuss reliability topics.
Successive Software Reliability Growth Model: A Modular Approachajeetmnnit
The document summarizes a presentation given at the International Applied Reliability Symposium in India in 2012. The presentation was titled "Successive Software Reliability Growth Model: A Modular Approach" and was delivered by Dr. Ajeet Kumar Pandey of Cognizant Technology Solution, Hyderabad, India. The presentation proposed a new Successive Software Reliability Growth Model (SSRGM) that uses software metrics and defect checklists to predict and fix faults at each phase of the software development life cycle, with the goal of improving reliability successively.
Many organisations operatin in highly regulated environments, such as healthcare, have concluded that in order to achieve the next level of product quality and safety improvements, not to mention enhanced competitiveness, adoption of a more Agile approach is required. In this presentation, you will learn how the Agile software development approach for high assurance systems addresses many of the challenges found in many highly regulated enterprise environments.
Presented by Craig Langenfeld
The document contains information about errata in course materials on software estimation, an upcoming lecture on software project management with opportunities for student presentations, and slides from a lecture on quality in software project management that discuss setting quality goals and mapping quality practices to goals and risks.
Quality Re Pres Ebert Rudorfer Med Conf2011 V4Arnold Rudorfer
This document summarizes a presentation on quality requirements engineering for medical systems. It discusses the challenges of developing safety and security critical medical devices. It provides an overview of quality requirements engineering and examples of how it was applied to a large medical device project at Siemens Healthcare involving thousands of requirements and hundreds of developers. The presentation outlines issues addressed through solutions like a feature model, forced ranking, architecture mapping, and a quality tree. It discusses positive results like improved reliability and reduced review efforts.
Quality Re Pres Ebert Rudorfer Med Conf2011 V5Arnold Rudorfer
This document discusses quality requirements engineering for medical systems. It provides an overview of quality requirements engineering challenges for medical device projects. It then discusses Siemens and Vector Consulting, the business environment for medical devices, and examples of applying quality requirements engineering for security. The document outlines some case studies and results, concluding that quality requirements engineering can improve system reliability and availability while reducing engineering effort.
Application of HALT at design stage becomes more and more common in electronics industry. Many discussions and disputations of the HALT test interpretation are in full swing. With our HALT test experiences in notebook, desktop and server products, we intend to share and discuss the safety factor of exact product operating limits to its operation specifications in temperature and vibration and common failure modes stimulated thereby. A general perspective of the test setup techniques by product types and its influence is also provided. The distinctive roles of HALT on board level and system level from thermal flow field point of view are also shared in this paper.
Invited Talk: C-SPIN, the Chicago Software Process Improvement Network. January 7, 2009, Schaumburg, Illinois. Overview of themes and concepts from ISSRE 2008.
This document discusses how data center automation can help organizations perform better. It notes that business is adopting cloud technologies faster than IT can keep up. By automating IT processes, organizations can reduce costs and risks while improving agility. The document promotes HP's data center automation software and services, claiming it can automate infrastructure, applications, compliance and service delivery across private and hybrid clouds. Case studies show customers achieving improvements in areas like incident response times and application deployment speeds.
This document discusses how data center automation can help organizations perform better. It notes that business is adopting cloud technologies faster than IT can keep up. Data center automation using tools from HP can automate infrastructure, applications, and processes to improve efficiency, reduce costs and risks, and accelerate service delivery. Several case studies are presented that demonstrate how HP solutions have helped automate tasks, maintain compliance, and improve service delivery for customers in various industries.
Here is an example operations list for a medical enteral pump system:
1. Power on pump
2. Navigate main menu
1. Set patient details
2. Set feeding program
1. Select feeding mode (continuous, intermittent)
2. Set feeding rate
3. Set feeding duration
3. Start/stop feeding
4. View feeding history
5. Adjust alarm settings
3. Acknowledge/silence alarms
4. Power off pump
This list was developed by walking through the menu structure and identifying the key operations a user could perform with the pump system. The numbering indicates sub-operations under main operations.
Omnikron is a consulting firm founded in 1980 that provides technology, project management, and human resources services. It has offices in Woodland Hills, California and Chennai, India. The document discusses Omnikron's history, areas of expertise, clientele, and values like absolute customer satisfaction.
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.
BQR Reliability Engineering Ltd. is an Israel-based consultancy established in 1989 that provides reliability, availability, maintainability, and safety (RAMS) consulting and integrated logistics support (ILS). It has worked on over 2,000 projects for more than 120 customers across various industries. BQR's main business is now software and distribution, having developed reliability analysis software tools like CARE, fiXtress, and apmOptimizer, which are supported by a team of reliability and maintenance engineering experts. The software tools provide reliability analysis across the entire product lifecycle from design to maintenance.
1) e-Zest's SLA Tracker (CWX) monitors application, platform, and infrastructure performance metrics in real-time for customers using Amazon AWS CloudWatch.
2) CWX defines application-level SLAs through an XML configuration and sends alerts by email and SMS when SLAs are breached to avoid heavy penalties.
3) The tool provides dashboards for end-user experience, application performance, platform components, and infrastructure components with metrics, alerts and is more cost effective than third-party options.
This document introduces the importance of requirements quality and methods for measuring it. It discusses how over 40% of critical success factors for software projects are related to requirements. Requirements should be unambiguous, consistent, traceable and verifiable. The ARM and RQA tools can help objectively measure requirements quality by analyzing documents for indicators of qualities like specificity, measurability and timeframes. RQA differs from ARM in providing more metrics, compatibility with DOORS/IRQA, customizability and use of semantics. Measuring requirements quality helps improve requirements and project success.
Understand Reliability Engineering, Scope, Use case, Methods, TrainingBryan Len
Reliability engineering performs good deals with the permanence and usefulness of parts, products and systems.
Reliability engineering is very much helpful for reliability engineers, as well as design engineers, quality engineers, or system and software engineers.
Tonex offers 17 different courses in the Reliability Engineering arena. These classes are mainly taught by some of the best instructors in the world — specialists in their areas with real world experience.
Understand Reliability Engineering, Scope,Use case, methods, training.
https://www.tonex.com/systems-engineering-training/reliability-engineering-training/
2011 RAMS Tutorial Effective Reliability Program Traits and ManagementAccendo Reliability
The document outlines key traits for effective reliability program management. It discusses setting reliability goals and metrics at multiple points in the product lifecycle. Goals should include intended function, operating environment, duration, and probability of success. Metrics provide milestones to track progress towards goals. The document provides an example of breaking down a system-level goal into goals for subsystems, and approaches for resolving gaps between goals and estimates.
Similar to Reliability doe the proper analysis approach for life data (20)
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 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.
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
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
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.
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.
Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
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.
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
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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.
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
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
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.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
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
2. ASQ Reliability Division
Chinese Webinar
Series
One of the monthly webinars
on topics of interest to
reliability engineers.
To view recorded webinar (available to ASQ Reliability
Division members only) visit asq.org/reliability
To sign up for the free and available to anyone live
webinars visit reliabilitycalendar.org and select English
Webinars to find links to register for upcoming events
http://reliabilitycalendar.org/The_Reli
ability_Calendar/Webinars_‐
_Chinese/Webinars_‐_Chinese.html
4. 2
Who is ReliaSoft
ReliaSoft 简介
ReliaSoft is a world leading software company. We provide training, consulting
and software tools for reliability and quality engineers around the world.
Software Training Consulting
GE
Weibull++ MSMT Reliability Foundations Advanced System
GM
Reliability/Maintainability Analysis
ALTA Pro John Deer
Effective FMEA Series Application of Fault Trees in Siemens
DOE++ Reliability, Maintainability and Risk
HP
Analysis
BlockSim FRACAS Principles and
Applications
Delphi
Kuwait Oil
Lambda Predict Simulation Modeling for Reliability and
RCM Principles and Applications
Risk Analysis Philips
RCM++ Allied Signal
Reliability and Maintainability Analysis Disney
XFMEA Standards Based Reliability for Repairable Systems
Prediction General Dynamic
RGA Fundamentals of Design for Reliability
Raytheon
Application of Reliability Growth (DFR) XEROX
XFracas Models in Developmental Testing
Dow Chemical
and Fielded Systems
RENO Introduction to Reliability Concepts, Sandia Lab
Principles and Applications
Advanced Accelerated Life Medtronic
Testing Analysis
DOE: Experiment Design and Analysis
...
Have trained more than 15,000 engineers from about 3,000 companies and government agencies.
5. 3
常用词中英文对照表
ANOVA: 方差分析 Censored Data: 删失数据
DOE: 实验设计 MLE: 极大似然估计
Factor: 因子 Likelihood function: 似然函数
Level: 水平 Life Characteristic: 寿命特征量
2-Level Factorial Design: 两 Life-Factor Relationship: 寿命-
水平因子实验 因子关系
2-Level Fractional Factorial Life-Stress Relationship:寿命-
Design: 两水平部分因子实验 应力关系
Response: 反应 Likelihood Ratio Test: 似然比
Main Effect: 主效应 检验
Interaction Effect: 交互效应 Probability density function
(pdf): 概率密度函数
Coefficient: 系数
Mean Squares (MS): 均方差
Critical Value: 关键值
Mean Squares of Error: (MSE):
Outlier: 离群值
残方差
6. 4
Introduction Example
引例
Consider an experiment
to improve the reliability
of fluorescent lights.
Five factors A-E are
investigated in the
experiment. A 25-2 design
with factor generators
D=AC and E=BC was
conducted*.
Objective: To identify
significant factors and
adjust them to improve
life.
*Taguchi, 1987, p. 930.
7. 5
Introduction Example (cont’d)
引例(继续)
A B C D E Failure Time
-1 -1 -1 1 1 14~16 20+
-1 -1 1 -1 -1 18~20 20+
-1 1 -1 1 -1 8~10 10~12
-1 1 1 -1 1 18~20 20+
1 -1 -1 -1 1 20+ 20+
1 -1 1 1 -1 12~14 20+
1 1 -1 -1 -1 16~18 20+
1 1 1 1 1 12~14 14~16
Two replicates at each treatment.
Inspections were conducted every two days.
Results have interval data and suspensions.
8. 6
Traditional DOE Approach
传统的DOE方法
Assumes that the response (life) is normally
distributed.
Treats suspensions as failures.
Uses the middle point of the interval data as
the failure time.
Problem: The above assumptions and
adjustments are incorrect and do not apply to
life data.
10. 8
Life Data Types
寿命数据类型
Complete Data
Censored Data
Right Censored (Suspended)
Interval Censored
11. 9
Complete and Censored Data
完全数据和删失数据
Complete Data
Censored Data
Right Censored
?
Interval Censored
?
12. 10
Complete Data: Example
完全数据例子
For example, if we tested five units and they all failed, we would
then have complete information as to the time of each failure in
the sample.
13. 11
Right Censored (Suspended) Data: Example
右删失数据 (终止): 例子
Imagine we tested five units and three failed. In this scenario,
our data set is composed of the times-to-failure of the three units
that failed and the running time of the other two units without
failure.
This is the most common censoring scheme and is used
extensively in the analysis of field data.
14. 12
Interval Censored Data: Example
区间删失数据: 例子
Imagine we are running a test on five units and inspecting them
every 100 hr. If a unit failed between inspections, we do not
know exactly when it failed, but rather that it failed between
inspections. This is also called “inspection data”.
15. 13
Censored Data Analysis Example
删失数据计算例子
100 pumps operated for three months.
One failed during the first month.
One failed during the second month.
Two failed during the third month.
What is the average time-to-failure?
1(1) 1(2) 2(3)
2.25?
4
You can’t answer this question without
assuming a model for the data.
16. 14
Common Distributions Used in Reliability
可靠性中常用的分布
Weibull distribution pdf:
1 t
t
f (t ) e
Lognormal distribution pdf:
2
1 ln( t )
1
2
f (t ) e
t 2
Exponential distribution pdf:
t
1
f (t ) e m
m
17. 15
Parameter Estimation:
Maximum Likelihood Estimation (MLE)
极大似然参数估计
Statistical (non-graphical) approach to parameter
estimation.
Given a data set, estimates the parameters that
maximize the probability that the data belong to that
distribution and that set of parameters.
Constructs likelihood function as product of densities,
assuming independence.
Uses calculus to find the values that maximize the likelihood
function.
Has elegant statistical properties when the sample size is
large.
18. 16
MLE Concept
极大似然参数估计概念
Which model is more likely if two values are observed:
-3 and 3?
19. 17
Likelihood Function: Complete Data
似然函数: 完全数据
If T is a continuous random variable with pdf:
f (T ;1 ,2 , , k )
where 1, 2, … , k are k unknown parameters that need to be estimated,
and we conduct an experiment and obtain N independent observations,
T1, T2, … , TN, then the likelihood function is given by:
N
L(1 , 2 , ,k T1 , T2 , , TN ) f (Ti ;1 , 2 , , k )
i 1
For a one-parameter distribution with a single parameter and data of
10, 20, 30, the likelihood of the function would be:
L( 10,20,30) f (10) f (20) f (30)
20. 18
Likelihood Function: Complete Data (cont‘d)
似然函数: 完全数据 (继续)
The logarithmic likelihood function is:
ln L(1 , 2 , , k T1 , T2 , , TN )
N
ln( f (Ti ;1 , 2 , , k ))
i 1
The maximum likelihood estimators (MLE) of 1, 2, … , k are obtained
by maximizing either L or .
By maximizing , which is much easier to work with than L, the
maximum likelihood estimators (MLE) of 1, 2, … , k are the
simultaneous solutions of k equations such that:
0, i 1, 2,...k
i
21. 19
Likelihood Function: Right Censored Data
似然函数: 右删失数据
The likelihood function for M suspension times,
S1,S2,…,SM, is given by:
L(1 , 2 ,..., k | S1 , S 2 ,..., S M )
M
1 F S j ;1 , 2 ,..., k
j 1
M
R S j ;1 , 2 ,..., k
j 1
22. 20
Likelihood Function: Interval Data
似然函数: 区间数据
The likelihood function for P intervals, IL1 , IU1; IL2 , IU2;…;
ILP , IUP, is given by:
L(1 , 2 ,..., k | I L1 , IU 1 , I L 2 , IU 2 ,..., I LP , IUP )
P
F IUl ;1 , 2 ,..., k F I Ll ;1 , 2 ,..., k
l 1
23. 21
The Complete Likelihood Function
完整的似然函数
After completing the likelihood function for the different types of
data, the likelihood function (without the constant) can now be
expressed in its complete form:
N M
L f Ti ;1 , 2 ,..., k R S j ;1 , 2 ,..., k
i 1 j 1
P
F IUl ;1 , 2 ,..., k F I Ll ;1 , 2 ,..., k
l 1
24. 22
MLE Parameter Estimation
极大似然解
The logarithmic likelihood function is:
ln L(1 ,2 , , k T1 , , TN , S1 ,..., S N , IU 1 , I L1 ,..., IUP , I LP )
The maximum likelihood estimators (MLE) of 1, 2, … , k
are the simultaneous solutions of k equations such that:
0, i 1, 2,...k
i
25. 23
EDUCATION
Combining Reliability and DOE
可靠性和DOE的结合
可靠性和DOE的结合
26. 24
Combining Reliability and DOE: Life-Factor Relationship
可靠性和DOE的结合: 寿命因子关系
可靠性和DOE的结合:
The graphic shows an example where life decreases when a factor is
changed from the low level to the high level.
It is seen that the pdf changes in scale only. The scale of the pdf is
compressed at the high level.
The failure mode remains the same. Only the time of occurrence
decreases at the high level.
27. 25
Life-Factor Relationship Simplify: Life Characteristic
简化寿命-因子关系:寿命特征量
Instead of considering the entire scale of the pdf, the life characteristic
can be chosen to investigate the effect of potential factors on life.
The life characteristic for the 3 commonly used distributions are:
Weibull: Lognormal: Exponential: m
28. 26
Life-Factor Relationship
寿命-因子关系
Using the life characteristic, the model to investigate
the effect of factors on life can be expressed as:
' 0 1 x1 2 x2 ... 12 x1 x2 ...
where:
' ln( ) or ' or ' ln( m)
xj : jth factor value
Note that a logarithmic transformation is applied to
the life characteristics of the Weibull and exponential
distributions.
This is because and m can take only positive values.
29. 27
MLE Based on Life-Factor Relationship
基于寿命-因子关系的极大似然解
Life-Factor Relationship i' 0 1 xi1 2 xi 2 ... 12 xi1 xi 2 ...
N
Failure Time Data L f f (Ti ; i, )
i 1
M
Suspension Data LS R( S j ; i, )
j 1
P
Interval Data LI F ( IUl ; i, ) F ( I Ll ; i, )
l 1
MLE
0 , 1 , 2 ,... and for lognormal
30. 28
Testing Effect Significance: Likelihood Ratio Test
检验效应的显著性: 似然比检验
Life-factor relationship is
i' 0 1 xi1 2 xi 2 ... 12 xi1 xi 2 ...
Likelihood ratio test
L(effect k removed )
LR(effect k ) 2 ln
L( full Model )
If LR(effect k ) 1,
2
then effect k is significant or active.
31. 29
Fluorescent Lights R-DOE: Data and Design
荧光灯可靠性DOE: 数据和实验设计
The design is identical to traditional DOE.
Data entered includes suspensions and interval data.
32. 30
Fluorescent Lights R-DOE: Results
荧光灯可靠性DOE: 结果
Life is assumed to follow the Weibull distribution.
33. 31
Fluorescent Lights R-DOE: Analyzing Model Fit
荧光灯可靠性DOE: 模型拟合分析
Residual Probability Plot
When using the Weibull distribution for life, the residuals from the
life-factor relationship should follow the extreme value distribution
with a mean of zero.
34. 32
Fluorescent Lights R-DOE: Analyzing Model Fit
(cont’d)
荧光灯的可靠性DOE: 模型拟合分析(继续)
Plot of residuals against run order
There should be no outliers or pattern.
35. 33
Fluorescent Lights R-DOE: Interpreting the Results
荧光灯的可靠性DOE: 理解结果
From the results, factors A,B, D and E are significant at the risk Level of
0.10. Therefore, attention should be paid to these factors.
In order to improve the life, factor A and E should be set to the high
level; while factors B and D should be set to the low level.
MLE Information
Term Coefficient
A:A 0.1052
B:B -0.2256
C:C -0.0294
D:D -0.2477
E:E 0.1166
36. 34
EDUCATION
Traditional DOE Approach
传统的DOE方法
传统的DOE方法
37. 35
Traditional DOE Approach: Model
传统的DOE方法: 模型
Traditional DOE uses ANOVA models.
y 0 1 x1 2 x2 12 x1 x2 ...
ˆ
…coefficients are estimated using least squares.
A B C D E Failure Time
-1 -1 -1 1 1 14~16 20+
-1 -1 1 -1 -1 18~20 20+
-1 1 -1 1 -1 8~10 10~12
-1 1 1 -1 1 18~20 20+
1 -1 -1 -1 1 20+ 20+
1 -1 1 1 -1 12~14 20+
1 1 -1 -1 -1 16~18 20+
1 1 1 1 1 12~14 14~16
For the first observation:
y1 0 1 (1) 2 (1) 3 (1) 4 (1) 5 (1)
ˆ
…assuming that the interactions are absent.
38. 36
Traditional DOE Approach: Effect Significance
传统的DOE方法: 效应显著性检验
The ANOVA model is
yi 0 1 xi1 2 xi 2 ... k xik 12 xi1 xi 2 ...
ˆ
F test
MS k
F0 ( k )
MS E
If F0 ( k ) f critical
then effect k is significant or active.
39. 37
Fluorescent Lights Example: Traditional DOE
Approach
荧光灯例子: 传统DOE分析方法
Suspensions are treated as failures.
Mid-points are used as failure times for interval data.
Life is assumed to follow the normal distribution.
40. 38
Fluorescent Lights Example: Traditional DOE
Approach Results
荧光灯例子:传统DOE分析结果
B and D come out to be significant using traditional DOE approach.
A, B, D and E were found to be significant using R-DOE.
Tradition DOE fails to identify A and E as an important factor at a
significance level of 0.1.
41. 39
Where to Get More Information
哪里可以找到更多的信息
1. http://www.itl.nist.gov/div898/handbook/
2. www.Weibull.com
42. Worldwide Headquarters (North America)
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E-mail: Sales@ReliaSoft.com
Web site: www.ReliaSoft.com
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40