ARA Module 11 – Repairable Systems Analysis

of the Applied Reliability Analysis Course

Using Reliasoft Weibull++

So far in this course we have been dealing with non-repairable systems where we model the time to the (first) failure.  Repairable systems analysis is concerned with understanding and modeling the time between failures (over time).  The terminology and models are different than the non-repairable case.  In this module we provide an overview of analysis of repairable systems.  

A little background, motivation, full course overview, and a welcome from the instructor, Steven Wachs

Training Objectives

The key training full course objectives are summarized below (highlighted objectives relate to this module)

• Understand reliability concepts and unique aspects of reliability data
• Understand underlying probability and statistical concepts for reliability analysis
• Develop competency in the modeling and analysis of time-to-failure data
• Use and interpret probability plots for distribution fitting
• Understand reliability metrics and how to estimate and report them
• Handle multiple failure modes in reliability estimation
• Utilize degradation Data and models to predict failure times for life data analysis
• Use nonparametric estimation methods when appropriate
• Determine if reliability specifications are met (at specified confidence level) or whether design improvements are required
• Estimate estimate uncertainty using confidence intervals and bounds
• Estimate reliability of subsystems and systems
• Handle basic series and parallel systems
• Become aware of system reliability activities such as Reliability Block diagramming, Reliability importance, and Reliability allocation
• Develop competency in the planning of reliability tests (sample sizes)
• Use simulation to support estimation test planning
• Develop reliability demonstration test plans (testing time vs. number of units tradeoffs)
• Analyze existing warranty data to predict future returns
• Analyze data from Accelerated Life Tests to estimate reliability at normal use conditions (single stress, multiple stress, time-varying stress models)
• Plan Accelerated Life tests (sample sizes, determination of test stress combinations, allocation of units to stress levels)
• Incorporate degradation data modeling into Accelerated Life Test analysis
• Perform stress-strength analysis using analytical methods, software, and simulation
• Model reliability from binary response data (i.e. pass/fail)
• Analyze repair data from repairable systems to predict the number of failures over time, conditional reliability, and failure rates
• Develop proficiency in the use of Reliasoft Weibull++ for Reliability analyses

Steven Wachs, Course Instructor

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Steve regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.

 

Thorough knowledge of subject matter and ability to communicate it – exceptional instructor.

— Steve Fleishman, Magna Powertrain

---

Why is This Course Important?

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 online reliability training courses also present 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 presents a difficult challenge in many industries with short product cycles and compressed product development timeframes.  This course provides an in-depth treatment of quantitative methods for predicting and demonstrating product reliability from data gathered from physical testing or from field data.

Typical Attendees

• Product Engineers
• Design Engineers
• Quality Engineers
• Reliability Engineering Training
• Project / Program Managers
• Manufacturing Personnel
• Six Sigma Professionals

Enroll in the ARA Module 11 – Repairable Systems Analysis course

Steve is an excellent instructor who is very good on a dry topic. His application ability is great.

Bruce Schubert, BAE Systems

ARA Module 10 – Analysis of Binary Response Data

of the Applied Reliability Analysis Course

Using Reliasoft Weibull++

This module covers modeling for summarized data resulting from pass/fail (binary) assessments.  That is, the unit either fails or doesn’t fail when exposed to some stress for a specified period of time.  In these problems similar methods are used, but often the probability of survival or failure is estimated as a function of stress level rather than time.  

A little background, motivation, full course overview, and a welcome from the instructor, Steven Wachs

Training Objectives

The key training full course objectives are summarized below (highlighted objectives relate to this module)

• Understand reliability concepts and unique aspects of reliability data
• Understand underlying probability and statistical concepts for reliability analysis
• Develop competency in the modeling and analysis of time-to-failure data
• Use and interpret probability plots for distribution fitting
• Understand reliability metrics and how to estimate and report them
• Handle multiple failure modes in reliability estimation
• Utilize degradation Data and models to predict failure times for life data analysis
• Use nonparametric estimation methods when appropriate
• Determine if reliability specifications are met (at specified confidence level) or whether design improvements are required
• Estimate estimate uncertainty using confidence intervals and bounds
• Estimate reliability of subsystems and systems
• Handle basic series and parallel systems
• Become aware of system reliability activities such as Reliability Block diagramming, Reliability importance, and Reliability allocation
• Develop competency in the planning of reliability tests (sample sizes)
• Use simulation to support estimation test planning
• Develop reliability demonstration test plans (testing time vs. number of units tradeoffs)
• Analyze existing warranty data to predict future returns
• Analyze data from Accelerated Life Tests to estimate reliability at normal use conditions (single stress, multiple stress, time-varying stress models)
• Plan Accelerated Life tests (sample sizes, determination of test stress combinations, allocation of units to stress levels)
• Incorporate degradation data modeling into Accelerated Life Test analysis
• Perform stress-strength analysis using analytical methods, software, and simulation
• Model reliability from binary response data (i.e. pass/fail)
• Analyze repair data from repairable systems to predict the number of failures over time, conditional reliability, and failure rates
• Develop proficiency in the use of Reliasoft Weibull++ for Reliability analyses

Steven Wachs, Course Instructor

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Steve regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.

 

Thorough knowledge of subject matter and ability to communicate it – exceptional instructor.

— Steve Fleishman, Magna Powertrain

---

Why is This Course Important?

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 online reliability training courses also present 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 presents a difficult challenge in many industries with short product cycles and compressed product development timeframes.  This course provides an in-depth treatment of quantitative methods for predicting and demonstrating product reliability from data gathered from physical testing or from field data.

Typical Attendees

• Product Engineers
• Design Engineers
• Quality Engineers
• Reliability Engineering Training
• Project / Program Managers
• Manufacturing Personnel
• Six Sigma Professionals

Enroll in the ARA Module 10 – Analysis of Binary Response Data course

Steve is an excellent instructor who is very good on a dry topic. His application ability is great.

Bruce Schubert, BAE Systems

ARA Module 9 – Stress Strength Analysis

of the Applied Reliability Analysis Course

Using Reliasoft Weibull++

This module introduces a method of quantifying risk by calculating failure probabilities based on a characterization of a stress distribution and a strength distribution.  A failure occurs when a unit randomly encounters a stress that exceeds its inherent strength.  The theory for normally distributed distributions is discussed and an example is worked through.  We also discuss the option of using simulation to estimate failure probabilities as well as Reliasoft software. This is especially useful when either the stress or strength distribution is not well described by the normal model. 

A little background, motivation, full course overview, and a welcome from the instructor, Steven Wachs

Training Objectives

The key training full course objectives are summarized below (highlighted objectives relate to this module)

• Understand reliability concepts and unique aspects of reliability data
• Understand underlying probability and statistical concepts for reliability analysis
• Develop competency in the modeling and analysis of time-to-failure data
• Use and interpret probability plots for distribution fitting
• Understand reliability metrics and how to estimate and report them
• Handle multiple failure modes in reliability estimation
• Utilize degradation Data and models to predict failure times for life data analysis
• Use nonparametric estimation methods when appropriate
• Determine if reliability specifications are met (at specified confidence level) or whether design improvements are required
• Estimate estimate uncertainty using confidence intervals and bounds
• Estimate reliability of subsystems and systems
• Handle basic series and parallel systems
• Become aware of system reliability activities such as Reliability Block diagramming, Reliability importance, and Reliability allocation
• Develop competency in the planning of reliability tests (sample sizes)
• Use simulation to support estimation test planning
• Develop reliability demonstration test plans (testing time vs. number of units tradeoffs)
• Analyze existing warranty data to predict future returns
• Analyze data from Accelerated Life Tests to estimate reliability at normal use conditions (single stress, multiple stress, time-varying stress models)
• Plan Accelerated Life tests (sample sizes, determination of test stress combinations, allocation of units to stress levels)
• Incorporate degradation data modeling into Accelerated Life Test analysis
• Perform stress-strength analysis using analytical methods, software, and simulation
• Model reliability from binary response data (i.e. pass/fail)
• Analyze repair data from repairable systems to predict the number of failures over time, conditional reliability, and failure rates
• Develop proficiency in the use of Reliasoft Weibull++ for Reliability analyses

Steven Wachs, Course Instructor

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Steve regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.

 

Thorough knowledge of subject matter and ability to communicate it – exceptional instructor.

— Steve Fleishman, Magna Powertrain

---

Why is This Course Important?

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 online reliability training courses also present 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 presents a difficult challenge in many industries with short product cycles and compressed product development timeframes.  This course provides an in-depth treatment of quantitative methods for predicting and demonstrating product reliability from data gathered from physical testing or from field data.

Typical Attendees

• Product Engineers
• Design Engineers
• Quality Engineers
• Reliability Engineering Training
• Project / Program Managers
• Manufacturing Personnel
• Six Sigma Professionals

Enroll in the ARA Module 9 – Stress Strength Analysis course

Steve is an excellent instructor who is very good on a dry topic. His application ability is great.

Bruce Schubert, BAE Systems

ARA Module 8 – Accelerated Life Testing

of the Applied Reliability Analysis Course

Using Reliasoft Weibull++

This module covers the planning and analysis of Accelerated Life Tests (ALT).  ALT is a useful methodology for obtaining failure information in less time than it takes for tests performed at normal use conditions.  Quantitative ALTs allow the estimation of reliability statistics at normal use conditions based on failures observes at various stress conditions.  While more complex than regular reliability tests, they can yield useful information is significantly less time.

A little background, motivation, full course overview, and a welcome from the instructor, Steven Wachs

Training Objectives

The key training full course objectives are summarized below (highlighted objectives relate to this module)

• Understand reliability concepts and unique aspects of reliability data
• Understand underlying probability and statistical concepts for reliability analysis
• Develop competency in the modeling and analysis of time-to-failure data
• Use and interpret probability plots for distribution fitting
• Understand reliability metrics and how to estimate and report them
• Handle multiple failure modes in reliability estimation
• Utilize degradation Data and models to predict failure times for life data analysis
• Use nonparametric estimation methods when appropriate
• Determine if reliability specifications are met (at specified confidence level) or whether design improvements are required
• Estimate estimate uncertainty using confidence intervals and bounds
• Estimate reliability of subsystems and systems
• Handle basic series and parallel systems
• Become aware of system reliability activities such as Reliability Block diagramming, Reliability importance, and Reliability allocation
• Develop competency in the planning of reliability tests (sample sizes)
• Use simulation to support estimation test planning
• Develop reliability demonstration test plans (testing time vs. number of units tradeoffs)
• Analyze existing warranty data to predict future returns
• Analyze data from Accelerated Life Tests to estimate reliability at normal use conditions (single stress, multiple stress, time-varying stress models)
• Plan Accelerated Life tests (sample sizes, determination of test stress combinations, allocation of units to stress levels)
• Incorporate degradation data modeling into Accelerated Life Test analysis
• Perform stress-strength analysis using analytical methods, software, and simulation
• Model reliability from binary response data (i.e. pass/fail)
• Analyze repair data from repairable systems to predict the number of failures over time, conditional reliability, and failure rates
• Develop proficiency in the use of Reliasoft Weibull++ for Reliability analyses

Steven Wachs, Course Instructor

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Steve regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.

 

Thorough knowledge of subject matter and ability to communicate it – exceptional instructor.

— Steve Fleishman, Magna Powertrain

---

Why is This Course Important?

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 online reliability training courses also present 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 presents a difficult challenge in many industries with short product cycles and compressed product development timeframes.  This course provides an in-depth treatment of quantitative methods for predicting and demonstrating product reliability from data gathered from physical testing or from field data.

Typical Attendees

• Product Engineers
• Design Engineers
• Quality Engineers
• Reliability Engineering Training
• Project / Program Managers
• Manufacturing Personnel
• Six Sigma Professionals

Enroll in the ARA Module 8 – Accelerated Life Testing course

Steve is an excellent instructor who is very good on a dry topic. His application ability is great.

Bruce Schubert, BAE Systems

ARA Module 7 – Reliability Test Planning

of the Applied Reliability Analysis Course

Using Reliasoft Weibull++

Reliability testing is a key component for ensuring that Reliability targets are achieved.  Planning for reliability tests is important to ensure that the results are likely to be useful.  In this module, we look at planning for both reliability estimation tests and reliability demonstration tests.  Essentially, we must test enough units for long enough to be able to adequately estimate reliability or demonstrate that targets are met.

A little background, motivation, full course overview, and a welcome from the instructor, Steven Wachs

Training Objectives

The key training full course objectives are summarized below (highlighted objectives relate to this module)

• Understand reliability concepts and unique aspects of reliability data
• Understand underlying probability and statistical concepts for reliability analysis
• Develop competency in the modeling and analysis of time-to-failure data
• Use and interpret probability plots for distribution fitting
• Understand reliability metrics and how to estimate and report them
• Handle multiple failure modes in reliability estimation
• Utilize degradation Data and models to predict failure times for life data analysis
• Use nonparametric estimation methods when appropriate
• Determine if reliability specifications are met (at specified confidence level) or whether design improvements are required
• Estimate estimate uncertainty using confidence intervals and bounds
• Estimate reliability of subsystems and systems
• Handle basic series and parallel systems
• Become aware of system reliability activities such as Reliability Block diagramming, Reliability importance, and Reliability allocation
• Develop competency in the planning of reliability tests (sample sizes)
• Use simulation to support estimation test planning
• Develop reliability demonstration test plans (testing time vs. number of units tradeoffs)
• Analyze existing warranty data to predict future returns
• Analyze data from Accelerated Life Tests to estimate reliability at normal use conditions (single stress, multiple stress, time-varying stress models)
• Plan Accelerated Life tests (sample sizes, determination of test stress combinations, allocation of units to stress levels)
• Incorporate degradation data modeling into Accelerated Life Test analysis
• Perform stress-strength analysis using analytical methods, software, and simulation
• Model reliability from binary response data (i.e. pass/fail)
• Analyze repair data from repairable systems to predict the number of failures over time, conditional reliability, and failure rates
• Develop proficiency in the use of Reliasoft Weibull++ for Reliability analyses

Steven Wachs, Course Instructor

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Steve regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.

 

Thorough knowledge of subject matter and ability to communicate it – exceptional instructor.

— Steve Fleishman, Magna Powertrain

---

Why is This Course Important?

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 online reliability training courses also present 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 presents a difficult challenge in many industries with short product cycles and compressed product development timeframes.  This course provides an in-depth treatment of quantitative methods for predicting and demonstrating product reliability from data gathered from physical testing or from field data.

Typical Attendees

• Product Engineers
• Design Engineers
• Quality Engineers
• Reliability Engineering Training
• Project / Program Managers
• Manufacturing Personnel
• Six Sigma Professionals

Enroll in the ARA Module 7 – Reliability Test Planning course

Steve is an excellent instructor who is very good on a dry topic. His application ability is great.

Bruce Schubert, BAE Systems

ARA Module 6 – Analysis of Field Data — Warranty Forecasting

of the Applied Reliability Analysis Course

Using Reliasoft Weibull++

In this introductory module, we define Reliability and cover many important reliability concepts.  We discuss why reliability performance is so important and the types of questions that may be answered by analyzing failure data.  Different types of reliability data are reviewed.  We introduce the concept of censored data which allows us to correctly utilize partial or incomplete data in the analysis of test or field data.  We cover the Bathtub Curve which illustrates the types of failures that may occur over a product’s lifetime.  Finally, we introduce the reliability function and discuss product reliability goals.

A little background, motivation, full course overview, and a welcome from the instructor, Steven Wachs

Training Objectives

The key training full course objectives are summarized below (highlighted objectives relate to this module)

• Understand reliability concepts and unique aspects of reliability data
• Understand underlying probability and statistical concepts for reliability analysis
• Develop competency in the modeling and analysis of time-to-failure data
• Use and interpret probability plots for distribution fitting
• Understand reliability metrics and how to estimate and report them
• Handle multiple failure modes in reliability estimation
• Utilize degradation Data and models to predict failure times for life data analysis
• Use nonparametric estimation methods when appropriate
• Determine if reliability specifications are met (at specified confidence level) or whether design improvements are required
• Estimate estimate uncertainty using confidence intervals and bounds
• Estimate reliability of subsystems and systems
• Handle basic series and parallel systems
• Become aware of system reliability activities such as Reliability Block diagramming, Reliability importance, and Reliability allocation
• Develop competency in the planning of reliability tests (sample sizes)
• Use simulation to support estimation test planning
• Develop reliability demonstration test plans (testing time vs. number of units tradeoffs)
• Analyze existing warranty data to predict future returns
• Analyze data from Accelerated Life Tests to estimate reliability at normal use conditions (single stress, multiple stress, time-varying stress models)
• Plan Accelerated Life tests (sample sizes, determination of test stress combinations, allocation of units to stress levels)
• Incorporate degradation data modeling into Accelerated Life Test analysis
• Perform stress-strength analysis using analytical methods, software, and simulation
• Model reliability from binary response data (i.e. pass/fail)
• Analyze repair data from repairable systems to predict the number of failures over time, conditional reliability, and failure rates
• Develop proficiency in the use of Reliasoft Weibull++ for Reliability analyses

Steven Wachs, Course Instructor

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Steve regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.

 

Thorough knowledge of subject matter and ability to communicate it – exceptional instructor.

— Steve Fleishman, Magna Powertrain

---

Why is This Course Important?

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 online reliability training courses also present 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 presents a difficult challenge in many industries with short product cycles and compressed product development timeframes.  This course provides an in-depth treatment of quantitative methods for predicting and demonstrating product reliability from data gathered from physical testing or from field data.

Typical Attendees

• Product Engineers
• Design Engineers
• Quality Engineers
• Reliability Engineering Training
• Project / Program Managers
• Manufacturing Personnel
• Six Sigma Professionals

Enroll in the ARA Module 6 – Analysis of Field Data – Warranty Forecasting course

Steve is an excellent instructor who is very good on a dry topic. His application ability is great.

Bruce Schubert, BAE Systems

ARA Module 5 – Introduction to Reliability of Systems

of the Applied Reliability Analysis Course

Using Reliasoft Weibull++

In Module 5, we extend the estimation of component reliability to the reliability estimation of a systemcomprised of multiple components.  The system reliability depends both on the component reliabilities as well as their configuration.  Redundant components allow the system reliability to achieve levels that exceed the component reliabilities.  We explore the basic series and parallel structures as well as k-out-of-n parallel systems.  We also introduce more complex systems and various aspects of system reliability including reliability importance and reliability allocation. 

A little background, motivation, full course overview, and a welcome from the instructor, Steven Wachs

Training Objectives

The key training full course objectives are summarized below (highlighted objectives relate to this module)

• Understand reliability concepts and unique aspects of reliability data
• Understand underlying probability and statistical concepts for reliability analysis
• Develop competency in the modeling and analysis of time-to-failure data
• Use and interpret probability plots for distribution fitting
• Understand reliability metrics and how to estimate and report them
• Handle multiple failure modes in reliability estimation
• Utilize degradation Data and models to predict failure times for life data analysis
• Use nonparametric estimation methods when appropriate
• Determine if reliability specifications are met (at specified confidence level) or whether design improvements are required
• Estimate estimate uncertainty using confidence intervals and bounds
• Estimate reliability of subsystems and systems
• Handle basic series and parallel systems
• Become aware of system reliability activities such as Reliability Block diagramming, Reliability importance, and Reliability allocation
• Develop competency in the planning of reliability tests (sample sizes)
• Use simulation to support estimation test planning
• Develop reliability demonstration test plans (testing time vs. number of units tradeoffs)
• Analyze existing warranty data to predict future returns
• Analyze data from Accelerated Life Tests to estimate reliability at normal use conditions (single stress, multiple stress, time-varying stress models)
• Plan Accelerated Life tests (sample sizes, determination of test stress combinations, allocation of units to stress levels)
• Incorporate degradation data modeling into Accelerated Life Test analysis
• Perform stress-strength analysis using analytical methods, software, and simulation
• Model reliability from binary response data (i.e. pass/fail)
• Analyze repair data from repairable systems to predict the number of failures over time, conditional reliability, and failure rates
• Develop proficiency in the use of Reliasoft Weibull++ for Reliability analyses

Steven Wachs, Course Instructor

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Steve regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.

 

Thorough knowledge of subject matter and ability to communicate it – exceptional instructor.

— Steve Fleishman, Magna Powertrain

---

Why is This Course Important?

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 online reliability training courses also present 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 presents a difficult challenge in many industries with short product cycles and compressed product development timeframes.  This course provides an in-depth treatment of quantitative methods for predicting and demonstrating product reliability from data gathered from physical testing or from field data.

Typical Attendees

• Product Engineers
• Design Engineers
• Quality Engineers
• Reliability Engineering Training
• Project / Program Managers
• Manufacturing Personnel
• Six Sigma Professionals

Enroll in the ARA Module 5 – Introduction to Reliability of Systems course

Steve is an excellent instructor who is very good on a dry topic. His application ability is great.

Bruce Schubert, BAE Systems

ARA Module 4 – Estimation of Reliability Metrics

of the Applied Reliability Analysis Course

Using Reliasoft Weibull++

This module is the core of life data analysis.  We start by providing an overview of the common methods for estimating the distribution parameters (e.g. Maximum Likelihood Estimation) and provide guidance for choosing a method.  Since quantifying uncertainty in our estimates is critical, we review the use of confidence intervals (or bounds) for this purpose.  Applying the concepts introduced previously, we learn how to utilize selected distributions to estimate reliability statistics of interest.  Treatment of censored data and multiple failure modes follow.  We also look at comparing multiple groups with respect to overall reliability performance.  Finally, we briefly look at nonparametric estimation which does not require any distribution assumption.  

A little background, motivation, full course overview, and a welcome from the instructor, Steven Wachs

Training Objectives

The key training full course objectives are summarized below (highlighted objectives relate to this module)

• Understand reliability concepts and unique aspects of reliability data
• Understand underlying probability and statistical concepts for reliability analysis
• Develop competency in the modeling and analysis of time-to-failure data
• Use and interpret probability plots for distribution fitting
• Understand reliability metrics and how to estimate and report them
• Handle multiple failure modes in reliability estimation
• Utilize degradation Data and models to predict failure times for life data analysis
• Use nonparametric estimation methods when appropriate
• Determine if reliability specifications are met (at specified confidence level) or whether design improvements are required
• Estimate estimate uncertainty using confidence intervals and bounds
• Estimate reliability of subsystems and systems
• Handle basic series and parallel systems
• Become aware of system reliability activities such as Reliability Block diagramming, Reliability importance, and Reliability allocation
• Develop competency in the planning of reliability tests (sample sizes)
• Use simulation to support estimation test planning
• Develop reliability demonstration test plans (testing time vs. number of units tradeoffs)
• Analyze existing warranty data to predict future returns
• Analyze data from Accelerated Life Tests to estimate reliability at normal use conditions (single stress, multiple stress, time-varying stress models)
• Plan Accelerated Life tests (sample sizes, determination of test stress combinations, allocation of units to stress levels)
• Incorporate degradation data modeling into Accelerated Life Test analysis
• Perform stress-strength analysis using analytical methods, software, and simulation
• Model reliability from binary response data (i.e. pass/fail)
• Analyze repair data from repairable systems to predict the number of failures over time, conditional reliability, and failure rates
• Develop proficiency in the use of Reliasoft Weibull++ for Reliability analyses

Steven Wachs, Course Instructor

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Steve regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.

 

Thorough knowledge of subject matter and ability to communicate it – exceptional instructor.

— Steve Fleishman, Magna Powertrain

---

Why is This Course Important?

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 online reliability training courses also present 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 presents a difficult challenge in many industries with short product cycles and compressed product development timeframes.  This course provides an in-depth treatment of quantitative methods for predicting and demonstrating product reliability from data gathered from physical testing or from field data.

Typical Attendees

• Product Engineers
• Design Engineers
• Quality Engineers
• Reliability Engineering Training
• Project / Program Managers
• Manufacturing Personnel
• Six Sigma Professionals

Enroll in the ARA Module 4 – Estimation of Reliability Metrics course

Steve is an excellent instructor who is very good on a dry topic. His application ability is great.

Bruce Schubert, BAE Systems

ARA Module 3 – Assessing & Selecting Models

of the Applied Reliability Analysis Course

Using Reliasoft Weibull++

Module 3 focuses on how we determine appropriate models for describing the time-to-failure data.  We introduce Probability plots and goodness-of-fit statistics that assist in determining which distributions may provide a good fit to the data.  We also learn how to handle censoring and multiple failure modes when attempting to find reasonable models for the data.

A little background, motivation, full course overview, and a welcome from the instructor, Steven Wachs

Training Objectives

The key training full course objectives are summarized below (highlighted objectives relate to this module)

• Understand reliability concepts and unique aspects of reliability data
• Understand underlying probability and statistical concepts for reliability analysis
• Develop competency in the modeling and analysis of time-to-failure data
• Use and interpret probability plots for distribution fitting
• Understand reliability metrics and how to estimate and report them
• Handle multiple failure modes in reliability estimation
• Utilize degradation Data and models to predict failure times for life data analysis
• Use nonparametric estimation methods when appropriate
• Determine if reliability specifications are met (at specified confidence level) or whether design improvements are required
• Estimate estimate uncertainty using confidence intervals and bounds
• Estimate reliability of subsystems and systems
• Handle basic series and parallel systems
• Become aware of system reliability activities such as Reliability Block diagramming, Reliability importance, and Reliability allocation
• Develop competency in the planning of reliability tests (sample sizes)
• Use simulation to support estimation test planning
• Develop reliability demonstration test plans (testing time vs. number of units tradeoffs)
• Analyze existing warranty data to predict future returns
• Analyze data from Accelerated Life Tests to estimate reliability at normal use conditions (single stress, multiple stress, time-varying stress models)
• Plan Accelerated Life tests (sample sizes, determination of test stress combinations, allocation of units to stress levels)
• Incorporate degradation data modeling into Accelerated Life Test analysis
• Perform stress-strength analysis using analytical methods, software, and simulation
• Model reliability from binary response data (i.e. pass/fail)
• Analyze repair data from repairable systems to predict the number of failures over time, conditional reliability, and failure rates
• Develop proficiency in the use of Reliasoft Weibull++ for Reliability analyses

Steven Wachs, Course Instructor

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Steve regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.

 

Thorough knowledge of subject matter and ability to communicate it – exceptional instructor.

— Steve Fleishman, Magna Powertrain

---

Why is This Course Important?

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 online reliability training courses also present 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 presents a difficult challenge in many industries with short product cycles and compressed product development timeframes.  This course provides an in-depth treatment of quantitative methods for predicting and demonstrating product reliability from data gathered from physical testing or from field data.

Typical Attendees

• Product Engineers
• Design Engineers
• Quality Engineers
• Reliability Engineering Training
• Project / Program Managers
• Manufacturing Personnel
• Six Sigma Professionals

Enroll in the ARA Module 3 – Assessing and Selecting Models course

Steve is an excellent instructor who is very good on a dry topic. His application ability is great.

Bruce Schubert, BAE Systems

ARA Module 2 – Probability & Reliability Statistics

of the Applied Reliability Analysis Course

Using Reliasoft Weibull++

In preparation for the reliability methods and tools to follow, this module covers many of the fundamental building blocks.  Since Reliability is a probability, we review some essential probability ideas and rules.  We introduce the concept of conditional probability since it plays a  role in various methods such as warranty forecasting and burn-in.  We discuss probability models that may be considered in describing time-to-failure data with some emphasis on the Weibull distribution.  The Weibull distribution is very popular due to its flexibility and meaningful parameters although we should not limit ourselves solely to this model.  Finally, we discuss some useful discrete distribution models and their applications to reliability.  

A little background, motivation, full course overview, and a welcome from the instructor, Steven Wachs

Use the course menu to navigate to the first lesson

Training Objectives

The key training full course objectives are summarized below (highlighted objectives relate to this module)

• Understand reliability concepts and unique aspects of reliability data
• Understand underlying probability and statistical concepts for reliability analysis
• Develop competency in the modeling and analysis of time-to-failure data
• Use and interpret probability plots for distribution fitting
• Understand reliability metrics and how to estimate and report them
• Handle multiple failure modes in reliability estimation
• Utilize degradation Data and models to predict failure times for life data analysis
• Use nonparametric estimation methods when appropriate
• Determine if reliability specifications are met (at specified confidence level) or whether design improvements are required
• Estimate estimate uncertainty using confidence intervals and bounds
• Estimate reliability of subsystems and systems
• Handle basic series and parallel systems
• Become aware of system reliability activities such as Reliability Block diagramming, Reliability importance, and Reliability allocation
• Develop competency in the planning of reliability tests (sample sizes)
• Use simulation to support estimation test planning
• Develop reliability demonstration test plans (testing time vs. number of units tradeoffs)
• Analyze existing warranty data to predict future returns
• Analyze data from Accelerated Life Tests to estimate reliability at normal use conditions (single stress, multiple stress, time-varying stress models)
• Plan Accelerated Life tests (sample sizes, determination of test stress combinations, allocation of units to stress levels)
• Incorporate degradation data modeling into Accelerated Life Test analysis
• Perform stress-strength analysis using analytical methods, software, and simulation
• Model reliability from binary response data (i.e. pass/fail)
• Analyze repair data from repairable systems to predict the number of failures over time, conditional reliability, and failure rates
• Develop proficiency in the use of Reliasoft Weibull++ for Reliability analyses

Steven Wachs, Course Instructor

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Steve regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.

 

Thorough knowledge of subject matter and ability to communicate it – exceptional instructor.

— Steve Fleishman, Magna Powertrain

---

Why is This Course Important?

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 online reliability training courses also present 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 presents a difficult challenge in many industries with short product cycles and compressed product development timeframes.  This course provides an in-depth treatment of quantitative methods for predicting and demonstrating product reliability from data gathered from physical testing or from field data.

Typical Attendees

• Product Engineers
• Design Engineers
• Quality Engineers
• Reliability Engineering Training
• Project / Program Managers
• Manufacturing Personnel
• Six Sigma Professionals

Enroll in the ARA Module 2 – Probability and Reliability Statistics course

Steve is an excellent instructor who is very good on a dry topic. His application ability is great.

Bruce Schubert, BAE Systems

ARA Module 1 – Reliability Concepts and Data

of the Applied Reliability Analysis Course

Using Reliasoft Weibull++

In this introductory module, we define Reliability and cover many important reliability concepts.  We discuss why reliability performance is so important and the types of questions that may be answered by analyzing failure data.  Different types of reliability data are reviewed.  We introduce the concept of censored data which allows us to correctly utilize partial or incomplete data in the analysis of test or field data.  We cover the Bathtub Curve which illustrates the types of failures that may occur over a product’s lifetime.  Finally, we introduce the reliability function and discuss product reliability goals.

A little background, motivation, full course overview, and a welcome from the instructor, Steven Wachs

Training Objectives

The key training full course objectives are summarized below (highlighted objectives relate to this module)

• Understand reliability concepts and unique aspects of reliability data
• Understand underlying probability and statistical concepts for reliability analysis
• Develop competency in the modeling and analysis of time-to-failure data
• Use and interpret probability plots for distribution fitting
• Understand reliability metrics and how to estimate and report them
• Handle multiple failure modes in reliability estimation
• Utilize degradation Data and models to predict failure times for life data analysis
• Use nonparametric estimation methods when appropriate
• Determine if reliability specifications are met (at specified confidence level) or whether design improvements are required
• Estimate estimate uncertainty using confidence intervals and bounds
• Estimate reliability of subsystems and systems
• Handle basic series and parallel systems
• Become aware of system reliability activities such as Reliability Block diagramming, Reliability importance, and Reliability allocation
• Develop competency in the planning of reliability tests (sample sizes)
• Use simulation to support estimation test planning
• Develop reliability demonstration test plans (testing time vs. number of units tradeoffs)
• Analyze existing warranty data to predict future returns
• Analyze data from Accelerated Life Tests to estimate reliability at normal use conditions (single stress, multiple stress, time-varying stress models)
• Plan Accelerated Life tests (sample sizes, determination of test stress combinations, allocation of units to stress levels)
• Incorporate degradation data modeling into Accelerated Life Test analysis
• Perform stress-strength analysis using analytical methods, software, and simulation
• Model reliability from binary response data (i.e. pass/fail)
• Analyze repair data from repairable systems to predict the number of failures over time, conditional reliability, and failure rates
• Develop proficiency in the use of Reliasoft Weibull++ for Reliability analyses

Steven Wachs, Course Instructor

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Steve regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.

 

Thorough knowledge of subject matter and ability to communicate it – exceptional instructor.

— Steve Fleishman, Magna Powertrain

---

Why is This Course Important?

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 online reliability training courses also present 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 presents a difficult challenge in many industries with short product cycles and compressed product development timeframes.  This course provides an in-depth treatment of quantitative methods for predicting and demonstrating product reliability from data gathered from physical testing or from field data.

Typical Attendees

• Product Engineers
• Design Engineers
• Quality Engineers
• Reliability Engineering Training
• Project / Program Managers
• Manufacturing Personnel
• Six Sigma Professionals

Enroll in the ARA Module 1 – Reliability Concepts and Data course

Steve is an excellent instructor who is very good on a dry topic. His application ability is great.

Bruce Schubert, BAE Systems