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

The seminar was great, as usual. The books are excellent resources when I am back on the job.
— D.J. Gray Intier Automotive

Get started with the ARA Module 11 – Repairable Systems Analysis course today

This module-focused course includes the course information section (participants guide, textbook, and example/exercise files), plus the 5 lessons found in module 11. This module may take 2 to 3 hours to complete. Once purchased, you will have 6 months of access to the module’s content.

The full course has 11 modules, 69 lessons, and approximately 32 hours of material, examples, and exercises. Steven is available to answer your questions and discuss course topics. Once purchased, you have full access for six months. The course is on-demand, so you can engage with the course in a way that fits your schedule and interests.

The course format is voice-over slides along with a ‘live’ view of Steven showing how to use Weibull++ with examples and when reviewing exercise solutions.

The course includes the recorded presentations, a Participant Guide (copies of the slides), a Supplemental Textbook, and a Reliasoft file that includes the datasets for examples and exercises. You can download the guide, textbook, and Reliasoft files in the third lesson of the Course Introduction module. You may also view the guide and textbook within that lesson online.

The course does include some statistical background and theory, yet the emphasis is on applying reliability analysis methods using Reliasoft Weibull++. We will examine the data setup, analysis, and interpretation.

Enroll in the Full Applied Reliability Analysis course

Steve Wachs was a wonderful instructor.
Gwen Case, Schrader-Bridgeport Int’l Inc.

Enroll in the ARA Module 11 – Repairable Systems Analysis course

If you have already signed up for the module 1 course, login and enjoy.

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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.