Introduction to the Course

A little background and motivation for the material in this course.

• Welcome
• Instructor Introduction / Background
• Course Format / Materials / Software – Minitab

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Training Objectives

The key training objectives are summarized below:

• 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
• Understand reliability metrics and how to estimate and report them
• Handle Multiple Failure Modes
• Estimate reliability of subsystems and systems
• Become aware of system reliability activities such as Reliability Block diagramming, Reliability importance, and Reliability allocation
• Determine if reliability specifications are met (at specified confidence level) or whether design improvements are required
• Develop competency in the planning of reliability tests (sample sizes)
• Develop reliability demonstration test plans (testing time vs. number of units tradeoffs)
• Analyze existing warranty data to predict future returns

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.

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Master Reliability Analysis Methods Using Minitab starting today.

Your On-demand Course with Instructor Support

Immediate access to all course lessons discussing concepts, reviewing procedures and flushing out context and applicability. We use Minitab software for the examples and exercises.

Plus, I’m here to support you upon request.

Lessons include text, video lectures, quick quizzes, exercises, and activities. The intent is to encourage you to immediately apply the lessons within your organization such that you can start improving process stability and quality.

How Long Will the Course Take?

This will depend on how many video lectures you view and how many of the sample exam problems you attempt. The course contains 9 modules, with a total of 52 lessons including 10 exercises. There are approximately 16 hours of lectures.

It is recommended that beyond the lectures, you plan on another 10 to 20 hours for reading and working the exercises. Plus, you are encouraged to ‘try this at work/home’ too.

You can always revisit a lesson or check a detail in the supporting student text.

What are the course prerequisites?

An interest in reliability analysis methods Or, more accurately, an interest in understanding how to analyze life data for your prototypes, products, or systems.

Familiarity with basic statistical concepts is not necessary for this course.

What is your return policy?

If you are not satisfied with the content, send me an email within 30 days for a full refund.

Module 1 Reliability Concepts & Data

Lesson M01-01

Text: Section 1 page 1

Duration: 18 minutes

Intense global competition and increasing customer expectationsIntense global competition and increasing customer expectations have increased the pressure on manufacturers to produce high-quality and reliable products. Improving reliability is a key element of the larger overall objective of improving product quality. The survival of any manufacturer today is dependent on its ability to produce reliable products.

Although a primary objective of reliability analysis is to improve product reliability, there are many possible reasons for collecting and analyzing reliability data. Several examples are the following:

• Assessing product reliability in the field
• Predicting product warranty costs
• Assessing the effect of a proposed design change
• Demonstrating product reliability to customers or government agencies
• Improving reliability through the use of laboratory experiments
• Providing risk assessment for safety-critical systems