Predicting Product Lifetime using Reliability Analysis Methods

Duration: 75 Minutes
Participants will gain awareness of the overall methodology for setting reliability targets, estimating product reliability from test data and/or field data, and determining whether or not reliability targets are achieved. Participants will also learn how to calculate sample sizes for reliability testing.
Reliability Analysis Methods
Instructor: Steven Wachs
Product ID: 501156
Objectives of the Presentation
  • Understand key aspects of reliability data
  • Learn what an effective reliability goal/target looks like
  • Learn how reliability performance is typically measured (e.g. Reliability Statistics)
  • How to determine appropriate probability distributions to model failure data
  • How to use reliability models to predict reliability performance
  • How much data is needed to estimate or demonstrate reliability
Why Should you Attend
Achieving high product reliability has become increasingly vital for manufacturers in order to meet customer expectations amid the threat of strong global competition. Poor reliability can doom a product and jeopardize the reputation of a brand or company. Inadequate reliability also presents financial risks from warranty, product recalls, and potential litigation. When developing new products, it is imperative that manufacturers develop reliability specifications and utilize methods to predict and verify that those reliability specifications will be met. This presents a difficult challenge in many industries with short product cycles and compressed product development timeframes. This webinar provides an overview of quantitative methods for predicting product reliability from data gathered from physical testing or from field data.

Areas Covered
RELIABILITY CONCEPTS AND RELIABILITY DATA
  • Reliability in product and process development
  • Unique characteristics of Reliability Data
  • Censored data
  • Setting reliability targets
PROBABILITY AND STATISTICS CONCEPTS
  • Probability distributions (e.g. Weibull, Lognormal, etc.)
  • Reliability and failure probability
  • Hazard rate
  • Mean time to failure
  • Percentiles
ASSESSING & SELECTING PARAMETRIC MODELS FOR FAILURE TIME DISTRIBUTIONS
  • Probability plotting
  • Identify the best distribution(s)
PARAMETRIC ESTIMATION OF RELIABILITY CHARACTERISTICS
  • Weibull analysis (and other distributions)
  • Precision of estimates/confidence intervals
INTRODUCTION TO RELIABILITY TEST PLANNING
  • Reliability estimation test plans
  • Reliability demonstration test plans
Who can Benefit
  • Quality Engineer
  • Quality Personnel
  • Reliability Engineer/Personnel
  • Program Manager
  • Design Engineering staff
  • Manufacturing personnel
  • Product Integrity staff
$375
Recorded Session for one participant
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Live Session - How it works
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  • Login to the audio conference on the scheduled date and time
  • Get answers to your queries through interactive Q&A sessions via chat at the end of the session
  • Download the Certificate of Attendance and Purchase Invoice from your OCP Account 24 hours after the completion of the session
  • Please let us know your thoughts and views at the end of the webinar, your valuable feedback will help us improve
Recorded Session - How it works
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  • Upon purchase of the recorded session a link will be updated on your OCP Account within 24 hours
  • Please click on the link to access the Recorded Session
  • Presentation handouts in downloadable PDF format will be updated on your OCP Account within 24 hours of the purchase of the product
  • Download the Certificate of Attendance and Purchase Invoice from your OCP Account after 48 hours of the product Purchase
  • Please share your valuable Feedback at the end of the session
Instructor Profile:
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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