Predicting Product Lifetimes Using Reliability Analysis Methods

Instructor: Steven Wachs
Product ID: 702096
  • 5
  • May 2017
  • 10:00 AM PDT | 01:00 PM EDT
    Duration: 75 Min

Live Online Training
May 05, Friday 10:00 AM PDT | 01:00 PM EDT | Duration: 75 Min

One Dial-in One Attendee
Group-Max. 10 Attendees/Location
(For multiple locations contact Customer Care)

recorded version

1x Person - Unlimited viewing for 6 Months
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Recorded Link and Ref. material will be available in My CO Section 48 hrs after completion of Live training

Training CD

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CD and Ref. material will be shipped within 15 business days after completion of Live training

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Read Frequently Asked Questions

In this training program, 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.

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 will provide an overview of quantitative methods for predicting product reliability from data gathered from physical testing or from field data.

Learning Objectives:

  • 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

Areas Covered in the Webinar:

  • 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 and Selecting Parametric Models for Failure Time Distributions
    • Probability Plotting
    • Identify the Best Distributions
  • 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 Will Benefit:

Anyone with a vested interest in product quality and reliability, such as:

  • Product Engineers
  • Reliability Engineers
  • Design Engineers
  • Quality Engineers
  • Quality Assurance Managers
  • Project/Program Managers
  • Manufacturing Personnel
Instructor Profile:
Steven Wachs

Steven Wachs
Principal Statistician, Integral Concepts, Inc

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. He 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.

Mr. Wachs 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. Mr. Wachs regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.

He has an M.A. in Applied Statistics from the University of Michigan, an M.B.A, Katz Graduate School of Business from the University of Pittsburgh, 1992, and a B.S., Mechanical Engineering from the University of Michigan.

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