Estimating Reliability Performance with Accelerated Life Tests

Speaker

Instructor: Steven Wachs
Product ID: 702095

Location
  • Duration: 75 Min
This webinar on Estimating Reliability Performance of products will show how you can use Accelerated Life Testing methods (ALT) to reduce costs, minimize risks and ensure adequate product performance prior to launch.
RECORDED TRAINING
Last Recorded Date: Feb-2017

 

$229.00
1 Person Unlimited viewing for 6 month info Recorded Link and Ref. material will be available in My CO Section
(For multiple locations contact Customer Care)

$299.00
Downloadable file is for usage in one location only. info Downloadable link along with the materials will be emailed within 2 business days
(For multiple locations contact Customer Care)

 

 

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Description:

Most manufacturers today recognize the criticality of developing products that will perform reliably during the useful life of the product. Inferior reliability performance alienates customers and significantly impairs brand and company reputations. While product development actions related to ensuring reliability targets are met have increased substantially in recent years, several challenges remain. They include:

  • Short product development timeframes
  • Difficulty in observing product failures (needed for reliability estimation) in available testing timeframe
  • Small sample sizes available for reliability testing

Accelerated Life Testing (ALT) methods may be utilized to overcome many of the challenges in standard reliability testing. Quantitative Accelerated Life Testing involves identifying stress conditions that will accelerate the failure mode(s) so that failures may be observed in a shorter time period. Failures observed at various stress conditions may be modeled and performance at normal use (or environmental) conditions may be predicted.

Why Should You Attend:

Superior reliability performance of your products can significantly improve brand and company reputation and boost customer acceptance. But several challenges still remain - short product development timeframes, inadequate testing timeframe and small sample sizes available for reliability testing.

This webinar will show how Accelerated Life Testing (ALT) methods may be utilized to overcome many of the challenges in standard reliability testing. Attendees will learn to:

  • Accelerate product testing to reduce costs while minimizing risk
  • Predict product performance rather than react to potential problems
  • Ensure adequate product performance prior to launch

Areas Covered in the Webinar:

  • Overview of Accelerated Life Testing.
  • Types of Stress Profiles.
  • Planning for an Accelerated Life Test (Sample Sizes, Stress Levels, Stress Profiles, etc.).
  • Analyzing Accelerated Life Test Data.
  • Predicting Product Performance.
  • Correlating Accelerated Life Test Results with Actual Reliability Performance.

Who Will Benefit:

  • Product Development Personnel
  • Research & Development Personnel
  • Reliability Professionals
  • Quality 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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