Predicting Product Life with Reliability Analysis Methods

Steven Wachs

Steven Wachs

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...
Read More
Pre-recorded
90 Mins
Steven Wachs

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.

Webinar 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

Webinar Agenda

This webinar will cover the following topics:

  • 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

Webinar Highlights

  • Understand key aspects of Reliability Data
  • How to establish an effective reliability goal/target looks like
  • Common measures of reliability performance (i.e. Reliability Statistics)
  • How to determine appropriate probability distributions to model failure data
  • How to use reliability models to predict reliability performance
  • How to determine how much data is needed to estimate or demonstrate reliability.

Who Should Attend

The target audience includes personnel involved in product/process development and manufacturing 

  • Product Engineers
  • Reliability Engineers
  • Design Engineers
  • Quality Engineers
  • Quality Assurance Managers
  • Project / Program Managers
  • Manufacturing Personnel
Event Registration
$199.00
$299.00
$299.00
$349.00
$299.00
$199.00
$299.00
$199.00
$199.00
$299.00
$299.00
$199.00
Purchase Options
×

Recommended:

Webinar Recording + PDF Transcript

Get webinar recording (in mp4) with presentation handouts and pdf transcript for the webinar

 

$299

Recording Only

Webinar recording (in mp4) with presentation handouts

$199

Make your Own Bundle

Choose your own learning format/s

$199

We also Recommend

Workplace Violence is Becoming All Too Common. Are You Prepared?

Joe Keenan | 60 Mins
On Demand | HR & Payroll

From Conflict to Resolution: Managing Toxic and Difficult Employees

Bob Churilla | 90 Mins
On Demand | HR & Payroll

View More