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Validity : 10th Dec'24 to 20th Dec'24
Predicting Product Life with Reliability Analysis Methods
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 that 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.
Course 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
Overview of the webinar
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.
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
Why should you attend?
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
Faculty - Mr.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 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 to estimate and reduce warranty. In addition to providing consulting services, Steve regularly conducts workshops in industrial statistical methods for companies worldwide.
Education:
M.A., Applied Statistics, University of Michigan, 2002
M.B.A, Katz Graduate School of Business, University of Pittsburgh, 1992
B.S., Mechanical Engineering, University of Michigan, 1986