Sign for Notice Everyday    Sign Up| Sign In| Link| English|

Our Sponsors

    Receive Latest News

    Feedburner
    Share Us


    Predicting Product Lifetime using Reliability Analysis Methods

    View: 232

    Website http://www.onlinecompliancepanel.com/webinar/RELIABILITY-ANALYSIS-501156/DEC-2015-ES-OURGLOCAL | Want to Edit it Edit Freely

    Category Reliability, Product Life, Weibull Analysis, Mean Time to Failure, Reliability Statistics, Reliability Testing

    Deadline: December 02, 2015 | Date: December 03, 2015

    Venue/Country: online Webinar, U.S.A

    Updated: 2015-11-24 15:29:26 (GMT+9)

    Call For Papers - CFP

    Predicting Product Lifetime using Reliability Analysis Methods

    Instructor: Steven Wachs

    Product ID: 501156

    Level: Intermediate

    Description

    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.

    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

    For Registration -

    http://www.onlinecompliancepanel.com/webinar/RELIABILITY-ANALYSIS-501156/DEC-2015-ES-OURGLOCAL

    Note : Use coupon code 1371 and get 10% off on Registration


    Keywords: Accepted papers list. Acceptance Rate. EI Compendex. Engineering Index. ISTP index. ISI index. Impact Factor.
    Disclaimer: ourGlocal is an open academical resource system, which anyone can edit or update. Usually, journal information updated by us, journal managers or others. So the information is old or wrong now. Specially, impact factor is changing every year. Even it was correct when updated, it may have been changed now. So please go to Thomson Reuters to confirm latest value about Journal impact factor.