Predicting Repair Rates with Plots

PREDICTING REPAIR RATES WITH PLOTS

Wayne B. Nelson, consultant

1. INTRODUCTION

  • Predict future population numbers of repairs, for example, during system warranty or design life,
  • Evaluate whether the population repair rate increases or decreases with age (this is useful for making decisions on factory burn-in, preventative replacement in service, and system retirement).
  • Nelson (2003) gives other repair and recurrent-event applications and information on
  • Comparing two or more data sets, which may come from different designs, vendors, production periods, environments, maintenance policies, etc.
  • Analyzing repair cost data or other numerical values associated with repairs (e.g., downtimes),
  • Analyzing data with a mix of types of repairs,
  • How plots reveal unexpected and useful information,
  • Analyzing availability data, including downtime for repairs,
  • Analyzing data with more complex censoring where system histories have gaps with missing repair data,
  • The minimal assumptions underlying the non-parametric estimate and confidence limits here.

2. REPAIR DATA

3. THE POPULATION MODEL AND ITS MEAN CUMULATIVE FUNCTION

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Reliability Engineering and Management Consultant focused on improving product reliability and increasing equipment availability.

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Fred Schenkelberg

Reliability Engineering and Management Consultant focused on improving product reliability and increasing equipment availability.

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