Repairable Systems Analysis
For reliability growth data analysis only.
Some of the models in Weibull++ can be used to analyze data from repairable systems operating in the field under typical customer usage conditions. Such data might be obtained from a warranty system, repair depot, operational testing, etc.
Specifically, you can use the power law or Crow-AMSAA (NHPP) models for repairable system analysis based on the assumption of minimal repair (i.e., the system is "as bad as old" after each repair) to calculate metrics such as the expected number of failures, rate of wearout or the optimum time to replace or overhaul a system to minimize life cycle costs.
The following topics describe how to perform repairable systems analysis in Reliability Growth Analysis (RGA), including:
- The applicable data types and models
- QCP results and plots for repairable systems analysis
- An example using the power law model to analyze failure/repair data from three race cars
The Reliability Growth and Repairable System Analysis Reference has more information on repairable systems analysis at https://help.reliasoft.com/reference/reliability_growth_and_repairable_system_analysis/rg_rsa/repairable_systems_analysis.html.
Tip: For even more advanced repairable systems analysis capabilities, you may wish to use ReliaSoft's BlockSim software. With BlockSim, you can use discrete event simulation to perform a wide variety of reliability, availability, maintainability and supportability (RAMS) analyses for repairable systems.
