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In life data analysis, it is assumed that the components being analyzed are non-repairable; that is, they are either discarded or replaced upon failure. When analyzing the failure behavior of non-repairable components, the data points are typically either times-to-failure or times-to-suspension. For a group of non-repairable units coming from a single population, the time-to-failure of one unit in the sample does not affect the time-to-failure of other units in the sample. Therefore, the lifetimes of non-repairable systems are considered to be independent and identically distributed (i.i.d).
On the other hand, for complex systems such as automobiles, computers, aircraft, etc., it is likely that the system will be repaired (not discarded) upon failure. Failures are recurring events in the life of a repairable system, and data from such a system are obtained by recording the age of the system at the time when each failure occurred. This type of data is known as recurrent event data.
The failure behavior of a repairable system is dependent on that system’s history of repairs; therefore, traditional life data analysis methods, such as the Weibull distribution, are not appropriate because those methods treat every failure event as identical and independent from the previous one. In order to analyze recurrent event data, Weibull++ includes a choice of two methods: non-parametric and parametric analysis.
The ReliaWiki resource portal has more information on recurrent event data analysis at http://www.reliawiki.org/index.php/Recurrent Event Data Analysis.
Non-parametric RDA:
Parametric RDA:
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