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In Big Data environment, one pressing challenge facing engineers is toperform reliability analysis for a large fleet of heterogeneous repairablesystems with covariates. In addition to static covariates, which includetime-invariant system attributes such as nominal operating conditions,geo-locations, etc., the recent advances of sensing technologies have also madeit possible to obtain dynamic sensor measurement of system operating andenvironmental conditions. As a common practice in the Big Data environment, themassive reliability data are typically stored in some distributed storagesystems. Leveraging the power of modern statistical learning, this paperinvestigates a statistical approach which integrates the Random Forestsalgorithm and the classical data analysis methodologies for repairable systemreliability, such as the nonparametric estimator for the Mean CumulativeFunction and the parametric models based on the Nonhomogeneous Poisson Process.We show that the proposed approach effectively addresses some common challengesarising from practice, including system heterogeneity, covariate selection,model specification and data locality due to the distributed data storage. Thelarge sample properties as well as the uniform consistency of the proposedestimator is established. Two numerical examples and a case study are presentedto illustrate the application of the proposed approach. The strengths of theproposed approach are demonstrated by comparison studies.

Analysis of Large Heterogeneous Repairable System Reliability Data with  Static System Attributes and Dynamic Sensor Measurement in Big Data  Environment