  In Big Data environment, one pressing challenge facing engineers is to
perform reliability analysis for a large fleet of heterogeneous repairable
systems with covariates. In addition to static covariates, which include
time-invariant system attributes such as nominal operating conditions,
geo-locations, etc., the recent advances of sensing technologies have also made
it possible to obtain dynamic sensor measurement of system operating and
environmental conditions. As a common practice in the Big Data environment, the
massive reliability data are typically stored in some distributed storage
systems. Leveraging the power of modern statistical learning, this paper
investigates a statistical approach which integrates the Random Forests
algorithm and the classical data analysis methodologies for repairable system
reliability, such as the nonparametric estimator for the Mean Cumulative
Function and the parametric models based on the Nonhomogeneous Poisson Process.
We show that the proposed approach effectively addresses some common challenges
arising from practice, including system heterogeneity, covariate selection,
model specification and data locality due to the distributed data storage. The
large sample properties as well as the uniform consistency of the proposed
estimator is established. Two numerical examples and a case study are presented
to illustrate the application of the proposed approach. The strengths of the
proposed approach are demonstrated by comparison studies.
