  Traditional reliability analysis has been using time to event data,
degradation data, and recurrent event data, while the associated covariates
tend to be simple and constant over time. Over the past years, we have
witnessed the rapid development of sensor and wireless technology, which
enables us to track how the product has been used and under which environmental
conditions it has been used. Nowadays, we are able to collect richer
information on covariates which provides opportunities for better reliability
predictions. In this chapter, we first review recent development on statistical
methods for reliability analysis. We then focus on introducing several specific
methods that were developed for different types of reliability data with
covariate information. Illustrations of those methods are also provided using
examples from industry. Test planning is also an important part of reliability
analysis. In addition to data analysis, we also provide a briefly review on
recent developments of test planning and then focus on illustrating the
sequential Bayesian design with an example of fatigue testing for polymer
composites. The paper is concluded with some discussions and remarks.
