  Successful modeling of degradation performance data is essential for accurate
reliability assessment and failure predictions of highly reliable product
units. The degradation performance measurements over time are highly
heterogeneous. Such heterogeneity can be partially attributed to external
factors, such as accelerated/environmental conditions, and can also be
attributed to internal factors, such as material microstructure characteristics
of product units. The latent heterogeneity due to the unobserved/unknown
factors shared within each product unit may also exists and need to be
considered as well. Existing degradation models often fail to consider (i) the
influence of both external accelerated/environmental conditions and internal
material information, (ii) the influence of unobserved/unknown factors within
each unit. In this work, we propose a generic degradation performance modeling
framework with mixed-type covariates and latent heterogeneity to account for
both influences of observed internal and external factors as well as unobserved
factors. Effective estimation algorithm is also developed to jointly quantify
the influences of mixed-type covariates and individual latent heterogeneity,
and also to examine the potential interaction between mixed-type covariates.
Functional data analysis and data augmentation techniques are employed to
address a series of estimation issues. A real case study is further provided to
demonstrate the superior performance of the proposed approach over several
alternative modeling approaches. Besides, the proposed degradation performance
modeling framework also provides interpretable findings.
