A Degradation Performance Model With Mixed-type Covariates and Latent Heterogeneity

TL;DR

This paper introduces a comprehensive degradation modeling framework that incorporates both observed external/internal factors and unobserved heterogeneity, improving reliability predictions for highly reliable products.

Contribution

It develops a novel degradation model with mixed-type covariates and latent heterogeneity, along with an effective estimation algorithm for better reliability assessment.

Findings

The proposed model outperforms existing approaches in a real case study.

It effectively captures the influence of both external and internal factors.

The framework provides interpretable insights into degradation processes.

Abstract

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.