Abstract In prognostics and health management (PHM), degradation modeling plays a central role in reliability analysis and lifetime prediction. The inverse Gaussian (IG) process has recently attracted increasing attention for its ability to describe monotonic and cumulative degradation with heavy-tailed behavior, analytical tractability, and clear physical interpretability. Meanwhile, the rapid development of artificial intelligence (AI) has created new opportunities to combine statistical modeling with learning-based approaches in reliability analysis. This paper presents a comprehensive review of IG-process-based degradation modeling, covering its theoretical foundations, model extensions, parameter estimation, and diagnostic methods. Applications in accelerated degradation test design, burn-in test, remaining useful life prediction, and maintenance optimization are systematically summarized. Recent progress on AI-integrated IG frameworks is also reviewed and critically assessed. In addition, key challenges and research opportunities are discussed to guide future developments in intelligent PHM.
Zhuang et al. (Sun,) studied this question.