Yu-Ying Liang

Abstract To address the phased-depended and random characteristics exhibited by products along their degradation process, this paper proposes a novel degradation modeling and reliability analysis method based on a two-phase Random Volatility Inverse Gaussian (RV-IG) process. Firstly, the Schwarz Information Criterion (SIC) is employed to identify change points in the product’s degradation trajectory.

References 1, , , and , “Health Diagnosis for Lithium-Ion Battery by Combining Partial Incremental Capacity and Deep Belief Network During Insufficient Discharge Profile,” IEEE Transactions on Industrial Electronics 70, no. 11 (2023): 11242–11250. 2, , , , and , “Reliability Modeling of Competing Failure Processes With Multi-Stage Degradation,” Quality & Reliability Engineering International 39, no. 4 (2023): 1494–1517.

1 INTRODUCTION Globally, arthritis serves as a disabling ubiquitous disease, impacting approximately 250 million individuals. Alarmingly, this condition represents one of the most rapidly proliferating health challenges worldwide (Cross et al., 2014; Theo et al., 2020). It is also a major cause of joint dysfunction, disability, and high medical costs. Arthritis is a general term for various types of joint inflammatory diseases.