Accelerated Degradation Life Prediction for Vibration Monitoring Sensors Based on a Wiener Process with Random Effects

Vibration monitoring sensors are widely used to acquire vibration signals from machinery and structural systems and therefore play an important role in condition monitoring, fault diagnosis, early warning, and operational safety assurance. Accurate lifetime prediction and reliability assessment of such sensors are essential for ensuring the long‐term stability of monitoring systems. However, many conventional accelerated Wiener degradation models assume that the drift parameter alone depends on stress and do not explicitly account for unit‐to‐unit heterogeneity, which may reduce the accuracy of degradation modeling, lifetime extrapolation, and reliability assessment. To address these limitations, this study develops an accelerated degradation model for vibration monitoring sensors based on a Wiener process with random effects. Random effects are introduced into the drift term to characterize unit‐to‐unit variability in degradation rates. Based on the constant acceleration factor principle, the drift and diffusion parameters are linked through a common stress‐dependent acceleration relationship, and a generalized Eyring‐based accelerated degradation model is established. The proposed method is validated using accelerated degradation data of sensor output voltage collected under combined temperature‐vibration stresses. The results show that, compared with a conventional accelerated degradation model, the proposed model provides closer agreement with the empirical reliability estimates and better captures the heterogeneity observed among individual sensors, thereby improving the reliability‐oriented lifetime assessment.