Enhancing Remaining Useful Life Predictions in Predictive Maintenance of MOSFETs: The Efficacy of Integrated Particle Filter-Gaussian Process Regression Models

Abstract

In the field of predictive maintenance, accurately predicting the remaining useful life (RUL) of equipment is critical to optimizing operations and avoiding unexpected failures. Traditional models such as Gaussian Process Regression (GPR), Particle Filter (PF), and Kalman Filter (KF) have been widely used, each with their own strengths and limitations. The motivation behind this study stems from the need to improve the accuracy and reliability of RUL predictions. Given the critical importance of predictive maintenance across a variety of industries, improved predictive models can provide significant operational and economic benefits. The main issues addressed are the limitations inherent in individual predictive models. GPR prediction gap or high initial error of his KF. This can lead to suboptimal RUL estimates. To address this problem, an integrated approach combining particle filtering and Gaussian process regression (PF-GPR) was proposed and developed. This integration aims to leverage the strengths of PF and GPR and potentially alleviate the limitations of the individual models. The performance of the PF-GPR model is evaluated and compared with the standalone His GPR, PF, and KF models using the prediction error and root mean square error (RMSE) at different points in the aging process of device #36. The results show that the PF-GPR model consistently outperforms the individual models in terms of both prediction error and RMSE, and significantly improves the accuracy and accuracy of RUL prediction. The PF-GPR model showed excellent performance in his RUL prediction for device #36, achieving the lowest prediction error and RMSE values at all time points as follows: B. The prediction error is only 2.30E-1 and the RMSE after 100 minutes is 7. 12E-3, which significantly outperforms his standalone GPR, PF, and KF models. The PF-GPR model demonstrated its excellent ability to provide robust and reliable predictions, highlighting the benefits of integrating different prediction methods into predictive maintenance applications.