# A New Method of Remaining Useful Lifetime Estimation for a Degradation Process with Random Jumps

## TL;DR

This paper introduces a new method for estimating remaining useful lifetime in systems with unpredictable degradation patterns.

## Contribution

A novel non-homogeneous jump diffusion model with particle filtering for robust RUL estimation in complex degradation processes.

## Key findings

- The proposed model outperforms CNN and LSTM in RUL estimation accuracy.
- The method effectively handles non-monotonic degradation with random jumps.
- Validation on real-world temperature sensor data shows practical effectiveness.

## Abstract

With the deepening of degradation, the stability and reliability of the degrading system usually becomes poor, which may lead to random jumps occurring in the degradation path. A non-homogeneous jump diffusion process model is introduced to more accurately capture this type of degradation. In this paper, the proposed degradation model is translated into a state–space model, and then the Monte Carlo simulation of the state dynamic model based on particle filtering is employed for predicting the degradation evolution and estimating the remaining useful life (RUL). In addition, a general model identification approach is presented based on maximization likelihood estimation (MLE), and an iterative model identification approach is provided based on the expectation maximization (EM) algorithm. Finally, the practical value and effectiveness of the proposed method are validated using real-world degradation data from temperature sensors on a blast furnace wall. The results demonstrate that our approach provides a more accurate and robust RUL estimation compared to CNN and LSTM methods, offering a significant contribution to enhancing predictive maintenance strategies and operational safety for systems with complex, non-monotonic degradation patterns.