Abstract:
Changes in the operating conditions of mechanical equipment not only increase the likelihood of performance degradation in mechanical components, but also impact the stability of the degradation process. This introduces challenges in predicting the remaining useful life (RUL) of mechanical equipment. Traditional Wiener process models are typically limited to the RUL prediction under a constant operating condition, leading to reduced accuracy in practical engineering scenarios. To address this problem, this paper proposes a Wiener process model that incorporates the influence of operating conditions. An exponential condition-dependent function is introduced to capture the relationship between the degradation process and operating conditions. Model parameters are estimated using the unit maximum likelihood estimation algorithm. Then, the variable parameter within the model is dynamically updated using an ensemble Kalman filter combined with a particle filter. Additionally, an analytical expression for the probability density function of the RUL is derived based on the constructed model. Finally, the effectiveness of the proposed prediction method is validated using a fatigue crack propagation dataset and a bearing degradation dataset. The experimental results show that the Akaike Information Criterion values of the proposed model are −185.64 and −537.76 for the two datasets, which are better than those of the traditional model. Additionally, the proposed prediction method achieves average Cumulative Relative Accuracy values of 0.93 and 0.86, and average Total Mean Square Error values of 25.74 and 28.34 for the two datasets, both of which outperform the prediction methods based on the particle filter and Bayesian principle.