A hybrid residual correction method with auxiliary particle filter and conditional kernel density estimation for remaining useful life prediction

Abstract

Predicting the remaining useful life (RUL) is crucial for prognosis and health management and provides an essential foundation for maintenance decisions. This study constructed a hybrid RUL prediction method based on residual correction to enhance prediction accuracy, minimize uncertainty, and mitigate errors caused by local fluctuations in the degeneration state within complex environments. This approach combined an auxiliary particle filter (APF), complementary ensemble empirical mode decomposition (CEEMD), and conditional kernel density estimation (CKDE). First, a Wiener degradation model was established, and the APF was used to acquire the initial state estimate of the equipment and residual difference between actual observations. Second, CEEMD was used to reconstruct the residual sequence and reduce interference. Subsequently, the nonparametric CKDE predicted the residual to correct the filter estimation. The corrected degeneration state value elucidated the local details of the system degradation process, and the point prediction of the RUL at the current time was obtained. The probability density distribution of the remaining life was estimated by substituting the modified degeneration state values into the kernel density estimation (KDE) model. Finally, the method was tested using an aircraft engine dataset. The results demonstrated the efficacy of the proposed method and its superiority over alternative approaches.