Estimation of Remaining Useful Life Based on Switching Kalman Filter Neural Network Ensemble

The proposed method is an extension of an existing Kalman Filter (KF) ensemble method. While the original method has shown great promise in the earlier PHM 2008 Data Challenge, the main limitation of the KF ensemble is that it is only applicable to linear models. In prognostics, degradation of mechanical systems is typically non-linear in nature, therefore limiting the applications of KF ensemble in this area. To circumvent this problem, this paper propose to approximate non-linear functions with piecewise linear functions. When estimating the RUL, the Switching Kalman Filter (SKF) is able to choose the most probable degradation mode and thus make better predictions. The implementation of the proposed SKF ensemble method is illustrated by implementing on NASA’s C-MAPSS Dataset as well as the PHM 2008 Data Challenge Dataset. The results show the effectiveness of the SKF in detecting the switching point between various degradation modes as well as the improved accuracy of the SKF ensemble method compared to other available methods in literature.