Real-time residual life prediction based on kernel density estimation considering abrupt change point detection

Abstract

The existence of abrupt change points in mechanical equipment degradation leads to inaccuracies in the prediction of its residual life. We propose a real-time residual life prediction method based on kernel density estimation (KDE) considering the influence of abrupt change points. First, a non-parametric cumulative sum method is used to detect abrupt change points in the degradation process. Then, integral mean square error is used to determine the abrupt change in the sample number that affects the accuracy of KDE. The weight coefficient is adaptively allocated according to the change in sample numbers relative to the minimum sample number before and after the abrupt change point in real-time monitoring. This method considers abrupt change states in the degradation process and uses KDE, which does not make model structure assumptions or parameter estimations for the degradation process. Finally, the effectiveness of this method is verified using the gear wear degradation data and compared with the real-time residual life prediction method based on KDE without considering the impact of the abrupt change point. The results show that this method can track the dynamic changes of a system more quickly and improve the accuracy of real-time residual life prediction.