Incipient Fault Diagnostics and Remaining Useful Life Prediction of Analog Filters

Remaining useful life (RUL) prediction can effectively prevent failure in analog filters. Few researchers consider incipient fault diagnosis before implementing RUL prediction of analog filters. To deal with this problem, a prognostic framework with incipient fault diagnosis included is proposed for analog filters. Wavelet features and Mahalanobis distance (MD) are used to diagnose incipient faults in analog filters. After the incipient fault is detected and isolated, a fault indicator (FI) which can monitor the degradation trend of analog filters is developed based on traditional frequency features. Moreover, a power function model is used to track the degradation of analog filters exhibited by FI. In order to manage the uncertainties, a particle filtering approach is applied to model adaption and RUL prediction. Two case studies involving a band-pass filter and a low-pass filter have been used to validate the performance of the approaches. The simulation results show that the proposed approaches can diagnose incipient faults effectively and predict RUL of analog filters with small errors and high precision.