Robust switching Kalman filter for diagnostics of long-term condition monitoring data in the presence of non-Gaussian noise

Abstract

Machinery condition prognosis system uses long-term historical data to predict remaining useful life (RUL). One of the critical steps to reach this purpose is to segment long-term data into two or several degradation stages (Healthy, Unhealthy, and Critic stage). Finding changing points between regimes may be a crucial preliminary task for further predicting the degradation process. However, finding the accurate partition into two or more regimes is a challenging task in the actual application when the noise inherent in the observed process is non-Gaussian. Therefore, this paper introduced a robust methodology based on switching Kalman filters to address the problems mentioned. This approach uses multiple dynamic system models to explain different degradation stages, utilizing robust Bayesian estimation. Also, based on this fact, this approach works based on dynamic behavior; a threshold for diagnostics is no longer needed. Ultimately, the proposed approach is applied for the online diagnosis of simulated data sets in the presence of Gaussian and non-Gaussian noise. The result of the applied methodology on simulated data sets proves the method’s efficacy.