Using long-term condition monitoring data with non-Gaussian noise for online diagnostics

The number of timely diagnoses based on condition monitoring data is increasing with the grow-ing usage of monitoring systems. In most of the methods used in these systems, a pre-established fault detection threshold is needed, while there are no specific limit values or thresholds in many cases, especially when the machine is unique. Also, in most actual applications, due to the kind of process and harsh environment, the noise inherent in the observed process exhibits non-Gaussian characteristics, making it a challenging task for diagnostics based on condition monitoring (CM) data. Therefore, this paper introduced a robust methodology based on the switching maximum correntropy Kalman filter (SMCKF) to address the mentioned problems (threshold and online diagnostics in the presence of non-Gaussian noise by using CM data). This approach uses multiple dynamic system models to explain different degradation stages, utilizing robust Bayesian estimation. As this approach is based on dynamic behavior, a threshold for diagnostics is no longer needed. Ultimately, the proposed approach is applied to the online diagnosis of simulated and actual data sets. The results of both simulated and real data sets prove the method's efficacy.

Automated summary

The number of timely diagnoses based on condition monitoring data is increasing, but the lack of specific limit values or thresholds poses challenges, especially in unique machine cases. Additionally, the non-Gaussian noise inherent in observed processes in many real applications makes diagnostics difficult. This paper introduces a robust methodology called switching maximum correntropy Kalman filter (SMCKF) that addresses the issues of threshold and online diagnostics in the presence of non-Gaussian noise using condition monitoring (CM) data. The proposed approach, based on dynamic behavior, eliminates the need for a diagnostic threshold and proves effective in both simulated and real data sets.