Estimation of machinery’s remaining useful life in the presence of non-Gaussian noise by using a robust extended Kalman filter

Shiri, Hamid, Zimroz, Pawel, Wyłomańska, Agnieszka and Zimroz, Radosław (2024) Estimation of machinery’s remaining useful life in the presence of non-Gaussian noise by using a robust extended Kalman filter. Measurement, 235, [114882]. (doi:10.1016/j.measurement.2024.114882).

Abstract

Estimation of the remaining useful life (RUL) of industrial machinery is essential for condition-based maintenance (CBM). While numerous papers have explored this issues, challenges arise as machinery often works in non-stationary conditions, particularly in harsh environments (like mining machines, wind turbines, helicopters, etc.). The data collected from such environments are affected by non-Gaussian noise, posing difficulties for traditional approaches to non-linear state estimation or prediction. The widely used extended Kalman filter (EKF) suffers from the non-Gaussian noise effect due to its recursive minimum L2-norm filtering. To address these issues, we propose a robust EKF based on the maximum correntropy criterion. This method effectively estimates the RUL of the time-varying degradation process in the presence of non-Gaussian noise, also enabling confidence interval computation for uncertainty management. The efficiency of our approach was confirmed through application to simulated and benchmark data sets, outperforming Kalman filter-based methods for both simulated and real-world scenarios.

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Contributors

Author: Hamid Shiri

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