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

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.