Intelligent bearing faults diagnosis featuring Automated Relative Energy based Empirical Mode Decomposition and novel Cepstral Autoregressive features

Abstract

Vibration signal analysis is a significant approach in fault diagnosis. This article presents a new vibration signal preprocessing scheme called Automated Relative Energy based Empirical Mode Decomposition (AREEMD) and a novel Cepstral Autoregressive (cAR) feature extraction. AREEMD generates the modes based on high relative energies and automates the signal reconstruction. The cAR features take advantage of the cepstrum information and provide discriminant markers for various machine faults. The weighted K-nearest neighbor delivered the best results when validated on four different datasets. The proposed scheme yielded promising performances of average accuracies of 99.3% using SUBF, 97.4% for CWRU, 100% on the Ottawa, and 99.1% using the combined dataset. The significant generalizability of the technique is demonstrated by its high performance in identifying faults that will aid in the detection of early warnings, regardless of motor speeds and size of faults, assuring a reduction in system shutdowns brought on by bearing failure.