Bearing Crack Diagnosis Using a Smooth Sliding Digital Twin to Overcome Fluctuations Arising in Unknown Conditions

Abstract

Bearings cause the most breakdowns in induction motors, which can result in significant economic losses. If faults in the bearings are not detected in time, they can cause the whole system to fail. System failures can lead to unexpected breakdowns, threats to worker safety, and huge economic losses. In this investigation, a new approach is proposed for fault diagnosis of bearings under variable low-speed conditions using a smooth sliding digital twin analysis of indirect acoustic emission (AE) signals. The proposed smooth sliding digital twin is designed based on the combination of the proposed autoregressive fuzzy Gauss–Laguerre bearing modeling approach and the proposed smooth sliding fuzzy observer. The proposed approach has four steps. The AE signals are resampled and the root mean square (RMS) feature is extracted from the AE signal in the first step. To estimate the resampled RMS bearing signal, a new smooth sliding digital twin is proposed in the second step. After that, the resampled RMS bearing residual signal is generated using the difference between the original and estimated signals. Next, a support vector machine (SVM) is proposed for crack detection and crack size identification. The effectiveness of this new approach is evaluated by AE signals provided by our lab’s bearing dataset, where the benchmark dataset consists of one normal and seven abnormal conditions: ball, outer, inner, outer-ball, inner-ball, inner-outer, and inner-outer-ball. The results demonstrated that the average accuracies of the anomaly diagnosis and crack size identification of AE signals for the bearings used in this new smooth sliding digital twin are 97.75% and 97.78%, respectively.