A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion

Abstract

Bearing fault diagnosis has extensively exploited vibration signals (VSs) because of their rich information about bearing health conditions. However, this approach is expensive because the measurement of VSs requires external accelerometers. Moreover, in machine systems that are inaccessible or unable to be installed in external sensors, the VS-based approach is impracticable. Otherwise, motor current signals (CSs) are easily measured by the inverters that are the available components of those systems. Therefore, the motor CS-based bearing fault diagnosis approach has attracted considerable attention from researchers. However, the performance of this approach is still not good as the VS-based approach, especially in the case of fault diagnosis for external bearings (the bearings that are installed outside of the electric motors). Accordingly, this article proposes a motor CS-based fault diagnosis method utilizing deep learning and information fusion (IF), which can be applied to external bearings in rotary machine systems. The proposed method uses raw signals from multiple phases of the motor current as direct input, and the features are extracted from the CSs of each phase. Then, each feature set is classified separately by a convolutional neural network (CNN). To enhance the classification accuracy, a novel decision-level IF technique is introduced to fuse information from all of the utilized CNNs. The problem of decision-level IF is transformed into a simple pattern classification task, which can be solved effectively by familiar supervised learning algorithms. The effectiveness of the proposed fault diagnosis method is verified through experiments carried out with actual bearing fault signals.