ABSTRACT
Deep learning (DL) architecture, which exploits multiple hidden layers to learn hierarchical representations automatically from massive input data, presents a promising tool for characterizing fault conditions. For induction motor fault diagnosis, monitoring signals from multiple sensors represent different fault information. To determine the specific fault type, it is necessary to fuse multi-sensor information. However, most of the existing researches usually utilize one single type of signal as input, which cannot fully characterize the state information of the induction motor. To address this issue, this chapter proposes a DL-based multi-signal fault diagnosis method that leverages the powerful feature learning ability of a convolutional neural network (CNN) in images. The proposed deep model is able to learn from multiple types of sensor signals simultaneously so that it can achieve robust performance and finally realize accurate induction motor fault recognition. Experimental results indicate that the proposed method outperforms traditional fault diagnosis methods, hence, demonstrating effectiveness in induction motor application.
