Novel Data-Driven Approach Based on Capsule Network for Intelligent Multi-Fault Detection in Electric Motors
Jianjun Chen, Weihao Hu, Di Cao, Man Zhang, Qi Huang, Zhe Chen, Frede Blaabjerg
Abstract
With the steady development of technology, electric motors (EMs) have become one of the most important components in modern industry. To ensure stable industrial production, detecting and classifying the EM faults is crucial. A novel intelligent deep-learning-based multi-fault detection method for EMs under varying working conditions is proposed in this article. This method involves two steps: first, a 2D convolution network without pooling layer is proposed to extract features from raw EM data. In addition, a long short-term memory (LSTM) network is applied to extract the fault features for comparison. Second, a capsule network (Caps-Net) based on a dynamic routing algorithm is used as a classifier to realize intelligent multi-fault detection and improve the generalization performance of the proposed model. The proposed method is applicable to raw physical signals of EMs, which improves the overall efficiency of the fault detection. Moreover, the proposed method has a strong generalization ability. The simulation results demonstrate that the proposed approach can achieve higher accuracy than various benchmark methods. Moreover, its fault detection accuracy is higher than those of other state-of-the-art models under two working conditions, in which the load type and size of the EM are changed, respectively.