A deep learning method for motor fault diagnosis based on a capsule network with gate-structure dilated convolutions

Abstract

Motor fault diagnosis is critical to predictive maintenance of electrical motor condition monitoring, whereas the conventional motor fault diagnosis method cannot effectively diagnose conditions caused by motors’ complex structure, non-stationary signals and mechanical big data. To solve the mentioned problems and enhance the fault diagnostic accuracy and generalization performance under different actual motor conditions, this study proposes an efficient, noise-resistant, end-to-end deep learning algorithm based on a novel capsule network with gate-structure dilated convolutions (GDCCN) for motor fault diagnosis; such algorithm is subtly incorporated with the input gate structure of long short-term memory network (LSTM), the dilated convolutions, as well as the capsule network. In the GDCCN model, the raw vibration signals are directly fed into the input gate structure of LSTM, which are employed to effectively remove noise and harvest more valuable information from the input sample. The dilated convolution is exploited in the output denoising feature maps to exponentially expand the receptive field of convolution kernel, so more redundant information can be acquired to reduce the effect of randomness. The capsule network is introduced to generate a set of vector neurons to represent an entity existing in the feature maps; as a result, more specific feature representations can be extracted, and the feature can be comprehended, thereby enhancing the diagnostic accuracy. As revealed from the experimental results, the GDCCN-based intelligent motor fault diagnosis method outperforms the other classical DL algorithms in diagnosis accuracy, noise resistance, generalization and transfer-learning performance of different workloads.