Compared to time-consuming and unreliable manual analysis, intelligent fault diagnosis techniques using deep learning models can improve the accuracy of intelligent fault diagnosis with their multi-layer nonlinear mapping capabilities. This paper proposes a model to perform fault diagnosis and classification by using a time series of vibration sensor data as the input. The model encodes the raw vibration signal into a two-dimensional image and performs feature extraction and classification by a deep convolutional neural network or improved capsule network. A fault diagnosis technique based on the Gramian Angular Field (GAF), the Markov Transition Field (MTF), and the Capsule Network is proposed. Experiments conducted on a bearing failure dataset from Case Western Reserve University investigated the impact of two coding methods and different network structures on the diagnosis accuracy. The results show that the GAF technique retains more complete fault characteristics, while the MTF technique contains a small number of fault characteristics but more dynamic characteristics. Therefore, the proposed method incorporates GAF images and MTF images as a dual-channel image input to the capsule network, enabling the network to obtain a more complete fault signature. Multiple sets of experiments were conducted on the bearing fault dataset at Case Western Reserve University, and the Capsule Network in the proposed model has an advantage over other convolutional neural networks and performs well in the comparison of fault diagnosis methods proposed by other researchers.
Motor failure can result in damage to resources and property. Real-time motor fault diagnosis technology can detect faults and diagnosis in time to prevent serious consequences caused by the continued operation of the machine. Neural network models can easily and accurately fault diagnose from vibration signals. However, they cannot notice faults in time. In this study, a deep learning model based on a temporal convolutional network (TCN) and attention is proposed for real-time motor fault diagnosis. TCN can extract features from shorter vibration signal sequences to allow the system to detect and diagnose faults faster. In addition, attention allows the model to have higher diagnostic accuracy. The experiments demonstrate that the proposed model is able to detect faults in time when they occur and has an excellent diagnostic accuracy.
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