Mingsheng Wang, Bo Huang, Chuanpeng He, Peipei Li, Jiahao Zhang, Yu Chen, Jie Tong
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A Fault Diagnosis Model for Complex Industrial Process Based on Improved TCN and 1D CNN
Fast and accurate fault diagnosis of strongly coupled, time-varying, multivariable complex industrial processes remain a challenging problem. We propose an industrial fault diagnosis model. This model is established on the base of the temporal convolutional network (TCN) and the one-dimensional convolutional neural network (1DCNN). We add a batch normalization layer before the TCN layer, and the activation function of TCN is replaced from the initial ReLU function to the LeakyReLU function. To extract local correlations of features, a 1D convolution layer is added after the TCN layer, followed by the multi-head self-attention mechanism before the fully connected layer to enhance the model's diagnostic ability. The extended Tennessee Eastman Process (TEP) dataset is used as the index to evaluate the performance of our model. The experiment results show the high fault recognition accuracy and better generalization performance of our model, which proves its effectiveness. Additionally, the model's application on the diesel engine failure dataset of our partner's project validates the effectiveness of it in industrial scenarios.
期刊介绍:
Wuhan University Journal of Natural Sciences aims to promote rapid communication and exchange between the World and Wuhan University, as well as other Chinese universities and academic institutions. It mainly reflects the latest advances being made in many disciplines of scientific research in Chinese universities and academic institutions. The journal also publishes papers presented at conferences in China and abroad. The multi-disciplinary nature of Wuhan University Journal of Natural Sciences is apparent in the wide range of articles from leading Chinese scholars. This journal also aims to introduce Chinese academic achievements to the world community, by demonstrating the significance of Chinese scientific investigations.