Fault Monitoring Method for the Process Industry System Based on the Improved Dense Connection Network

Jiarula Yasenjiang (), Zhigang Lan, Kai Wang, Luhui Lv, Chao He, Yingjun Zhao, Wenhao Wang and Tian Gao
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Jiarula Yasenjiang: College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumqi 830017, China
Zhigang Lan: College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumqi 830017, China
Kai Wang: College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumqi 830017, China
Luhui Lv: College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumqi 830017, China
Chao He: College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumqi 830017, China
Yingjun Zhao: College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumqi 830017, China
Wenhao Wang: College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumqi 830017, China
Tian Gao: College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumqi 830017, China

Mathematics, 2024, vol. 12, issue 18, 1-20

Abstract: The safety of chemical processes is of critical importance. However, traditional fault monitoring methods have insufficiently studied the monitoring accuracy of multi-channel data and have not adequately considered the impact of noise on industrial processes. To address this issue, this paper proposes a neural network-based model, DSCBAM-DenseNet, which integrates depthwise separable convolution and attention modules to fuse multi-channel data features and enhance the model’s noise resistance. We simulated a real environment by adding Gaussian noise with different signal-to-noise ratios to the Tennessee Eastman process dataset and trained the model using multi-channel data. The experimental results show that this model outperforms traditional models in both fault diagnosis accuracy and noise resistance. Further research on a compressor unit engineering instance validated the superiority of the model.

Keywords: densely connected convolution network; deeply separable convolution; attention mechanism; Tennessee Eastman process (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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