Research on Industrial Process Fault Diagnosis Method Based on DMCA-BiGRUN

Feng Yu (), Changzhou Zhang and Jihan Li
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Feng Yu: College of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China
Changzhou Zhang: College of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China
Jihan Li: School of Artificial Intelligence, Shenyang Aerospace University, Shenyang 110136, China

Mathematics, 2025, vol. 13, issue 15, 1-26

Abstract: With the rising automation and complexity level of industrial systems, the efficiency and accuracy of fault diagnosis have become a critical challenge. The convolutional neural network (CNN) has shown some success in the fault diagnosis field. However, typical convolutional kernels are commonly fixed-sized, which makes it difficult to capture multi-scale features simultaneously. Additionally, the use of numerous fixed-size convolutional filters often results in redundant parameters. During the feature extraction process, the CNN often struggles to take inter-channel dependencies and spatial location information into consideration. There are also limitations in extracting various time-scale features. To address these issues, a fault diagnosis method on the basis of a dual-path mixed convolutional attention-BiGRU network (DMCA-BiGRUN) is proposed for industrial processes. Firstly, a dual-path mixed CNN (DMCNN) is designed to capture features at multiple scales while effectively reducing the parameter count. Secondly, a coordinate attention mechanism (CAM) is designed to help the network to concentrate on main features more effectively during feature extraction by combining the channel relationship and position information. Finally, a bidirectional gated recurrent unit (BiGRU) is introduced to process sequences in both directions, which can effectively learn the long-range temporal dependencies of sequence data. To verify the fault diagnosis performance of the proposed method, simulation experiments are implemented on the Tennessee Eastman (TE) and Continuous Stirred Tank Reactor (CSTR) datasets. Some deep learning methods are compared in the experiments, and the results confirm the feasibility and superiority of DMCA-BiGRUN.

Keywords: multi-scale feature extraction; convolutional neural network; TE process; fault diagnosis; attention mechanism; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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