Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Industrial Process Monitoring and Fault Diagnosis Based on Temporal Attention Augmented Deep Network

  • Mu, Ke (Dept. of Information and Control Engineering, Liaoning Shihua University) ;
  • Luo, Lin (Dept. of Information and Control Engineering, Liaoning Shihua University) ;
  • Wang, Qiao (Dept. of Information and Control Engineering, Liaoning Shihua University) ;
  • Mao, Fushun (Synthetic Detergent Factory of Fushun Petrochemical Company, China National Petroleum Corporation)
  • Received : 2020.11.18
  • Accepted : 2021.01.06
  • Published : 2021.04.30
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Abstract

Following the intuition that the local information in time instances is hardly incorporated into the posterior sequence in long short-term memory (LSTM), this paper proposes an attention augmented mechanism for fault diagnosis of the complex chemical process data. Unlike conventional fault diagnosis and classification methods, an attention mechanism layer architecture is introduced to detect and focus on local temporal information. The augmented deep network results preserve each local instance's importance and contribution and allow the interpretable feature representation and classification simultaneously. The comprehensive comparative analyses demonstrate that the developed model has a high-quality fault classification rate of 95.49%, on average. The results are comparable to those obtained using various other techniques for the Tennessee Eastman benchmark process.

Keywords

Acknowledgement

This paper is supported by National Natural Science Foundation of China (No. 61703191), the Foundation of Liaoning Educational Committee (No. L2017LQN028), the Scientific Research Foundation of Liaoning Shihua University (No. 2017XJJ-012).

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