Industrial Process Monitoring and Fault Diagnosis Based on Temporal Attention Augmented Deep Network


Ke Mu, Lin Luo, Qiao Wang, and Fushun Mao, Journal of Information Processing Systems Vol. 17, No. 2, pp. 242-252, Apr. 2021  

10.3745/JIPS.04.0211
Keywords: Deep Learning, Online Fault Classification, Recurrent Neural Networks, Temporal Attention Mechanism
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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.


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Cite this article
[APA Style]
Ke Mu, Lin Luo, Qiao Wang, & and Fushun Mao (2021). Industrial Process Monitoring and Fault Diagnosis Based on Temporal Attention Augmented Deep Network. Journal of Information Processing Systems, 17(2), 242-252. DOI: 10.3745/JIPS.04.0211.

[IEEE Style]
K. Mu, L. Luo, Q. Wang and a. F. Mao, "Industrial Process Monitoring and Fault Diagnosis Based on Temporal Attention Augmented Deep Network," Journal of Information Processing Systems, vol. 17, no. 2, pp. 242-252, 2021. DOI: 10.3745/JIPS.04.0211.

[ACM Style]
Ke Mu, Lin Luo, Qiao Wang, and and Fushun Mao. 2021. Industrial Process Monitoring and Fault Diagnosis Based on Temporal Attention Augmented Deep Network. Journal of Information Processing Systems, 17, 2, (2021), 242-252. DOI: 10.3745/JIPS.04.0211.