| 한글제목(Korean Title) |
Industrial Process Monitoring and Fault Diagnosis Based on Temporal Attention Augmented Deep Network |
| 영문제목(English Title) |
Industrial Process Monitoring and Fault Diagnosis Based on Temporal Attention Augmented Deep Network |
| 저자(Author) |
Ke Mu
Lin Luo
Qiao Wang
Fushun Mao
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| 원문수록처(Citation) |
VOL 17 NO. 02 PP. 0242 ~ 0252 (2021. 04) |
한글내용
(Korean Abstract) |
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영문내용
(English 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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| 키워드(Keyword) |
Deep Learning
Online Fault Classification
Recurrent Neural Networks
Temporal Attention Mechanism
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| 파일첨부 |
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