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Research on TE process fault diagnosis method based on DBN and dropout
Author(s) -
Wei Yuqin,
Weng Zhengxin
Publication year - 2020
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.23750
Subject(s) - overfitting , dropout (neural networks) , deep belief network , artificial intelligence , computer science , process (computing) , deep learning , machine learning , fault (geology) , generalization , nonlinear system , representation (politics) , feature (linguistics) , pattern recognition (psychology) , artificial neural network , mathematics , mathematical analysis , physics , quantum mechanics , seismology , politics , political science , law , geology , operating system , linguistics , philosophy
Abstract In recent years, deep learning has shown outstanding performance and potential in pattern recognition and feature extraction, which has attracted an increasing amount of attention from engineering researchers and academics. Fault diagnosis methods based on deep learning have also become the focus of a significant amount of research. In this paper, a nonlinear process fault diagnosis and identification method based on DBN‐dropout is proposed. The deep belief network (DBN) has significant advantages in dealing with nonlinear processes, and it can extract the abstract representation of nonlinear process data to build a deep network to achieve the real‐time monitoring of process operations. Dropout technology can reduce overfitting and improve the generalization ability of the model. Afterwards, the Tennessee Eastman (TE) process is employed to analyze the performance of the proposed approach.