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Fault detection and classification with feature representation based on deep residual convolutional neural network
Author(s) -
Ren Xuemei,
Zou Yiping,
Zhang Zheng
Publication year - 2019
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3170
Subject(s) - convolutional neural network , computer science , pattern recognition (psychology) , artificial intelligence , deep learning , residual , benchmark (surveying) , fault detection and isolation , preprocessor , feature (linguistics) , data pre processing , fault (geology) , representation (politics) , data set , process (computing) , external data representation , artificial neural network , data mining , algorithm , linguistics , philosophy , seismology , geology , politics , political science , law , actuator , geodesy , geography , operating system
This paper proposes a novel fault detection and classification method via deep residual convolutional neural network (DRCNN). The DRCNN captures the deep process features represented by convolutional layers from local to global. Unlike traditional methods, this feature representation can extract the deep fault information and learn the latent fault patterns. Besides, a data preprocessing approach is also proposed to transform the shape of original data into the shape available for convolutional neural network. Finally, experiments based on the data set of Tennessee Eastman process (TEP), a chemical industrial process benchmark, show that the proposed method achieves superior fault detection and better classification performance compared with the state‐of‐the‐art methods.