Robust Monitoring and Fault Isolation of Nonlinear Industrial Processes Using Denoising Autoencoder and Elastic Net

Robust process monitoring and reliable fault isolation in industrial processes usually encounter different challenges, including process nonlinearity and noise interference. In this brief, a novel method denoising autoencoder and elastic net (DAE-EN) is proposed to solve the aforementioned issues by...

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Published in:	IEEE transactions on control systems technology Vol. 28; no. 3; pp. 1083 - 1091
Main Authors:	Yu, Wanke, Zhao, Chunhui
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.05.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Coders Data mining Denoising autoencoder (DAE) Discriminant analysis elastic net (EN) fault isolation Feature extraction Kernel kernel density estimation (KDE) Monitoring Neurons Noise reduction Nonlinearity Principal component analysis process monitoring Robustness Statistical methods
ISSN:	1063-6536, 1558-0865
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Abstract	Robust process monitoring and reliable fault isolation in industrial processes usually encounter different challenges, including process nonlinearity and noise interference. In this brief, a novel method denoising autoencoder and elastic net (DAE-EN) is proposed to solve the aforementioned issues by effectively integrating DAE and EN. The DAE is first trained to robustly capture the nonlinear structure of the industrial data. Then, the encoder network is updated into a sparse model using EN, so that the key variables associated with each neuron can be selected. After that two statistics are developed based on the extracted systematic structure and the retained residual information. In addition, another statistic is also constructed by combining the aforementioned two statistics to provide an overall measurement for the process sample. In this way, a robust monitoring model can be constructed to monitor the abnormal status in industrial processes. After the fault is detected, the faulty neurons are identified by the sparse exponential discriminant analysis, so that the associated faulty variables along each faulty neuron can thus be isolated. Two real industrial processes are used to validate the performance of the proposed method. Experimental results show that the proposed method can effectively detect the abnormal samples in industrial processes and accurately isolate the faulty variables from the normal ones.
AbstractList	Robust process monitoring and reliable fault isolation in industrial processes usually encounter different challenges, including process nonlinearity and noise interference. In this brief, a novel method denoising autoencoder and elastic net (DAE–EN) is proposed to solve the aforementioned issues by effectively integrating DAE and EN. The DAE is first trained to robustly capture the nonlinear structure of the industrial data. Then, the encoder network is updated into a sparse model using EN, so that the key variables associated with each neuron can be selected. After that two statistics are developed based on the extracted systematic structure and the retained residual information. In addition, another statistic is also constructed by combining the aforementioned two statistics to provide an overall measurement for the process sample. In this way, a robust monitoring model can be constructed to monitor the abnormal status in industrial processes. After the fault is detected, the faulty neurons are identified by the sparse exponential discriminant analysis, so that the associated faulty variables along each faulty neuron can thus be isolated. Two real industrial processes are used to validate the performance of the proposed method. Experimental results show that the proposed method can effectively detect the abnormal samples in industrial processes and accurately isolate the faulty variables from the normal ones.
Author	Yu, Wanke Zhao, Chunhui
Author_xml	– sequence: 1 givenname: Wanke orcidid: 0000-0002-3927-5656 surname: Yu fullname: Yu, Wanke email: yuwanke@zju.edu.cn organization: State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China – sequence: 2 givenname: Chunhui orcidid: 0000-0002-0254-5763 surname: Zhao fullname: Zhao, Chunhui email: chhzhao@zju.edu.cn organization: State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China
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SubjectTerms	Coders Data mining Denoising autoencoder (DAE) Discriminant analysis elastic net (EN) fault isolation Feature extraction Kernel kernel density estimation (KDE) Monitoring Neurons Noise reduction Nonlinearity Principal component analysis process monitoring Robustness Statistical methods
Title	Robust Monitoring and Fault Isolation of Nonlinear Industrial Processes Using Denoising Autoencoder and Elastic Net
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