A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant

Min Wang; Li Sheng; Donghua Zhou; Maoyin Chen

doi:10.1109/JAS.2022.105467

Volume 9 Issue 4

Apr. 2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(4): 719-727

M. Wang, L. Sheng, D. H. Zhou, and M. Y. Chen, “A feature weighted mixed naive Bayes model for monitoring anomalies in the fan system of a thermal power plant,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 4, pp. 719–727, Apr. 2022. doi: 10.1109/JAS.2022.105467

Citation:

M. Wang, L. Sheng, D. H. Zhou, and M. Y. Chen, “A feature weighted mixed naive Bayes model for monitoring anomalies in the fan system of a thermal power plant,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 4, pp. 719–727, Apr. 2022. doi: 10.1109/JAS.2022.105467

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A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant

doi: 10.1109/JAS.2022.105467

Min Wang^1
,,
Li Sheng^2
,,
Donghua Zhou^{3, 4
,
,},
Maoyin Chen^1
,

1.
Department of Automation, Tsinghua University, Beijing 100084, China
2.
College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China
3.
College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
4.
Department of Automation, Tsinghua University, Beijing 100084, China

Funds: This work was supported by the National Natural Science Foundation of China (62033008, 61873143)

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Author Bio:
Min Wang (Graduate Student Member, IEEE) received the B.Eng. degree from the School of Information Engineering, Chang’an University, China, in 2015, and the M.Eng. degree from the School of Reliability and Systems Engineering, Beihang University, Beijing, China, in 2018. He is currently a Ph.D. candidate at the Department of Automation, Tsinghua University, China. His research interests include process monitoring, fault diagnosis, statistical learning, and ensemble learning

Li Sheng (Member, IEEE) received the B.Sc. degree in computer science and technology in 2003, and the M.Sc. degree in computer software and theory in 2006, both from Shandong Normal University, Jinan, China, and the Ph.D. degree in control theory and control engineering in 2010 from Jiangnan University, Wuxi, China. From October 2008 to October 2009, he was a Visiting Ph.D. Student in the Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA. From March 2015 to March 2016, he was a Visiting Scholar in the Department of Information Systems and Computing, Brunel University London, UK. He is currently a Professor in the College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China. His current research interests include stochastic control and filtering, networked control systems, and fault detection and diagnosis for modern systems. He has published more than 30 papers in refereed international journals. He is a very active reviewer for many international journals

Donghua Zhou (Fellow, IEEE) received the B.Eng., M.Sci., and Ph.D. degrees all in electrical engineering from Shanghai Jiaotong University, China, in 1985, 1988, and 1990, respectively. He was an Alexander von Humboldt Research Fellow with the University of Duisburg, Germany from 1995 to 1996, and a Visiting Scholar with Yale University, USA from 2001 to 2002. He joined Tsinghua University in 1996, and was promoted as Full Professor in 1997, he was the head of the Department of Automation, Tsinghua University, during 2008 and 2015. He is now a Vice President, Shandong University of Science and Technology, and a Joint Professor of Tsinghua University. His research interests include fault diagnosis, fault-tolerant control, reliability prediction, and optimal maintenance. He has authored and coauthored over 220 peer-reviewed international journal papers and 7 monographs in the areas of fault diagnosis, fault-tolerant control and operational safety evaluation. He is a Fellow of IEEE, CAA and IET, a Member of the International Federationof Automatic Control (IFAC) Technical Committee (TC) on SAFEPROCESS, an Associate Editor of Journal of Process Control, the Vice Chairman of Chinese Association of Automation (CAA), the TC Chair of the SAFEPROCESS Committee, CAA. He was also the National Organizing Committee (NOC) Chair of the 6th IFAC Symposium on SAFEPROCESS 2006

Maoyin Chen (Member, IEEE) received the B.S. degree in mathematics and the M.S. degree in control theory and control engineering from Qufu Normal University, Shandong, China, in 1997 and 2000, respectively, and the Ph.D. degree in control theory and control engineering from Shanghai Jiaotong University, Shanghai, China, in 2003. From 2003 to 2005, he was a Postdoctoral Researcher with the Department of Automation, Tsinghua University, Beijing, China. From 2006 to 2008, he visited Potsdam University, Potsdam, Germany, as an Alexander von Humboldt Research Fellow. Since October 2008, he has been an Associated Professor with the Department of Automation, Tsinghua University. His research interests include fault prognosis and complex systems. He has authored and coauthored over 90 peer-reviewed international journal papers. He has won the first prize in natural science (2011, ranked first) and the second prize (2019, ranked first) of CAA
Corresponding author: Donghua Zhou, e-mail: zdh@tsinghua.edu.cn
Received Date: 2021-02-15
Revised Date: 2021-07-23
Accepted Date: 2021-09-23

Available Online: 2022-03-03

Abstract

Abstract

With the increasing intelligence and integration, a great number of two-valued variables (generally stored in the form of 0 or 1) often exist in large-scale industrial processes. However, these variables cannot be effectively handled by traditional monitoring methods such as linear discriminant analysis (LDA), principal component analysis (PCA) and partial least square (PLS) analysis. Recently, a mixed hidden naive Bayesian model (MHNBM) is developed for the first time to utilize both two-valued and continuous variables for abnormality monitoring. Although the MHNBM is effective, it still has some shortcomings that need to be improved. For the MHNBM, the variables with greater correlation to other variables have greater weights, which can not guarantee greater weights are assigned to the more discriminating variables. In addition, the conditional probability ${P( {{{x}_{j}}| {{{x}_{j'}},{y} = k} } )}$ must be computed based on historical data. When the training data is scarce, the conditional probability between continuous variables tends to be uniformly distributed, which affects the performance of MHNBM. Here a novel feature weighted mixed naive Bayes model (FWMNBM) is developed to overcome the above shortcomings. For the FWMNBM, the variables that are more correlated to the class have greater weights, which makes the more discriminating variables contribute more to the model. At the same time, FWMNBM does not have to calculate the conditional probability between variables, thus it is less restricted by the number of training data samples. Compared with the MHNBM, the FWMNBM has better performance, and its effectiveness is validated through numerical cases of a simulation example and a practical case of the Zhoushan thermal power plant (ZTPP), China.
- Abnormality monitoring,
- continuous variables,
- feature weighted mixed naive Bayes model (FWMNBM),
- two-valued variables,
- thermal power plant

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This paper focus on the anomaly monitoring with both continuous and two-valued variables and a model known as the feature weighted mixed naive Bayes model (FWMNBM) is proposed.
The variables that are more correlated to the class have greater weights, which makes the more discriminating variables contribute more to the model. An effective consistent characterization technique is developed for the correlation of mixed variables, and the corresponding feasibility analysis is conducted.
The effectiveness of FWMNBM is validated through the simulations of a numerical example and a practical vibration fault case.

A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant

doi: 10.1109/JAS.2022.105467

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