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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	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
刊名	IEEE/CAA Journal of Automatica Sinica
	2022
卷号	9 期号:4 页码:719-727
关键词	Abnormality monitoring,continuous variables,feature weighted mixed naive Bayes model (FWMNBM),two-valued variables,thermal power plant
ISSN号	2329-9266
DOI	10.1109/JAS.2022.105467
英文摘要	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.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/47229]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Min Wang, Li Sheng, Donghua Zhou, Maoyin Chen. A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant[J]. IEEE/CAA Journal of Automatica Sinica,2022,9(4):719-727.
APA	Min Wang, Li Sheng, Donghua Zhou, Maoyin Chen.(2022).A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant.IEEE/CAA Journal of Automatica Sinica,9(4),719-727.
MLA	Min Wang, Li Sheng, Donghua Zhou, Maoyin Chen."A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant".IEEE/CAA Journal of Automatica Sinica 9.4(2022):719-727.