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Volume 9 Issue 4
Apr.  2022

IEEE/CAA Journal of Automatica Sinica

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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

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

     

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    Highlights

    • 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.

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