A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 9 Issue 4
Apr.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Contents
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.

# 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)
• 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.

•  [1] Y. Yang, X. Shi, X. Liu, and H. Li, “A novel MDFA-MKECA method with application to industrial batch process monitoring,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 5, pp. 1446–1454, 2020. [2] Q. Zhu, “Latent variable regression for supervised modeling and monitoring,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 3, pp. 800–811, 2020. [3] A. Imtiaz, D. Aldo, and D. Yu, “Unsupervised anomaly detection based on minimum spanning tree approximated distance measures and its application to hydropower turbines,” IEEE Trans. Autom. Sci. Eng., vol. 16, no. 2, pp. 654–667, 2018. [4] D. Wu, L. Sheng, D. Zhou, and M. Chen, “Dynamic stationary subspace analysis for monitoring nonstationary dynamic processes,” Ind. Eng. Chem. Res., vol. 59, no. 47, pp. 20787–20797, 2020. [5] H. Ji, H. Xiao, J. Shang, and D. Zhou, “Incipient fault detection with smoothing techniques in statistical process monitoring,” Control Eng. Pract., vol. 62, pp. 11–21, 2017. [6] K. Zhong, M. Han, and B. Han, “Data-driven based fault prognosis for industrial systems: A concise overview,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 02, pp. 19–34, 2020. [7] G. Anagnostou, F. Boem, and S. Kuenzel, “Observer-based anomaly detection of synchronous generators for power systems monitoring,” IEEE Trans. Power Syst., vol. 33, no. 4, pp. 4228–4237, 2018. [8] X. Zhang and Z. Ge, “Automatic deep extraction of robust dynamic features for industrial big data modeling and soft sensor application,” IEEE Trans. Ind. Informat., vol. 16, no. 7, pp. 4456–4467, 2020. [9] H. Ji, K. Huang, and D. Zhou, “Incipient sensor fault isolation based on augmented mahalanobis distance,” Control Eng. Pract., vol. 86, pp. 144–154, 2019. [10] J. Shi, J. Sun, Y. Yang, and D. Zhou, “Distributed self-triggered formation control for multi-agent systems,” Sci. China Inf. Sci., vol. 63, no. 10, Article No. 209207, 2020. [11] Y. Wang, D. Zhou, M. Chen, and M. Wang, “Weighted part mutual information related component analysis for quality-related process monitoring,” J. Process Control, vol. 88, pp. 111–123, 2020. [12] P. Y. Zhang, S. Shu, and M. C. Zhou, “An online fault detection model and strategies based on SVM-grid in clouds,” IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 2, pp. 445–456, 2018. [13] S. Yin, H. Gao, J. Qiu, and O. Kaynak, “Fault detection for nonlinear process with deterministic disturbances: A just-in-time learning based data driven method,” IEEE Trans. Cybern., vol. 47, no. 11, pp. 3649–3657, 2017. [14] Z. Ge, Z. Song, and F. Gao, “Review of recent research on data-based process monitoring,” Ind. Eng. Chem. Res., vol. 52, no. 10, pp. 3543–3562, 2013. [15] L. Yao and Z. Ge, “Scalable semisupervised GMM for big data quality prediction in multimode processes,” IEEE Trans. Ind. Electron., vol. 66, no. 5, pp. 3681–3692, 2019. [16] V. Agrawal, B. K. Panigrahi, and P. M. V. Subbarao, “Review of control and fault diagnosis methods applied to coal mills,” J. Process Control, vol. 32, pp. 138–153, 2015. [17] Z. Gao, C. Cecati, and S. X. Ding, “A survey of fault diagnosis and fault-tolerant techniques-part I: Fault diagnosis with model-based and signal-based approaches,” IEEE Trans. Ind. Electron., vol. 62, no. 6, pp. 3757–3767, 2015. [18] K. Huang, H. Wen, H. Ji, L. Cen, X. Chen, and C. Yang, “Nonlinear process monitoring using kernel dictionary learning with application to aluminum electrolysis process,” Control Eng. Pract., vol. 89, pp. 94–102, 2019. [19] M. Wang, L. Sheng, D. Zhou, and M. Chen, “A feature weighted mixed naive Bayes model for monitoring anomalies in the fan system of a thermal power plant, ” arXiv preprint arXiv: 2012.07230, 2020. [20] R. Dunia, S. J. Qin, T. F. Edgar, and T. J. McAvoy, “Identification of faulty sensors using principal component analysis,” AIChE J., vol. 42, no. 10, pp. 2797–2812, 1996. [21] W. Li, H. H. Yue, S. Valle-Cervantes, and S. J. Qin, “Recursive PCA for adaptive process monitoring,” J. Process Control, vol. 10, no. 5, pp. 471–486, 2000. [22] S. J. Qin, “Recursive PLS algorithms for adaptive data modeling,” Comput. Chem. Eng., vol. 22, no. 4–5, pp. 503–514, 1998. [23] N. Sheng, Q. Liu, S. J. Qin, and T. Chai, “Comprehensive monitoring of nonlinear processes based on concurrent kernel projection to latent structures,” IEEE Trans. Autom. Sci. Eng., vol. 13, no. 2, pp. 1129–1137, 2016. [24] Q. P. He, S. J. Qin, and W. Jin, “A new fault diagnosis method using fault directions in Fisher discriminant analysis,” AIChE J., vol. 51, no. 2, pp. 555–571, 2005. [25] Q. He and J. Wang, “Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes,” IEEE Trans. Semicond. Manuf., vol. 20, no. 4, pp. 345–354, 2007. [26] A. Widodo and B.-S. Yang, “Support vector machine in machine condition monitoring and fault diagnosis,” Mech. Syst. Signal Pr., vol. 21, no. 6, pp. 2560–2574, 2007. [27] Z. Ge, Z. Song, S. X. Ding, and B. Huang, “Data mining and analytics in the process industry: The role of machine learning,” IEEE Access, vol. 5, pp. 20590–20616, 2017. [28] M. Wang, D. Zhou, M. Chen, and Y. Wang, “Anomaly detection in the fan system of a thermal power plant monitored by continuous and two-valued variables,” Control Eng. Pract., vol. 102, Article No. 104522, 2020. [29] M. Ozuysal, M. Calonder, V. Lepetit, and P. Fua, “Fast keypoint recognition using random ferns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 3, pp. 448–461, 2010. [30] E. J. D. Fortuny, D. Martens, and F. Provost, “Wallenius Bayes,” Mach. Learn., vol. 107, no. 2, pp. 1–25, 2018. [31] L. Zhang, L. Jiang, C. Li, and G. Kong, “Two feature weighting approaches for naive Bayes text classifiers,” Knowl. Based Syst., vol. 100, no. 15, pp. 137–144, 2016. [32] L. Jiang, L. Zhang, C. Li, and J. Wu, “A correlation-based feature weighting filter for naive Bayes,” IEEE Trans. Knowl. Data Eng., vol. 31, no. 2, pp. 201–213, 2019. [33] C. Ratanamahatana, E. Keogh, A. J. Bagnall, and S. Lonardi, A Novel Bit Level Time Series Representation With Implication of Similarity Search and Clustering. Berlin Heidelberg: Springer, 2005. [34] Y. Wu and L. Liu, “Conditional entropy and mutual information in random cascading processes,” Physical Review D, vol. 43, no. 9, pp. 3077–3079, 1991. [35] M. Collins, Parameter Estimation for Statistical Parsing Models: Theory and Practice of Distribution-Free Methods. Netherlands: Springer, 2004. [36] N. Balakrishnan and V. B. Nevzorov, Bernoulli Distribution. UK.: Springer, 2008. [37] Y. Sheng and R. Steven, “Decision tree-based methodology for high impedance fault detection,” IEEE Trans. Power Del., vol. 19, no. 2, pp. 533–536, 2004. [38] T. Zhang, D. Yue, Y. Gu, Y. Wang, and G. Yu, “Adaptive correlation analysis in stream time series with sliding windows,” Comput. Math. Appl., vol. 57, no. 6, pp. 937–948, 2009.

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