Abstract
The wireless sensor network (WSN) has great potential in monitoring equipment and processes of nuclear power plants (NPPs). The WSN can not only lower the cost of regular monitoring, but also enable the capability to achieve intelligent monitoring. The massive and heterogeneous monitoring data collected by the WSN can contribute various monitoring applications, including the cyber and physical security defense, the fault detection and diagnosis, and the advanced operation and maintenance. The Wi-Fi technology is promising to be the underlying platform for the WSN in NPPs. Specific data-driven statistical algorithms for anomaly detection and identification are demonstrated.
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Bi D, Zhang J (2010) Applications of wireless Technology in nuclear power plants and research on its key issue (in Chinese). Process Automation Instrumentation 31:47–53.
Cui J, Wang YR (2011) A novel approach of analog circuit fault diagnosis using support vector machines classifier. Measurement 44:281–289.
Elaissi I, Jaffel I, Taouali O et al (2013) Online prediction model based on the SVD–KPCA method. ISA Transactions 52:96–104.
Gan LV, Cheng HZ, Zhai HB et al (2005) Fault diagnosis of power transformer based on multi-layer SVM classifier. Electric Power Systems Research 7:1–7.
Gryllias KC, Antoniadis IA (2012) A Support Vector Machine approach based on physical model training for rolling element bearing fault detection in industrial environments. Engineering Applications of Artificial Intelligence 25:326–344.
Hashemian HM (2011) Wireless sensors for predictive maintenance of rotating equipment in research reactors. Annals of Nuclear Energy 38:665–680.
Jiang S, Chai B, Cao Y et al (2010) Study of the technical solution to the dedicated wireless communication system in nuclear power plant (in Chinese). Chinese Journal of Nuclear Science and Engineering 30:275–279.
Lee JM, Yoo CK, Choi SW (2004) Nonlinear process monitoring using kernel principal component analysis. Chemical Engineering Science 59:223–234.
Nguyen VH, Golinval JC (2010) Fault detection based on kernel principal component analysis. Engineering Structures 32(11):3683–3691.
Qin SJ (2012) Survey on data-driven industrial process monitoring and diagnosis. Annual Reviews in Control 36:220–234.
Acknowledgements
This paper is jointly supported by the National S&T Major Project (Grant No. ZX06901) and National Natural Science Foundation of China (Grant No. 61502270).
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Li, J., Kang, X., Long, Z., Meng, J., Huang, X. (2017). The Application of the Wireless Sensor Network in Intelligent Monitoring of Nuclear Power Plants. In: Xu, Y. (eds) Nuclear Power Plants: Innovative Technologies for Instrumentation and Control Systems. SICPNPP 2016. Lecture Notes in Electrical Engineering, vol 400. Springer, Singapore. https://doi.org/10.1007/978-981-10-3361-2_20
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DOI: https://doi.org/10.1007/978-981-10-3361-2_20
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