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Science and Technology of Nuclear Installations
Volume 2018 (2018), Article ID 7689305, 16 pages
Research Article

Condition Monitoring of Sensors in a NPP Using Optimized PCA

Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001, China

Correspondence should be addressed to Wei Li; moc.621@323078sseccus and Minjun Peng; moc.361@jmpueh

Received 15 August 2017; Accepted 13 December 2017; Published 8 January 2018

Academic Editor: Michael I. Ojovan

Copyright © 2018 Wei Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


An optimized principal component analysis (PCA) framework is proposed to implement condition monitoring for sensors in a nuclear power plant (NPP) in this paper. Compared with the common PCA method in previous research, the PCA method in this paper is optimized at different modeling procedures, including data preprocessing stage, modeling parameter selection stage, and fault detection and isolation stage. Then, the model’s performance is greatly improved through these optimizations. Finally, sensor measurements from a real NPP are used to train the optimized PCA model in order to guarantee the credibility and reliability of the simulation results. Meanwhile, artificial faults are sequentially imposed to sensor measurements to estimate the fault detection and isolation ability of the proposed PCA model. Simulation results show that the optimized PCA model is capable of detecting and isolating the sensors regardless of whether they exhibit major or small failures. Meanwhile, the quantitative evaluation results also indicate that better performance can be obtained in the optimized PCA method compared with the common PCA method.