Fault detectability analysis in PCA method during condition monitoring of sensors in a nuclear power plant

Abstract Principal component analysis (PCA) is applied in this paper for sensor condition monitoring in a nuclear power plant (NPP). In order to estimate the fault detectability of the proposed PCA model, non-detection zones of sensors in the model are calculated. Then the diagnosis ability of the PCA model is more specific, and the diagnosis results are more credible and reliable. Meanwhile an improved statistics-based method is applied to directly reduce the false alarms during monitoring which makes contribution to the improvement of model performance. Finally, the credibility of the non-detection zones of sensors are evaluated with sensor measurements from a real NPP. According to simulation results, the non-detection zones of sensors are proved to be credible and reliable, and the false alarm reducing method also makes contribution to the condition monitoring performance of the developed PCA model.

[1]  H. Hashemian.  On-line monitoring applications in nuclear power plants , 2011 .

[2]  Wei Li,et al.  A hybrid data-driven modeling method on sensor condition monitoring and fault diagnosis for power plants , 2015 .

[3]  Shengwei Wang,et al.  Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method , 2005 .

[4]  Tao Chen,et al.  On reducing false alarms in multivariate statistical process control , 2010 .

[5]  Ali Ajami,et al.  Data driven approach for fault detection and diagnosis of turbine in thermal power plant using Independent Component Analysis (ICA) , 2012 .

[6]  Gabriel Maciá-Fernández,et al.  Hierarchical PCA-based multivariate statistical network monitoring for anomaly detection , 2016, 2016 IEEE International Workshop on Information Forensics and Security (WIFS).

[7]  A. Massi Pavan,et al.  On-line fault detection of a fuel rod temperature measurement sensor in a nuclear reactor core using ANNs , 2015 .

[8]  Karim Salahshoor,et al.  Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers , 2010 .

[9]  B. R. Upadhyaya,et al.  Fault Diagnosis of Helical Coil Steam Generator Systems of an Integral Pressurized Water Reactor Using Optimal Sensor Selection , 2012, IEEE Transactions on Nuclear Science.

[10]  Wei Li,et al.  Fault detection, identification and reconstruction of sensors in nuclear power plant with optimized PCA method , 2018 .

[11]  Jin Wen,et al.  A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform , 2014 .

[12]  Jin Jiang,et al.  Applications of fault detection and diagnosis methods in nuclear power plants: A review , 2011 .

[13]  Chung-Horng Lung,et al.  Entropy-based robust PCA for communication network anomaly detection , 2014, 2014 IEEE/CIC International Conference on Communications in China (ICCC).

[14]  Enrico Zio,et al.  An ensemble approach to sensor fault detection and signal reconstruction for nuclear system control , 2010 .

[15]  S. Qin,et al.  Selection of the Number of Principal Components: The Variance of the Reconstruction Error Criterion with a Comparison to Other Methods† , 1999 .

[16]  Muhammad Abid,et al.  Fault diagnosis of Pakistan Research Reactor-2 with data-driven techniques , 2016 .

[17]  George van Schoor,et al.  An integrated approach to sensor FDI and signal reconstruction in HTGRs. Part I. Theoretical framework , 2016 .

[18]  Long-Sheng Chen,et al.  Using SVM based method for equipment fault detection in a thermal power plant , 2011, Comput. Ind..

[19]  Fan Li,et al.  Dynamic Modeling, Sensor Placement Design, and Fault Diagnosis of Nuclear Desalination Systems , 2011 .

[20]  Gabriel Maciá Fernández,et al.  PCA-based Multivariate Statistical Network Monitoring for Anomaly Detection , 2016 .

[21]  Tao Chen,et al.  Robust probabilistic PCA with missing data and contribution analysis for outlier detection , 2009, Comput. Stat. Data Anal..

[22]  Huanxin Chen,et al.  Sensitivity analysis for PCA-based chiller sensor fault detection , 2016 .

[23]  Chudong Tong,et al.  Fault detection and diagnosis of dynamic processes using weighted dynamic decentralized PCA approach , 2017 .