Improved PCA method for sensor fault detection and isolation in a nuclear power plant

Abstract An improved principal component analysis (PCA) method is applied for sensor fault detection and isolation (FDI) in a nuclear power plant (NPP) in this paper. Data pre-processing and false alarm reducing methods are combined with general PCA method to improve the model performance in practice. In data pre-processing, singular points and random fluctuations in the original data are eliminated with various techniques respectively. In fault detecting, a statistics-based method is proposed to reduce the false alarms of T2 and Q statistics. Finally, the effects of the proposed data pre-processing and false alarm reducing techniques are evaluated with sensor measurements from a real NPP. They are proved to be greatly beneficial to the improvement on the reliability and stability of PCA model. Meanwhile various sensor faults are imposed to normal measurements to test the FDI ability of the PCA model. Simulation results show that the proposed PCA model presents favorable performance on the FDI of sensors no matter with major or small failures.

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

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

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

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

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

[6]  Diego H. Milone,et al.  Wavelet shrinkage using adaptive structured sparsity constraints , 2015, Signal Process..

[7]  R. H. Myers,et al.  Probability and Statistics for Engineers and Scientists , 1978 .

[8]  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 .

[9]  Tian Liang.  Combustion control optimization systems based on information fusion technology , 2009 .

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

[11]  David Kim,et al.  A Bayesian network-based approach for fault analysis , 2017, Expert Syst. Appl..

[12]  Andrew Kusiak,et al.  Sensor Fault Detection in Power Plants , 2009 .

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

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

[15]  Frédéric Kratz,et al.  Detection, isolation, and identification of sensor faults in nuclear power plants , 1996, IEEE Trans. Control. Syst. Technol..

[16]  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 .

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

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

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