Kernel approaches for fault detection and classification in PARR-2

Abstract Safety and reliability of nuclear power plants is of utmost importance. For that purpose, modern fault detection and classification (FDC) techniques are being devised to compliment the existing hardware redundancy and limit checking techniques. Among these modern techniques, Fisher discriminant analysis (FDA) and support vector machines (SVM) have been shown to be successful for FDC of nuclear reactors. By considering the fact that both FDA and SVM are basically established for linear systems and nuclear reactors are highly nonlinear processes, it becomes more intuitive to utilize some nonlinear FDC technique. To this end, application of kernel based non-linear approaches including kernel FDA (KFDA) and kernel SVM (KSVM) is proposed in this paper for fault detection and classification in Pakistan Research Reactor-2. Control rod withdrawal and accidental external reactivity insertion faults are manually executed at PARR-2, and training data is collected from the reactor based on which KFDA and KSVM models are developed. The online data is subsequently tested using the developed models which resulted into reliable fault classification.

[1]  George C. Verghese,et al.  Optimally robust redundancy relations for failure detection in uncertain systems , 1986, Autom..

[2]  Jie Chen,et al.  Observer-based fault detection and isolation: robustness and applications , 1997 .

[3]  Zhi-Bo Zhu,et al.  Fault diagnosis based on imbalance modified kernel Fisher discriminant analysis , 2010 .

[4]  Muhammad Abid,et al.  Exponential discriminant analysis for fault diagnosis , 2016, Neurocomputing.

[5]  C. Yoo,et al.  Nonlinear process monitoring using kernel principal component analysis , 2004 .

[6]  G. Baudat,et al.  Generalized Discriminant Analysis Using a Kernel Approach , 2000, Neural Computation.

[7]  B. Upadhyaya,et al.  Model Based Approach for Fault Detection and Isolation of Helical Coil Steam Generator Systems Using Principal Component Analysis , 2006, IEEE Transactions on Nuclear Science.

[8]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[9]  Jason Weston,et al.  A user's guide to support vector machines. , 2010, Methods in molecular biology.

[10]  Fugee Tsung,et al.  A kernel-distance-based multivariate control chart using support vector methods , 2003 .

[11]  Bo-Suk Yang,et al.  Support vector machine in machine condition monitoring and fault diagnosis , 2007 .

[12]  Ishtiaq Hussain Bokhari,et al.  Safety analysis for core conversion (from HEU to LEU) of Pakistan research reactor-2 (PARR-2) , 2010 .

[13]  M. Iqbal,et al.  Neutronic analysis for core conversion (HEU–LEU) of Pakistan research reactor-2 (PARR-2) , 2008 .

[14]  J. W. Hines,et al.  A hybrid approach for detecting and isolating faults in nuclear power plant interacting systems , 1996 .

[15]  Stephen P. Boyd,et al.  Optimal kernel selection in Kernel Fisher discriminant analysis , 2006, ICML.

[16]  Rolf Isermann,et al.  Fault-Diagnosis Applications: Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-tolerant Systems , 2011 .

[17]  Jiawei Han,et al.  Efficient Kernel Discriminant Analysis via Spectral Regression , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[18]  Junhong Li,et al.  Improved kernel fisher discriminant analysis for fault diagnosis , 2009, Expert Syst. Appl..

[19]  Leo H. Chiang,et al.  Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis , 2000 .

[20]  A. Willsky,et al.  Analytical redundancy and the design of robust failure detection systems , 1984 .

[21]  B. R. Upadhyaya,et al.  Control and Instrumentation Strategies for Multi-Modular Integral Nuclear Reactor Systems , 2011, IEEE Transactions on Nuclear Science.

[22]  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.

[23]  Chuancai Liu,et al.  Determine the Kernel Parameter of KFDA Using a Minimum Search Algorithm , 2007, ICIC.

[24]  Bernhard Schölkopf,et al.  A Generalized Representer Theorem , 2001, COLT/EuroCOLT.

[25]  Hyun-Woo Cho.  Nonlinear feature extraction and classification of multivariate data in kernel feature space , 2007, Expert Syst. Appl..

[26]  Hassan Salarieh,et al.  Design of a fault tolerated intelligent control system for a nuclear reactor power control: Using extended Kalman filter , 2014 .

[27]  Haesun Park,et al.  Nonlinear Discriminant Analysis Using Kernel Functions and the Generalized Singular Value Decomposition , 2005, SIAM J. Matrix Anal. Appl..

[29]  Xi Zhang,et al.  Nonlinear biological batch process monitoring and fault identification based on kernel fisher discriminant analysis , 2007 .

[30]  Hyun-Woo Cho,et al.  Identification of contributing variables using kernel-based discriminant modeling and reconstruction , 2007, Expert Syst. Appl..

[31]  David A. Petti,et al.  A Scenario of the Three Mile Island Unit 2 Accident , 1989 .

[32]  Cheng Haozhong,et al.  Fault diagnosis of power transformer based on multi-layer SVM classifier , 2005 .

[33]  Enrico Zio,et al.  A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems , 2007, Reliab. Eng. Syst. Saf..

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

[35]  Peiling Cui,et al.  Kernel scatter-difference-based discriminant analysis for nonlinear fault diagnosis , 2008 .