A multi-fault diagnosis method for sensor systems based on principle component analysis

Sensors

Volumn 10, Issue 1, 2010, Pages 241-253

  Zhu, Daqi ^a   Bai, Jie ^a   Yang, Simon X ^b  

^a SHANGHAI MARITIME UNIVERSITY   (China)

^b UNIVERSITY OF GUELPH   (Canada)

Author keywords

Fault detection; Fault isolation; Multi fault diagnosis; Principal component analysis; Signal reconstruction

Indexed keywords

COMBINED SENSORS; COMPARISON STUDY; FAULT ISOLATION; MODEL-BASED OPC; MULTI-FAULT DIAGNOSIS; PCA (PRINCIPAL COMPONENT ANALYSIS); PRINCIPLE COMPONENT ANALYSIS; SQUARED PREDICTION ERRORS;

ELECTRIC FAULT CURRENTS; FAULT DETECTION; PRINCIPAL COMPONENT ANALYSIS; SIGNAL RECONSTRUCTION;

SENSORS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; EQUIPMENT; INSTRUMENTATION; METHODOLOGY; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL MODEL; TRANSDUCER;

ALGORITHMS; COMPUTER SIMULATION; EQUIPMENT FAILURE ANALYSIS; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS; TRANSDUCERS;

EID: 77953681757     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s100100241     Document Type: Article

References (15)

1. Puyati, W.Y.; Walairacht, A. Efficiency Improvement for Unconstrained Face Recognition by Weight Probability Values of Modular PCA and Wavelet PCA. In Proceedings of 10th International Conference on Advanced Communication Technology, Gangwon-Do, Korea, 2008; pp. 1449-1453.
2. Peng, D.Z.; Zhang, Y. Dynamics of Generalized PCA and MCA Learning Algorithms. IEEE Trans. Neural Netw. 2007, 18, 1777-1784.
3. Zhao, K.; Upadhyaya, B.R. Model Based Approach for Fault Detection and Isolation of Helical Coil Steam Generator Systems Using Principal Component Analysis. IEEE Trans. Nucl. Sci. 2006, 53, 2343-2352.
4. Dunia, R.; Qin, S.J.; Edgar, T.F.; McAvoy, T.J. Identification of Faulty Sensors Using Principal Component Analysis. AIChE J. 1996, 42, 2797-2812.
5. Ricardo, D.; Qin, S.J.; Thomas, F.E.; McAvoy, T.J. Use of Principal Component Analysis for Sensor Fault Identification. Comput. Chem. Eng. 1996, 20, 713-718.
6. Hui, D.; Liu, J.H. Drift Reduction of Gas Sensor by Wavelet and Principal Component Analysis. Sens. Actuat. 2003, 96, 354-361.
7. Li, R.Y.; Rong, G. Fault Isolation by Partial Dynamic Principal Component Analysis in Dynamic Process. Chinese J. Chem. Eng. 2006, 14, 486-493.
8. Hsieh, W.W. Nonlinear Principal Component Analysis by Neural Networks. Tellus Ser. A 2001, 53, 599-615.
9. Harkat, M.F.; Mourot, G.; Ragot, J. Nonlinear Pca Combining Principal Curves and Rbf-Networks for Process Monitoring. In Proceedings 42th IEEE Conference on Decision and Control, Maui, HI, USA, 2003; pp. 1956-1961.
10. Harkat, M.F.; Mourot, G. Variable Reconstruction Using RBF-NLPCA for Process Monitoring. In IFAC Symposium on Fault Detection, Supervision and Safety for Technical Process, Washington, DC, USA, 2003; pp. 55-63.
11. Kramer, M.A. Nonlinear Principal Component Analysis Using Auto-Associative Neural Networks. AIChE J. 1991, 37, 233-243.
12. Albus, J.S. A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC). Dyn. Syst. Measur. Control 1975, 97, 220-227.
13. Albus, J.S. Data Storage in Cerebeller Model Articulation Controller (CMAC). ASME J. Dynam. Syst. Meas. Control 1975, 97, 228-233.
14. Zhu, D.Q.; Kong, M. Adaptive Fault-Tolerant Control of Non-Linear Systems: An Improved Cmac-Based Fault Learning Approach. Int. J. Control 2007, 80, 1576-1594.
15. Feng, S.S.; Ted, T.; Hung, T.H. Credit Assigned Cmac and Its Application to Online Learning Robust Controllers. IEEE Trans. Syst. Man Cybern. 2003, 33, 202-213.