Differential feature based hierarchical PCA fault detection method for dynamic fault

Neurocomputing

Volumn 202, Issue , 2016, Pages 27-35

  Zhou, Funa ^a,b   Park, Ju H ^b   Liu, Yajuan ^b  

^a HENAN UNIVERSITY   (China)

^b Yeungnam University   (South Korea)

Author keywords

Differential feature; Dynamic fault; Hierarchical PCA; Zero cross point

Indexed keywords

FEATURE EXTRACTION; PRINCIPAL COMPONENT ANALYSIS; SIGNAL DETECTION;

ALTERNATING CURRENT; DETECTION CRITERIA; DIFFERENTIAL FEATURE; DYNAMIC FAULTS; FAULT DETECTION TECHNIQUES; FEATURE EXTRACTION METHODS; HIERARCHICAL PCA; ZERO-CROSS POINT;

FAULT DETECTION;

ARTICLE; INFORMATION PROCESSING; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; STATISTICAL ANALYSIS;

EID: 84979492203     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2016.03.007     Document Type: Article

References (36)

1. Mehra R.K., Reschon J. An innovation approach to fault detection and diagnosis in dynamics. Automatica 1971, 7(5):637-640.
2. Frank P.M. Fault diagnosis in dynamics systems using analytical and knowledge-based redundancy. a survey and some new results. Automatica 1990, 26(3):459-474.
3. Kresta J.V., MacGregor J.F., Marlin T.E. Multivariate statistical monitoring of process performance. Can. J. Chem. Eng. 1991, 69(1):35-47.
4. Xiaobin Xu, Haishan Feng, Zhi Wang, Chenglin Wen An information fusion method of fault diagnosis based on interval basic probability assignment. Chinese J. Electron. 2011, 20(2):255-260.
5. ChengLin Wen, AiBing Qiu, Bin Jiang An output delay approach to fault estimation for sampled-data systems. Sci. China Inf. Sci. (Science in China Series F) 2012, 55(9):2128-2138.
6. Shen H., Song X., Wang Z. Robust fault tolerant control of uncertain fractional-order systems against actuator faults. IET Control Theory Appl. 2013, 7(9):1233-1241.
7. S. Yin, P. Shi, and H.Y. Yang. Adaptive fuzzy control of strict-feedback nonlinear time-delay systems with unmodeled dynamics. IEEE Trans. Cybern. 2015, available online, . doi:10.1109/TCYB.2015.2457894.
8. Zhang D., Wang Q.G., Yu L., Song H.Y. Fuzzy-model-based fault detection for a class of nonlinear systems with networked measurements. IEEE Trans. Instrum. Meas. 2013, 62(12). 3418-3159.
9. Yin S., Ding S.X., Xie X., Luo H. A review on basic data-driven approaches for industrial process monitoring. IEEE Trans. Ind. Electron. 2014, 61(11):6418-6428.
10. Yin S., Li X.W., Gao H.J., Kaynak O. Data-based techniques focused on modern industry. an overview. IEEE Trans. Ind. Electron. 2015, 62(1):657-667.
11. Venkat V., Raghunathan R., Kewn Y., Surya N.K. A review of process fault detection and diagnosis Part I. quantitative model-based methods. Comput. Chem. Eng. 2003, 27(3):293-311.
12. Zhang D., Zhang W.A., Yu L., Wang Q.G. Distributed fault detection for a class of large-scale systems with multiple incomplete measurements. J. Frankl. Inst. 2015, 352(9):3730-3749.
13. Zeng J.S., Kruger U., Geluk J., Wang X., Xie L. Detecting abnormal situations using the Kullback-Leibler divergence. Automatica 2014, 50(11):2777-2786.
14. Adil M., Abid M., Khan A.Q., Mustafa G., Ahmed N. Exponential discriminant analysis for fault diagnosis. Neurocomputing 2016, 171:1344-1353.
15. Jing C., Hou J. SVM and PCA based fault classification approaches for complicated industrial process. Neurocomputing 2015, 167(1):636-642.
16. Bin Shams M.A., Budman H.M., Duever T.A. Fault detection, identification and diagnosis using CUSUM based PCA. Chem. Eng. Sci. 2011, 66:4488-4498.
17. Zhang Z.D., Peng B.C., Xie L.B., Peng L. Process monitoring based on recursive probabilistic PCA for Multi-mode Process. IFAC-Papers OnLine 2015, 48(8):1294-1299.
18. Pollanen K., Hakkinen A., Reinikainen S., Rantanen J., Minkkinen P. Dynamic PCA-based MSPC charts for nucleation prediction in batch cooling crystallization processes. Chemometrics Intell. Lab. Syst. 2006, 84(1):126-133.
19. Cai L.F., Tian X.M., Chen S. A process monitoring method based on noisy independent component analysis. Neurocomputing 2014, 127(15):231-246.
20. Ge Z.Q., Gao F.R., Song Z.H. Mixture probabilistic PCR model for soft sensing of multimode processes. Chemometrics Intell. Lab. Syst. 2011, 105:91-105.
21. Wen C.L., Zhou F.N. An extended multi-scale principal component analysis and application in fault detection. Chinese J. Electron. 2012, 21(3):471-479.
22. Navi M., Davoodi M.R., Meskin N. Sensor fault detection and isolation of an industrial gas turbine using partial kernel PCA. IFAC-Papers OnLine 2015, 48(21):1389-1396.
23. Cai L.F., Tian X.M., Chen S. A process monitoring method based on noisy independent component analysis. Neurocomputing 2014, 127(15):231-246.
24. Peng D.Z., Zhang Y. Dynamics of generalized PCA and MCA learning algorithms. IEEE Trans. Netw. 2007, 18(6):1777-1784.
25. S. Yin, G. Wang, H.J. Gao. Data-driven process monitoring based on modified orthogonal projections to latent structures, IEEE Trans. Control Syst. Technol. available online, 2015, . doi:10.1109/TCST.2015.2481318.
26. Harrou F., Kadri F., Chaabane S., Tahon C., Sun Y. Improved principal component analysis for fault detection. application to an emergency department. Comput. Ind. Eng. 2015, 88:63-77.
27. Hu J., Wen C.L., Li P., Yuan T.Q. Direct projection to latent variable space for fault detection. J. Frankl. Inst. 2014, 351(3):1226-1250.
28. Gao Y., Wang X., Wang Z.L. Fault detection for a class of industrial processes based on recursive multiple models. Neurocomputing 2015, 169(2):430-438.
29. Yin S., Huang Z.H. Performance monitoring for vehicle suspension system via fuzzy positivistic C-Means clustering based on accelerometer measurements. IEEE/ASME Trans. Mechatronics 2015, 20(5):2613-2620.
30. Liu L., Wang Z.S. H.G. Adaptive NN fault-tolerant control for discrete-time systems in triangular forms with actuator fault. Neurocomputing 2015, 152(25):209-221.
31. Gharavian M.H., Almas Ganj F., Ohadi A.R., Heidari Bafroui H. Comparison of FDA-based and PCA-based features in fault diagnosis of automobile gearboxes. Neurocomputing 2013, 121(9):150-159.
32. Asuhaimi A., Zin M., Saini M., Mustafa M.W., Sultan A.R. New algorithm for detection and fault classification on parallel transmission line using DWT and BPNN based on Clarkes transformation. Neurocomputing 2015, 168(30):983-993.
33. Tian Y., Fu M.Y., Wu F. Steel plates fault diagnosis on the basis of support vector machines. Neurocomputing 2015, 151(3):296-303.
34. Xiao Y.Q., He Y.G. A novel approach for analog fault diagnosis based on neural networks and improved kernel PCA. Neurocomputing 2011, 74(7):1102-1115.
35. Li F., Wang J.X., Tang B.Q. Weak fault diagnosis of rotating machinery based on feature reduction with supervised orthogonal local Fisher discriminant analysis. Neurocomputing 2015, 168(30):505-519.
36. Downs J.J., Vogel E.F. A plant-wide industrial process control problem. Comput. Chem. Eng. 1993, 17:245-255.