Exponential discriminant analysis for fault diagnosis

Neurocomputing

Volumn 171, Issue , 2016, Pages 1344-1353

  Adil, M ^a   Abid, M ^a   Khan, A Q ^a   Mustafa, G ^a   Ahmed, N ^a  

^a PAKISTAN INSTITUTE OF ENGINEERING AND APPLIED SCIENCES   (Pakistan)

Author keywords

Exponential discriminant analysis; Fault diagnosis; Fisher discriminant analysis; Small sample size problem; Tennessee Eastman process

Indexed keywords

DISCRIMINANT ANALYSIS; ELECTRIC FAULT CURRENTS; FACE RECOGNITION; FAILURE ANALYSIS; FISHER INFORMATION MATRIX;

DIFFERENT CLASS; DIMENSIONALITY REDUCTION TECHNIQUES; DISCRIMINANT POWER; FAULT DETECTION AND ISOLATION; FISHER DISCRIMINANT ANALYSIS; HIGHER-DIMENSIONAL; SMALL SAMPLE SIZE PROBLEMS; TENNESSEE EASTMAN PROCESS;

FAULT DETECTION;

ALGORITHM; ARTICLE; COUPLED LIQUID TANKS SYSTEM; DISCRIMINANT ANALYSIS; ENGINEERING; EXPONENTIAL DISCRIMINANT ANALYSIS; FAULT DIAGNOSIS; FISHER DISCRIMINANT ANALYSIS; INDUSTRIAL PRODUCTION; INDUSTRY; MULTIFACTOR DIMENSIONALITY REDUCTION; PRIORITY JOURNAL; PROCESS CONTROL; PROCESSING; SAMPLE SIZE; SIMULATION; TENNESSEE EASTMAN PROCESS;

EID: 84944511398     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.07.099     Document Type: Article

References (40)

1. Chen J., Liao C.-M. Dynamic process fault monitoring based on neural network and PCA. J. Process Control 2002, 12(2):277-289.
2. Cheng Y.-Q., Zhuang Y.-M., Yang J.-Y. Optimal Fisher discriminant analysis using the rank decomposition. Pattern Recognit. 1992, 25(1):101-111.
3. Chiang L.H., Pell R.J. Genetic algorithms combined with discriminant analysis for key variable identification. J. Process Control 2004, 14(2):143-155.
4. Chiang L.H., Russel E., Braatz R. Fault Detection and Diagnosis in Industrial Systems 2001, Springer-Verlag, London.
5. Deng X., Tian X. Nonlinear process fault pattern recognition using statistics kernel PCA similarity factor. Neurocomputing 2013, 121:298-308.
6. S. Ding, P. Zhang, E. Ding, P. Engel, W. Gui, A survey of the application of basic data-driven and model-based methods in process monitoring and fault diagnosis. In: Proceedings of the 18th IFAC World Congress, 2011.
7. Ding S.X. Model-based fault diagnosis techniques - design schemes, algorithms and tools 2012, Springer-Verlag, London. 2nd Edition.
8. Ding S.X. Data-driven Design of Fault Diagnosis and Fault-Tolerant Control Systems 2014, Springer-Verlag, London.
9. J. Dong, K. Zhang, Y. Huang, G. Li, K. Peng, Adaptive total PLS based quality-relevant process monitoring with application to the Tennessee Eastman process. Neurocomputing 154, 2015, 77-85.
10. Elshenawy L.M., Yin S., Naik A.S., Ding S.X. Efficient recursive principal component analysis algorithms for process monitoring. Ind. Eng. Chem. Res. 2009, 49(1):252-259.
11. Frank P.M. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy-a survey and some new results. Automatica 1990, 26:459-474.
12. M. Fuente, D. Garcia-Alvarez, G.I. Sainz-Palmero, T. Villegas, Fault detection and identification method based on multivariate statistical techniques. In: IEEE Conference on Emerging Technologies Factory Automation. ETFA 2009, September 2009, pp. 1-6.
13. Gharavian M., Ganj F.A., Ohadi A., Bafroui H.H. Comparison of FDA-based and PCA-based features in fault diagnosis of automobile gearboxes. Neurocomputing 2013, 121(0):150-159.
14. He Q.P., Qin S.J., Wang J. A new fault diagnosis method using fault directions in Fisher discriminant analysis. AIChE J. 2005, 51(2):555-571.
15. Howland P., Wang J., Park H. Solving the small sample size problem in face recognition using generalized discriminant analysis. Pattern Recognit. 2006, 39(2):277-287.
16. Isermann R. Fault-diagnosis Systems: An Introduction form Fault Detection to Fault Tolerance 2006, Springer-Verlag, Berlin, Heidelberg.
17. W. Jian, H. Zhiyan, F. Jian, Kernel local Fisher discriminant analysis for fault diagnosis in chemical process. In: IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), 2013 pp. 607-611.
18. F. Kousar, M. Abid, A.Q. Khan, Sensor fault detection in coupled liquid tanks system. In: FIT, 2012, pp. 318-324.
19. Kresta J.V., MacGregor J.F., Marlin T.E. Multivariate statistical monitoring of process operating performance. Can. J. Chem. Eng. 1991, 69(1):35-47.
20. Lau C., Ghosh K., Hussain M., Che Hassan C. Fault diagnosis of Tennessee Eastman process with multi-scale PCA and ANFIS. Chemom. Intell. Lab. Syst. 2013, 120:1-14.
21. Li J., Cui P. Kernel scatter-difference-based discriminant analysis for nonlinear fault diagnosis. Chemom. Intell. Lab. Syst. 2008, 94(1):80-86.
22. Li J., Cui P. Improved kernel Fisher discriminant analysis for fault diagnosis. Expert Syst. Appl. 2009, 36(2):1423-1432.
23. Mai Q. A review of discriminant analysis in high dimensions. Wiley Interdiscip. Rev.: Comput. Stat. 2013, 5(3):190-197.
24. Misra M., Yue H.H., Qin S.J., Ling C. Multivariate process monitoring and fault diagnosis by multi-scale PCA. Comput. Chem. Eng. 2002, 26(9):1281-1293.
25. K. Patan, Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes, vol. 377, Springer-Verlag, Berlin, Heidelberg, 2008.
26. Qiao Z., Zhou L., Huang J.Z. Sparse linear discriminant analysis with applications to high dimensional low sample size data. Int. J. Appl. Math. 2009, 39.
27. Qin S.J. Statistical process monitoring. basics and beyond. J. Chemom. 2003, 17(8-9):480-502.
28. Qin S.J. Survey on data-driven industrial process monitoring and diagnosis. Annu. Rev. Control 2012, 36(2):220-234.
29. Tao E., Shen W., Liu T., Chen X. Fault diagnosis based on PCA for sensors of laboratorial wastewater treatment process. Chemom. Intell. Lab. Syst. 2013, 128:49-55.
30. W. Wang, X. Ma, R. Kang, Fisher discriminant analysis for fault classification. In: IEEE Conference on Prognostics and System Health Management (PHM), May 2012, pp. 1-5.
31. Yin S., Ding S., Xie X., Luo H. A review on basic data-driven approaches for industrial process monitoring. IEEE Trans. Ind. Electron. 2014, 61(11):6418-6428.
32. Yin S., Ding S.X., Haghani A., Hao H., Zhang P. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. J. Process Control 2012, 22(9):1567-1581.
33. S. Yin, Z. Huang, Performance monitoring for vehicle suspension system via fuzzy positivistic C-means clustering based on accelerometer measurements, IEEE/ASME Trans. Mechatronics (2014), http://dx.doi.org/10.1109/TMECH.2014.2358674.
34. Yin S., Li X., Gao H., Kaynak O. Data-based techniques focused on modern industry. an overview. IEEE Trans. Ind. Electron. 2015, 62(1):657-667.
35. Yin S., Zhu X., Kaynak O. Improved PLS focused on key-performance-indicator-related fault diagnosis. IEEE Trans. Ind. Electron. 2015, 62(3):1651-1658.
36. Yoon S., MacGregor J.F. Fault diagnosis with multivariate statistical models Part I. using steady state fault signatures. J. Process Control 2001, 11(4):387-400.
37. C. Yu, Q. Pan, Y. Cheng, H. Zhang, Small sample size problem of fault diagnosis for process industry. In: 8th IEEE International Conference on Control and Automation (ICCA), IEEE, Xiamen, China, 2010, pp. 1721-1725.
38. P. Zhang, J. Peng, N. Riedel, Discriminant analysis: a unified approach. In: Fifth IEEE International Conference on Data Mining, 2005.
39. Zhang T., Fang B., Tang Y.Y., Shang Z., Xu B. Generalized discriminant analysis. a matrix exponential approach. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 2010, 40(1):186-197.
40. Zhong S., Wen Q., Ge Z. Semi-supervised Fisher discriminant analysis model for fault classification in industrial processes. Chemom. Intell. Lab. Syst. 2014, 138:203-211.