Sensor Multifault Diagnosis with Improved Support Vector Machines

IEEE Transactions on Automation Science and Engineering

Volumn 14, Issue 2, 2017, Pages 1053-1063

  Deng, Fang ^a   Guo, Su ^a   Zhou, Rui ^a   Chen, Jie ^a  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Error correcting output codes (ECOC); online sparse least squares support vector machine (OS LSSVM); sensor fault diagnosis; support vector machine (SVM)

Indexed keywords

MATHEMATICAL TRANSFORMATIONS; SUPPORT VECTOR MACHINES; VECTORS;

ERROR CORRECTING OUTPUT CODE; FAULT IDENTIFICATIONS; IDENTIFICATION ACCURACY; LEAST SQUARES SUPPORT VECTOR MACHINES; NON-LINEAR TRANSFORMATIONS; ON-LINE IDENTIFICATION; SENSOR FAULT DETECTION; SENSOR FAULT DIAGNOSIS;

FAULT DETECTION;

EID: 85027715431     PISSN: 15455955     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASE.2015.2487523     Document Type: Article

References (38)

1. I. Jolliffe, Principal Component Analysis. New York: Springer-Verlag, 1986.
2. R. A. Fisher, "The use of multiple measurements in taxonomic problems," Annals of Eugenics, vol. 7, pp. 179-188, 1936.
3. B. Scholkopf, A. J. Smola, and K.-R. Muller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Computation, vol. 10, no. 5, pp. 1299-1319, 1998.
4. G. Baudat and F. Anouar, "Generalized discriminant analysis using a kernel approach," Neural Computation, vol. 12, no. 10, pp. 2385-2404, 2000.
5. J. Zhang and Y. Yong, "A wavelet-based approach to abrupt fault detection and diagnosis of sensor," IEEE Trans. Instrum. Meas., vol. 50, May, no. 5, pp. 1389-1396, 2001.
6. J. Na, J. Yang, X. Wu, and Y. Guo, "Robust adaptive parameter estimation of sinusoidal signals," Automatica, vol. 53, pp. 376-384.
7. X. D. Zhang, Q. Zhang, and N. Sonti, "Diagnosis of process faults and sensor faults in a class of nonlinear uncertain systems," J. Syst. Eng. Electron., vol. 22, no. 1, pp. 22-32, 2011.
8. S. Sahoo, S. Bansal, and R. Tiwari, "Multiclass fault diagnosis in gears using support vector machine algorithms based on frequency domain data," Measurement, vol. 46, pp. 3469-3481, 2013.
9. K. He, Q. Xu, and M. Jia, "Modeling and predicting surface roughness in hard turning using a Bayesian inference-based HMM-SVM model," IEEE Trans. Autom. Sci. Eng., vol. PP, no. 99, pp. 1-12, 2014.
10. C. M. Vong and P. K. Wong, "Engine ignition signal diagnosis with wavelet packet transform and multi-class least squares support vector machines," Expert Systems With Applications, vol. 38, pp. 8563-8570, 2011.
11. J. Li and J. Wang, "Research of temperature predictive control based on LSSVM optimized by improved PSO for thick steel plate Roller hearth Normalizing Furnace," in Proc. Intell. Control Autom., 2010, pp. 3717-3721.
12. L. Xu, T. Cai, and F. Deng, "Sensor fault diagnosis based on least squares support vector," in Proc. IEEE 5th Int. Conf. Robot., Autom. Mechatron., 2011, pp. 275-279.
13. J. Suykens and J. Vandewalle, "Least squares support vector machine classifiers," Neural Process. Lett., vol. 9, no. 3, pp. 293-300, 1999.
14. A. J. Chen and Z. H. Song, "Modeling method of least squares support vector regression based on vector base learning," Control Theory and Applications, vol. 24, no. 1, pp. 1-5, 2007.
15. X. X. Wang, "Sparse support vector regression based on orthogonal forward selection for the generalised kernel model," Neurocomputing, vol. 70, pp. 462-474, 2006.
16. J. C. Wang, C. H. Lin, B. W. Chen, and M. K. Tsai, "Gabor-based nonuniform scale-frequency map for environmental sound classification in home automation," IEEE Trans. Autom. Sci. Eng., vol. 11, no. 2, pp. 607-613, Apr. 2013.
17. L. Chen, X. Zhang, and C. Song, "An automatic screening approach for obstructive sleep apnea diagnosis based on single-lead electrocardiogram," IEEE Trans. Autom. Sci. Eng., vol. 12, no. 1, pp. 106-115, jan. 2014.
18. W. Achmad, "Support vector machine in machine condition monitoring and fault diagnosis," Mech. Syst. Signal Process., vol. 2007, no. 21, pp. 2560-2574.
19. H. Yin, X. Jiao, Y. Chai, and B. Fang, "Scene classification based on single-layer SAE and SVM," Expert Syst. With Applications, vol. 42, no. 7, pp. 3368-3380, 2015.
20. M. Arun Kumar and M. Gopal, "Reduced one-against-all method for multiclass SVM classification," Expert Syst.With Applications, vol. 38, no. 11, pp. 14238-14248, 2011.
21. J. A. K. Suykens and J. Vandewalle, "Multiclass least squares support vector machines," in Proc. Int. Joint Conf. Neural Networks, 1999, pp. 900-903.
22. Y. Bai and Q. Zhang, "Notice of retraction structural risk minimization principle on sugeno space," in Proc. Int. Conf. Mach. Learning and Cybernetics, 2008, vol. 2, pp. 829-834.
23. S. Granville and A. Rodrigo de Miranda Alves, "Active-reactive coupling in optimal reactive dispatch: A solution via Karush-Kuhn-Tucker optimality conditions," IEEE Trans. Power Syst., vol. 9, pp. 1774-1779, 1994.
24. C. W. Li and K. F. Jea, "An adaptive approximation method to discover frequent itemsets over sliding-window-based data streams," Expert Systems With Applications, vol. 38, no. 10, pp. 13386-13404, 2011.
25. S. Guo, F. Deng, and J. Sun, "Sensor fault detection with online sparse least squares support vector machine," in Proc. 32nd Chin. Control Conf., 2013, pp. 6220-6224.
26. G. T. Dietterich and G. Bakiri, "Solving multiclass learning problems via error correcting output codes," J. Artif. Intell. Res., vol. 2, no. 1, pp. 263-286, 1995.
27. Q. Guo and C. Zhong, "Error correcting output codes based ensemble feature extraction," Pattern Recognition, vol. 46, pp. 1091-1100, 2013.
28. S. Escalera, D. M. J. Tax, O. Pujol, P. Radeva, and R. P. W. Duin, "Subclass problem-dependent design for error-correcting output codes," IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 6, pp. 1041-1054, Jun. 2008.
29. T. G. Dietterich and G. Bakiri, "Solving multiclass learning problems via error-correcting output codes," J. Artif. Intell. Res., vol. 2, pp. 263-286, 1995.
30. N. Nilsson, Learning Machines. : McGraw-Hill, 1965.
31. T. Hastie and R. Tibshirani, "Classification by pairwise coupling," Ann. Statistics, vol. 26, no. 2, pp. 451-471, 1998.
32. E. L. Allwein, R. E. Schapire, and Y. Singer, "Reducing multiclass to binary: A unifying approach for margin classifiers," J. Mach. Learning Res., pp. 113-141, 2000.
33. A. Yin, X. Xie, and J. Kuang, "Application of Hadamard ECOC in multi-class problems based on SVM," Acta Electronica Sinica, vol. 36, no. 1, pp. 122-126, 2008.
34. Y. Shang and H. Wang, "Effective method of sensor fault diagnosis," Computer Eng. Applications, vol. 48, no. 2, pp. 22-25, 2012.
35. J. C. Platt, "Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods," in Advances in Large Margin Classifiers. Cambridge, MA, USA: MIT, 1999, pp. 61-74.
36. Y. Lei, Z. He, Y. Zi, and Q. Hu, "Mechanical fault diagnosis model based on feature evaluation and neural networks," J. Xi'an Jiaotong Univ., vol. 40, no. 5, pp. 558-562, 2006.
37. B. S. Yang, T. Han, and J. L. An, "ART-KOHONEN neural network for fault diagnosis of rotating machinery," Mech. Syst. Signal Process., p. 18, 2004.
38. M. H. Park, S. S. Kim, C. K. Ryoo, and K. Choi, "Development of alpha sensor for unmanned aerial systems," in Proc. Choonbae Park SICE Annu. Conf., 2008, pp. 2131-2134.