Fault prognostic based on hybrid method of state judgment and regression

Advances in Mechanical Engineering

Volumn 2013, Issue , 2013, Pages

  Li, Xiaobin ^a,b   Qian, Jiansheng ^a   Wang, Gai Ge ^b  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

^b JIANGSU NORMAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84890027379     PISSN: 16878132     EISSN: 16878140     Source Type: Journal    
DOI: 10.1155/2013/149562     Document Type: Article

References (28)

1. Jardine A. K. S., Lin D., Banjevic D., A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 2006 20 7 1483 1510 2-s2.0-33646534620 10.1016/j.ymssp.2005. 09.012
2. Heng A., Zhang S., Tan A. C. C., Mathew J., Rotating machinery prognostics: state of the art, challenges and opportunities. Mechanical Systems and Signal Processing 2009 23 3 724 739 2-s2.0-58049190180 10.1016/j.ymssp.2008. 06.009
3. Pecht M., Mathew J., Ma L., Tan A., Weijnen M., Lee J., A Prognostics and Health Management for Information and Electronics-Rich Systems Engineering Asset Management and Infrastructure Sustainability 2012 London, UK Springer
4. Si X.-S., Wang W., Hu C.-H., Zhou D.-H., Remaining useful life estimation-a review on the statistical data driven approaches. European Journal of Operational Research 2011 213 1 1 14 2-s2.0-79955584653 10.1016/j.ejor.2010. 11.018
5. Godwin J. L., Matthews P., Watson C., Classification and detection of electrical control system faults through SCADA data analysis. Chemical Engineering Transactions 2013 33 985 990
6. Lo B. W., Macdonald R. L., Baker A., Levine M. A., Clinical outcome prediction in aneurysmal subarachnoid hemorrhage using Bayesian neural networks with fuzzy logic inferences. Computational and Mathematical Methods in Medicine 2013 2013 10 904860 10.1155/2013/904860
7. Zhang Z., Wang Y., Wang K., Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks. The International Journal of Advanced Manufacturing Technology 2013 68 1-4 763 773
8. Zhou X., Xiang Z., Liu M., Xiang J., A multiwavelet support vector machine prediction algorithm for avionics PHM. Intelligent Computing Theories 2013 Springer 295 304
9. Thatoi D. N., Das H. C., Parhi D. R., Review of techniques for fault diagnosis in damaged structure and engineering system. Advances in Mechanical Engineering 2012 2012 11 327569 10.1155/2012/327569
10. Bunks C., McCarthy D., Al-Ani T., Condition-based maintenance of machines using hidden Markov models. Mechanical Systems and Signal Processing 2000 14 4 597 612 2-s2.0-0034215428
11. Baruah P., Chinnam R. B., HMMs for diagnostics and prognostics in machining processes. International Journal of Production Research 2005 43 6 1275 1293 2-s2.0-27744505094 10.1080/00207540412331327727 (Pubitemid 41633077)
12. Chinnam R. B., Baruah P., Autonomous diagnostics and prognostics through competitive learning driven HMM-based clustering. 4 Proceedings of the International Joint Conference on Neural Networks July 2003 2466 2471 2-s2.0-0141815690
13. Camci F., Chinnam R. B., Health-state estimation and prognostics in machining processes. IEEE Transactions on Automation Science and Engineering 2010 7 3 581 597 2-s2.0-77954382543 10.1109/TASE.2009.2038170
14. Ocak H., Loparo K. A., Discenzo F. M., Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: a method for bearing prognostics. Journal of Sound and Vibration 2007 302 4-5 951 961 2-s2.0-33947210318 10.1016/j.jsv.2007.01.001 (Pubitemid 46435577)
15. Tai A. H., Ching W.-K., Chan L. Y., Detection of machine failure: hidden Markov Model approach. Computers and Industrial Engineering 2009 57 2 608 619 2-s2.0-68649097825 10.1016/j.cie.2008.09.028
16. Zhou Z.-J., Hu C.-H., Xu D.-L., Chen M.-Y., Zhou D.-H., A model for real-time failure prognosis based on hidden Markov model and belief rule base. European Journal of Operational Research 2010 207 1 269 283 2-s2.0-77953873857 10.1016/j.ejor.2010.03.032
17. Tobon-Mejia D. A., Medjaher K., Zerhouni N., Tripot G., A data-driven failure prognostics method based on mixture of Gaussians hidden Markov models. IEEE Transactions on Reliability 2012 61 2 491 503 2-s2.0-84860579243 10.1109/TR.2012.2194177
18. Liu Z., Li Q., Liu X., Mu C., A hybrid LSSVR/HMM-based prognostic approach. Sensors 2013 13 5 5542 5560
19. Rabiner L. R., A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 1989 77 2 257 286 2-s2.0-0024610919 10.1109/5.18626
20. Li W., Wang Z. W., Zhu Z. C., Zhou G. B., Chen G. A., Design of online monitoring and fault diagnosis system for belt conveyors based on wavelet packet decomposition and support Vector machine. Advances in Mechanical Engineering 2013 2013 10 797183 10.1155/2013/797183
21. Suykens J. A. K., Vandewalle J., Least squares support vector machine classifiers. Neural Processing Letters 1999 9 3 293 300 2-s2.0-0032638628
22. Espinoza M., Suykens J. A. K., De Moor B., Fixed-size least squares support vector machines: a large scale application in electrical load forecasting. Computational Management Science 2006 3 2 113 129 2-s2.0-33645018466 10.1007/s10287-005-0003-7 (Pubitemid 43414562)
23. Williams C., Seeger M., Using the nystroem method to speed up Kernel machines. Advances in Neural Information Processing Systems 2001 13 682 688
24. Saxena A., Goebel K., C-MAPSS Data Set. NASA Ames Prognostics Data Repository, NASA Ames, Moffett Field, Calif, USA, 2008, http://ti.arc.nasa.gov/ tech/dash/pcoe/prognostic-data-repository/
25. Collobert R., Weston J., A unified architecture for natural language processing: deep neural networks with multitask learning. Proceedings of the 25th International Conference on Machine Learning July 2008 160 167 2-s2.0-56449095373
26. Saxena A., Celaya J., Saha B., Saha S., Goebel K., Metrics for offline evaluation of prognostic performance. International Journal of Prognostics and Health Management 2010 1 1 20
27. Saxena A., Celaya J., Balaban E., Goebel K., Saha B., Saha S., Schwabacher M., Metrics for evaluating performance of prognostic techniques. Proceedings of the International Conference on Prognostics and Health Management 2008 1 17
28. Huang G.-B., Zhu Q.-Y., Siew C.-K., Extreme learning machine: theory and applications. Neurocomputing 2006 70 1-3 489 501 2-s2.0-33745903481 10.1016/j.neucom.2005.12.126 (Pubitemid 44615772)