Engine Fault Diagnosis Based on Support Vector Machine and Noise Analysis

Gao Huan Xu; Jun Xiang Ye

doi:10.4028/www.scientific.net/AMR.546-547.97

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 546-547Engine Fault Diagnosis Based on Support Vector...

Engine Fault Diagnosis Based on Support Vector Machine and Noise Analysis

Abstract:

Engine is a core component. It’s performance is important to the production safety, it will affect production efficiency. At the conclusion of other types of fault diagnosis method, proposed the engine fault diagnosis technology is based on SVM and noise analysis and do some pilot studies. The method can be used to early diagnosis, and can quickly and easily find fault.

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Periodical:

Advanced Materials Research (Volumes 546-547)

Pages:

97-101

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.546-547.97

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Online since:

July 2012

Authors:

Gao Huan Xu, Jun Xiang Ye

Keywords:

Engine, Fault Diagnosis, Noise Signal, Support Vector Machine (SVM)

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References

[1] Yunkui Xiao. Automotive Fault Diagnosis, Beijing: Beijing Institute of Technology Press, (2006).

Google Scholar

[2] D M Tullsen, S J Eggers, H M Levy, et al. Simultaneous multithreading: Maximizing on-chip parallelism[C]. In: Proc of the 22nd Annual Int'l Symposium on Computer Architecture. Los Alamitos, CA: IEEE Computer Society Press, 1995. 392-403.

DOI: 10.1109/isca.1995.524578

Google Scholar

[3] Longhan Cao. Diesel intelligent fault diagnosis, Beijing: National Defense Industry Press, Beijing: (2005).

Google Scholar

[4] Longhan Cao, Changxiu Cao, Yikai Sun. Status and Prospects of Diesel Engine Fault Diagnosis Technology [J]. Chongqing University, 2001. 24(6): 134-138.

Google Scholar

[5] C. Cortes & V. Vapnik. Support-vector networks, Machine Learning. v. 20, 1995: 273-297.

DOI: 10.1007/bf00994018

Google Scholar

[6] Han J, Pei J, Yin Y. Mining Frequent patterns without candidate generation. ACM SIGMOD International Conference Management of Data. Dallas, 2000. 1-12.

DOI: 10.1145/342009.335372

Google Scholar

[7] V. Vapnik, LevinE, LeCunY. Measuring the VC-dimension of A Learning Machine. Nueral Compution, 1994, 6: 851-876.

Google Scholar

[8] Vapnik V.N. The nature of statistical learning theory. New ork: Springer-Verlag, (2000).

Google Scholar

[9] Steve R Gunn. Support vector machines for clsssification and regression[R]. England: University of Southampton, (1998).

Google Scholar

[10] C. Cortes & V. Vapnik. Support-vector networks, Machine Learning. v. 20, 1995: 273-297.

DOI: 10.1007/bf00994018

Google Scholar

[11] Vapnik, V. N. An overview of statistical learning theory. IEEE Trans. Neural Networks, 10, 1999: 988-999.

DOI: 10.1109/72.788640

Google Scholar