Wire Rope Fault Diagnosis Based on Wavelet Analysis and Support Vector Machine (SVM)

Ya Nan Qin; Guo Hua Gao; Mei Juan Lian; Yue Liu

doi:10.4028/www.scientific.net/AMR.971-973.1396

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 971-973Wire Rope Fault Diagnosis Based on Wavelet...

Wire Rope Fault Diagnosis Based on Wavelet Analysis and Support Vector Machine (SVM)

Abstract:

A diagnosis of corrosion failure in steel wire ropes based on wavelet and support vector machine (SVM) is investigated. Wavelet decomposition and reconstruction algorithm is used to denoise the magnetic flux leakage (MFL) signals, then peak value, trough value, peak-to-peak value and energy parameters are extracted as fault feature vectors, and a SVM classifier is designed and used to conduct the fault pattern recognition. The experimental results show that the accuracy of the SVM classification achieves to 100%.

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Periodical:

Advanced Materials Research (Volumes 971-973)

Pages:

1396-1399

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.971-973.1396

Citation:

Cite this paper

Online since:

June 2014

Authors:

Ya Nan Qin*, Guo Hua Gao, Mei Juan Lian, Yue Liu

Keywords:

Fault Diagnosis, SVM, Wavelet Analysis, Wire Rope

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* - Corresponding Author

References

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