Fault Diagnosis of Bearing Based on KPCA and KNN Method

Qiang Wang; Yong Bao Liu; Xing He; Shu Yong Liu; Jian Hua Liu

doi:10.4028/www.scientific.net/AMR.986-987.1491

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 986-987Fault Diagnosis of Bearing Based on KPCA and KNN...

Fault Diagnosis of Bearing Based on KPCA and KNN Method

Abstract:

Selection of secondary variables is an effective way to reduce redundant information and to improve efficiency in nonlinear system modeling. The combination of Kernel Principal Component Analysis (KPCA) and K-Nearest Neighbor (KNN) is applied to fault diagnosis of bearing. In this approach, the integral operator kernel functions is used to realize the nonlinear map from the raw feature space of vibration signals to high dimensional feature space, and structure and statistics in the feature space to extract the feature vector from the fault signal with the principal component analytic method. Assessment method using the feature vector of the Kernel Principal Component Analysis, and then enter the sensitive features to K-Nearest Neighbor classification. The experimental results indicated that this method has good accuracy.

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Periodical:

Advanced Materials Research (Volumes 986-987)

Pages:

1491-1496

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.986-987.1491

Citation:

Cite this paper

Online since:

July 2014

Authors:

Qiang Wang*, Yong Bao Liu, Xing He, Shu Yong Liu, Jian Hua Liu

Keywords:

Bearing, Fault Diagnosis, KNN, KPCA

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* - Corresponding Author

References

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