Gear crack detection using kernel function approximation

Authors:
Weihua Li;Tielin Shi;Kang Ding
Affiliations:
School of Automotive Engineering, South China University of Technology, Guangzhou, China;School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China;School of Automotive Engineering, South China University of Technology, Guangzhou, China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

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Abstract

Failure detection in machine condition monitoring involves a classification mainly on the basis of data from normal operation, which is essentially a problem of one-class classification. Inspired by the successful application of KFA (Kernel Function Approximation) in classification problems, an approach of KFA-based normal condition domain description is proposed for outlier detection. By selecting the feature samples of normal condition, the boundary of normal condition can be determined. The outside of this normal domain is considered as the field of outlier. Experiment results indicated that this method can be effectively and successfully applied to gear crack diagnosis.