Fault Diagnosis of Multi-information Fusion by SVM &amp; Improved Evidence Theory

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Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (13) : 71-77.

Fault Diagnosis of Multi-information Fusion by SVM & Improved Evidence Theory

XIANG Yang-hui, ZHANG Gan-qing, PANG You-xia

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Abstract

To comprehensively reasonably utilize much feature information of the equipment to improve the accuracy of the fault diagnosis, a method of multi-information fusion fault diagnosis is proposed which is based on support vector machine(SVM) and improved evidence theory. It gets the reliability of the local diagnosis evidence of each SVM to every failure mode by the confusion matrix, by which gives different weight coefficient. The basic probability assignment that is constructed by hard output decision matrix from the local diagnosis of each SVM, which effectively combines SVM and improved evidence theory in multi-information fusion fault diagnosis. The experimental results show that the weighted fusion treatment of the local diagnosis evidence from each SVM can significantly reduce the conflict between the local diagnoses, and that the proposed method can effectively improve the accuracy of fault diagnosis.

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Support vector machine / Evidence theory / Fault diagnosis / Multi-information fusion

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XIANG Yang-hui, ZHANG Gan-qing, PANG You-xia. Fault Diagnosis of Multi-information Fusion by SVM & Improved Evidence Theory[J]. Journal of Vibration and Shock, 2015, 34(13): 71-77

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