Bearing Fault Diagnosis Based on Probability Boxes Theory and SVM Method

Abstract:

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To solve the information loss on the feature extraction process in the traditional fault diagnosis, this paper proposes a new method which based on probability boxes and Dempster Shafer Structure (DSS). The DSS was extracted from the raw data and then transformed into a probability box. The bearing fault diagnosis was done by the probability boxes images recognition. To solve the excessive computing cost caused by large sample frequency and the overlaps among p-boxes, the Support Vector Machine (SVM) was involved. The SVM features database was established by some cumulative uncertainty measures methods of p-boxes. The test result shows that this method is fast, not sensitive to noise and has high recognition rate with high accuracy.

Info:

Periodical:

Edited by:

Yiyi Zhouzhou and Qi Luo

Pages:

93-98

DOI:

10.4028/www.scientific.net/AMM.79.93

Citation:

Y. Du et al., "Bearing Fault Diagnosis Based on Probability Boxes Theory and SVM Method", Applied Mechanics and Materials, Vol. 79, pp. 93-98, 2011

Online since:

July 2011

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Price:

$35.00

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