Bearing Fault Diagnosis Based on Probability Boxes Theory and SVM Method

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volume 79)
• Pages: 93-98
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.79.93
• Online since: July 2011
• Authors: Yi Du, Yi Lin Chi, Xing Wu
• Keywords: Dempster Shafer Structure, Fault Diagnosis, Probability Box, Rolling Bearing

References

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