Bearing Fault Diagnosis Based on Probability Boxes Theory and SVM Method
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.
Yiyi Zhouzhou and Qi Luo
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