A Rolling Bearing Fault Signal Modeling Method Based on Probability Box Theory

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Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (19) : 31-37.

DU Yi 1 DING Jiaman 2 LIU liqiang 1

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Abstract

Feature extractions lead to information loss，and multi-segments-averages lead to data uncertainties discarding. In order to solve the above problems in mechanical fault diagnosis, a new modeling method for mechanical fault signals based on probability box (p-box) theory was proposed. The fault signals of the rolling bearing were the research objects. The raw data’s probability types were analyzed. The uncertainty interval of probability distribution’s parameter was gotten. The p-box modeling method based on normal distribution was proposed. In order to solve the identification difficulty of fault signal data’s probability distribution, the raw data’s characteristics were extracted, and the orderliness of characteristics was used, and a p-box modeling method based on feature extraction was proposed. The similarities and differences of skewness p-box and kurtosis p-box were contrasted. Based on the p-box’s definition, the raw data’s uncertainties were projected into the p-box’s bounds, and another more effective p-box modeling method which was directly based on raw data was proposed, which does not need data’s probability distribution identification. The effectiveness and applicability of the three methods were compared by the rolling bearing’s measure data. The method’s validity is verified by compared with conventional feature extraction method.

Key words

rolling bearing / fault diagnosis / uncertainty / probability box theory / Dempster Shafer Structure

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DU Yi 1 DING Jiaman 2 LIU liqiang 1 . A Rolling Bearing Fault Signal Modeling Method Based on Probability Box Theory[J]. Journal of Vibration and Shock, 2016, 35(19): 31-37

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