Fault diagnosis of rolling bearings based on improved TrAdaBoost multi-classification algorithm

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Journal of Vibration and Shock ›› 2019, Vol. 38 ›› Issue (15) : 36-41.

CHEN Renxiang1,2, CHEN Siyang1, YANG Lixia3, XU Xiangyang1, DONG Shaojiang1, TANG Linlin1

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Abstract

In engineering practice, rolling bearings are affected by working conditions and runtime environment.The acquired data can’t easily satisfy conditions of training data and tested ones being independent and having the same distribution, and enough training samples in traditional machine learning to directly affect fault diagnosis rate.Here, a rolling bearing fault diagnosis method based on the improved TrAdaBoost multi-classification algorithm was proposed.Firstly, a large amount of auxiliary labeled data and a small amount of target labeled data were introduced to form a joint training set, and make training samples enough.The heterogeneous distribution weighted random sampling was used to reconstruct the joint training set in TrAdaBoost iteration process, acquire the optimal joint training set with the approximate same distribution as that of the tested set,and reduce the diversity among different distribution data.Secondly, the internal classifier model after iteration ending was taken as output to change the output mechanism of TrAdaBoost, and make it adapt to the multi-class task.Finally, to weaken effects of random sampling on diagnosis results, results of multiple samplings were voted with consistence to get the final diagnosis results.The test results verified the feasibility and effectiveness of the proposed method.

Key words

rolling bearings / fault diagnosis / weighted random sampling / TrAdaBoost

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CHEN Renxiang1,2, CHEN Siyang1, YANG Lixia3, XU Xiangyang1, DONG Shaojiang1, TANG Linlin1. Fault diagnosis of rolling bearings based on improved TrAdaBoost multi-classification algorithm[J]. Journal of Vibration and Shock, 2019, 38(15): 36-41

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