Multi-period fault diagnosis of rolling bearings based on the OHF Elman-AdaBoost algorithm

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (6) : 71-78.

ZHUO Pengcheng1，XIA Tangbin1,2，ZHENG Meimei1，ZHENG Yu1，XI Lifeng1,2

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Abstract

In order to meet the needs of the multi-period fault diagnosis of rolling bearings under random noise, the OHF Elman-AdaBoost (output hidden feedback Elman-adaptive boosting) algorithm was proposed to achieve the accurate fault diagnosis of rolling bearings.The original signal was decomposed, denoised and reconstructed by the ensemble empirical mode decomposition (EEMD).The OHF Elman neural network improved the memory function for its dynamic data by adding feedback from the output layer to the hidden layer based on the Elman neural network.Then, a strong regressor (OHF Elman-AdaBoost algorithm) was integrated by selecting the OHF Elman neural network as the weak regressor and combining with the AdaBoost algorithm.The experimental results show that the OHF Elman-AdaBoost algorithm not only has a good diagnostic effect on different periods of rolling bearing faults, but also improves the diagnostic accuracy of the full sample data, providing a new tool and effective solution for the fault diagnosis.

Key words

rolling bearing / OHF Elman-AdaBoost / neural network / ensemble empirical mode decomposition (EEMD) / multi-period fault diagnosis

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ZHUO Pengcheng1，XIA Tangbin1,2，ZHENG Meimei1，ZHENG Yu1，XI Lifeng1,2. Multi-period fault diagnosis of rolling bearings based on the OHF Elman-AdaBoost algorithm[J]. Journal of Vibration and Shock, 2021, 40(6): 71-78

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