A fault diagnosis method of mechanical bearing based on the deep forest

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (12) : 107-113.

DING Jiaman1，WU Yehui1，LUO Qingbo1，DU Yi2

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Abstract

There is a complex parameter adjustment process in mechanical fault diagnosis by using deep neural network, and the assignment of parameters has a great influence on the diagnosis results.In order to solve this problem, this paper introduced the idea of deep learning and proposed a bearing fault diagnosis method based on the deep forest.Firstly, time-domain and frequency-domain features were extracted by resampling technique.Then, a deep forest model was constructed by training several groups of bearing experimental data under simple working conditions, and the key parameters of the diagnosis model were determined based on the analysis of the influence of over-parameters on the model.Finally, the model was applied to complex conditions, and compared with a random forest model and a deep neural network model, the experimental results show that the method in this paper is not only effective but also has a strong generalization ability.

Key words

bearing fault / fault diagnosis / deep forest / deep learning

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DING Jiaman1，WU Yehui1，LUO Qingbo1，DU Yi2. A fault diagnosis method of mechanical bearing based on the deep forest[J]. Journal of Vibration and Shock, 2021, 40(12): 107-113

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