Features distribution fitting and intelligent fault diagnosis of planet bearings under time-varying condition

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (14) : 252-260.

ZHAO Chuan1,2， FENG Zhipeng2

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Abstract

The intricate kinematics of planet bearings in planetary gearboxes leads to complex and even non-linear vibration signals.Besides, the characteristic frequencies change with time under time-varying working condition, which makes the fault diagnosis of planet bearings manually more difficult.For addressing these issues, an intelligent fault diagnosis method was proposed based on the adversarial variational auto-encoder.Firstly, the time-frequency representation of a sample was obtained to reveal the time-varying properties.Then, the variational auto-encoder of the model was utilized to extract features of a time-frequency image.In order to make the latent features exposed in an explicit meaning, a multivariate independent Gaussian distribution was introduced, and samples were collected from the distribution.After that, the samples were transformed into new ones according to the label information and made to follow a new multivariate independent Gaussian distribution to ensure each element in the sample has its own distribution.Through an adversarial game, the latent features were made to have the same distribution as the corresponding transformed samples and follow the new distribution so that the unknown features distribution could be fitted by a given prior distribution.And the discriminability of the features among different classes was enhanced by controlling the distribution to improve their performance for pattern identification.Finally, a classifier was trained and tested by the optimized features.The method was validated via some planetary gearbox data sets.The results show that the model enables extracted features among different classes to follow an explicit distribution, improves their clustering performance for pattern identification and effectively diagnoses planet bearing faults.It outperforms to a certain extent the traditional auto-encoder and variational auto-encoder.

Key words

planet bearing / intelligent fault diagnosis / multivariate independent Gaussian distribution / adversarial variational auto-encoder / time-varying condition

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ZHAO Chuan， FENG Zhipeng. Features distribution fitting and intelligent fault diagnosis of planet bearings under time-varying condition[J]. Journal of Vibration and Shock, 2021, 40(14): 252-260

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