Remaining Useful Life Prediction of Planet Bearings Based on Conditional Deep Recurrent Generative Adversarial Network and  Action Discovery

doi:10.3969/j.issn.1000-1093.2020.11.003

Abstract

Abstract: In order to address the problem about the remaining useful life (RUL) prediction of planet bearings under small samples and varying operating conditions, a RUL prediction method of planet bearings based on conditional deep recurrent generative adversarial network (C-DRGAN) and action discovery (AD) is presented. The gated recurrent unit neural network is integrated with conditional generative adversarial network to construct the C-DRGAN, which extracts the fault features from the nonlinear and non-stationary signals so as to realize the RUL prediction of planet bearings under small samples and varying operating conditions conditions. Then the training algorithm based on AD is employed to train the C-DRGAN to enhance the convergence speed and reduce the training time. Finally, according to the C-DRGAN after training, a multiple linear regression classifier is employed to predict the RUL of planet bearings in test samples. The effectiveness of the proposed method is validated through an accelerated fatigue life experiment of planet bearings. The results show that the proposed method processes strong processing adaptability to the nonlinear and non-stationary signals and obtains the excellent performance in the case of small samples.

Key words: planetbearing, remainingusefullifeprediction, gatedrecurrentunitneuralnetwork, conditionalgenerativeadversarialnetwork, actiondiscovery, smallsample

CLC Number:

TJ810.3⁺21

YU Jun， LIU Ke， GUO Shuai, YU Guangbin， GUO Zhenyu. Remaining Useful Life Prediction of Planet Bearings Based on Conditional Deep Recurrent Generative Adversarial Network and Action Discovery[J]. Acta Armamentarii, 2020, 41(11): 2170-2178.

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Remaining Useful Life Prediction of Planet Bearings Based on Conditional Deep Recurrent Generative Adversarial Network and Action Discovery

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