Variational eligibility trace meta-reinforcement recurrent network for residual life prediction of space rolling bearings

Abstract

Traditional sequence recurrent neural networks (SRNNs) have the defect of long time dependence in the prediction of time series, resulting in their poor generalization ability. Moreover, it is required to traverse the whole training data set to realize supervised learning by SRNNs, which increases the time complexity and leads to their low prediction accuracy and high computation cost in the residual life prediction of space rolling bearings in the ground simulated space environment. In view of this, a novel SRNN named variational eligibility trace meta-reinforcement recurrent network (VETMRRN) is proposed for achieving higher residual life prediction accuracy and lower computation cost. In the proposed VETMRRN, a new sequence recurrent network structure is constructed to increase the memory amount of historical information, thus improving the long-term memory capacity of VETMRRN. Then, a hyperparameter self-initialization meta-learning network with an oracle gate mechanism is designed to self-initialize the hyperparameters of VETMRRN for fast determination of the optimal review sequence length. Hence, VETMRRN can adapt to different input sequence lengths and avoid the defect of long time dependence of traditional SRNNs. Furthermore, a variational auto-encoding meta policy gradient learning algorithm with an eligibility trace operator is designed to improve the training speed and enhance the global optimization effect for VETMRRN parameters. Based on the above advantages of VETMRRN, a new residual life prediction method of space rolling bearings in the ground simulated space environment is proposed. Firstly, the time-frequency fusion features are extracted by Shapely-value feature fusion from the vibration acceleration data of space rolling bearing as the performance degradation features. Then, the performance degradation features are input into VETMRRN to predict the performance degradation feature trends of space rolling bearings. Finally, a Weibull-distribution reliability model is established based on the performance degradation feature trend values to predict the residual life of space rolling bearings. The effectiveness of the proposed VETMRRN-based prediction method is verified by the vibration acceleration data collected from the self-built vibration monitoring platform of space rolling bearings in the ground simulated space environment. The results indicate that compared to traditional SRNNs, deep sparse auto-encoding neural network (DSAE-NN), and multi-kernel least-square support vector machine (MK-LSSVM), the proposed method can improve the prediction accuracy and reduce the computation cost in the residual life prediction of space rolling bearings. In the future, the generalization performance of VETMRRN still needs to be further improved.