关键词: 强化学习单元匹配循环神经网络, 强化学习, 奇异谱熵, 状态趋势预测, 滚动轴承

Abstract: To solve the problems of poor prediction accuracy and low computational efficiency of the existing artificial intelligence-based prediction methods in state trend prediction of rolling bearings,a novel state trend prediction method was proposed based on Reinforcement Learning Unit Matching Recurrent Neural Network (RLUMRNN).The moving average singular spectral entropy was used as the state degradation feature of rolling bearing,and then the feature was input to RLUMRNN to accomplish the state trend prediction.In RLUMRNN,the monotone trend discriminator was constructed by using the least square linear regression method for dividing the whole state degradation trend of rolling bearing into the following three kinds of monotonic trend units: ascending unit,descending unit and stationary unit;by virtue of reinforcement learning,the RNN with the hidden layer number and hidden layer node number fitted to corresponding monotone trend unit was selected to enhance the nonlinear approximation ability and generalization performance of RLUMRNN.Besides,three monotonic trend units and different hidden layer and node numbers were respectively used to represent the status and action of Q value table,and a new reward function associated with RNN output errors was constructed to clarify the purpose of reinforcement learning,which made the output error of RNN smaller,avoided the blind search of agent (decision function) in the update of Q value table and improved the convergence speed of RLUMRNN.The example of state degradation trend prediction for double row roller bearing demonstrated the higher prediction accuracy and higher calculation efficiency of the proposed method.

Key words: reinforcement learning unit matching recurrent neural network, reinforcement learning, singular spectral entropy, state trend prediction, rolling bearing