Abstract:In order to obtain the optimal number of hidden layer elements in bidirectional gated cyclic element network under non-empirical guidance and realize the performance degradation trend prediction of rolling bearings, a bearing performance degradation trend prediction method was proposed based on sparrow search algorithm to optimize the depth bidirectional gated cyclic element. Firstly, the forward gating loop unit network is added to the reverse gating loop unit network to construct the deep bi-directional gating loop unit prediction network. Then, the mean square error between the predicted value and the true value was used as the fitness value, and the parameters were updated according to the sparrows finder and predator. After training, the depth bidirectional gated cyclic element network with the optimal hidden layer element parameters was obtained. Finally, the performance degradation trend is predicted by the full connection layer. The validity and feasibility of the proposed method are verified by experiments on public and measured data sets.
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