Method for predicting remaining useful life of bearings based on a gated recurrent neural network

Abstract

The present invention provides a method for predicting remaining useful life of bearings based on a gated recurrent neural network, comprising the following steps: S1. obtaining full life cycle vibration signals of bearings, extracting the vibration distribution features and creating a training set of gated recurrent neural network; S2. constructing a gated recurrent neural network model, and introducing an attention mechanism that directly calculates weights to improve the integrity of extracting temporal information; S3. adding a Bayesian layer to construct a nonlinear mapping relationship between temporal information and remaining useful life; S4. taking vibration signals of a test bearing as input, the output result of the gated recurrent neural network model is the remaining useful life of the test bearing at the current time. The present invention does not need to add additional neural network layers, which avoids the problem of increasing the complexity of the model. The integrity of extracted information is improved through weighted fusion of temporal information extracted at different time. Moreover, by adding a Bayesian layer, the traditional point prediction results are converted into interval predictions, to consider the prediction uncertainty of remaining useful life of bearings.