Abstract:In order to solve the problem that the traditional methods in the field of mechanical fault diagnosis have poor adaptability and the selection of parameters is too dependent on manual work, this paper proposes a mechanical fault diagnosis algorithm based on recurrent neural networks(RNN). This method uses the mechanical vibration signal after preprocessing to build a fault diagnosis model of bidirectional gated recurrent unit(BiGRU), and optimizes the model based on attention mechanism to improve the efficiency of feature extraction. It has been verified by the bearing data set of Cassegrain University and the self collected diesel engine fault test data. Compared with the traditional neural network algorithm, it improves the calculation efficiency and diagnosis accuracy, and shows a good anti noise ability. The results show that the method can be effectively applied to fault diagnosis based on mechanical vibration signals, and has good engineering application value.
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