In view of the time-varying working conditions in the actual operation for agricultural machinery equipment,a fault diagnosis algorithm of agricultural machinery bearing based on hierarchical feature attention decoupling was proposed.Firstly,a Transformer network improved by Long Short-Term Memory(LSTM)neural network was used as the backbone network,and a hierarchical feature set of agricultural machinery bearing fault data was constructed according to the Multi-head mechanism of Transformer.Then,a cross-attention mechanism was employed to explore the correlations between different layers of features,and enhance the expression ability of agricultural machinery bearing fault features.Finally,by employing the multi-label diagnosis of agricultural machinery bearing faults,the mixed features were decoupled into multiple independent sets of bearing fault features.The decoupled features were used to predict corresponding labels,and achieve the diagnosis of various types of agricultural machinery bearing faults.The experimental results showed that the proposed model can achieve an average recognition accuracy of 96.58％and can diagnose multiple types of bearing faults in a fine-grained manner.
维普
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