基于注意力机制与群智能优化的液压系统故障诊断OA

Addressing the challenges of high nonlinearity,strong coupling,and concealed faults in hydraulic systems,a fault diagnosis method integrating channel attention enhancement and swarm intelligence collaborative optimization is proposed.The method is designed to extract spatial features of multi-source heterogeneous sensor data such as pressure and flow by constructing multi-scale convolutional neural networks,and dynamic feature enhancement of key fault-sensitive channels is achieved using a channel attention mechanism.A deeply coupled architecture integrating a particle swarm optimization algorithm with bidirectional gated recurrent units(BiGRUs)is designed to optimize the hidden layer dimensions and network hyperparameters of BiGRU,achieving global optimization of bidirectional temporal features.A collaborative diagnosis mechanism combining channel feature enhancement with swarm intelligence optimization is established,coupled with a hybrid dynamic learning rate strategy to facilitate rapid model convergence.Experimental results demonstrate that this method achieves an accuracy of 98.09%in valve fault diagnosis and 100%accuracy in accumulator failure diagnosis.Dependence on expert experience for fault diagnosis in complex hydraulic systems is effectively reduced through automated feature enhancement and model optimization.Concurrently,the efficiency,accuracy,and intelligence level of the diagnostic process are significantly enhanced.This approach provides strong technical support for ensuring the safe,stable,and efficient operation of hydraulic systems.