基于改进CNN-GRU网络的多源传感器故障诊断方法

A fault diagnosis method for multi-source sensors in complex systems was proposed. Based on the correlation between multi-source sensor data, a convolutional neural network (CNN) was used to extract the connections and features between different sensors. In the convolutional neural network, a sensor data calibration module was designed to make the network pay more attention to learning sensor data related to fault signals. Recurrent neural networks were used to model the time series of sensors, and jump connections and auxiliary loss functions were added to the network to reduce the difficulty of network training. Finally, based on the temporal and spatial characteristics, the results of the fault classification and the estimated values of the fault parameters were obtained at one time. The simulation results show that the improved CNN-GRU network can accurately diagnose fixed deviation fault and drift deviation fault of sensors in real time. The sensor data calibration module and the jump connection can effectively improve the accuracy and precision of the diagnosis algorithm.