Abstract: Aiming at the fault diagnosis problem of the main components in complex hydraulic system， a fault diagnosis model based on one-dimensional convolutional neural network （1D-CNN） and bidirectional long-term memory network （BiLSTM） is proposed to achieve multi-sensor information fusion and make fault diagnosis of piston pump and throttle valve. In the proposed model， the signals collected by various sensors are carried out data-stage fusion firstly， then the fault characteristics of the fusion signal are extracted by CNN and dimensionality reduction is performed， and then the forward and reverse data characteristics in the signal are learned by BiLSTM， finally the Softmax is used for classification， which realizes the diagnosis of piston pump and throttle valve fault. The experimental results show that the proposed method can automatically extract the fault characteristics in the signal and consider the positive and negative data characteristics contained in the signal. The diagnostic accuracy of the plunger pump can reach 96.3%， and the diagnostic accuracy of the throttle valve can reach 94.28%， which realizes the accurate and reliable diagnosis of the fault state of the plunger pump and the throttle valve.

Key words: hydraulic system, fault diagnosis, convolutional neural networks, long and short time memory networks, multi-sensor fusion