Intelligent Fault Diagnosis of Electrohydraulic Servo Valve Based on Kalman Filter and BiLSTM‐ANN Hybrid Deep Model

The electrohydraulic servo valve plays crucial roles as key component in combustion control system of an aeroengine, and its fault forms are diverse, nonlinear, and closed. At the same time, limited installation space makes it difficult to ensure the installation of a large number of sensors, and traditional neural network fault diagnosis methods cannot meet the demand for fault data. Therefore, in order to accurately diagnose and predict electrohydraulic servo valve faults, a fault diagnosis method based on digital‐analog fusion based on the state space model of electrohydraulic servo valve is innovatively proposed. First, electrohydraulic servo valve state space model is established by the method of mechanism modeling. Then, the Kalman filter model is established to determine the operating state of electrohydraulic servo valve based on the pressure, current command and other signals. Combining the hybrid deep learning models of BiLSTM (Bi‐directional Long Short‐Term Memory)‐ANN (Artificial Neural Network) to identify fault forms of electrohydraulic servo valve, the fault diagnosis strategy with the fusion of Kalman filter and artificial neural network is obtained. Finally, based on the experimental platform of electrohydraulic servo valve performance accelerated degradation, the fault diagnosis accuracy proposed in this paper is verified to be 97.5%. This paper ensures the accuracy of fault diagnosis for electro‐hydraulic servo valves under limited data, and simultaneously achieves the explainability of faults, providing a favorable reference for the research of fault diagnosis algorithms of hydraulic components in practical engineering applications.