This invention belongs to the field of hydraulic
system technology and discloses an AI-based online monitoring method for external leakage in aircraft hydraulic systems. It aims to solve the technical problems of poor real-time performance, high
false alarm rate, and difficulty in adapting to dynamic operating conditions in existing
monitoring methods. The aircraft hydraulic
system is the power source for critical functions such as door opening and closing,
landing gear retraction and extension, braking, and control surface actuation. External leakage can easily lead to serious safety accidents. Existing
monitoring methods have obvious defects, insufficient generalization ability, and inadequate real-time performance. The steps of this method are as follows: First, select multiple leak-
free flight parameter data, extract multi-dimensional parameters such as pressure and temperature, and construct a
feature vector set after preprocessing; second, based on AI algorithms, construct and
train a
fuel tank oil volume prediction model under leak-free operating conditions; third, input real-time collected multi-dimensional time-
series data into the model and output the predicted oil volume value; finally, compare the predicted value with the measured value from the
level sensor, determine the leakage status, sensor failure, or model deviation through the volume difference, and realize
online model updates. This invention integrates multi-dimensional parameter features, improves the accuracy of leak identification and anti-interference ability, can output monitoring results in real time and provide early warning of leakage, enhances the robustness of hydraulic
system leak monitoring, reduces operation and maintenance costs, and can provide
technical support for early warning of leaks in aircraft hydraulic systems, thus having significant
engineering application value.