The improved multi-scale convolution method for fault diagnosis of civil aircraft hydraulic systems based on QAR data

Abstract As a core system of the aircraft, the stability, response speed, and efficiency of the hydraulic system are directly linked to flight safety, control performance, and operational reliability. Hydraulic system failures can lead to catastrophic accidents. This paper proposes a diagnostic method for civil aircraft hydraulic systems based on quick access recorder (QAR) data. First, based on guidance from an airline’s maintenance records, eight typical hydraulic system faults were identified. Next, under the guidance of the hydraulic system’s working principles, 28 sensor parameters most relevant to hydraulic system faults were selected from over 3000 parameters collected by the aircraft’s sensors. Subsequently, using preprocessed QAR fault data, an improved deep learning model based on multi-scale convolution (MSC) was developed to diagnose hydraulic system faults. The model incorporates an enhanced MSC design, which employs cross-channel convolution on top of MSC and integrates a multi-head attention mechanism, enabling the model to extract multi-dimensional features from QAR data more quickly and accurately. Experimental results on the QAR fault dataset demonstrate high prediction accuracy and fast diagnostic speed, achieving an accuracy of 98.2%. This model effectively and accurately diagnoses hydraulic system faults, contributing to flight safety and improving maintenance efficiency.