Fault Diagnosis for Aero-engine Accessory Gearbox by Mechanism Constraint Graph Weight Enhancement Networks under the Large Revolution Fluctuations

doi:10.3901/JME.2024.12.126

Abstract

Abstract: Aiming at the problem that the existing deep learning models are easily affected by the large revolution fluctuation in the process of fault feature extraction, which leads to the low accuracy of aero-engine accessory gearbox fault diagnosis, a mechanism constraint graph weight enhancement networks (MCGWENet) is proposed for accessory gearbox fault diagnosis. First, the adjacency matrix of the graph is constrained by measuring the euclidean distance of the time and frequency domain characteristics of the vibration signal, and the physical mechanism is embedded into the constructed graph structure. Then the results of wavelet packet decomposition of vibration signals are used as node features; and the designed adjacency matrix is combined to construct a graph that can be used for the fault diagnosis of aero-engine accessory gearbox. Finally, the designed graph weight enhancement layer is used to suppress the influence of large revolution fluctuation conditions on fault feature extraction and improve the fault diagnosis accuracy of the proposed model. The experimental results show that the proposed method can effectively identify faults and can be used for health management of aero-engine accessory gearbox.

Key words: aero-engine accessory gearbox, mechanism constraint, graph weight enhancement network, large revolution fluctuation, fault diagnosis

CLC Number:

TH17

YU Xiaoxia, TANG Baoping, WEI Jing, ZHANG Zhigang. Fault Diagnosis for Aero-engine Accessory Gearbox by Mechanism Constraint Graph Weight Enhancement Networks under the Large Revolution Fluctuations[J]. Journal of Mechanical Engineering, 2024, 60(12): 126-136.

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