| Citation: | WAN Anping, ZHANG Hua, ZHANG Jin, et al. Fault diagnosis of aeroengine bearings using multi-source fusion STFT-IncepNext method[J]. Journal of Aerospace Power, 2025, 40（10）：20240049 doi: 10.13224/j.cnki.jasp.20240049
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To address the challenge of reliably monitoring aviation engine bearing faults under complex operating conditions with limited information from a single sensor, the STFT-IncepNext model was proposed for bearing fault diagnosis. Initially, sensor data from different positions within the same time window were concatenated to enrich the vibration information of bearings across various spatial dimensions. Subsequently, to capture the transient changes of fault components in the vibration signal, the short-time Fourier transform (STFT) was applied to convert multi-sensor vibration signals into time-frequency representations. Finally, a lightweight IncepNext network extracted global features of fault information embedded in the time-frequency representations, and a Softmax classifier identified the fault category. Experimental results demonstrated that the proposed approach effectively enhanced signal fault characteristics, and improved the discriminability of vibration features under various bearing states. Under specific experimental conditions, the method achieved an accuracy rate of 100% for diagnosing vibration faults in aeroengine bearings. Compared with STFT-EdgeNeXt, STFT-ResNeXt, STFT-ShuffleNet, and STFT-ResNet18, the method exhibited superior performance, providing a feasible approach for diagnosing faults in aeroengine bearings.
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