【摘要】 针对单传感器信息难以满足复杂工况下航空发动机轴承故障状态的稳定监测问题，提出一种STFT-IncepNext的航空发动机轴承故障诊断模型。首先，将同一时间窗口内的异位传感器数据进行拼接，以补充轴承在不同空间下的振动信息。其次，为了捕捉振动信号中故障成分的瞬时变化，利用短时傅里叶变换（short-time Fourier transform, STFT）将多传感器振动信号转换为时频图。最后，通过轻量化的IncepNext网络来提取时频图中蕴含的故障信息全局特征，由分类器给出识别的故障类别。实验结果表明：所提方法能够有效地增强信号的故障特征，提高轴承在不同状态下振动特征的辨识度。在特定的实验条件下，该方法实现了航空发动机轴承振动故障诊断准确率达100%，相较于STFT-EdgeNeXt、 STFT-ResNeXt、 STFT-ShuffleNet、 STFTResNet18均取得了更好的性能，为航空发动机轴承故障诊断提供一种可行方法。更多还原

【Abstract】 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 timefrequency 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.更多还原