Affected by the transmission path, it is very difficult to diagnose the vibration signal of the rolling bearing on the aircraft engine casing. A fault diagnosis method based on convolutional neural network is proposed for the weak vibration signal of the casing under the excitation of rolling bearing fault. Firstly, the processing method of vibration signal is studied. Through comparison and analysis, it is found that the fault characteristics of rolling bearing are more easily expressed by continuous wavelet scale spectrum, and a better recognition rate is obtained. Finally, the experiment was carried out with an aero-engine rotor tester with a casing, and the method based on wavelet scale spectrum and convolutional neural network was used for diagnosis. The results were compared with the support vector machine method. The results show that the method has a high recognition rate for the weak fault signals of different fault types collected on the aero engine case, and its fault recognition rate reaches 95.82 %, which verifies the superiority and potential of the method for rolling bearing fault diagnosis.

受传动路径的影响，很难诊断飞机发动机壳体上滚动轴承的振动信号。针对滚动轴承故障引起的壳体弱振动信号，提出了一种基于卷积神经网络的故障诊断方法。首先，研究了振动信号的处理方法。通过比较和分析，发现滚动轴承的故障特征更容易用连续小波尺度谱表示，并且获得了更好的识别率。最后，用带壳体的航空发动机转子测试仪进行了实验，并采用基于小波尺度谱和卷积神经网络的方法进行了诊断。将结果与支持向量机方法进行比较。