Rolling bearing fault convolutional neural network diagnosis method based on casing signal

• Published in: Journal of mechanical science and technology Vol. 34; no. 6; pp. 2307 - 2316
• Main Authors: Zhang, Xiangyang, Chen, Guo, Hao, Tengfei, He, Zhiyuan
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society of Mechanical Engineers 01.06.2020; Springer Nature B.V; 대한기계학회
• Subjects: Aerospace engines; Aircraft engines; Artificial neural networks; Control; Dynamical Systems; Engineering; Fault diagnosis; Industrial and Production Engineering; Mechanical Engineering; Neural networks; Original Article; Recognition; Roller bearings; Signal processing; Support vector machines; Vibration; Vibration analysis; 기계공학; Fault diagnosis; Aero-engine; Casing; Convolutional neural network; Rolling bearing; Wavelet scale spectrum
• ISSN: 1738-494X; 1976-3824
• DOI: 10.1007/s12206-020-0506-8

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.