Abstract
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.
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Acknowledgments
This research is sponsored by the National Natural Science Foundation of China (No. 51675263), and National Science and Technology Major Project (2017-IV-0008-0045).
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Recommended by Editor Chongdu Cho
X. Y. Zhang is a Master’s student at the College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China. His current research interests include deep learning and pattern recognition, and their applications in bearing fault diagnosis.
G. Chen received a Ph.D. degree in the School of Mechanical Engineering from the Southwest Jiaotong University, Chengdu, P. R. China, in 2000. Now he works at the College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China. His current research interests include the whole aero-engine vibration, rotor-bearing dynamics, rotating-machine fault diagnosis, pattern recognition and machine learning, signal analysis and processing.
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Zhang, X., Chen, G., Hao, T. et al. Rolling bearing fault convolutional neural network diagnosis method based on casing signal. J Mech Sci Technol 34, 2307–2316 (2020). https://doi.org/10.1007/s12206-020-0506-8
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DOI: https://doi.org/10.1007/s12206-020-0506-8