Abstract
Compressive sensing is an efficient machinery monitoring framework, which just needs to sample and store a small amount of observed signal. However, traditional reconstruction and fault detection methods cost great time and the accuracy is not satisfied. For this problem, a 1D convolutional neural network (CNN) is adopted here for fault diagnosis using the compressed signal. CNN replaces the reconstruction and fault detection processes and greatly improves the performance. Since the main information has been reserved in the compressed signal, the CNN is able to extract features from it automatically. The experiments on compressed gearbox signal demonstrated that CNN not only achieves better accuracy but also costs less time. The influencing factors of CNN have been discussed, and we compared the CNN with other classifiers. Moreover, the CNN model was also tested on bearing dataset from Case Western Reserve University. The proposed model achieves more than 90 % accuracy even for 50 % compressed signal.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under the grants 71871220. I would like to express my gratitude to all those who helped me during the writing of this paper. A special acknowledgment should be shown to my supervisor Professor Xisheng Jia from whose useful instructions I benefited greatly.
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Yun-fei Ma received his M.Sc. in 2017 from Army Engineering University; now he is a Ph.D. candidate in Army Engineering University. His main research interests include compressive sensing, deep learning, and fault diagnosis.
Xi-sheng Jia received his Ph.D. in 2001 from University of Salford; now he is a Professor and Ph. D supervisor at Army Engineering University. His main research interests include Condition Based Maintenance (CBM), Prognostic and Health Management (PHM).
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Ma, Y., Jia, X., Bai, H. et al. A new fault diagnosis method based on convolutional neural network and compressive sensing. J Mech Sci Technol 33, 5177–5188 (2019). https://doi.org/10.1007/s12206-019-1007-5
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DOI: https://doi.org/10.1007/s12206-019-1007-5