Abstract
As a novel representation method, two dimensional (2D) segmentation is gaining ground as an effective condition monitoring method due to its high-level information descriptional ability. However, the accuracy of extracting frequency information is still limited by the finite gray-level and the extraction ability of distinguishable texture for each fault. To overcome these drawbacks, this research proposes a bearing fault diagnosis method using the converted 2D vibrational signal matrices. In this method, 1D vibration signals are converted into 2D matrices to exploit the fault signatures from the converted images. Curvature filtering (mean curvature) algorithm is applied to eliminate the overwhelming interfering contents and preserves the necessary edge information contained in the 2D matrix. In addition, the histogram of oriented gradients features is employed for the effective fault feature extraction. Finally, a one-versus-one support vector machine is utilized for the automatically fault classification. An experimental investigation was carried out for the performance evaluation of the proposed method. Comparison results indicate that the established method is capable of bearing fault detection with considerable accuracy.
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Acknowledgments
This work was financially supported by Fundamental Scientific Research Project of Wenzhou (No. G20190013) and National Natural Science Foundation of China (No. 51605403). Authors are also grateful to School of Mechanical Engineering of Xi’an Jiaotong University for providing equipment for this research. The statements made herein are solely the responsibility of the authors.
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Weifeng Sun was born in Xiangyang, Hubei Province, China in 1988. He received the M.S. degree from Huazhong Agricultural University, Wuhan, China, in 2014, and the Ph.D. degree from Xiamen university, Xiamen, China, in 2018. He is currently an Assistant Professor of College of Mechanical and Electrical Engineering, Wenzhou University. His research interests include dynamic modeling and diagnosis of electromechanical system, digital information analysis and artificial intelligence method.
Xincheng Cao was born in Weifang, Shandong, China, in 1992. He received the Bachelor12206_2020_504_JobSheet_100.xmls and Master’s degrees in Mechanical Engineering from the School of Aerospace Engineering, Xiamen University, China, in 2015 and 2018, respectively, and the Ph.D. degree from the School of Aerospace Engineering, Xiamen University. His main research interests include intelligent equipment and smart manufacturing, and structural health monitoring of equipment.
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Sun, W., Cao, X. Curvature enhanced bearing fault diagnosis method using 2D vibration signal. J Mech Sci Technol 34, 2257–2266 (2020). https://doi.org/10.1007/s12206-020-0501-0
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DOI: https://doi.org/10.1007/s12206-020-0501-0