Abstract
Defective rolling bearing response is often characterized by the presence of periodic impulses. However, the in-situ sampled vibration signal is ordinarily mixed with ambient noises and easy to be interfered even submerged. The hybrid approach combining the second generation wavelet denoising with morphological filter is presented. The raw signal is purified using the second generation wavelet. The difference between the closing and opening operator is employed as the morphology filter to extract the periodicity impulsive features from the purified signal and the defect information is easily to be extracted from the corresponding frequency spectrum. The proposed approach is evaluated by simulations and vibration signals from defective bearings with inner race fault, outer race fault, rolling element fault and compound faults, respectively. Results show that the ambient noises can be fully restrained and the defect information of the above defective bearings is well extracted, which demonstrates that the approach is feasible and effective for the fault detection of rolling bearing.
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Ling-Jie Meng received the B.S. degree in Mechanical Engineering from Guilin University of Electronic Technology, China, in 2010. He is currently a postgraduate student in Wenzhou University. His research interests include faults detection of mechanical systems.
Jia-Wei Xiang received the B.S. degree in mechatronics from Hunan University, China, in 1997, the M.S. degree from Guangxi University, China, in 2003, and the Ph.D. from Xi’an Jiaotong University, China, in 2006. He is currently a professor at the college of Mechanical and Electrical Engineering, Wenzhou University, China. His research interests are the health monitoring of mechanical systems using numerical simulation and signal processing techniques.
Yong-Teng Zhong received the B.S. degree in Mechanical Engineering from Wuhan Textile University, China, in 2006, the M.S. degree from Guilin University of Electronic Technology, China, in 2010, and the Ph.D. from Nanjing University of Aeronautics and Astronautics, China, in 2014. He is currently a Lecturer at the college of Mechanical and Electrical Engineering, Wenzhou University, China. His research interests are the structural health monitoring of mechanical systems.
Wen-Lei Song received the B.S. degree in Mechanical Engineering from Laocheng University, China, in 2014. He is currently a postgraduate student in Wenzhou University. His research interests include faults detection of mechanical systems.
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Meng, L., Xiang, J., Zhong, Y. et al. Fault diagnosis of rolling bearing based on second generation wavelet denoising and morphological filter. J Mech Sci Technol 29, 3121–3129 (2015). https://doi.org/10.1007/s12206-015-0710-0
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DOI: https://doi.org/10.1007/s12206-015-0710-0