Abstract
Considering the random noise and the false IMF component which will led to the decrease of the quality of the EEMD decomposition, a fault diagnosis method is presented based on SVD and improved EEMD. First of all, using the SVD method to denoise fault signals for pretreatment, then using the correlation coefficient norm to eliminate the false IMF components which are gained by EEMD decomposition, then refactor the effective IMF components that are bigger than setting threshold, finally gain fault characteristic frequency of fault signal by using the Hilbert transform envelop demodulation. In rotating machinery fault platform QPZZ-II, fault signals of broken teeth, cracked gear and worn gear are acquired, respectively. Using the method proposed in this paper, finally successfully extract the fault characteristic frequency of different type.
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Acknowledgments
This paper was partially supported by the research projects: “Fujian Natural Science Foundation”, Grant #2015J01643; “Education Science Project of Young and Middle-aged Teachers of Colleges and Universities in Fujian Province”, Grant #JA15545 and #JZ160396; “Ningde City Science and Technology Project”, Grant #20150034; “Talents Cultivation Program for Outstanding Young Scientists in Fujian Universities”, Grant #MIN Education (2015) 54; “Scientific Innovation Team of Ningde Normal University”, Grant #2015T07 and Grant #2015Z03.
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Song, M., Xiao, S. (2017). A Fault Diagnosis Method of Gear Based on SVD and Improved EEMD. In: Yue, D., Peng, C., Du, D., Zhang, T., Zheng, M., Han, Q. (eds) Intelligent Computing, Networked Control, and Their Engineering Applications. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 762. Springer, Singapore. https://doi.org/10.1007/978-981-10-6373-2_7
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DOI: https://doi.org/10.1007/978-981-10-6373-2_7
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