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International Standard Serial Number:
ISSN 1001-4551
Sponsor:
Zhejiang University;
Zhejiang Machinery and Electrical Group
Edited by:
Editorial of Journal of Mechanical & Electrical Engineering
Chief Editor:
ZHAO Qun
Vice Chief Editor:
TANG ren-zhong,
LUO Xiang-yang
Tel:
86-571-87041360,87239525
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86-571-87239571
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No.9 Gaoguannong,Daxue Road,Hangzhou,China
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310009
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meem_contribute@163.com
Abstract: The gear fault signal had typical modulation characteristics, and the instantaneous frequency trajectories of its individual frequency components had proportional characteristics. Meanwhile, the traditional time-frequency method had the problems of low concentration of time-frequency energy and drowning of weak component signals. To address this problem, a time-ous frequency trajectory of the signal to obtain the time-frequency domain representation using the new kernel function, and the time-frequency spine was extracted using the spine search method. Then, the synchro-reassign operator was constructed using the time-frequency spine and the Dirac function to redistribute the obtained time-frequency results to further improve the time-frequency energy concentration. Finally, the effectiveness of the PFSRLCT method was verified by simulated signals, and it was compared with other methods. In addition, the method was applied to the diagnosis of actual gearbox fault signals, and the meshing frequency fm and its harmonics and side frequency bands were clearly distinguished from the obtained time-frequency results, and the diagnosed fault types were consistent with the preset fault types, which verified the applicability of the method. The research results show that PFSRLCT has good performance in processing signals with proportional characteristics of instantaneous frequency trajectories, and the obtained frequency estimation errors are less than 0.01 at signal-to-noise ratios of -2 dB to 10 dB. The obtained Rényi entropy is only 4.745 1 at signal-to-noise ratios of -1 dB, which is significantly smaller than other methods. It is proved that the noise robustness of this method is stronger and the energy concentration is higher than other methods. Meanwhile, when this method is applied to the actual gear fault signal, the Rényi entropy obtained is only 3.069 8, which is significantly lower than other methods, indicating that it obtains higher timefrequency energy concentration and has better performance capability.
Key words: subway gear fault diagnosis; proportional frequency reassignment linear Chirplet transform(PFSRLCT); time-frequency analysis method; fault signal modulation; ridge extraction; time-frequency reassigning
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