期刊
IEEE ACCESS
卷 8, 期 -, 页码 166753-166767出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3023306
关键词
Fault diagnosis; Rolling bearings; Feature extraction; Optimization; Noise reduction; Support vector machines; Genetic algorithms; Wavelet threshold de-noising; genetic algorithm; variational modal decomposition; von Neumann topology; rolling bearing
资金
- Program of the State Key Laboratory of Mechanical Transmissions [SKLMT-KFKT-201808]
- National Natural Science Foundation of China [51505361]
- Innovative Talents Promotion Plan in Shaanxi Province [2017KJXX-58]
- Natural Science Basic Research Program of Shaanxi [2020JM-564]
To improve the fault identification accuracy of rolling bearings due to the problems of parameter optimization and low convergence accuracy, a novel fault diagnosis method for rolling bearings combining wavelet threshold de-noising, genetic algorithm optimization variational mode decomposition (GA-VMD) and the whale optimization algorithm based on the von Neumann topology optimization least squares support vector machine (VNWOA-LSSVM) is proposed in this manuscript. First, wavelet threshold de-noising is used to preprocess the vibration signal to reduce the noise and improve the signal-to-noise ratio (SNR). Second, a genetic algorithm (GA) is utilized to optimize the parameters of variational mode decomposition (VMD), and optimized VMD is adopted to extract the fault feature information. The VNWOA-LSSVM fault diagnosis model is built to train and identify the fault feature vectors. The proposed method is validated by experimental data. The results show that this method can not only effectively diagnose various damage positions and extents of rolling bearings but also has good identification accuracy.
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