东南大学学报（英文版）2020，Vol.36Issue(1)：32-40,9.DOI:10.3969/j.issn.1003-7985.2020.01.005
基于阈值降噪同步压缩变换和CNN的滚动轴承故障诊断方法
Fault diagnosis method of rolling bearing based on threshold denoising synchrosqueezing transform and CNN
摘要
Abstract
The rolling bearing vibration signal is non-stationary and is easily disturbed by background noise, so it is difficult to accurately diagnose bearing faults. A fault diagnosis method of rolling bearing based on the time-frequency threshold denoising synchrosqueezing transform ( TDSST ) and convolutional neural network ( CNN) is proposed. Since the traditional methods of wavelet threshold denoising and wavelet adjacent coefficient denoising are greatly affected by the estimation accuracy of noise variance, a time-frequency denoising method based on the STFT spectral correlation coefficient threshold optimization is adopted, which is combined with a synchrosqueezing transform. The ability of the TDSST to reduce noise and improve time-frequency resolution was verified by simulated impact fault signals of rolling bearings. Finally, the CNN is utilized to diagnose the time-frequency diagrams obtained by the TDSST. The diagnostic results of the rolling bearing experimental data show that the proposed method can effectively improve the accuracy of diagnosis. When the SNR of the bearing signal is larger than 0 dB, the accuracy is over 95% , even when the SNR reduces to - 4 dB, the accuracy is still around 80% . Moreover, the standard deviation of multiple test results is small, which means that the method has good robustness.关键词
阈值降噪/同步压缩变换/卷积神经网络/滚动轴承Key words
threshold denoising/synchrosqueezing transform/convolutional neural network/rolling bearing分类
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吴佳晨,胡建中,徐亚东..基于阈值降噪同步压缩变换和CNN的滚动轴承故障诊断方法[J].东南大学学报（英文版）,2020,36(1):32-40,9.
