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Fault diagnosis method of rolling bearing based onthreshold denoising synchrosqueezing transform and CNN

Journal of Southeast University(English Edition) 2020年第1期36卷 32-40页

作者： Wu Jiachen Hu Jianzhong Xu Yadong School of Mechanical Engineering Southeast University Nanjing 211189 China

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 *** 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 *** ability of the TDSST to reduce noise and improve time-frequency resolution was verified by simulated impact fault signals of rolling ***,the CNN is utilized to diagnose the time-frequency diagrams obtained by the *** diagnostic results of the rolling bearing experimental data show that the proposed method can effectively improve the accuracy of *** 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： threshold denoising synchrosqueezing transform convolutional neural network rolling bearing

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Wind Turbine Planetary Gearbox Fault Diagnosis via Proportion-Extracting synchrosqueezing Chirplet transform

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Journal of Dynamics, Monitoring and Diagnostics 2023年第3期2卷 177-182页

作者： Dong Zhang Zhipeng Feng School of Mechanical Engineering University of Science and Technology BeijingBeijingChina

Wind turbine planetary gearboxes usually work under time-varying conditions,leading to nonstationary vibration *** signals often consist of multiple time-varying components with close instantaneous ***,high-quality time-frequency analysis(TFA)is needed to extract the time-frequency feature from such nonstationary signals for fault ***,it is difficult to obtain high-quality timefrequency representations(TFRs)through conventional TFA methods due to low resolution and time-frequency *** address this issue,we propose a new TFA method termed the proportion-extracting synchrosqueezing chirplet transform(PESCT).Firstly,the proportion-extracting chirplet transform is employed to generate highresolution underlying ***,the energy concentration of the underlying TFRs is enhanced via the synchrosqueezing ***,wind turbine planetary gearbox fault can be diagnosed by analysis of the dominant time-varying components revealed by the concentrated TFRs with high *** proposed PESCT is suitable for achieving high-quality TFRs for complicated nonstationary *** and experimental analyses validate the effectiveness of the PESCT in characterizing the nonstationary signals from wind turbine planetary gearboxes.

关键词： nonstationary signal planetary gearbox synchrosqueezing transform time-frequency analysis wind turbine

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An Improved Second-Order Multisynchrosqueezing transform for the Analysis of Nonstationary Signals

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Journal of Dynamics, Monitoring and Diagnostics 2023年第3期2卷 183-189页

作者： Kewen Wang Yajun Shang Yongzheng Lu Tianran Lin Qingdao Key Rail Transportation Laboratory for Noise and Vibration Control and Automated Fault Diagnostic Qingdao University of TechnologyQingdao 266525China

Second-order multisynchrosqueezing transform(SMSST),an effective tool for the analysis of nonstationary signals,can significantly improve the time-frequency resolution of a nonstationary *** the noise energy in the signal can also be enhanced in the transform which can largely affect the characteristic frequency component identification for an accurate fault *** improved algorithm termed as an improved second-order multisynchrosqueezing transform(ISMSST)is then proposed in this study to alleviate the problem of noise interference in the analysis of nonstationary *** the study,the time-frequency(TF)distribution of a nonstationary signal is calculated first using SMSST,and then aδfunction is constructed based on a newly proposed time-frequency operator(TFO)which is then substituted back into SMSST to produce a noisefree time frequency *** effectiveness of the technique is validated by comparing the TF results obtained using the proposed algorithm and those using other TFA techniques in the analysis of a simulated signal and an experimental *** result shows that the current technique can render the most accurate TFA result within the TFA techniques employed in this study.

关键词： fault diagnosis nonstationary signals synchrosqueezing transform time-frequency operator

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Time-Frequency Decomposition of Sunspot Number Fluctuations 36

Time-Frequency Decomposition of Sunspot Number Fluctuations

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第36届中国控制会议

作者： Yu Lan Qin Zhong Cao Yu Feng Song Department of mechanical and electrical engineering Yunnan Land and Resources Vocational College Key Laboratory of Computer Technology Application Kunming University of Science and Technology

ISBN: (纸本)9781538629185

Time-frequency decomposition has been widely used to analyze those long periodic signals in solar *** mode decomposition（EMD） is the popular methods to decompose them from time domain to time-frequency ***,the approach brings mode mixing in cycle decomposition and can cause inaccurate results and conclusion in solar *** this paper,a new time-frequency decomposition approach based on synchrosqueezing transform（SST） is *** built a synthetic signal and used an echolocation pulse of a bat to illustrate the performance of the SST approach to the ***,the sunspot number fluctuations（SNF） was analyzed with the SST,and several new periods are extracted that fail to found with the EMD *** result demonstrates that the SST can accurately decompose those intrinsic cycles of SNF,and better understand the phenomenon of solar activity.

关键词： Time-Frequency Analysis Sunspot Number synchrosqueezing transform Empirical Mode Decomposition

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