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Wind Turbine Planetary Gearbox Fault Diagnosis via Proportion-Extracting Synchrosqueezing Chirplet Transform 被引量:2
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作者 Dong Zhang Zhipeng Feng 《Journal of Dynamics, Monitoring and Diagnostics》 2023年第3期177-182,共6页
Wind turbine planetary gearboxes usually work under time-varying conditions,leading to nonstationary vibration signals.These signals often consist of multiple time-varying components with close instantaneous frequenci... Wind turbine planetary gearboxes usually work under time-varying conditions,leading to nonstationary vibration signals.These signals often consist of multiple time-varying components with close instantaneous frequencies.Therefore,high-quality time-frequency analysis(TFA)is needed to extract the time-frequency feature from such nonstationary signals for fault diagnosis.However,it is difficult to obtain high-quality timefrequency representations(TFRs)through conventional TFA methods due to low resolution and time-frequency blurs.To 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 TFRs.Then,the energy concentration of the underlying TFRs is enhanced via the synchrosqueezing transform.Finally,wind turbine planetary gearbox fault can be diagnosed by analysis of the dominant time-varying components revealed by the concentrated TFRs with high resolution.The proposed PESCT is suitable for achieving high-quality TFRs for complicated nonstationary signals.Numerical and experimental analyses validate the effectiveness of the PESCT in characterizing the nonstationary signals from wind turbine planetary gearboxes. 展开更多
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 被引量:1
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作者 Kewen Wang Yajun Shang +1 位作者 Yongzheng Lu Tianran Lin 《Journal of Dynamics, Monitoring and Diagnostics》 2023年第3期183-189,共7页
Second-order multisynchrosqueezing transform(SMSST),an effective tool for the analysis of nonstationary signals,can significantly improve the time-frequency resolution of a nonstationary signal.Though the noise energy... Second-order multisynchrosqueezing transform(SMSST),an effective tool for the analysis of nonstationary signals,can significantly improve the time-frequency resolution of a nonstationary signal.Though 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 diagnostic.An 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 signals.In 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 result.The 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 data.The result shows that the current technique can render the most accurate TFA result within the TFA techniques employed in this study. 展开更多
关键词 fault diagnosis nonstationary signals synchrosqueezing transform time-frequency operator
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Fault diagnosis method of rolling bearing based onthreshold denoising synchrosqueezing transform and CNN
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作者 Wu Jiachen Hu Jianzhong Xu Yadong 《Journal of Southeast University(English Edition)》 EI CAS 2020年第1期32-40,共9页
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... 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. 展开更多
关键词 threshold denoising synchrosqueezing transform convolutional neural network rolling bearing
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Spectrum Sensing and AM-FM Decomposition through Synchrosqueezing
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作者 K. Vandhana P. V. S. Sowmya +3 位作者 P. Roshni K. Divya S. Ashwin K. A. Narayanankutty 《Wireless Engineering and Technology》 2013年第3期134-138,共5页
In this paper we have accomplished one of the tasks of cognitive radio i.e. dynamic spectrum sensing by using wavelet based Synchrosqueezing transform [1], a novel technique, which was proposed to analyze a signal in ... In this paper we have accomplished one of the tasks of cognitive radio i.e. dynamic spectrum sensing by using wavelet based Synchrosqueezing transform [1], a novel technique, which was proposed to analyze a signal in time-frequency plane. The distinctive feature of this transform compared to other techniques is that it enables us to decompose amplitude and frequency modulated signals and allows individual reconstruction of these components. The objective is also to separate the occupied band into amplitude modulated and frequency modulated bands. 展开更多
关键词 COGNITIVE RADIO synchrosqueezing AM-FM SPECTRUM SENSING TIME-FREQUENCY Reassignment Empirical Mode DECOMPOSITION
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Separation of inhomogeneous blended seismic data 被引量:2
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作者 刘强 韩立国 +2 位作者 陈竞一 陈雪 张显娜 《Applied Geophysics》 SCIE CSCD 2015年第3期327-333,466,共8页
The frequencies of sources involved m conventional blended acquisition are the same. Each source transmits the full frequency band, and in general, significant effort is required to successfully produce and operate wi... The frequencies of sources involved m conventional blended acquisition are the same. Each source transmits the full frequency band, and in general, significant effort is required to successfully produce and operate wideband sources. To solve this problem, inhomogeneous blended or decentralized blended acquisition is used, in which the dominant frequency and bandwidth of the source units in a blended array are not equal. When the inhomogeneous and conventional blending acquisitions adopt the same geometry and separation methods, the former has low signal-to-blending noise ratio. Therefore, we present a new separation method for such blended acquisition based on the synchrosqueezed wavelet transform. The proposed method offers better separation quality and decreases the computation time to approximately 1/3. 展开更多
关键词 Inhomogeneous blended acquisition synchrosqueezed wavelet transform deblending
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Harmonic signal extraction from noisy chaotic interference based on synchrosqueezed wavelet transform 被引量:1
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作者 汪祥莉 王文波 《Chinese Physics B》 SCIE EI CAS CSCD 2015年第8期142-148,共7页
For the harmonic signal extraction from chaotic interference, a harmonic signal extraction method is proposed based on synchrosqueezed wavelet transform(SWT). First, the mixed signal of chaotic signal, harmonic signal... For the harmonic signal extraction from chaotic interference, a harmonic signal extraction method is proposed based on synchrosqueezed wavelet transform(SWT). First, the mixed signal of chaotic signal, harmonic signal, and noise is decomposed into a series of intrinsic mode-type functions by synchrosqueezed wavelet transform(SWT) then the instantaneous frequency of intrinsic mode-type functions is analyzed by using of Hilbert transform, and the harmonic extraction is realized. In experiments of harmonic signal extraction, the Duffing and Lorenz chaotic signals are selected as interference signal, and the mixed signal of chaotic signal and harmonic signal is added by Gauss white noises of different intensities.The experimental results show that when the white noise intensity is in a certain range, the extracting harmonic signals measured by the proposed SWT method have higher precision, the harmonic signal extraction effect is obviously superior to the classical empirical mode decomposition method. 展开更多
关键词 harmonic extraction noisy chaotic interference synchrosqueezed wavelet transform
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A Hybrid Coordinated Design Method for Power System Stabilizer and FACTS Device Based on Synchrosqueezed Wavelet Transform and Stochastic Subspace Identification
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作者 Ayda Faraji Ali Hesami Naghshbandy Arman Ghaderi Baayeh 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2021年第4期910-918,共9页
The occurrence of low-frequency electromechanical oscillations is a major problem in the effective operation of power systems. The scrutiny of these oscillations provides substantial information about power system sta... The occurrence of low-frequency electromechanical oscillations is a major problem in the effective operation of power systems. The scrutiny of these oscillations provides substantial information about power system stability and security. In this paper, a new method is introduced based on a combination of synchrosqueezed wavelet transform and the stochastic subspace identification (SSI) algorithm to investigate the low-frequency electromechanical oscillations of large-scale power systems. Then, the estimated modes of the power system are used for the design of the power system stabilizer and the flexible alternating current transmission system (FACTS) device. In this optimization problem, the control parameters are set using a hybrid approach composed of the Prony and residual methods and the modified fruit fly optimization algorithm. The proposed mode estimation method and the controller design are simulated in MATLAB using two test case systems, namely IEEE 2-area 4-generator and New England-New York 68-bus 16-generator systems. The simulation results demonstrate the high performance of the proposed method in estimation of local and inter-area modes, and indicate the improvements in oscillation damping and power system stability. 展开更多
关键词 Low‐frequency oscillation modified fruit fly optimization algorithm Prony analysis stochastic subspace identification(SSI)algorithm synchrosqueezed wavelet transform(SSWT)
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