Harmonic signal extraction from noisy chaotic interference based on synchrosqueezed wavelet transform

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.

Wind Turbine Planetary Gearbox Fault Diagnosis via Proportion-Extracting Synchrosqueezing Chirplet Transform

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.

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

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.

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

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.

Separation of inhomogeneous blended seismic data

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.

A Hybrid Coordinated Design Method for Power System Stabilizer and FACTS Device Based on Synchrosqueezed Wavelet Transform and Stochastic Subspace Identification

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.