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.

Anti-aliasing nonstationary signals detecion algorithm based on interpolation in the frequency domain using the short time Fourier transform

To eliminate the aliasing that appeared during the measurement of multi-components nonstationary signals, a novel kind of anti-aliasing algorithm based on the short time Fourier transform （STFT） is brought forward. First the physical essence of aliasing that occurs is analyzed; second the interpolation algorithm model is setup based on the Hamming window; then the fast implementation of the algorithm using the Newton iteration method is given. Using the numerical simulation the feasibility of algorithm is validated. Finally, the electrical circuit experiment shows the practicality of the algorithm in the electrical engineering.

Extraction of Strain Characteristic Signals from Wind Turbine Blades Based on EEMD-WT

Analyzing the strain signal of wind turbine blade is the key to studying the load of wind turbine blade,so as to ensure the safe and stable operation of wind turbine in natural environment.The strain signal of the wind turbine blade under continuous crosswind state has typical non-stationary and unsteady characteristics.The strain signal contains a lot of noise,which makes the analysis error.Therefore,it is very important to denoise and extract features of measured signals before signal analysis.In this paper,the joint algorithm of ensemble empirical mode decomposition(EEMD)and wavelet transform(WT)is used for the first time to achieve sufficient noise reduction and effectively extract the feature signals of non-stationary strain signals.The application process of EEMD-WT is optimized.This optimization can avoid the repeated selection of wavelet basis function and the number of decomposition layers due to different crosswind conditions.EEMD adaptively decomposes the strain signal into intrinsic mode functions,to judge the frequency of IMFs,remove the high-frequency noise components,retain the useful components.The useful components are denoised twice by the wavelet transform,the components and residual terms after the secondary denoising are reconstructed to obtain the characteristic signal.The EEMD-WT was applied to process the simulating signals andmeasured the strain signals.The results were compared with the results of the EEMD.The results showed that the EEMD-WTmethod has better noise reduction performance,and can effectively extract the characteristics of strain signals,which lays a solid foundation for accurate analysis of wind turbine blade strain signals under crosswind conditions.

Feature Extraction and Classification of Echo Signal of Ground Penetrating Radar

Automatic feature extraction and classification algorithm of echo signal of ground penetrating radar is presented. Dyadic wavelet transform and the average energy of the wavelet coefficients are applied in this paper to decompose and extract feature of the echo signal. Then, the extracted feature vector is fed up to a feed forward muhi layer perceptron classifier. Experimental results based on the measured GPR, echo signals obtained from the Mei shan railway are presented.

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.

Vibration Feature Abstraction and Classification of Diesel Fault

ion and classification is put forward for periodic cyclic nonstationary vibration signal. The proposed method is applied to experimental data of an ISUZU C240 diesel engine. Experiment results show the effectiveness of the proposed method in classification of engine faults.