An improved time-frequency analysis method for structural instantaneous frequency identification based on generalized S-transform and synchroextracting transform

2022 ◽  
Vol 252 ◽  
pp. 113657
Author(s):  
Ping-Ping Yuan ◽  
Jian Zhang ◽  
Jia-Qi Feng ◽  
Hang-Hang Wang ◽  
Wei-Xin Ren ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Instantaneous Frequency ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Generalized S Transform ◽  
Frequency Identification

scholarly journals The Synchrosqueezing Algorithm Based on Generalized S-transform for High-Precision Time-Frequency Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 769 ◽  
Author(s):  
Hui Chen ◽  
Lingqi Lu ◽  
Dan Xu ◽  
Jiaxing Kang ◽  
Xuping Chen
Keyword(s):  
High Precision ◽  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Generalized S Transform ◽  
Precision Time

scholarly journals SEMG Feature Extraction Based on Stockwell Transform Improves Hand Movement Recognition Accuracy

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4457 ◽  
Author(s):  
She ◽  
Zhu ◽  
Tian ◽  
Wang ◽  
Yokoi ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Recognition Accuracy ◽  
Hand Movement ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods ◽  
Movement Recognition ◽  
S Transform

Feature extraction, as an important method for extracting useful information from surfaceelectromyography (SEMG), can significantly improve pattern recognition accuracy. Time andfrequency analysis methods have been widely used for feature extraction, but these methods analyzeSEMG signals only from the time or frequency domain. Recent studies have shown that featureextraction based on time-frequency analysis methods can extract more useful information fromSEMG signals. This paper proposes a novel time-frequency analysis method based on the Stockwelltransform (S-transform) to improve hand movement recognition accuracy from forearm SEMGsignals. First, the time-frequency analysis method, S-transform, is used for extracting a feature vectorfrom forearm SEMG signals. Second, to reduce the amount of calculations and improve the runningspeed of the classifier, principal component analysis (PCA) is used for dimensionality reduction of thefeature vector. Finally, an artificial neural network (ANN)-based multilayer perceptron (MLP) is usedfor recognizing hand movements. Experimental results show that the proposed feature extractionbased on the S-transform analysis method can improve the class separability and hand movementrecognition accuracy compared with wavelet transform and power spectral density methods.

Parameter Optimization of Generalized S Transform Based on Improved Genetic Algorithm

2019 ◽  
Vol 13 (4) ◽  
pp. 433-441
Author(s):  
Yun Lin ◽  
Xiaowan Yu ◽  
Chunguang Ma
Keyword(s):  
Genetic Algorithm ◽  
Fourier Transform ◽  
Frequency Analysis ◽  
Frequency Modulation ◽  
Improved Genetic Algorithm ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods ◽  
S Transform ◽  
Generalized S Transform

Background: For the traditional Fourier Transform (FT), it cannot effectively detect the frequency of non-stationary signals with time. Analyzing the local characteristics of time-varying signal by using FT is hard to achieve. Therefore, many time-frequency analysis methods which can meet different needs have been proposed on the basis of the traditional Fourier transform, like the Short Time Fourier Transform (STFT), the widely used Continuous Wavelet Transform (CWT), Wigner-Ville Distribution (WVD) and so on. However, the best time and frequency resolution cannot be achieved at the same time due to the uncertainty criterion. Methods: From the point of view of optimizing time-frequency performance, a new Generalized S Transform (GST) window function optimization method is proposed by combining time frequency aggregation with an improved genetic algorithm in this paper. Results: In the noiseless condition, the Linear Frequency Modulation (LFM), Nonlinear Frequency Modulation (NLFM) and binary Frequency Shift Keying (2FSK) signals are simulated. The simulation results prove that the method can improve the time-frequency concentration and decreasing Rényi entropy effectively. Compared with other four traditional time-frequency analysis methods, the improved GST has more advantages. Conclusion: In this paper, a new method of optimizing the window function in GST based on improved GA is proposed in this paper. Experiments on LFM, NLFM and 2FSK signals show that the proposed method not only has the advantages of high resolution, but also determines the optimal parameters via the time frequency focusing criterion and the Rényi entropy. Compared with the other four kinds of time-frequency analysis methods, the optimized GST based on improved GA in this paper has the best time-frequency focusing.

scholarly journals An Improved Time-Frequency Analysis Method for Instantaneous Frequency Estimation of Rolling Bearing

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Zengqiang Ma ◽  
Wanying Ruan ◽  
Mingyi Chen ◽  
Xiang Li
Keyword(s):  
Frequency Analysis ◽  
Instantaneous Frequency ◽  
Frequency Estimation ◽  
Rolling Bearing ◽  
Analysis Method ◽  
Instantaneous Frequency Estimation ◽  
Time Frequency Analysis ◽  
Noise Interference ◽  
Time Frequency ◽  
Frequency Representation

Instantaneous frequency estimation of rolling bearing is a key step in order tracking without tachometers, and time-frequency analysis method is an effective solution. In this paper, a new method applying the variational mode decomposition (VMD) in association with the synchroextracting transform (SET), named VMD-SET, is proposed as an improved time-frequency analysis method for instantaneous frequency estimation of rolling bearing. The SET is a new time-frequency analysis method which belongs to a postprocessing procedure of the short-time Fourier transform (STFT) and has excellent performance in energy concentration. Considering nonstationary broadband fault vibration signals of rolling bearing under variable speed conditions, the time-frequency characteristics cannot be obtained accurately by SET alone. Thus, VMD-SET method is proposed. Firstly, the signal is decomposed into several intrinsic mode functions (IMFs) with different center frequency by VMD. Then, effective IMFs are selected by mutual information and kurtosis criteria and are reconstructed. Next, the SET method is applied to the reconstructed signal to generate the time-frequency representation with high resolution. Finally, instantaneous frequency trajectory can be accurately extracted by peak search from the time-frequency representation. The proposed method is free from time-varying sidebands and is robust to noise interference. It is proved by numerical simulated signal analysis and is further validated by lab experimental rolling bearing vibration signal analysis. The results show this method can estimate the instantaneous frequency with high precision without noise interference.

Self-Adaptive Generalized S-Transform and Its Application in Seismic Time–Frequency Analysis

2019 ◽  
Vol 57 (10) ◽  
pp. 7849-7859 ◽  
Author(s):  
Naihao Liu ◽  
Jinghuai Gao ◽  
Bo Zhang ◽  
Qian Wang ◽  
Xiudi Jiang
Keyword(s):  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Generalized S Transform ◽  
Self Adaptive

A generalized S transform and applications to seismic time-frequency analysis

2018 ◽  
Author(s):  
Naihao Liu ◽  
Jinghuai Gao ◽  
Bo Zhang ◽  
Xiudi Jiang ◽  
Zhen Li
Keyword(s):  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Generalized S Transform
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