Research on Extraction Method of Time-Frequency Feature of Human Motion

2019 ◽  
Author(s):  
Wang Tianrun ◽  
Su Zhong ◽  
Li Chao ◽  
Liu Ning
Keyword(s):  
Extraction Method ◽  
Human Motion ◽  
Time Frequency ◽  
Frequency Feature

scholarly journals Time-Frequency Feature Extraction Method of the Multi-Source Shock Signal Based on Improved VMD and Bilateral Adaptive Laplace Wavelet

2021 ◽  
Author(s):  
Nanyang Zhao ◽  
Jinjie Zhang ◽  
Zhiwei Mao ◽  
Zhinong Jiang ◽  
He Li
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Economic Losses ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Noise Interference ◽  
Time Frequency ◽  
Fault Recognition ◽  
Penalty Factor ◽  
Frequency Feature

Abstract Reciprocating machinery, e.g., diesel engines and reciprocating compressors, is the key power component in petroleum, petrochemical, nuclear power, and shipbuilding industries. Vibration signals have the characteristics of multi-source strong shock coupling and strong noise interference owing to the complex structure of reciprocating machinery; therefore, it is difficult to extract, analyze, and diagnose mechanical fault features. Moreover, failures occur frequently every year, causing serious economic losses. To accurately and efficiently extract sensitive features from the strong noise interference and unsteady monitoring signals of reciprocating machinery, a study on the time-frequency feature extraction method of multi-source shock signals was conducted. Combining the characteristics of reciprocating mechanical vibration signals, a targeted optimization method considering the variational modal decomposition (VMD) mode number K and second penalty factor was proposed, which completed the adaptive decomposition of coupled signals. Aiming at the bilateral asymmetric attenuation characteristics of reciprocating mechanical shock signals, a new bilateral adaptive Laplace wavelet (BALW) was established. A search strategy for wavelet local parameters of multi-impact signals was proposed using the harmony search (HS) method. A multi-source shock simulation signal was established and actual data of the valve fault were obtained through diesel engine fault experiments. The test results demonstrated that the new method achieved adaptive extraction of local shock features of non-stationary multi-source shock signals and was superior to the original method in terms of signal decomposition effect, sensitive feature extraction, fault recognition accuracy, and parameter search time. The fault recognition rate of the intake and exhaust valve clearance was above 90% and the extraction accuracy of the shock start position was improved by 10°.

scholarly journals A New Time–Frequency Feature Extraction Method for Action Detection on Artificial Knee by Fractional Fourier Transform

Micromachines ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 333 ◽  
Author(s):  
Tianrun Wang ◽  
Ning Liu ◽  
Zhong Su ◽  
Chao Li
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Extraction Method ◽  
Fractional Fourier Transform ◽  
Feature Vector ◽  
Action Detection ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Frequency Feature ◽  
New Time

With the aim of designing an action detection method on artificial knee, a new time–frequency feature extraction method was proposed. The inertial data were extracted periodically using the microelectromechanical systems (MEMS) inertial measurement unit (IMU) on the prosthesis, and the features were extracted from the inertial data after fractional Fourier transform (FRFT). Then, a feature vector composed of eight features was constructed. The transformation results of these features after FRFT with different orders were analyzed, and the dimensions of the feature vector were reduced. The classification effects of different features and different orders are analyzed, according to which order and feature of each sub-classifier were designed. Finally, according to the experiment with the prototype, the method proposed above can reduce the requirements of hardware calculation and has a better classification effect. The accuracies of each sub-classifier are 95.05%, 95.38%, 91.43%, and 89.39%, respectively; the precisions are 78.43%, 98.36%, 98.36%, and 93.41%, respectively; and the recalls are 100%, 93.26%, 86.96%, and 86.68%, respectively.

Research on a feature extraction method for local faults in planetary gearboxes based on improved dynamic time warping

2021 ◽  
Vol 63 (8) ◽  
pp. 465-471
Author(s):  
Shang Zhiwu ◽  
Yu Yan ◽  
Geng Rui ◽  
Gao Maosheng ◽  
Li Wanxiang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Extraction Method ◽  
Dynamic Time Warping ◽  
Spectral Feature ◽  
Time Warping ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Dynamic Time

Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised.

Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine

2010 ◽  
Vol 22 (10) ◽  
pp. 2441-2447
Author(s):  
金晶 Jin Jing ◽  
魏彪 Wei Biao ◽  
冯鹏 Feng Peng ◽  
唐跃林 Tang Yuelin ◽  
周密 Zhou Mi
Keyword(s):  
Support Vector Machine ◽  
Feature Analysis ◽  
Support Vector ◽  
Time Frequency ◽  
Fission Neutrons ◽  
Frequency Feature

A Blind Extraction Method of FH Signals Based on Improved Symmetric Co-Occurrence Matrix in HF Channel

2014 ◽  
Vol 543-547 ◽  
pp. 2570-2574
Author(s):  
Pei Gu ◽  
Bin Wang
Keyword(s):  
Extraction Method ◽  
Blind Detection ◽  
Threshold Method ◽  
Time Frequency ◽  
Low Snr ◽  
Interference Signals ◽  
Noise Threshold ◽  
Blind Extraction ◽  
Occurrence Matrix ◽  
Hf Channel

To remove the noise and interference signals in the blind detection of FH signals from HF channel, this paper gives a FH signals extraction method based on the improved symmetric GLCM, by combining the time-frequency analysis with the threshold method of GLCM. Firstly, the paper defines the calculation of improved symmetric GLCM on the direction of frequency and time. Then, the noise threshold is estimated based on the noise probability in the improved GLCM of frequency and the FH signals from the improved GLCM of time can be extracted by using the noise threshold. Simulation results show that the method can extract integrated FH signals under low SNR without any prior information, and that the estimation of noise threshold is more accurate and stable. Furthermore the method is simple with a small amount of computation, which is easy to be applied in the engineering.

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