Diagnosis of damaged wind turbine blade by noise characteristics

2020 ◽  
Vol 68 (2) ◽  
pp. 146-156
Author(s):  
Chao-Nan Wang ◽  
Tang-Yao Chi
Keyword(s):  
Feature Extraction ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Frequency Analysis ◽  
Time Domain ◽  
Wind Turbine Blade ◽  
Statistical Regression ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Noise Characteristics

This study has proposed two estimation models of noise signal characteristic diagnosis based on time-domain and time-frequency analysis. The diagnosis of time domain was based on the fractal theory, and the result of fractal dimensions was converted into Gauss distribution, so as to provide a feature extraction for abnormality diagnosis of damaged blade. In addition, for time-frequency analysis, the wavelet methodwas used as the basis of signal analysis. The Morlet transform and mother wavelet were used for wavelet analysis of signal to obtain the result of time-frequency analysis. When the time axis was integrated, the marginal spectrum of frequency domain was obtained, and statistical regression analysis was used to provide another method of feature extraction diagnosis. The wind turbine blade signal was measured in actual wind turbine operation at Changhua Coastal Industrial Park for diagnostic analysis, so as to provide a multi-diagnostic model of wind turbine blade prewarning and health management models.

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.

Time Frequency Domain Analysis of the Dispersion of Guided Wave Modes

2004 ◽  
Author(s):  
Youn-Ho Cho ◽  
Yong-Kwon Kim ◽  
Ik-Keun Park
Keyword(s):  
Wavelet Transform ◽  
Group Velocity ◽  
Frequency Analysis ◽  
Time Domain ◽  
Excitation Frequency ◽  
Guided Wave ◽  
Time Frequency Analysis ◽  
Mode Identification ◽  
Time Frequency ◽  
Wave Modes

One of unique characteristics of guided waves is a dispersive behavior that guided wave velocity changes with an excitation frequency and mode. In practical applications of guided wave techniques, it is very important to identify propagating modes in a time-domain waveform for determination of defect location and size. Mode identification can be done by measurement of group velocity in a time-domain waveform. Thus, it is preferred to generate a single or less dispersive mode. But, in many cases, it is difficult to distinguish a mode clearly in a time-domain waveform because of superposition of multi modes and mode conversion phenomena. Time-frequency analysis is used as efficient methods to identify modes by presenting wave energy distribution in a time-frequency. In this study, experimental guided wave mode identification is carried out in a steel plate using time-frequency analysis methods such as wavelet transform. The results are compared with theoretically calculated group velocity dispersion curves. The results are in good agreement with analytical predictions and show the effectiveness of using the wavelet transform method to identify and measure the amplitudes of individual guided wave modes.

The Time Domain Analysis of the Flutter of Wind Turbine Blade Combined with Eigenvalue Approach

2013 ◽  
Vol 860-863 ◽  
pp. 342-347
Author(s):  
Hao Wang ◽  
Jiao Jiao Ding ◽  
Bing Ma ◽  
Shuai Bin Li
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Time Domain ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Wind Turbine Blade ◽  
Eigenvalue Approach ◽  
Characteristic Roots ◽  
Eigenvalue Method ◽  
The Time Domain

The aeroelasticity and the flutter of the wind turbine blade have been emphasized by related fields. The flutter of the wind turbine blade airfoil and its condition will be focused on. The eigenvalue method and the time domain analysis method will be used to solve the flutter of the wind turbine blade airfoil respectively. The flutter problem will be firstly solved using eigenvalue approach. The flutter region, where the flutter will occur and anti-flutter region, where the flutter will not occur, will be obtained directly by judging the sign of the real part of the characteristic roots of the blade system. Then the time domain analysis of flutter of wind turbine blade will be carried out through the use of the four-order Runge-Kutta numerical methods, the flutter region and the anti-flutter region will be gotten in another way. The time domain analysis can give the changing treads of the aeroelastic responses in great detail than those of the eigenvalue method. The flap displacement of wind turbine blade airfoil will change from convergence to divergence, and change from divergence to convergence extremely suddenly. During the flutter region, the flutter of wind turbine blade will occur extremely dramatically. The flutter region provided by the time domain analysis of the flutter of the blade airfoil accurately coincides with the results of eigenvalue approach, therefore the simulation results are reliable and credible.

Extracting of Micro Doppler Parameter of Helicopter Rotor Based on Time-Frequency Analysis

2011 ◽  
Vol 130-134 ◽  
pp. 2696-2700 ◽  
Author(s):  
Lei Zhang ◽  
Guo Qing Huang
Keyword(s):  
Frequency Domain ◽  
Frequency Analysis ◽  
Time Domain ◽  
Good Method ◽  
Helicopter Rotor ◽  
Echo Signal ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Radar Echo ◽  
Doppler Parameter

The micro Doppler effect of the radar echo signal of helicopter rotor is studied, and the formula of helicopter rotor echo is obtained. Then the received echo signal of helicopter rotor simulated is analyzed in time domain, frequency domain and time-frequency domain respectively, the analysis results show that it is a good method to extract micro Doppler of helicopter rotor echo by time-frequency analysis. According to analysis results, obtained a method to determine parity of blades and velocity of helicopter rotor, these methods can be used to identify helicopter.

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