scholarly journals An Effective Feature Extraction Method of Quantitative Recognition of Ultrasonic Echo for Bonding Quality of Composite Sheet

CONVERTER ◽  
2021 ◽  
pp. 25-40
Author(s):  
Dong Li, Bateer, Bingqing Gao
Keyword(s):  
Feature Extraction ◽  
Thin Plate ◽  
Extraction Method ◽  
Echo Signal ◽  
Bonding Quality ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Signal Parameters ◽  
Ultrasonic Echo ◽  
Quantitative Identification

In this paper, a set of effective ultrasonic echo feature extraction method is proposed for the quantitative identification of thin plate composite bonding quality, which opens up a new research idea for the research of ultrasonic echo feature extraction for quantitative identification of thin plate bonding quality, and also lays a foundation for further quantitative identification of bonding structure debonding degree of thin plate. In addition to the time and frequency domain analysis, the empirical mode time-frequency analysis method is used to highlight the time-frequency characteristics of the echo signal, improve the resolution of the signal analysis, and expand the space for the echo signal feature extraction. The feature validity evaluation theory of fault diagnosis is introduced to evaluate the feature validity of high-dimensional signal parameters, and the invalid signal parameters are eliminated to obtain an effective echo signal feature set with low dimension. Using the exhaustive search strategy of feature selection theory, geometric distance measurement and information measurement evaluation criteria, and through the classification verification of support vector machine classifier, the effective feature vector for quantitative identification of adhesive quality is obtained.

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.

Research on Feature Extraction for Ultrasonic Echo Signal Based on EEMD Approach

2013 ◽  
Vol 321-324 ◽  
pp. 1311-1316 ◽  
Author(s):  
Jian Ming Yu ◽  
Ze Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Thin Plates ◽  
Echo Signal ◽  
Stationary Characteristic ◽  
Bonding Quality ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Ultrasonic Echo

The bonding quality of composite materials have a critical influence on the quality of the product in modern industry, while the current technology can only make judgments on bonding and de-bonding instead of quantitative evaluation of different de-bonding degrees. We present HHT method to extract features of echo signals used for quantitative recognition of bonding quality of thin plates. For the non-stationary characteristic of the ultrasonic echo signal, empirical mode decomposition(EMD) and ensemble empirical mode decomposition(EEMD) are put forward to decompose the signal and calculate its energy torque. The HHT method highlights the time-frequency performance of echo signals effectively. The simulated signals verify that EEMD has more excellent decomposition performance than EMD, that is, EEMD diminishes the mode mixing to some extent generated from EMD decomposition.

scholarly journals Mechanical Fault Diagnosis Using Color Image Recognition of Vibration Spectrogram Based on Quaternion Invariable Moment

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Liang Hua ◽  
Yujian Qiang ◽  
Juping Gu ◽  
Ling Chen ◽  
Xinsong Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Learning Algorithm ◽  
Probabilistic Neural Network ◽  
Invariant Moment ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Neural Network Algorithm

Automatic extraction of time-frequency spectral image of mechanical faults can be achieved and faults can be identified consequently when rotating machinery spectral image processing technology is applied to fault diagnosis, which is an advantage. Acquired mechanical vibration signals can be converted into color time-frequency spectrum images by the processing of pseudo Wigner-Ville distribution. Then a feature extraction method based on quaternion invariant moment was proposed, combining image processing technology and multiweight neural network technology. The paper adopted quaternion invariant moment feature extraction method and gray level-gradient cooccurrence matrix feature extraction method and combined them with geometric learning algorithm and probabilistic neural network algorithm, respectively, and compared the recognition rates of rolling bearing faults. The experimental results show that the recognition rates of quaternion invariant moment are higher than gray level-gradient cooccurrence matrix in the same recognition method. The recognition rates of geometric learning algorithm are higher than probabilistic neural network algorithm in the same feature extraction method. So the method based on quaternion invariant moment geometric learning and multiweight neural network is superior. What is more, this algorithm has preferable generalization performance under the condition of fewer samples, and it has practical value and acceptation on the field of fault diagnosis for rotating machinery as well.

Transient Feature Extraction method based on adaptive TQWT sparse optimization

2020 ◽  
Author(s):  
xue liu ◽  
ao sun ◽  
jian hu
Keyword(s):  
Feature Extraction ◽  
Frequency Band ◽  
Characteristic Frequency ◽  
Extraction Method ◽  
Vibration Signal ◽  
Measured Signal ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Transient Feature ◽  
The Impact

Abstract Aiming at the problem of strong impact, short response period and wide resonance frequency bandwidth of transient vibration signals, a transient feature extraction method based on adaptive Tunable Q-factor Wavelet Transform (TQWT) was proposed. Firstly, the characteristic frequency band of the vibration signal was selected according to the time-frequency distribution. Based on the characteristic frequency band, the sub-band average energy weighted wavelet Shannon entropy was used to optimize the number of decomposition layers, quality factor and redundancy of TQWT, so as to achieve the adaptive optimal matching of the impact characteristic components in the vibration signal. Then, according to the characteristics of the transient impact of the telemetry vibration signal, the TQWT decomposition coefficients were sparse reconstructed to obtain more sparse impact characteristics, and the weighted power spectrum kurtosis was used as the impact characteristic index to select the optimal sub-band, Finally, the inverse transform of TQWT was used to reconstruct the optimal sub-band to enhance its weak impact features. The simulation and measured signal processing results verify the effectiveness of the algorithm.

scholarly journals Transient feature extraction method based on adaptive TQWT sparse optimization

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xue Liu ◽  
Ao Sun ◽  
Jian Hu
Keyword(s):  
Feature Extraction ◽  
Frequency Band ◽  
Characteristic Frequency ◽  
Extraction Method ◽  
Vibration Signal ◽  
Measured Signal ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Transient Feature ◽  
The Impact

AbstractAiming at the problem of strong impact, short response period and wide resonance frequency bandwidth of transient vibration signals, a transient feature extraction method based on adaptive tunable Q-factor wavelet transform (TQWT) was proposed. Firstly, the characteristic frequency band of the vibration signal was selected according to the time–frequency distribution. Based on the characteristic frequency band, the sub-band average energy weighted wavelet Shannon entropy was used to optimize the number of decomposition layers, quality factor and redundancy of TQWT, so as to achieve the adaptive optimal matching of the impact characteristic components in the vibration signal. Then, according to the characteristics of the transient impact of the telemetry vibration signal, the TQWT decomposition coefficients were sparse reconstructed to obtain more sparse impact characteristics, and the weighted power spectrum kurtosis was used as the impact characteristic index to select the optimal sub-band, Finally, the inverse transform of TQWT was used to reconstruct the optimal sub-band to enhance its weak impact features. The simulation and measured signal processing results verify the effectiveness of the algorithm.

Sign in / Sign up

Export Citation Format

Share Document