scholarly journals Damage Localization in Composite Plates Using Wavelet Transform and 2-D Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5825
Author(s):  
Guillermo Azuara ◽  
Mariano Ruiz ◽  
Eduardo Barrera
Keyword(s):  
Neural Networks ◽  
Wavelet Transform ◽  
Guided Waves ◽  
Lamb Waves ◽  
Composite Plates ◽  
Time Frequency ◽  
Damage Location ◽  
Using Data ◽  
Fiber Reinforced Material ◽  
Physical Features

Nondestructive evaluation of carbon fiber reinforced material structures has received special attention in the last decades. Usage of Ultrasonic Guided Waves (UGW), particularly Lamb waves, has become one of the most popular techniques for damage location, due to their sensitivity to defects, large range of inspection, and good propagation in several material types. However, extracting meaningful physical features from the response signals is challenging due to several factors, such as the multimodal nature of UGW, boundary conditions and the geometric shape of the structure, possible material anisotropies, and their environmental dependency. Neural networks (NN) are becoming a practical and accurate approach to analyzing the acquired data using data-driven methods. In this paper, a Convolutional-Neural-Network (CNN) is proposed to predict the distance-to-damage values from the signals corresponding to a transmitter-receiver path of transducers. The NN input is a 2D image (time-frequency) obtained as the Wavelet transform of the acquired experimental signals. The distances obtained with the NN are the input of a novel damage location algorithm which outputs a bidimensional image of the structure’s surface showing the estimated damage locations with a deviation of the actual position lower than 15 mm.

Determination of Vibration Source Locations in Laminated Composite Plates Using Lamb Wave Analysis

2010 ◽  
Vol 123-125 ◽  
pp. 899-902
Author(s):  
Chao Du ◽  
Qing Qing Ni ◽  
Toshiaki Natsuki
Keyword(s):  
Lamb Wave ◽  
Guided Waves ◽  
Lamb Waves ◽  
Laminated Composite ◽  
Composite Plates ◽  
Wave Dispersion ◽  
Laminated Plates ◽  
Vibration Source ◽  
Arrival Times ◽  
Wave Velocities

Signals propagate on plate-like structures as ultrasonic guided waves, and analysis of Lamb waves has been widely used for on-line monitoring. In this study, the wave velocities of symmetric and anti-symmetric modes in various directions of propagation were investigated. Since the wave velocities of these two modes are different, it is possible to compute the difference in their arrival times when these waves propagated the distance from the vibration source to sensor. This paper presents an evaluation formulation of wave velocity and describes a generalized algorithm for locating a vibration source on a thin, laminated plate. With the different velocities of two modes based on Lamb wave dispersion, the method uses two sensors to locate the source on a semi-infinite interval of a plate. The experimental procedure supporting this method employs pencil lead breaks to simulate vibration sources on quasi-isotropic and unidirectional laminated plates. The transient signals generated in this way are transformed using a wavelet transform. The vibration source locations are then detected by utilizing the distinct wave velocities and arrival times of the symmetric and anti-symmetric wave modes. The method is an effective technique for identifying impact locations on plate-like structures.

scholarly journals Impact Localisation in Composite Plates of Different Stiffness Impactors under Simulated Environmental and Operational Conditions

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3659 ◽  
Author(s):  
Seno ◽  
Aliabadi
Keyword(s):  
Composite Structures ◽  
Lamb Waves ◽  
Real Life ◽  
Composite Plates ◽  
Information Criterion ◽  
Reference Database ◽  
Time Of Arrival ◽  
Operational Conditions ◽  
Contact Behaviour ◽  
Using Data

A parametric investigation of the effect of impactor stiffness as well as environmental and operational conditions on impact contact behaviour and the subsequently generated lamb waves in composite structures is presented. It is shown that differing impactor stiffness generates the most significant changes in contact area and lamb wave characteristics (waveform, frequency, and amplitude). A novel impact localisation method was developed based on the above observations that allows for variations due to differences in impactor stiffness based on modifications of the reference database method and the Akaike Information Criterion (AIC) time of arrival (ToA) picker. The proposed method was compared against a benchmark method based on artificial neural networks (ANNS) and the normalised smoothed envelope threshold (NSET) ToA extraction method. The results indicate that the proposed method had comparable accuracy to the benchmark method for hard impacts under various environmental and operational conditions when trained only using a single hard impact case. However, when tested with soft impacts, the benchmark method had very low accuracy, whilst the proposed method was able to maintain its accuracy at an acceptable level. Thus, the proposed method is capable of detecting the location of impacts of varying stiffness under various environmental and operational conditions using data from only a single impact case, which brings it closer to the application of data driven impact detection systems in real life structures.

scholarly journals Warped Wigner-Hough Transform for Defect Reflection Enhancement in Ultrasonic Guided Wave Monitoring

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Luca De Marchi ◽  
Emanuele Baravelli ◽  
Giampaolo Cera ◽  
Nicolò Speciale ◽  
Alessandro Marzani
Keyword(s):  
Hough Transform ◽  
Guided Waves ◽  
Lamb Waves ◽  
Guided Wave ◽  
Nonlinear Signal Processing ◽  
Localization Accuracy ◽  
Time Frequency ◽  
Ultrasonic Guided Wave ◽  
Inspection Systems ◽  
Nonlinear Signal

To improve the defect detectability of Lamb wave inspection systems, the application of nonlinear signal processing was investigated. The approach is based on a Warped Frequency Transform (WFT) to compensate the dispersive behavior of ultrasonic guided waves, followed by a Wigner-Ville time-frequency analysis and the Hough Transform to further improve localization accuracy. As a result, an automatic detection procedure to locate defect-induced reflections was demonstrated and successfully tested by analyzing numerically simulated Lamb waves propagating in an aluminum plate. The proposed method is suitable for defect detection and can be easily implemented for real-world structural health monitoring applications.

scholarly journals Improvement of Accuracy in Damage Localization Using Frequency Slice Wavelet Transform

2012 ◽  
Vol 19 (4) ◽  
pp. 585-596 ◽  
Author(s):  
Xinglong Liu ◽  
Zhongwei Jiang ◽  
Zhonghong Yan
Keyword(s):  
Wavelet Transform ◽  
Damage Identification ◽  
Lamb Waves ◽  
Group Velocity Dispersion ◽  
Propagation Distance ◽  
Primary Objective ◽  
Frequency Resolution ◽  
Damage Localization ◽  
Arrival Times ◽  
Time Frequency

Damage localization is a primary objective of damage identification. This paper presents damage localization in beam structure using impact-induced Lamb wave and Frequency Slice Wavelet Transform (FSWT). FSWT is a new time-frequency analysis method and has the adaptive resolution feature. The time-frequency resolution is a vital factor affecting the accuracy of damage localization. In FSWT there is a unique parameter controlling the time-frequency resolution. To improve the accuracy of damage localization, a generalized criterion is proposed to determine the parameter value for achieving a suitable time-frequency resolution. For damage localization, the group velocity dispersion curve (GVDC) of A0Lamb waves in beam is first accurately estimated using FSWT, and then the arrival times of reflection wave from the crack for some individual frequency components are determined. An average operation on the calculated propagation distance is then performed to further improve the accuracy of damage localization.

Guided Waves in Steel Rails – Experimental Works and Wavelet Signal Processing

2007 ◽  
Vol 347 ◽  
pp. 115-120
Author(s):  
Magdalena Rucka ◽  
Krzysztof Wilde
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Guided Waves ◽  
Flaw Detection ◽  
Transverse Waves ◽  
Dispersive Waves ◽  
Time Frequency ◽  
Time Signals ◽  
Frequency Components ◽  
Short Time

This paper presents experimental study on dispersive waves propagation in steel rails. The propagation of longitudinal and transverse waves was generated by an impulse hammer and measured in three points. Wavelet transform (WT) and short time Fourier transform (STFT) were applied to analyze the time signals. Analysis of signal by STFT does not provide a proper timefrequency representation due to a fixed size window. The wavelet transform can effectively identify the time-frequency components in waves. The wavelet signal processing of the experimental wave propagation signals is intended to be used for rail flaw detection.

Damage Localization in Complex Composite Panels Using Guided Wave Based Structural Health Monitoring System

2011 ◽  
Author(s):  
Yingtao Liu ◽  
Seung Bum Kim ◽  
Aditi Chattopadhyay ◽  
Derek Doyle
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Composite Structures ◽  
Composite Plates ◽  
Guided Wave ◽  
Damage Localization ◽  
Composite Panels ◽  
Time Frequency ◽  
Structural Health ◽  
Damage Location

Knowledge of the damage location in composite structures is a necessary output for both Non-Destructive Evaluation (NDE) and Structural Health Monitoring (SHM). Although several damage localization approaches using a triangulation method and Time-of-Flight (ToF) of guided waves have been reported in literature, the damage localization technique is still not mature for composite structures with complex material properties, varying thickness and complex geometries. This paper investigates the development of a new approach for SHM and damage localization using a guided wave based active sensing system. In contrast to the traditional ellipse method, the proposed method does not require the information of structural thickness, ToF, or the estimation of group velocities of each guided wave mode at different propagation angles, which is one of the main limitations of most current ToF methodologies involving composites. This approach uses time-frequency analysis to calculate the difference of the ToF of the converted modes for each sensor signal. The damage location and the group velocity are obtained by solving a set of nonlinear equations. The proposed method can be used for composite structures with unknown lay-up and thickness. To validate the proposed method, experiments were conducted on both composite plates and stiffened composite panels. Eight piezoelectric (PZT) transducers were surface-bonded on each composite specimen and used in four pairs. The PZT transducers in each pair were bonded close to each other. In the PZT array, one PZT transducer from one PZT pair was used as the actuator and the other three pairs were used as sensors. A windowed cosine signal was used as the excitation signal. The locations of the delaminations in the composite specimens were validated using a flash thermography system. The accuracy of the proposed method in localizing delaminations was examined through comparison with the experimental measurements.

scholarly journals Lamb Wave-Based Damage Localization Feature Enhancement and Extraction Method for Stator Insulation of Large Generators Using VMD and Wavelet Transform

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4205
Author(s):  
Ruihua Li ◽  
Jing Luo ◽  
Bo Hu
Keyword(s):  
Wavelet Transform ◽  
Lamb Wave ◽  
Background Noise ◽  
Lamb Waves ◽  
Signal To Noise Ratio ◽  
Scattered Wave ◽  
Damage Location ◽  
Wave Detection ◽  
Mode Decomposition ◽  
Reconstructed Signal

Lamb waves are used to locate any damage in the stator insulation structure of large generators. However, it is difficult to extract the features of Lamb wave signals in a strong background noise environment, thus significantly reducing the accuracy with which the damage is located. This paper proposes a method based on variational mode decomposition (VMD) and wavelet transform to enhance and extract the location features of stator insulation damage signals of large motors. First, considering that the characteristics of VMD are sensitive to noise, the Lamb wave detection signal is decomposed, denoised, and reconstructed; the reconstructed signal is then wavelet-transformed to extract the time of flight (TOF) of the damage-scattered wave as the damage location feature; finally, the damage location is determined using the TOF features. The proposed method is experimentally tested and verified under various noise environments. The results show that the VMD and wavelet transform methods can significantly improve the signal-to-noise ratio of Lamb wave detection signals and the accuracy with which the damage is located under strong background noise. This study extends the applicability of Lamb wave-based non-destructive detection of stator insulation damage in complex environments.

scholarly journals Atrial Fibrillation Beat Identification Using the Combination of Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaoyan Xu ◽  
Shoushui Wei ◽  
Caiyun Ma ◽  
Kan Luo ◽  
Li Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Convolutional Neural Networks ◽  
Test Data ◽  
Heart Diseases ◽  
Time Frequency ◽  
Beat Classification ◽  
Electrocardiogram Ecg ◽  
New Framework

Atrial fibrillation (AF) is a serious cardiovascular disease with the phenomenon of beating irregularly. It is the major cause of variety of heart diseases, such as myocardial infarction. Automatic AF beat detection is still a challenging task which needs further exploration. A new framework, which combines modified frequency slice wavelet transform (MFSWT) and convolutional neural networks (CNNs), was proposed for automatic AF beat identification. MFSWT was used to transform 1 s electrocardiogram (ECG) segments to time-frequency images, and then, the images were fed into a 12-layer CNN for feature extraction and AF/non-AF beat classification. The results on the MIT-BIH Atrial Fibrillation Database showed that a mean accuracy (Acc) of 81.07% from 5-fold cross validation is achieved for the test data. The corresponding sensitivity (Se), specificity (Sp), and the area under the ROC curve (AUC) results are 74.96%, 86.41%, and 0.88, respectively. When excluding an extremely poor signal quality ECG recording in the test data, a mean Acc of 84.85% is achieved, with the corresponding Se, Sp, and AUC values of 79.05%, 89.99%, and 0.92. This study indicates that it is possible to accurately identify AF or non-AF ECGs from a short-term signal episode.

Time-Frequency Analysis of Partial Discharge Phenomena in SF6 gas using Wavelet Transform

1997 ◽  
Vol 117 (3) ◽  
pp. 338-345 ◽  
Author(s):  
Masatake Kawada ◽  
Masakazu Wada ◽  
Zen-Ichiro Kawasaki ◽  
Kenji Matsu-ura ◽  
Makoto Kawasaki
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Partial Discharge ◽  
Time Frequency Analysis ◽  
Time Frequency

Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market

2020 ◽  
Vol 13 (1) ◽  
pp. 16-26
Author(s):  
Sumit Saroha ◽  
Sanjeev K. Aggarwal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Wind Power ◽  
Electricity Market ◽  
General Regression Neural Network ◽  
Percentage Error ◽  
Wind Power Forecasting ◽  
General Regression ◽  
Power Forecasting

Objective: The estimation accuracy of wind power is an important subject of concern for reliable grid operations and taking part in open access. So, with an objective to improve the wind power forecasting accuracy. Methods: This article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique. Results: The results of the proposed model are compared with four different models namely naïve benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric. Conclusion: The historical data used by the presented models has been collected from the Ontario Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years (28 months) from November 2012 to February 2015 with one month estimation moving window.

Sign in / Sign up

Export Citation Format

Share Document