Guided Wave Based Fatigue Crack Detection and Localization in Aluminum Aerospace Structures

2012 ◽  
Author(s):  
Kevin Hensberry ◽  
Narayan Kovvali ◽  
Kuang C. Liu ◽  
Aditi Chattopadhyay ◽  
Antonia Papandreou-Suppappola
Keyword(s):  
Signal Processing ◽  
Crack Detection ◽  
Health Management ◽  
Lamb Waves ◽  
Matching Pursuit ◽  
Probabilistic Approach ◽  
Guided Wave ◽  
Signal Processing Technique ◽  
Time Frequency ◽  
Detection And Localization

The work presented in this paper provides an insight into the current challenges to detect incipient damage in complex metallic structural components. The goal of this research is to improve the confidence level in diagnosis and damage localization technologies by developing a robust structural health management (SHM) framework. Improved methodologies are developed for reference-free localization of fatigue induced cracks in complex metallic structures. The methodologies for damage interrogation involve damage feature extraction using advanced signal processing tools and a probabilistic approach for damage detection and localization. Specifically, piezoelectric transducers are used in pitch-catch mode to interrogate the structure with guided Lamb waves. A novel time-frequency (TF) based signal processing technique based on the matching pursuit decomposition (MPD) algorithm is developed to extract time-of-flight damage features from dispersive guided wave sensor signals, followed by a Bayesian probabilistic approach used to optimally fuse multi-sensor information and localize the crack tip. The MPD algorithm decomposes a signal using localized TF atoms and can provide a highly concentrated TF representation. The Bayesian probabilistic framework enables the effective quantification and management of uncertainty. Experiments are conducted to validate the proposed detection and localization methods. Results presented will illustrate the usefulness of the developed approaches in detection and localization of damage in aluminum lug joints.

scholarly journals Application of UAV in Search and Rescue Actions in Underground Mine—A Specific Sound Detection in Noisy Acoustic Signal

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3725
Author(s):  
Paweł Zimroz ◽  
Paweł Trybała ◽  
Adam Wróblewski ◽  
Mateusz Góralczyk ◽  
Jarosław Szrek ◽  
...  
Keyword(s):  
Signal Processing ◽  
Acoustic Signal ◽  
Processing Technique ◽  
Underground Mine ◽  
Search And Rescue ◽  
Reference Spectrum ◽  
Signal Processing Technique ◽  
Time Frequency ◽  
Communication Device ◽  
Frequency Representation

The possibility of the application of an unmanned aerial vehicle (UAV) in search and rescue activities in a deep underground mine has been investigated. In the presented case study, a UAV is searching for a lost or injured human who is able to call for help but is not able to move or use any communication device. A UAV capturing acoustic data while flying through underground corridors is used. The acoustic signal is very noisy since during the flight the UAV contributes high-energetic emission. The main goal of the paper is to present an automatic signal processing procedure for detection of a specific sound (supposed to contain voice activity) in presence of heavy, time-varying noise from UAV. The proposed acoustic signal processing technique is based on time-frequency representation and Euclidean distance measurement between reference spectrum (UAV noise only) and captured data. As both the UAV and “injured” person were equipped with synchronized microphones during the experiment, validation has been performed. Two experiments carried out in lab conditions, as well as one in an underground mine, provided very satisfactory results.

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.

Noise filtering techniques for Lamb waves in structural health monitoring

2018 ◽  
Vol 14 (4) ◽  
pp. 676-694
Author(s):  
Ambuj Sharma ◽  
Sandeep Kumar ◽  
Amit Tyagi
Keyword(s):  
Crack Detection ◽  
Lamb Waves ◽  
Random Noise ◽  
Matched Filtering ◽  
Noise Filtering ◽  
Content Type ◽  
Time Frequency ◽  
Structural Health ◽  
Filtering Technique ◽  
Filtering Techniques

Purpose The real challenges in online crack detection testing based on guided waves are random noise as well as narrow-band coherent noise; and to achieve efficient structural health assessment methodology, magnificent extraction of noise and analysis of the signals are essential. The purpose of this paper is to provide optimal noise filtering technique for Lamb waves in the diagnosis of structural singularities. Design/methodology/approach Filtration of time-frequency information of guided elastic waves through the noisy signal is investigated in the present analysis using matched filtering technique which “sniffs” the signal buried in noise and most favorable mother wavelet based denoising methods. The optimal wavelet function is selected using Shannon’s entropy criterion and verified by the analysis of root mean square error of the filtered signal. Findings Wavelet matched filter method, a newly developed filtering technique in this work and which is a combination of the wavelet transform and matched filtering method, significantly improves the accuracy of the filtered signal and identifies relatively small damage, especially in enormously noisy data. A comparative study is also performed using the statistical tool to know acceptability and practicability of filtered signals for guided wave application. Practical implications The proposed filtering techniques can be utilized in online monitoring of civil and mechanical structures. The algorithm of the method is easy to implement and found to be successful in accurately detecting damage. Originality/value Although many techniques have been developed over the past several years to suppress random noise in Lamb wave signal but filtration of interferences of wave modes and boundary reflection is not in a much matured stage and thus needs further investigation. The present study contains detailed information about various noise filtering methods, newly developed filtration technique and their efficacy in handling the above mentioned issues.

A Wavelet-Based Damage Detection Approach for Acousto-Ultrasonic In Situ Monitoring Systems

2011 ◽  
Vol 471-472 ◽  
pp. 809-814 ◽  
Author(s):  
Chin Kian Liew ◽  
Martin Veidt
Keyword(s):  
Damage Detection ◽  
Laminate Plate ◽  
Processing Technique ◽  
Guided Wave ◽  
Signal Processing Technique ◽  
Piezoceramic Actuator ◽  
Time Frequency ◽  
Wave Response ◽  
Detection Approach ◽  
Normalisation Factor

In this research, an advanced signal processing technique using wavelet analysis has been developed for a guided wave structural health monitoring system. The approach was applied for the detection of delamination in carbon fibre reinforced composites. A monolithic piezoceramic actuator was attached to a laminate plate for wave generation while laser vibrometry was used to facilitate the measurements of the wave response in a sensor network. This database of wave response was then processed using the continuous wavelet transform to obtain the positional frequency content. Transforms between damaged and undamaged states were compared to ascertain the presence of defects by evaluating the total energy of the time-frequency density function. Results show high damage detection indices depending on the location of the sensor and normalisation factor applied while there are positive indications that this methodology can be extended for damage characterisation.

The Present Development Situation of Laser Ultrasonic Nondestructive Testing Signal Processing System

2012 ◽  
Vol 532-533 ◽  
pp. 369-373
Author(s):  
Lie Xiang Xia ◽  
Yan Zhao ◽  
Yong Zhong Ma ◽  
Xiao Ning An
Keyword(s):  
Signal Processing ◽  
Nondestructive Testing ◽  
Detection System ◽  
Matching Pursuit ◽  
Ultrasonic Inspection ◽  
Processing System ◽  
Guided Wave ◽  
Ultrasonic Nondestructive Testing ◽  
Laser Ultrasonic ◽  
Signal Processing System

Currently, laser ultrasonic nondestructive testing is the best application prospect of nondestructive testing methods. This paper expounds the mechanism and the qualitative analysis of the common defects of the laser ultrasonic inspection and introduces the application of matching pursuit algorithm guided wave testing technology in signal processing. This paper focuses on the laser ultrasonic detection system and analysis of the detected data using the finite element method.

scholarly journals A Reservoir Information Extraction Method Based on Improved Hilbert-Huang Transform

2019 ◽  
pp. 85-90
Author(s):  
Qingmi Yang
Keyword(s):  
Signal Processing ◽  
Seismic Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Intrinsic Mode Functions ◽  
Signal Processing Technique ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Non Stationary Signal

Hilbert-Huang transform (HHT) is a nonlinear non-stationary signal processing technique, which is more effective than traditional time-frequency analysis methods in complex seismic signal processing. However, this method has problems such as modal aliasing and end effect. The problem causes the accuracy of signal processing to drop. Therefore, this paper introduces the method of combining the Ensemble Empirical Mode Decomposition (EEMD) and the Normalized Hilbert transform (NHT) to extract the instantaneous properties. The specific process is as follows: First, the EEMD method is used to decompose the seismic signal to a series of Intrinsic Mode Functions (IMF), and then The IMFs is screened by using the relevant properties, and finally the NHT is performed on the IMF to obtain the instantaneous properties.

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