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.

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.

Transient Resonance Scattering and Target Identification

1997 ◽  
Vol 50 (3) ◽  
pp. 131-148 ◽  
Author(s):  
G. C. Gaunaurd ◽  
H. C. Strifors
Keyword(s):  
Frequency Domain ◽  
Target Identification ◽  
Resonance Scattering ◽  
Convergent Series ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Time Frequency ◽  
Transient Scattering ◽  
Transient Resonance ◽  
Active Classification

The article presents an overview of transient resonance scattering, emphasizing one of its most important applications—the active classification of sonar and radar targets. It discusses traditional, classical techniques such as the Watson-Sommerfeld method (WSM) to transform classical, and slowly convergent normal-mode series in the frequency domain, to rapidly convergent series in the domain of the complex generalization, λ, of the mode-order, n. In view of its analytical complexity and the advent of computers that can overcome slow convergence difficulties, the WSM is not as popular today as it once was. Its main advantage remains its ability to extract physical interpretations from the mathematical results. Resonance scattering focuses on the resonance spectral region of targets. Of these, the penetrable (ie, elastic or dielectric) ones are the subjects of main interest here, particularly those insonified/illuminated by (finite) pulses of various types. The authors describe the exact isolation and extraction of the resonances contained within the scattering cross-section of a penetrable target by subtraction of suitable, background, geometrical contributions. These backgrounds are often given by the solution for an identical, but impenetrable target. This seems to be the main usefulness of impenetrable target solutions in underwater acoustics, which, generally, are physically unrealistic idealizations. The resonances identify the target as its fingerprint. Examples are shown to illustrate various transient scattering phenomena in acoustics and electromagnetism. The article shows exactly how the broadband pulses emitted by an impulse sonar (or radar) extract a substantial number of resonances from the echoes of penetrable targets. Further, it is shown how these are actually used to identify all physical characteristics of various analyzed targets, thus, indeed identifying them. The application of a novel signal processing technique that analyzes the echoes in the joint time-frequency domain is examined. This shows much promise for target identification purposes. Many distributions of the Wigner-type were used by us to generate simulated and experimental echo-displays in time-frequency that show the advantages of the process. The present overview supplements two earlier ones [23, 48] on closely related subjects. The article includes 101 references.

Advanced signal processing technique for damage detection in steel tubes

2016 ◽  
Author(s):  
Umar Amjad ◽  
Susheel Kumar Yadav ◽  
Cac Minh Dao ◽  
Kiet Dao ◽  
Tribikram Kundu
Keyword(s):  
Signal Processing ◽  
Damage Detection ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Steel Tubes

CARDIAC MURMUR ANALYSIS USING THE SHORT-TIME FOURIER TRANSFORM

2006 ◽  
Vol 06 (03) ◽  
pp. 273-284 ◽  
Author(s):  
S. M. DEBBAL ◽  
F. BEREKSI-REGUIG
Keyword(s):  
Fourier Transform ◽  
Processing Technique ◽  
Graphic Representation ◽  
Heart Valve Disease ◽  
Cardiac Auscultation ◽  
Short Time Fourier Transform ◽  
Signal Processing Technique ◽  
Cardiac Murmur ◽  
Time Frequency ◽  
Short Time

This paper describes a signal processing technique aimed at complementing cardiac auscultation in the detection of heart valve disease. The method provides a means for keeping objective records by analyzing the characteristics of the cardiac murmurs. The Short-time Fourier Transform (STFT) is used here to provide a graphic representation of the time-frequency information of the cardiac murmurs from eight different pathology cases. The graphic representation obtained shows the variation in frequency and intensity during the murmur. Some interesting observations show characteristic rising and falling tones, suggesting degrees of the pathology severity.

Spectral balancing GPR data using time-variant bandwidth in the t-f domain

Geophysics ◽  
2010 ◽  
Vol 75 (3) ◽  
pp. J19-J27 ◽  
Author(s):  
Nikos Economou ◽  
Antonis Vafidis
Keyword(s):  
Time Windows ◽  
Attenuation Factor ◽  
Amplitude Spectrum ◽  
Dominant Frequency ◽  
Processing Technique ◽  
Frequency Noise ◽  
Signal Processing Technique ◽  
Time Frequency ◽  
Text Filtering ◽  
Ground Penetrating

Ground-penetrating radar (GPR) sections encounter a resolution reduction with depth because, for electromagnetic (EM) waves propagating in the subsurface, attenuation is typically more pronounced at higher frequencies. To correct for these effects, we have applied a spectral balancing technique, using the S-transform (ST). This signal-processing technique avoids the drawbacks of inverse [Formula: see text] filtering techniques, namely, the need for estimation of the attenuation factor [Formula: see text] from the GPR section and instability caused by scattering effects that result from methods of dominant frequency-dependent estimation of [Formula: see text]. The method designs and applies a gain in the time-frequency ([Formula: see text]) domain and involves the selection of a time-variant bandwidth to reduce high-frequency noise. This method requires a reference amplitude spectrum for spectral shaping. It performs spectral balancing, which works efficiently for GPR data when it is applied in very narrow time windows. Furthermore, we have found that spectral balancing must be applied prior to deconvolution, instead of being an alternative technique.

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.

ENHANCING WELLBORE LEAK LOCALIZATION WITH CONTINUOUS LOGGING DATA FROM A SONIC SENSOR ARRAY

2021 ◽  
Author(s):  
Yao Ge ◽  
◽  
Yadong Wang ◽  
Xiang Wu ◽  
Ruijia Wang ◽  
...  
Keyword(s):  
Field Data ◽  
Array Signal Processing ◽  
Noise Spectrum ◽  
Processing Technique ◽  
Guided Wave ◽  
Acquisition Time ◽  
Flow Profile ◽  
Road Noise ◽  
Signal Processing Technique ◽  
Leak Location

Early detection and localization of downhole leaks are essential to maintain well integrity, reduce cost, and minimize downtime. New technology has been developed to detect leak locations in a well quickly and to characterize the flow profile of the leak by using an array of hydrophones. The technology uses advanced modeling and beamforming algorithm to map out the flow pattern in a 2D image within the well’s completion structure. However, during continuous logging, the leak signal may be contaminated by guided wave noises such as the road noise from the tool string, and the logging results will be compromised. This paper demonstrates a method to estimate and remove guided-wave noise to enhance the leak detection answer products. The data from continuous logging may be contaminated with significant road noise due to equipment contacting the casing or borehole which produces Stoneley or tube waves. For single and dual hydrophone tools, additional runs may be needed to stop these tools at selected locations to record data without this contamination, but this approach prolongs the acquisition time and limits the vertical resolution. In order to obtain depth-continuous and high-resolution leak information, an advanced array signal-processing technique has been developed to enhance the signal quality. Extensive studies on field data were conducted to extract the features and characteristics of the leak noise, even when those features overlap in time or frequency with contamination noise. The processing method employs multiple steps that analyze the hydrophone array to remove the contamination noise in the time or frequency domain, leaving the leak noise for flow and leak location analysis. The proposed method has successfully identified high noise activity at certain depths as road noise in continuous logging data. Road noise may increase in amplitude within a limited depth due to a momentary change in logging activity. The elevated noise generated can be identified as guided-wave noise instead of a potential leak. The method can be implemented in realtime and the results will save additional rig time conducting further stationary logging at the non-leak depths. Field data results also suggest that the proposed method improves the signal-to-noise ratio of the continuous logging data significantly and delivers quality noise spectrum and leak location logs for the industry. The proposed method has been proven to be effective in identifying and enhancing leak signals and removing contaminating signals due to guided wave noises. It has greatly enhanced the quality of the detection, resolution, and location of leaks in wellbore tubulars produced from continuous logging data. These high-quality continuous logging results will help field engineers to make more accurate decisions quickly during logging operations and could avoid costly and time-lengthy stationary logging programs.

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 Enhancement of Ultrasonic Guided Wave Signals Using a Split-Spectrum Processing Method

2018 ◽  
Vol 8 (10) ◽  
pp. 1815 ◽  
Author(s):  
Seyed Kamran Pedram ◽  
Peter Mudge ◽  
Tat-Hean Gan
Keyword(s):  
Structural Integrity ◽  
Signal To Noise Ratio ◽  
Processing Technique ◽  
Guided Wave ◽  
Signal Processing Technique ◽  
Pipeline Inspection ◽  
Ultrasonic Guided Wave ◽  
Small Defect ◽  
Split Spectrum ◽  
Wave Modes

Ultrasonic guided wave (UGW) systems are broadly utilised in several industry sectors where the structural integrity is of concern, in particular, for pipeline inspection. In most cases, the received signal is very noisy due to the presence of unwanted wave modes, which are mainly dispersive. Hence, signal interpretation in this environment is often a challenging task, as it degrades the spatial resolution and gives a poor signal-to-noise ratio (SNR). The multi-modal and dispersive nature of such signals hampers the ability to detect defects in a given structure. Therefore, identifying a small defect within the noise level is a challenging task. In this work, an advanced signal processing technique called split-spectrum processing (SSP) is used firstly to address this issue by reducing/removing the effect of dispersive wave modes, and secondly to find the limitation of this technique. The method compared analytically and experimentally with the conventional approaches, and showed that the proposed method substantially improves SNR by an average of 30dB. The limitations of SSP in terms of sensitivity to small defects and distances are also investigated, and a threshold has been defined which was comparable for both synthesised and experimental data. The conclusions reached in this work paves the way to enhance the reliability of UGW inspection.

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