Disbond Detection Using Peak Amplitude of Pulse-Echo Signals for Various Thicknesses and Transducer Frequencies

Abedin, M. Nurul; Johnston, Patrick H.; Prabhu, D. R.

doi:10.1007/978-1-4615-2848-7_197

Cited by 5 publications

(6 citation statements)

References 4 publications

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“…Each signal is a time series of length n = 258. Details about the data set can be found in the work of Abedin et al 38 There are considerable variabilities among signals within each class due to various sources, including the type of structure (eg, lap joint or reinforcing doubler), the thicknesses of bond layers, epoxy, paint, and coating, and the amount of material loss. Figure 3 shows two signals from each class.…”

Section: Case Studymentioning

confidence: 99%

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From zero crossings to quantile‐frequency analysis of time series with an application to nondestructive evaluation

2019

Appl Stoch Models Bus & Ind

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Represented by the pioneering works of Professor Benjamin Kedem, zero crossings of time-series data have been proven useful for characterizing oscillatory patterns in many applications such as speech recognition and brainwave analysis. Robustness against outliers and nonlinear distortions is one of the advantages of zero crossings in comparison with traditional spectral analysis techniques. This paper introduces a new tool of spectral analysis for time-series data that goes beyond zero crossings. It is called quantile-frequency analysis (QFA). Constructed from trigonometric quantile regression, QFA transforms a time series into a bivariate function of quantile level and frequency variable. For each fixed quantile level, it corresponds to a periodogram-like function, called the quantile periodogram, which characterizes the oscillatory behavior of the time series round the quantile. By coupling QFA with functional principal component analysis, new dimension-reduced features are proposed for discriminant analysis of time series. The usefulness of these features is demonstrated by a case study of classifying real-world ultrasound signals for nondestructive evaluation of aircraft panels. Various machine learning classifiers are trained and tested by cross-validation. The results show a clear advantage of the QFA method over its ordinary-periodogram-based counterpart in delivering higher out-of-sample classification accuracy.

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Section: Case Studymentioning

confidence: 99%

“…It comprises 708 pulse‐echo ultrasound signals acquired by a broadband transducer, of which 324 are labeled “bond” and 384 “disbond.” Each signal is a time series of length n =258. Details about the data set can be found in the work of Abedin et al 38 …”

Section: Case Studymentioning

confidence: 99%

From zero crossings to quantile‐frequency analysis of time series with an application to nondestructive evaluation

2019

Appl Stoch Models Bus & Ind

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“…Spectral features derived from the power spectrum, also known as the periodogram, have been widely used for signal classification in these applications. One such example is nondestructive evaluation (NDE) of mechanical systems [3]- [6]. In this application, the time series data comprise echoes reflected from a mechanical system when probed by ultrasound waves.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, our experiment considers the multilayer perceptron (MLP) classifier and the convolutional neural network (CNN) classifier [14]. These classifiers are designed to take the two-dimensional functions of QFA as input and classify a set of ultrasonic signals collected from aircraft panels with or without structural defects [3]. The performances of these classifiers are evaluated using a cross-validation scheme and compared with the performances of similar classifiers using the power spectrum as input instead of QFA.…”

Section: Introductionmentioning

confidence: 99%

Quantile-Frequency Analysis and Deep Learning for Signal Classification

2023

J Nondestruct Eval

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This paper proposes a new method for signal classification based on a combination of recently introduced nonlinear spectral analysis technique called quantile-frequency analysis (QFA) and deep-learning (DL) image classifiers. The QFA method converts a one-dimensional signal into a two-dimensional representation of the signal's oscillatory behavior in the frequency domain at different quantiles or a sequence of such representations that are localized in time. The DL image classifiers utilize these representations for signal classification. The benefit of this QFA-DL classification method, especially in comparison with the traditional method based on the power spectrum and spectrogram, is demonstrated by a numerical experiment using real-world data from a nondestructive evaluation (NDE) application.

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“…[l-3] Research results for characterization of disbond and corrosion based on normal-incidence pulse-echo measurement geometries are showing promise, but are limited by the single-site nature of the measurement which requires manual or mechanical scanning to inspect an area. [4][5][6][7] One approach to developing efficient systems may be to transfer specific aspects of current medical imaging technology to the NDT arena. Ultrasonic medical imaging systems offer many desirable attributes for large scale inspection.…”

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confidence: 99%

Detection of Disbonded Regions in Bonded Aluminum Plates Using an Ultrasonic 7.5 MHz Linear Array Medical Imaging System

Holland

Johnston

Handley

et al. 1995

Review of Progress in Quantitative Nondestructive Evaluation

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INTRODUCTION Current concern for ensuring the air-worthiness of the aging commercial air fleet has prompted the establishment of broad-agency programs to develop NDT technologies that address specific aging-aircraft issues.[l, 2] One of the crucial technological needs that has been identified is the development of rapid, quantitative systems for depot-level inspection of bonded aluminum lap joints on aircraft.[l-3] Research results for characterization of disbond and corrosion based on normal-incidence pulse-echo measurement geometries are showing promise, but are limited by the single-site nature of the measurement which requires manual or mechanical scanning to inspect an area. [4-7] One approach to developing efficient systems may be to transfer specific aspects of current medical imaging technology to the NDT arena. Ultrasonic medical imaging systems offer many desirable attributes for large scale inspection. They are portable, provide real-time imaging, and have integrated video tape recorder and printer capabilities available for documentation and post-inspection review. Furthermore, these systems are available at a relatively low cost (approximately $50,000 to $200,000) and can be optimized for use with metals with straightforward modifications. As an example, ultrasonic phased-array and linear array imaging technology, which was first developed for use in the medical industry, has been successfully implemented for some NDT applications by other investigators. [8-1 0] In this paper we explore the feasibility of implementing medical linear array imaging technology as a viable ultrasonic-based nondestructive evaluation method to inspect and characterize bonded aluminum lap joints. We present an image, obtained using an unmodified medical ultrasonic imaging system, of an epoxy-bonded aluminum plate specimen with an intentionally disbonded region. This image is compared with corresponding conventional ultrasonic contact transducer measurements in order to assess whether this image can detect disbonded regions and provide information regarding the nature of the disbonded region. The results of this investigation may provide a step toward the development of a rapid, real-time, and portable method of adhesive bond inspection and characterization based on linear array technology. BACKGROUND

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Disbond Detection Using Peak Amplitude of Pulse-Echo Signals for Various Thicknesses and Transducer Frequencies

Cited by 5 publications

References 4 publications

From zero crossings to quantile‐frequency analysis of time series with an application to nondestructive evaluation

From zero crossings to quantile‐frequency analysis of time series with an application to nondestructive evaluation

Quantile-Frequency Analysis and Deep Learning for Signal Classification

Detection of Disbonded Regions in Bonded Aluminum Plates Using an Ultrasonic 7.5 MHz Linear Array Medical Imaging System

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