A Reference Matching-Based Temperature Compensation Method for Ultrasonic Guided Wave Signals.

The ultrasonic guided wave-based structural damage diagnosis method has broad application prospects in different fields. However, some environmental factors such as temperature and loads will significantly affect the monitoring results. In this paper, a reference matching-based temperature compensation for ultrasonic guided wave signals is proposed to eliminate the effect of temperature. Firstly, the guided wave signals measured at different temperatures are used as reference signals to establish the relationship between the features of the reference signals and temperature. Then the matching algorithm based on Gabor function is used to establish the relationship between the amplitude influence coefficient obtained by the reference signal and the corresponding temperature. Finally, through these two relationships, the values of the phase and amplitude influence coefficients of the guided wave signals at other temperatures are obtained in a way of interpolation in order to reconstruct the compensation signals at the temperature. The effect of temperature on the amplitude and phase of the guided wave signal is eliminated. The proposed temperature compensation method is featured such that the compensation performance can be improved by multiple iteration compensation of the residual signal. The ultrasonic guided wave test results at different temperatures show that the first iterative compensation of the proposed method can achieve compensation within the temperature range greater than 7 °C, and the compensation within the temperature range greater than 18 °C can be achieved after three iterations

Lamb waves defects imaging research based on multi-parameter compensation and pixel optimization

Ultrasonic guided waves detecting technology has promising application prospects in structural health monitoring. In order to detect defects in the aluminum sheet, a kind of defect localization imaging algorithm, combining multi-parameter compensation and pixel partitioning optimization is proposed in this paper. Based on the analysis of imaging principles, the waveform signal of time domain was mapped onto the wavenumber domain through a backward propagation compensation, so dispersion parameters can be compensated. Reference signal compensation can be achieved by the baseline signal differential method from wavenumber domain, which overcame influences of environmental changes. During the imaging process, a reasonable threshold was used for pixel partitioning and optimization to improve image quality. Experimental results demonstrated that positioning error about the algorithm is small, defects imaging of sheet is clear and intuitive, this optimization and compensation of guided-wave defects imaging can be used in structural health monitoring

Stochastic analysis of guided wave structural health monitoring for aeronautical composites

This thesis presents new methods developed for improvement of the reliability of Guided Wave Structural Health Monitoring (GWSHM) systems for aeronautical composite. Particular attention is devoted to the detection and localisation of barely visible impact damage (BVID) in Carbon-Fibre Reinforced Polymer (CFRP) structures. A novel sensor installation method is developed that offers ease of application and replacement as well as excellent durability. Electromechanical Impedance (EMI) is used to assess the durability of the sensor installation methods in simulated aircraft operational conditions, including thermal cycles, fatigue loading and hot-wet conditions. The superiority of the developed method over existing installation methods is demonstrated through extensive tests. Damage characterisation using GWSHM is investigated in different CFRP structures. Key issues in guided wave based damage identification are addressed, including wave mode /frequency selection, the influence of dynamic load, the validity of simulated damage, sensitivity of guided wave to impact damage in different CFRP materials. Identification of barely visible impact damage (BVID) are investigated on three simple CFRP panels and two stiffened CFRP panels. BVID is detected using three different damage index and located using RAPID, Delay-and-sum, Rayleigh maximum likelihood estimation (RMLE) and Bayesian inference (BI). The influence of temperature on guided wave propagation in anisotropic CFRP structures is addressed and a novel baseline reconstruction approach for temperature compensation is proposed. The proposed temperature compensation method accommodates various sensor placement and can be established using coupon level structures for the application in larger scale structures. Finally, a multi-level hierarchical approach is proposed for the quantification of ultrasonic guided wave based structural health monitoring (GWSHM) system. The hierarchical approach provides a systemic and practical way of establishing GWSHM systems for different structures under uncertainties and assessing system performance. The proposed approach is demonstrated in aircraft CFRP structures from coupon level to sub-component level.Open Acces

Ultrasonic guided wave imaging via sparse reconstruction

Structural health monitoring (SHM) is concerned with the continuous, long-term assessment of structural integrity. One commonly investigated SHM technique uses guided ultrasonic waves, which travel through the structure and interact with damage. Measured signals are then analyzed in software for detection, estimation, and characterization of damage. One common configuration for such a system uses a spatially-distributed array of fixed piezoelectric transducers, which is inexpensive and can cover large areas. Typically, one or more sets of prerecorded baseline signals are measured when the structure is in a known state, with imaging methods operating on differences between follow-up measurements and these baselines. Presented here is a new class of SHM spatially-distributed array algorithms that rely on sparse reconstruction. For this problem, damage over a region of interest (ROI) is considered to be sparse. Two different techniques are demonstrated here. The first, which relies on sparse reconstruction, uses an a priori assumption of scattering behavior to generate a redundant dictionary where each column corresponds to a pixel in the ROI. The second method extends this concept by using multidimensional models for each pixel, with each pixel corresponding to a "block" in the dictionary matrix; this method does not require advance knowledge of scattering behavior. Analysis and experimental results presented demonstrate the validity of the sparsity assumption. Experiments show that images generated with sparse methods are superior to those created with delay-and-sum methods; the techniques here are shown to be tolerant of propagation model mismatch. The block-sparse method described here also allows the extraction of scattering patterns, which can be used for damage characterization.Ph.D

Acoustic emission testing and acousto-ultrasonics for structural health monitoring

The global trends in the construction of modern structures require the integration of sensors together with data recording and analysis modules so that its integrity can be continuously monitored for safe-life, economic and ecological reasons. This process of measuring and analysing the data from a distributed sensor network all over a structural system in order to quantify its condition is known as structural health monitoring (SHM). The research presented in this thesis is motivated by the need to improve the inspection capabilities and reliability of SHM systems based on ultrasonic guided waves with focus on the acoustic emission and acousto-ultrasonics techniques. The use of a guided wave-based approach is driven by the fact that these waves are able to propagate over relatively long distances, interact sensitively with and/or being related to different types of defect. The main emphasis of the thesis is concentrated on the development of different methodologies based on signal analysis together with the fundamental understanding of wave propagation for the solution of problems such as damage detection, localisation and identification. The behaviour of guided waves for both techniques is predicted through modelling in order to investigate the characteristics of the modes being propagated throughout the evaluated structures and support signal analysis. The validity of the developed model is extensively investigated by contrasting numerical simulations and experiments. In this thesis special attention is paid to the development of efficient SHM methodologies. This fact requires robust signal processing techniques for the correct interpretation of the complex ultrasonic waves. Therefore, a variety of existing algorithms for signal processing and pattern recognition are evaluated and integrated into the different proposed methodologies. Additionally, effects such as temperature variability and operational conditions are experimentally studied in order to analyse their influence on the performance of developed methodologies. At the end, the efficiency of these methodologies are experimentally evaluated in diverse isotropic and anisotropic composite structures.Nach den heutigen Standards zur Konstruktion moderner Leichtbaustrukturen ist es zur Strukturüberwachung aufgrund von wirtschaftlichen, ökologischen und Sicherheitsaspekten unerlässlich, Sensoren und Module zur Datenspeicherung und –analyse in diese Strukturen zu integrieren. Den Prozess der Strukturüberwachung anhand der Messung und Analyse von Daten aus einem dezentralen Sensornetzwerk wird als „Structural Health Monitoring (SHM)“ bezeichnet. Die vorliegende Arbeit und die darin vorgestellten Untersuchungen reagieren auf den Bedarf an verbesserter Genauigkeit und höherer Zuverlässigkeit von SHM-Systemen, die auf geführten Ultraschallwellen basieren, wobei der Fokus der Untersuchung auf Schallemissions- und Acousto-Ultraschalltechniken liegt. Da geführte Wellen lange Wege zurückzulegen können und mit hoher Empfindlichkeit und Genauigkeit auf verschiedene Schadenstypen reagieren, eignen sie sich sehr gut für die Überwachung dünnwandiger Strukturen. Der Schwerpunkt der Arbeit liegt in der Entwicklung verschiedener Methoden zur Signalanalyse zur Lösung von Problemen wie Schadenserkennung, lokalisierung und identifizierung. Dies ist nicht ohne ein grundlegendes Verständnis der Wellenausbreitungsmechanismen möglich, sodass ein Modell entwickelt wird, anhand dessen die Charakteristiken der angeregten Moden sowie die Wellenausbreitung in den zu untersuchenden Strukturen analysiert werden können, um so die Signalanalyse zu unterstützen. Die Validität des entwickelten Modells wird eingehend anhand von verschiedenen numerischen Simulationen und Experimenten untersucht. Um besonders effiziente Methoden des SHMs zu entwickeln, sind robuste Signalverarbeitungstechniken zur zuverlässigen Interpretation komplexer Ultraschallwellen notwending. Aus diesem Grund erfolgt die Auswertung einer Vielzahl existierender Algorithmen zur Signalverarbeitung und Mustererkennung, die in die hier vorgestellten Methoden integriert werden. Des Weiteren wird experimentell untersucht, welchen Einfluss Effekte wie Temperaturschwankungen und Betriebsbedingungen auf diese Methoden haben. Abschließend wird experimentell die Effizienz der entwickelten Methoden bei der Überwachung diverser isotroper und anisotroper Faserverbundstrukturen nachgewiesen

Model-based Filtering of Interfering Signals in Ultrasonic Time Delay Estimations

In dieser Arbeit werden modellbasierte algorithmische Ansätze zur Interferenz-invarianten Zeitverschiebungsschätzung vorgestellt, die speziell für die Schätzung kleiner Zeitverschiebungsdifferenzen mit einer notwendigen Auflösung, die deutlich unterhalb der Abtastzeit liegt, geeignet sind. Daher lassen sich die Verfahren besonders gut auf die Laufzeit-basierte Ultraschalldurchflussmessung anwenden, da hier das Problem der Interferenzsignale besonders ausgeprägt ist. Das Hauptaugenmerk liegt auf der Frage, wie mehrere Messungen mit unterschiedlichen Zeitverschiebungen oder Prozessparametern zur Unterdrückung der Interferenzsignale in Ultraschalldurchflussmessungen verwendet werden können, wobei eine gute Robustheit gegenüber additivem weißen Gauß\u27schen Rauschen und eine hohe Auflösung erhalten bleiben sollen. Zu diesem Zweck wird ein Signalmodell angenommen, welches aus stationären Interferenzsignalen, die nicht von wechselnden Zeitverschiebungen abhängig sind, und aus Zielsignalen, die den Messeffekt enthalten, besteht. Zunächst wird das Signalmodell einer Ultraschalldurchflussmessung und sein dynamisches Verhalten bei Temperatur- oder Zeitverschiebungsschwankungen untersucht. Ziel ist es, valide Simulationsdatensätze zu erzeugen, mit denen die entwickelten Methoden sowohl unter der Prämisse, dass die Daten perfekt zum Signalmodell passen, als auch unter der Prämisse, dass Modellfehler vorliegen, getestet werden können. Dabei werden die Eigenschaften der Signalmodellkomponenten, wie Bandbreite, Stationarität und Temperaturabhängigkeit, identifiziert. Zu diesem Zweck wird eine neue Methode zur Modellierung der Temperaturabhängigkeit der Interferenzsignale vorgestellt. Nach der Charakterisierung des gesamten Messsystems wird das Signalmodell -- angepasst an die Ultraschalldurchflussmessung -- als Grundlage für zwei neue Methoden verwendet, deren Ziel es ist, die Auswirkungen der Interferenzsignale zu reduzieren. Die erste vorgeschlagene Technik erweitert die auf der Signaldynamik basierenden Ansätze in der Literatur, indem sie die Voraussetzungen für die erforderliche Varianz der Zeitverschiebungen abschwächt. Zu diesem Zweck wird eine neue Darstellung von mehreren Messsignalen als Punktwolken eingeführt. Die Punktwolken werden dann mithilfe der Hauptkomponentenanalyse und B-Splines verarbeitet, was entweder zu Interferenz-invarianten Zeitverschiebungsschätzungen oder geschätzten Interferenzsignalen führt. In diesem Zusammenhang wird eine neuartige gemeinsame B-Spline- und Registrierungsschätzung entwickelt, um die Robustheit zu erhöhen. Der zweite Ansatz besteht in einer regressionsbasierten Schätzung der Zeitverschiebungsdifferenzen durch das Erlernen angepasster Signalunterräume. Diese Unterräume werden effizient durch die Analytische Wavelet Packet Transformation berechnet, bevor die resultierenden Koeffizienten in Merkmale transformiert werden, die gut mit den Zeitverschiebungssdifferenzen korrelieren. Darüber hinaus wird ein neuartiger, unbeaufsichtigter Unterraum-Trainingsansatz vorgeschlagen und mit den konventionellen Filter- und Wrapper-basierten Merkmalsauswahlmethoden verglichen. Schließlich werden beide Methoden in einem experimentellen Ultraschalldurchflussmesssystem mit einem hohen Maß an vorhandenen Interferenzsignalen getestet, wobei sich zeigt, dass sie in den meisten Fällen den Methoden aus der Literatur überlegen sind. Die Qualität der Methoden wird anhand der Genauigkeit der Zeitverschiebungsschätzung bewertet, da die Grundwahrheit für die Interferenzsignale nicht zuverlässig bestimmt werden kann. Anhand verschiedener Datensätze werden die Abhängigkeiten von den Hyperparametern, den Prozessbedingungen und, im Falle der regressionsbasierten Methode, dem Trainingsdatensatz analysiert

Model-based Filtering of Interfering Signals in Ultrasonic Time Delay Estimations

This work presents model-based algorithmic approaches for interference-invariant time delay estimation, which are specifically suited for the estimation of small time delay differences with a necessary resolution well below the sampling time. Therefore, the methods can be applied particularly well for transit-time ultrasonic flow measurements, since the problem of interfering signals is especially prominent in this application

Denoising autoencoder in damage detection of pipeline using guided ultrasonic wave

Pipeline condition monitoring is essential in critical sectors such as the petrochemical, nuclear and energy sectors. The guided ultrasonic wave (GUW) monitoring system is an available pipeline condition monitoring system that is gaining much attention owing to its portability, long coverage and high sensitivity to damage. However, environmental and operational conditions (EOCs) effects, especially temperature and random noise may generate unwanted peaks, which are falsely identified as damage. Attempts to deal with EOC effects have not solved the problem, especially for small damage cases (damage equal to or less than 5% cross sectional area loss (CSAL)). In this study, a new damage feature extraction method based on the residual reliability criterion (RRC) is proposed. The performance of the proposed method is measured using the established receiver operating characteristics (ROCs) performance evaluation method. The findings show that this method performs well, with an AUC value greater than 0.9, based on numerical model under 40 ? variations and 10% random noise level, and that the application of RRC is intuitively simple. To ensure the practicality of the method, a 6 metre long, 8 inches diameter experimental pipe model filled with liquid is used to form a GUW database of small damage under 30 ? variations by using Torsional T(0,1) excitation mode at 26 kHz centre frequency. However, the RRC underperformed when experimental data is used because the random noise generated by healthy and damaged signals interferes and generates high amplitude noise. Therefore, this study proposed a denoising autoencoder (DAE) neural network to deal with the effects of EOCs. A DAE decodes high-dimensional data into low-dimensional features and reconstructs the original data from these low-dimensional features. By providing GUW signals at a reference temperature, this structure forces the DAE to learn the essential features hidden within complex data. The proposed DAE showed perfect detection (AUC value of 1.0) using numerical model and performs well (AUC greater than 0.9) using experimental model in terms of small damage identification. Moreover, the proposed method showed superiority among other advanced EOC compensation techniques using both numerical and experimental models

Model-based Filtering of Interfering Signals in Ultrasonic Time Delay Estimations

This work presents model-based algorithmic approaches for interference-invariant time delay estimation, which are specifically suited for the estimation of small time delay differences with a necessary resolution well below the sampling time. Therefore, the methods can be applied particularly well for transit-time ultrasonic flow measurements, since the problem of interfering signals is especially prominent in this application