47 research outputs found
Master of Science
thesisCarbon fiber-reinforced composite materials have been increasingly used in aerospace and aeronautics industries due to their superior strength over metals, low fatigue life, high corrosion resistance, and temperature resistance. Since most damage, such as delaminations, manifest inside the composite material, we often cannot detect damage through visual inspection. As a replacement for visual inspection, ultrasonic guided waves have been widely researched to remotely detect, locate, and characterize damage in structures due to their unique capability to travel long distances and inspect inaccessible locations for damage. Yet the anisotropic nature of composites makes it difficult to identify the velocity characteristics of the guided waves and utilize them for damage localization. To address this challenge, we use sparse wavenumber analysis to determine anisotropic multimodal and dispersive frequency-wavenumber characteristics of guided waves. We then use these multimodal and dispersive properties to predict how guided waves propagate in the anisotropic plate through sparse wavenumber synthesis. Finally, these predictions, which form a wave propagation model for the composite, are integrated with matched field processing, a model-based localization framework, to locate damage on the composite
Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities
The analysis of ultrasonic NDE data has traditionally been addressed by a
trained operator manually interpreting data with the support of rudimentary
automation tools. Recently, many demonstrations of deep learning (DL)
techniques that address individual NDE tasks (data pre-processing, defect
detection, defect characterisation, and property measurement) have started to
emerge in the research community. These methods have the potential to offer
high flexibility, efficiency, and accuracy subject to the availability of
sufficient training data. Moreover, they enable the automation of complex
processes that span one or more NDE steps (e.g. detection, characterisation,
and sizing). There is, however, a lack of consensus on the direction and
requirements that these new methods should follow. These elements are critical
to help achieve automation of ultrasonic NDE driven by artificial intelligence
such that the research community, industry, and regulatory bodies embrace it.
This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by
DL methodologies. The review is organised by the NDE tasks that are addressed
by means of DL approaches. Key remaining challenges for each task are noted.
Basic axiomatic principles for DL methods in NDE are identified based on the
literature review, relevant international regulations, and current industrial
needs. By placing DL methods in the context of general NDE automation levels,
this paper aims to provide a roadmap for future research and development in the
area.Comment: Accepted version to be published in NDT & E Internationa
Compressive Sensing and Imaging of Guided Ultrasonic Wavefields
Structural health monitoring (SHM) and Nondestructive Evaluation (NDE) technologies can be used to predict the structural remaining useful life through appropriate diagnosis and prognosis methodologies. The main goal is the detection and characterization of defects that may compromise the integrity and the operability of a structure. The use of Lamb waves, which are ultrasonic guided waves (GW), have shown potential for detecting damage in specimens as a part of SHM or NDT systems. These methods can play a significant role in monitoring and tracking the integrity of structures by estimating the presence, location, severity, and type of damage. One of the advantages of GW is their capacity to propagate over large areas with excellent sensitivity to a variety of damage types while guaranteeing a short wavelength, such that the detectability of large structural damages is guaranteed.
The Guided ultrasonic wavefield imaging (GWI) is an advanced technique for Damage localization and identification on a structure. GWI is generally referred to as the analysis of a series of images representing the time evolution of propagating waves and, possibly, their interaction with defects. This technique can provide useful insights into the structural conditions. Nowadays, high-resolution wavefield imaging has been widely studied and applied in damage identification. However, full wavefield imaging techniques have some limitations, including slow data acquisition and lack of accuracy.
The objectives of this dissertation are to develop novel and high resolution Guided Wavefield Imaging techniques able to detect defects in metals and composite materials while reducing the acquisition time without losing in detection accuracy
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
Compressed Sensing for Open-ended Waveguide Non-Destructive Testing and Evaluation
Ph. D. ThesisNon-destructive testing and evaluation (NDT&E) systems using open-ended waveguide (OEW) suffer from critical challenges. In the sensing stage, data acquisition is time-consuming by raster scan, which is difficult for on-line detection. Sensing stage also disregards demand for the latter feature extraction process, leading to an excessive amount of data and processing overhead for feature extraction. In the feature extraction stage, efficient and robust defect region segmentation in the obtained image is challenging for a complex image background. Compressed sensing (CS) demonstrates impressive data compression ability in various applications using sparse models. How to develop CS models in OEW NDT&E that jointly consider sensing & processing for fast data acquisition, data compression, efficient and robust feature extraction is remaining challenges.
This thesis develops integrated sensing-processing CS models to address the drawbacks in OEW NDT systems and carries out their case studies in low-energy impact damage detection for carbon fibre reinforced plastics (CFRP) materials. The major contributions are:
(1) For the challenge of fast data acquisition, an online CS model is developed to offer faster data acquisition and reduce data amount without any hardware modification. The images obtained with OEW are usually smooth which can be sparsely represented with discrete cosine transform (DCT) basis. Based on this information, a customised 0/1 Bernoulli matrix for CS measurement is designed for downsampling. The full data is reconstructed with orthogonal matching pursuit algorithm using the downsampling data, DCT basis, and the customised 0/1 Bernoulli matrix. It is hard to determine the sampling pixel numbers for sparse reconstruction when lacking training data, to address this issue, an accumulated sampling and recovery process is developed in this CS model. The defect region can be extracted with the proposed histogram threshold edge detection (HTED) algorithm after each recovery, which forms an online process. A case study in impact damage detection on CFRP materials is carried out for validation. The results show that the data acquisition time is reduced by one order of magnitude while maintaining equivalent image quality and defect region as raster scan.
(2) For the challenge of efficient data compression that considers the later feature extraction, a feature-supervised CS data acquisition method is proposed and evaluated. It reserves interested
features while reducing the data amount. The frequencies which reveal the feature only occupy a small part of the frequency band, this method finds these sparse frequency range firstly to supervise the later sampling process. Subsequently, based on joint sparsity of neighbour frame and the extracted frequency band, an aligned spatial-spectrum sampling scheme is proposed. The scheme only samples interested frequency range for required features by using a customised 0/1 Bernoulli measurement matrix. The interested spectral-spatial data are reconstructed jointly, which has much faster speed than frame-by-frame methods. The proposed feature-supervised CS data acquisition is implemented and compared with raster scan and the traditional CS reconstruction in impact damage detection on CFRP materials. The results show that the data amount is reduced greatly without compromising feature quality, and the gain in reconstruction speed is improved linearly with the number of measurements.
(3) Based on the above CS-based data acquisition methods, CS models are developed to directly detect defect from CS data rather than using the reconstructed full spatial data. This method is robust to texture background and more time-efficient that HTED algorithm. Firstly, based on the histogram is invariant to down-sampling using the customised 0/1 Bernoulli measurement matrix, a qualitative method which only gives binary judgement of defect is developed. High probability of detection and accuracy is achieved compared to other methods. Secondly, a new greedy algorithm of sparse orthogonal matching pursuit (spOMP)-based defect region segmentation method is developed to quantitatively extract the defect region, because the conventional sparse reconstruction algorithms cannot properly use the sparse character of correlation between the measurement matrix and CS data. The proposed algorithms are faster and more robust to interference than other algorithms.China Scholarship Counci
The Application of PSO in Structural Damage Detection: An Analysis of the Previously Released Publications (2005–2020)
The structural health monitoring (SHM) approach plays a key role not only in structural engineering but also in other various engineering disciplines by evaluating the safety and performance monitoring of the structures. The structural damage detection methods could be regarded as the core of SHM strategies. That is because the early detection of the damages and measures to be taken to repair and replace the damaged members with healthy ones could lead to economic advantages and would prevent human disasters. The optimization-based methods are one of the most popular techniques for damage detection. Using these methods, an objective function is minimized by an optimization algorithm during an iterative procedure. The performance of optimization algorithms has a significant impact on the accuracy of damage identification methodology. Hence, a wide variety of algorithms are employed to address optimization-based damage detection problems. Among different algorithms, the particle swarm optimization (PSO) approach has been of the most popular ones. PSO was initially proposed by Kennedy and Eberhart in 1995, and different variants were developed to improve its performance. This work investigates the objectives, methodologies, and results obtained by over 50 studies (2005-2020) in the context of the structural damage detection using PSO and its variants. Then, several important open research questions are highlighted. The paper also provides insights on the frequently used methodologies based on PSO, the computational time, and the accuracy of the existing methodologies
Guided-wave structural health monitoring
Guided-wave (GW) approaches have shown potential in various initial
laboratory demonstrations as a solution to structural health
monitoring (SHM) for damage prognosis. This thesis starts with an
introduction to and a detailed survey of this field. Some critical
areas where further research was required and those that were chosen
to be addressed herein are highlighted. Those were modeling, design
guidelines, signal processing and effects of elevated temperature.
Three-dimensional elasticity-based models for GW excitation and
sensing by finite dimensional surface-bonded piezoelectric wafer
transducers and anisotropic piezocomposites are developed for various
configurations in isotropic structures. The validity of these models
is extensively examined in numerical simulations and experiments.
These models and other ideas are then exploited to furnish a set of
design guidelines for the excitation signal and transducers in GW SHM
systems. A novel signal processing algorithm based on chirplet
matching pursuits and mode identification for pulse-echo GW SHM is
proposed. The potential of the algorithm to automatically resolve and
identify overlapping, multimodal reflections is discussed and explored
with numerical simulations and experiments. Next, the effects of
elevated temperature as expected in internal spacecraft structures on
GW transduction and propagation are explored based on data from the
literature incorporated into the developed models. Results from the
model are compared with experiments. The feasibility of damage
characterization at elevated temperatures is also investigated. An
extension of the modeling effort for GW excitation by
finite-dimensional piezoelectric wafer transducers to composite plates
is also proposed and verified by numerical simulations. At the end,
future directions for research to make this technology more easily
deployable in field applications are suggested.Ph.D.Aerospace EngineeringUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/77498/1/Raghavan_PhD_thesis_GWSHM.pd
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Lamb Wave Mode Decomposition and Its Applications in Structural Health Monitoring
Structural health monitoring (SHM) systems perform automated non-destructive damage detection and characterization for a variety of large structures including civil structures such as bridges and aerospace structures such as aircrafts and space vehicles. The goals of SHM include preventing catastrophic structural failures, increasing reliability, reducing maintenance costs, and increasing the useful life span of the structures monitored by the systems. Guided waves have been extensively studied as means for monitoring the state of large structures. Guided waves, such as Lamb waves, propagate within the thickness of the structure, and are sensitive to damage. This makes them attractive for use in SHM systems. However, they are characterized by complex propagation characteristics such as dispersion, and multimodal and frequency-dependent attenuation, which often complicate analysis. In my dissertation research, we developed and evaluated four important components of a reliable guided wave-based SHM system for aerospace structures made out of composite materials and metals. These are: 1. A cross Wigner-Ville distribution-based mode decomposition algorithm to separate overlapped modes in sensor signals. Separating the mode components in sensor signals has several applications in SHM. Algorithms (2) and (3) are two examples where separated mode components are used. 2. A sparse tomographic reconstruction algorithm based on decomposed mode components to estimate the extent of damage on the structure. Estimating the extent of damage allows us to reliably predict the remaining useful life of the structure. The anomaly-imaging algorithm estimates damage extent with accuracies comparable to manual ultrasonic inspection techniques such as C-scan when the sensor density is sufficiently high. 3. An algorithm to compensate for the effect of temperature on sensor signals. The damage characterization algorithm developed in (2) requires a set of baseline signals recorded on the structure before the introduction of damage. Temperature changes can introduce changes in sensor signals that maybe interpreted as damage. The temperature compensation algorithm will mitigate difficulties caused by such changes in sensor signals. 4. A baseline-free damage detection algorithm from sensor signals under varying environmental conditions. Baseline comparison methods for SHM in time-varying environments require training on data recorded from damaged structures. The baseline-free damage detection algorithm overcomes this challenge. The algorithm is trained using only signals acquired from the damage-free structures. The four algorithms presented in this dissertation have the potential to form the basis for the next generation of SHM systems for aerospace structures and provide unprecedented accuracy in terms of detecting damage and estimating its extent for better residual structural analysis. Such a system will facilitate safer air travel. In addition, it will hasten the transition from currently employed schedule-based maintenance to a condition-based maintenance strategy resulting in less downtime time and reduced maintenance costs for aerospace structures