Academic literature on the topic 'Acoustic emissions, structural health monitoring, pattern recognition'

Alkali-silica reaction has caused damage to concrete structures, endangering structural serviceability and integrity. This is of concern in sensitive structures such as nuclear power plants. In this study, acoustic emission (AE) was employed as a structural health monitoring strategy in large-scale, reinforced concrete specimens affected by alkali-silica reaction with differing boundary conditions resembling the common conditions found in nuclear containments. An agglomerative hierarchical algorithm was utilized to classify the AE data based on energy-frequency based features. The AE signals were transferred into the frequency domain and the energies in several frequency bands were calculated and normalized to the total energy of signals. Principle component analysis was used to reduce feature redundancy. Then the selected principal components were considered as features in an input of the pattern recognition algorithm. The sensor located in the center of the confined specimen registered the largest portion of AE energy release, while in the unconfined specimen the energy is distributed more uniformly. This confirms the results of the volumetric strain, which shows that the expansion in the confined specimen is oriented along the thickness of the specimen.

Purpose In recent years, the mechanical industries began to apply many investments in research and technological development to obtain efficient methods to analyze the integrity of structures and prevent disasters and/or accidents, ensuring people’s lives and preventing economic losses. Any structure, whether mechanical or aeronautical, before being put into use undergoes a structural integrity assessment and testing. In this case, non-destructive evaluations are performed, aiming to estimate the degree of safety and reliability of the structure. For this, there are techniques traditionally used such as ultrasonic inspection, X-ray, acoustic emission tests, among other techniques. The traditional techniques may even have a good instrumental apparatus and be well formulated for structural integrity assessment; however, these techniques cannot meet growing industrial needs, even more so when structures are in motion. The purpose of this paper is to demonstrate artificial immune systems (AISs), ate and strengthen the emergence of an innovative technological tool, the biological immune systems and AISs, and these are presented as computing methods in the field of structural health monitoring (SHM). Design/methodology/approach The concept of SHM is based on a fault detection mechanism used in industries, and in other applications, involving the observation of a structure or a mechanical system. This observation occurs through the dynamic response of periodic measurements, later related to the statistical analysis, determining the integrity of the system. This study aims to develop a methodology that identifies and classifies a signal in normal signals or in faults, using an algorithm based on artificial immunological systems, being the negative selection algorithm, and later, this algorithm classifies the failures in probabilities of failure and degree of fault severity. The results demonstrate that the proposed SHM is efficient and robust for prognosis and failure detection. Findings The present study aims to develop different fast access methodologies for the prognosis and detection of failures, classifying and judging the types of failures based on AISs. The authors declare that the present study was neither published in any other vehicle of scientific information nor is under consideration for publication in another scientific journal, and that this paper strictly followed the ethical procedures of research and publication as requested. Originality/value This study is original by the fact that conventional structural integrity monitoring methods need improvements, which intelligent computing techniques can satisfy. Intelligent techniques are tools inspired by natural and/or biological processes and belong to the field of computational intelligence. They present good results in problems of pattern recognition and diagnosis and thus can be adapted to solve problems of monitoring and identifying structural failures in mechanical and aeronautical engineering. Thus, the proposal of this study demonstrates and strengthens the emergence of an innovative technological tool, the biological immune system and the AIS, and these are presented as computation methods in the field of SHM in rotating systems – a topic not yet addressed in the literature.

In the context of Structural Health Monitoring, the Acoustic Emission Technique may be efficiently used to detect damage on aerospace structures. This study focuses on the development of a source identification algorithm to distinguish different acoustic emission events in aluminium sheets, which have been collected during experimental tests. The future goal will be the design of a Holistic Structural Health Monitoring System which will make the complete aircraft an intelligent structure able to diagnose its own structural damage based on the condition of the structure while maintaining safety.

Le contrôle de santé structural ou Structural Health Monitoring (SHM) des matériaux constitue une démarche fondamentale pour la maîtrise de la durabilité et de la fiabilité des structures en service. Au-delà des enjeux industriels et humains qui ne cessent de s’accroître en termes de sécurité et de fiabilité, le contrôle de santé doit faire face à des exigences de plus en plus élaborées. Les nouvelles stratégies de contrôle de santé doivent non seulement détecter et identifier l’endommagement mais aussi quantifier les différents phénomènes qui en sont responsables. Pour atteindre cet objectif, il est nécessaire d’accéder à une meilleure connaissance des processus d’endommagement. Par ailleurs, ceux-ci surviennent fréquemment sous l’effet de sollicitations mécaniques et environnementales. Ainsi, il est indispensable, d’une part, d’élaborer des méthodes de traitement des signaux permettant d’estimer les effets des conditions environnementales et opérationnelles, dans un contexte de l’analyse des événements précurseurs des mécanismes d’endommagement, et, d’autre part, de définir les descripteurs d’endommagement les plus adaptés à cette analyse. Cette étude propose donc des méthodes de traitement du signal permettant d’atteindre cet objectif, dans un premier temps, pour l’estimation des effets externes sur les ondes multidiffusées dans un contexte de contrôle de santé actif et, dans un second temps, pour l’extraction d’un indicateur d’endommagement à partir de l’analyse des signaux d’émission acoustique dans un contexte de contrôle de santé passif. Dans la première partie de ce travail, quatre méthodes de traitement du signal sont proposées. Celles-ci permettent de prendre en compte les variations des conditions environnementales dans la structure, qui dans le cadre de cette thèse, se sont limitées au cas particulier du changement de la température. En effet, les variations de température ont pour effet de modifier les propriétés mécaniques du matériau et par conséquent la vitesse de propagation des ondes ultrasonores. Ce phénomène entraîne alors une dilatation temporelle des signaux acoustiques qu’il convient d’estimer afin de suivre les variations de température. Quatre estimateurs de coefficients de dilatation sont alors étudiés : Il s’agit de l’intercorrélation à fenêtre glissante, utilisée comme méthode de référence, la méthode du stretching, l’estimateur à variance minimale et la transformée exponentielle. Les deux premières méthodes ont été déjà validées dans la littérature alors que les deux dernières ont été développées spécifiquement dans le cadre de cette étude. Par la suite, une évaluation statistique de la qualité des estimations est menée grâce à des simulations de Monte-Carlo utilisant des signaux de synthèse. Ces signaux sont basés sur un modèle de signal multidiffusé prenant en compte l’influence de la température. Une estimation sommaire de la complexité algorithmique des méthodes de traitement du signal complète également cette phase d’évaluation. Enfin, la validation expérimentale des méthodes d’estimation est réalisée sur deux types de matériaux : Tout d’abord, dans une plaque d’aluminium, milieu homogène dont les caractéristiques sont connues, puis, dans un second temps dans un milieu fortement hétérogène prenant la forme d’une plaque composite en verre/epoxy. Dans ces expériences, les plaques sont soumises à différentes températures dans un environnement thermique contrôlé. Les estimations de température sont alors confrontées à un modèle analytique décrivant le comportement du matériau. La seconde partie de ce travail concerne la caractérisation in situ des mécanismes d’endommagement par émission acoustique dans des matériaux hétérogènes. Les sources d’émission acoustique génèrent des signaux non stationnaires
Structural health monitoring (SHM) of materials is a fundamental measure to master thedurability and the reliability of structures in service. Beyond the industrial and human issuesever increasing in terms of safety and reliability, health monitoring must cope with demandsincreasingly sophisticated. New health monitoring strategies must not only detect and identifydamage but also quantify the various phenomena involved in it. To achieve this objective, itis necessary to reach a better understanding of the damage process. Moreover, they frequentlyoccur as a result of mechanical and environmental stresses. Thus, it is essential, first, to developsignal processing methods for estimating the effects of environmental and operational conditions,in the context of the analysis of precursor events of damage mechanisms, and on theother hand, to define the damage descriptors that are the most suitable to this analysis. Thisstudy proposes signal processing methods to achieve this goal. At first, to the estimation ofexternal effects on the scattered waves in an active health control context, in a second step, tothe extraction of a damage indicator from the signals analysis of acoustic emission in a passivehealth monitoring context.In the first part of this work, four signal processing methods are proposed. These allow takinginto account the variation of environmental conditions in the structure, which in this thesis,were limited to the particular case of temperature change. Indeed, temperature changes have theeffect of altering the mechanical properties of the material and therefore the propagation velocityof ultrasonic waves. This phenomenon then causes a dilation of the acoustic signals that shouldbe estimated in order to monitor changes in temperature. Four estimators of dilation coefficientsare then studied: the intercorrelation sliding window, used as reference method, the stretchingmethod, the minimum variance estimator and the exponential transform. The first two methodshave already been validated in the literature while the latter two were developed specificallyin the context of this study. Thereafter, a statistical evaluation of the quality of estimates isconducted through Monte Carlo simulations using synthetic signals. These signals are basedon a scattered signal model taking into account the influence of temperature. A raw estimateof the computational complexity of signal processing methods also completes this evaluationphase. Finally, the experimental validation of estimation methods is performed on two types ofmaterial: First, in an aluminum plate, homogeneous medium whose characteristics are known,then, in a second step in a highly heterogeneous environment in the form of a compositeglass/epoxy plate. In these experiments, the plates are subjected to different temperatures in acontrolled thermal environment. The temperature estimates are then faced with an analyticalmodel describing the material behavior.The second part of this work concerns in situ characterization of damage mechanisms byacoustic emission in heterogeneous materials. Acoustic emission sources generate non-stationarysignals. The Hilbert-Huang transform is thus proposed for the discrimination of signals representativeof four typical sources of acoustic emission in composites: matrix cracking, debondingfiber/matrix, fiber breakage and delamination. A new time-frequency descriptor is then definedfrom the Hilbert-Huang transform and is introduced into an online classification algorithm. Amethod of unsupervised classification, based on the k-means method, is then used to discriminatethe sources of acoustic emission and the data segmentation quality is evaluated. Thesignals are recorded from blank samples, using piezoelectric sensors stuck to the surface of thematerial and sensitive samples (sensors integrated within the material)