On acoustic emission for failure investigation in CFRP: Pattern recognition and peak frequency analyses
TL;DR: In this article, the authors investigated failure in Carbon Fibre Reinforced Plastics CFRP using Acoustic Emission (AE) signals collected and post-processed for various test configurations: tension, Compact Tension (CT), Compact Compression (CC), Double Cantilever Beam (DCB), and four-point bend End Notched Flexure (4-ENF).
About: This article is published in Mechanical Systems and Signal Processing.The article was published on 2011-05-01. It has received 457 citations till now. The article focuses on the topics: Acoustic emission.
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TL;DR: The technical comparative analysis of the different physical and material based types of HSSs illustrates the paradoxical inherent features, including gravimetric and volumetric storage densities and parameters associated with storage and release processes, among these systems.
Abstract: Hydrogen storage systems (HSSs), are the backbone of feasible hydrogen economy. To provide a reliable renewable energy system, safe, cost effective and compact HSS is due. Physical storage systems involve the compressed gas, liquid and cryo-compressed techniques while material based one involves adsorptive materials, metal hydrides and chemical storage materials. In this paper, the features of a variety of HSSs are impartially discussed. The technical comparative analysis of the different physical and material based types of HSSs illustrates the paradoxical inherent features, including gravimetric and volumetric storage densities and parameters associated with storage and release processes, among these systems. Accordingly, no ideal hydrogen storage technique can be considered the best-fit for all stationary and automotive applications. Therefore, not only a unique HSS solution can properly provide the needs, but a set of complementary HSS solutions which may offer the system designer several options. This set of options can be hardly interpretable in case of the unclear definition of the application needs which may be time variant. Inside this review, the critical insights and recommendations about suitable applications for storage systems are provided. Different standards and codes alongside the corresponding tests are demonstrated for the different storage technologies. Moreover, storage vessels research work is overviewed for the different hydrogen storage technologies. In addition, the failure behaviour, criteria and prediction models are investigated for composite vessels subjected to high pressures and extreme temperatures degrading their mechanical behaviour and failure resistance.
232 citations
TL;DR: In this article, the authors adopted the acoustic emission technique to study the failure mechanisms and damage evolution of carbon fiber/epoxy composite laminates, and studied the effects of different lay-up patterns and hole sizes on the acoustic response.
177 citations
TL;DR: A comprehensive review on the use of acoustic emission (AE) for damage characterization in laminated composites is presented in this paper, where the authors discuss the literature for damage diagnostics and damage type identification and damage localization.
Abstract: Damage characterization of laminated composites has been thoroughly studied the last decades where researchers developed several damage models, and in combination with experimental evidence, contributed to better understanding of the structural behavior of these structures. Experimental techniques played an essential role on this progress and among the techniques that were utilized, acoustic emission (AE) was extensively used due to its advantages for in-situ damage monitoring with high sensitivity and its capability to inspect continuously a relatively large area. This paper presents a comprehensive review on the use of AE for damage characterization in laminated composites. The review is divided into two sections; the first section discusses the literature for damage diagnostics and it is presented in three subsections: damage initiation detection, damage type identification and damage localization, while the second section is devoted to damage prognostics and it focuses on the remaining useful life (RUL) and residual strength prediction of composite structures using AE data. In every section, efforts have been made to analyze the most relevant literature, discuss in a critical manner the results and conclusions, and identify possibilities for future work.
175 citations
TL;DR: In this paper, peak amplitude and peak frequency were selected as the best cluster-definition features from nine AE parameters by Laplacian score and correlation analysis, principal component analysis and k-means++ algorithm and repeatability and similarity analysis of the clusters in AE registration of different specimens.
174 citations
TL;DR: In this article, the authors investigated the use of acoustic emission (AE) to locate and classify the type of damage occurring in a composite, carbon fiber panel during buckling using delta-T mapping.
Abstract: Classifying the type of damage occurring within a structure using a structural health monitoring system can allow the end user to assess what kind of repairs, if any, that a component requires. This paper investigates the use of acoustic emission (AE) to locate and classify the type of damage occurring in a composite, carbon fibre panel during buckling. The damage was first located using a bespoke location algorithm developed at Cardiff University, called delta-T mapping. Signals identified as coming from the regions of damage were then analysed using three AE classification techniques; Artificial Neural Network (ANN) analysis, Unsupervised Waveform Clustering (UWC) and corrected Measured Amplitude Ratio (MAR). A comparison of results yielded by these techniques shows a strong agreement regarding the nature of the damage present in the panel, with the signals assigned to two different damage mechanisms, believed to be delamination and matrix cracking. Ultrasonic C-scan images and a digital image correlation (DIC) analysis of the buckled panel were used as validation. MAR’s ability to reveal the orientation of recorded signals greatly assisted the identification of the delamination region, however, ANN and UWC have the ability to group signals into several different classes, which would prove useful in instances where several damage mechanisms were generated. Combining each technique’s individual merits in a multi-technique analysis dramatically improved the reliability of the AE investigation and it is thought that this cross-correlation between techniques will also be the key to developing a reliable SHM system.
167 citations
Cites background from "On acoustic emission for failure in..."
...Summarised by Gutkin [10], early approaches looked simply to classify damage based on a single AE parameter, such as the study by Valentin et al. [11] which used the peak amplitude of the waveforms to distinguish between matrix cracking and fibre break signals....
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...Summarised by Gutkin [10], early approaches looked simply to classify damage based on a single AE parameter, such as the study by Valentin et al....
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References
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01 Sep 1990
TL;DR: The self-organizing map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications, and an algorithm which order responses spatially is reviewed, focusing on best matching cell selection and adaptation of the weight vectors.
Abstract: The self-organized map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications. The self-organizing map has the property of effectively creating spatially organized internal representations of various features of input signals and their abstractions. One result of this is that the self-organization process can discover semantic relationships in sentences. Brain maps, semantic maps, and early work on competitive learning are reviewed. The self-organizing map algorithm (an algorithm which order responses spatially) is reviewed, focusing on best matching cell selection and adaptation of the weight vectors. Suggestions for applying the self-organizing map algorithm, demonstrations of the ordering process, and an example of hierarchical clustering of data are presented. Fine tuning the map by learning vector quantization is addressed. The use of self-organized maps in practical speech recognition and a simulation experiment on semantic mapping are discussed. >
7,883 citations
TL;DR: The two-stage procedure--first using SOM to produce the prototypes that are then clustered in the second stage--is found to perform well when compared with direct clustering of the data and to reduce the computation time.
Abstract: The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In this paper, different approaches to clustering of the SOM are considered. In particular, the use of hierarchical agglomerative clustering and partitive clustering using K-means are investigated. The two-stage procedure-first using SOM to produce the prototypes that are then clustered in the second stage-is found to perform well when compared with direct clustering of the data and to reduce the computation time.
2,387 citations
Book•
01 May 1988
TL;DR: In this paper, competitive learning is applied to parallel networks of neuron-like elements to discover salient, general features which can be used to classify a set of stimulus input patterns, and these feature detectors form the basis of a multilayer system that serves to learn categorizations of stimulus sets which are not linearly separable.
Abstract: This paper reporis the results of our studies with an unsupervised learning paradigm which we have called “Competitive Learning” We have examined competitive learning using both computer simulation and formal analysis and hove found that when it is applied to parallel networks of neuron-like elements, many potentially useful learning tasks can be accomplished We were attracted to competitive learning because it seems to provide o way to discover the salient, general features which can be used to classify o set of patterns We show how o very simply competitive mechanism con discover a set of feature detectors which capture important aspects of the set of stimulus input patterns We 0150 show how these feature detectors con form the basis of o multilayer system that con serve to learn categorizations of stimulus sets which ore not linearly separable We show how the use of correlated stimuli con serve IX o kind of “teaching” input to the system to allow the development of feature detectors which would not develop otherwise Although we find the competitive learning mechanism o very interesting and powerful learning principle, we do not, of course, imagine thot it is the only learning principle Competitive learning is cm essentially nonassociative stotisticol learning scheme We certainly imagine that other kinds of learning mechanisms will be involved in the building of associations among patterns of activation in o more complete neural network We offer this analysis of these competitive learning mechanisms to further our understanding of how simple adaptive networks can discover features importont in the description of the stimulus environment in which the system finds itself
1,319 citations
TL;DR: This paper shows how a set of feature detectors which capture important aspects of the set of stimulus input patterns are discovered and how these feature detectors form the basis of a multilayer system that serves to learn categorizations of stimulus sets which are not linearly separable.
881 citations