Damage classification in reinforced concrete beam by acoustic emission signal analysis

Acoustic Emission (AE) is a non-destructive testing technique which can be used to identify both the damage level and the nature of that damage such as tensile cracks and shear movements at critical zones within a structure. In this work, the acoustic emission parameters of amplitude, rise time, average frequency and signal strength were used to classify the damage and to determine the damage level. Laboratory experiments were performed on a beam (150 x 250 x 1900 mm). The acoustic emission analysis was successfully used to determine crack movements and classify damage levels in accordance with the observations made during an increasing loading cycle

Optimized placement of parasitic vibration energy harvesters for autonomous structural health monitoring

Energy harvesting, based on sources including vibration and thermal gradients, has been exploited in recent years to power telemetry, small devices, or to charge batteries or capacitors. Generating the higher levels of power which have thus far been required to run sensor systems such as those needed for structural health monitoring has been more challenging. In addition, harvesters such as those required to capture vibration often require additional elements (e.g. cantilevers) to be added to the structure and harvest over a relatively narrow band of frequencies. In aerospace applications, where weight is at a premium and vibrations occur over a broader range of frequencies, this is non-ideal. With the advent of new, lower power monitoring systems, the potential for energy harvesting to be utilized is significantly increased. This article optimizes the placement of a set of parasitic piezoelectric patches to harvest over the broad band of frequencies found in an aircraft wing and validates the results experimentally. Results are compared with the requirements of a low-power structural health monitoring system, with a closing of the gap between the energy generated and that required being demonstrated

Acoustic emission source location in complex structures using full automatic delta T mapping technique

An easy to use, fast to apply, cost-effective, and very accurate non-destructive testing (NDT) technique for damage localisation in complex structures is key for the uptake of structural health monitoring systems (SHM). Acoustic emission (AE) is a viable technique that can be used for SHM and one of the most attractive features is the ability to locate AE sources. The time of arrival (TOA) technique is traditionally used to locate AE sources, and relies on the assumption of constant wave speed within the material and uninterrupted propagation path between the source and the sensor. In complex structural geometries and complex materials such as composites, this assumption is no longer valid. Delta T mapping was developed in Cardiff in order to overcome these limitations; this technique uses artificial sources on an area of interest to create training maps. These are used to locate subsequent AE sources. However operator expertise is required to select the best data from the training maps and to choose the correct parameter to locate the sources, which can be a time consuming process. This paper presents a new and improved fully automatic delta T mapping technique where a clustering algorithm is used to automatically identify and select the highly correlated events at each grid point whilst the “Minimum Difference” approach is used to determine the source location. This removes the requirement for operator expertise, saving time and preventing human errors. A thorough assessment is conducted to evaluate the performance and the robustness of the new technique. In the initial test, the results showed excellent reduction in running time as well as improved accuracy of locating AE sources, as a result of the automatic selection of the training data. Furthermore, because the process is performed automatically, this is now a very simple and reliable technique due to the prevention of the potential source of error related to manual manipulation

Characterisation of fatigue damage in composites using an Acoustic Emission Parameter Correction Technique

In industrial applications of composite materials, accurate characterisation of damage is vital. Acoustic Emission (AE) can be utilised to achieve this, however, in large-scale complex geometry components, traditional AE approaches have limitations. In this study a large carbon fibre specimen was used to generate different damage mechanisms under fatigue loading. The Delta T Mapping technique was used to locate damage and signal features were corrected using the Parameter Correction Technique (PCT). A comparison between results obtained using traditional signal features and those obtained using PCT is given. The results are validated using C-scanning and computed tomography. Matrix cracking and delamination were successfully identified using the PCT approach and improved location accuracy was achieved

Buckling and postbuckling behaviour of Glare laminates containing splices and doublers. Part 1:Instrumented Tests

The progressive damage and fracture behaviour of Glare® fibre-metal laminates (FMLs) was investigated experimentally for the buckling and post- buckling regimes of laminates containing internal ‘splice’ and ‘doubler’ joints. Specimens were either ‘pristine’ or contained artificial delaminations in the form of strips of release film to represent manufacturing defects. Each was tested under in-plane compression. Tests were monitored using digital image correlation (DIC) for visualisation of three-dimensional full-field displacements whilst acoustic emission (AE) monitoring – combined with the novel Delta-T location algorithm – was used for the first time to detect and locate damage events in these FML structures. Results were validated using Scanning Electron Microscopy (SEM) to determine the damage mechanisms present. Large numbers of AE events were recorded at the splice and doubler locations during initial loading and throughout the postbuckling regime,suggesting that the novel AE location algorithm used is suitable for the monitoring of delaminations and matrix cracks in internal features in Glare® laminates. Moreover, AE events located away from internal features correlated well with buckling and postbuckling deformation as identified by the full- field DIC data. Finally, good correlation was observed between the onset of buckling and a rapid increase in cumulative AE energy, demonstrating that as well as locating damage, AE monitoring is able to indicate quite clearly when the buckling load has been reached

A new methodology for automating acoustic emission detection of metallic fatigue fractures in highly demanding aerospace environments: An overview

The acoustic emission (AE) phenomenon has many attributes that make it desirable as a structural health monitoring or non-destructive testing technique, including the capability to continuously and globally monitor large structures using a sparse sensor array and with no dependency on defect size. However, AE monitoring is yet to fulfil its true potential, due mainly to limitations in location accuracy and signal characterisation that often arise in complex structures with high levels of background noise. Furthermore, the technique has been criticised for a lack of quantitative results and the large amount of operator interpretation required during data analysis. This paper begins by introducing the challenges faced in developing an AE based structural health monitoring system and then gives a review of previous progress made in addresing these challenges. Subsequently an overview of a novel methodology for automatic detection of fatigue fractures in complex geometries and noisy environments is presented, which combines a number of signal processing techniques to address the current limitations of AE monitoring. The technique was developed for monitoring metallic landing gear components during pre-flight certification testing and results are presented from a full-scale steel landing gear component undergoing fatigue loading. Fracture onset was successfully identify automatically at 49,000 fatigue cycles prior to final failure (validated by the use of dye penetrant inspection) and the fracture position was located to within 10. mm of the actual location

Lupus-related single nucleotide polymorphisms and risk of diffuse large B-cell lymphoma

Objective: Determinants of the increased risk of diffuse large B-cell lymphoma (DLBCL) in SLE are unclear. Using data from a recent lymphoma genome-wide association study (GWAS), we assessed whether certain lupus-related single nucleotide polymorphisms (SNPs) were also associated with DLBCL. Methods: GWAS data on European Caucasians from the International Lymphoma Epidemiology Consortium (InterLymph) provided a total of 3857 DLBCL cases and 7666 general-population controls. Data were pooled in a random-effects meta-analysis. Results: Among the 28 SLE-related SNPs investigated, the two most convincingly associated with risk of DLBCL included the CD40 SLE risk allele rs4810485 on chromosome 20q13 (OR per risk allele=1.09, 95% CI 1.02 to 1.16, p=0.0134), and the HLA SLE risk allele rs1270942 on chromosome 6p21.33 (OR per risk allele=1.17, 95% CI 1.01 to 1.36, p=0.0362). Of additional possible interest were rs2205960 and rs12537284. The rs2205960 SNP, related to a cytokine of the tumour necrosis factor superfamily TNFSF4, was associated with an OR per risk allele of 1.07, 95% CI 1.00 to 1.16, p=0.0549. The OR for the rs12537284 (chromosome 7q32, IRF5 gene) risk allele was 1.08, 95% CI 0.99 to 1.18, p=0.0765. Conclusions: These data suggest several plausible genetic links between DLBCL and SLE

Genetically predicted longer telomere length is associated with increased risk of B-cell lymphoma subtypes

Evidence from a small number of studies suggests that longer telomere length measured in peripheral leukocytes is associated with an increased risk of non-Hodgkin lymphoma (NHL). However, these studies may be biased by reverse causation, confounded by unmeasured environmental exposures and might miss time points for which prospective telomere measurement would best reveal a relationship between telomere length and NHL risk. We performed an analysis of genetically inferred telomere length and NHL risk in a study of 10 102 NHL cases of the four most common B-cell histologic types and 9562 controls using a genetic risk score (GRS) comprising nine telomere length-associated single-nucleotide polymorphisms. This approach uses existing genotype data and estimates telomere length by weighing the number of telomere length-associated variant alleles an individual carries with the published change in kb of telomere length. The analysis of the telomere length GRS resulted in an association between longer telomere length and increased NHL risk [four B-cell histologic types combined; odds ratio (OR) = 1.49, 95% CI 1.22–1.82, P-value = 8.5 × 10−5]. Subtype-specific analyses indicated that chronic lymphocytic leukemia or small lymphocytic lymphoma (CLL/SLL) was the principal NHL subtype contributing to this association (OR = 2.60, 95% CI 1.93–3.51, P-value = 4.0 × 10−10). Significant interactions were observed across strata of sex for CLL/SLL and marginal zone lymphoma subtypes as well as age for the follicular lymphoma subtype. Our results indicate that a genetic background that favors longer telomere length may increase NHL risk, particularly risk of CLL/SLL, and are consistent with earlier studies relating longer telomere length with increased NHL risk

The psychological science accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data