AE Data Research Articles

Optimization of Acoustic Emission Data Clustering by a Genetic Algorithm Method

The segmentation of acoustic emission data collected during mechanical tests is one of the current challenges to allow further analysis of damaged materials. Among the existing clustering methods, one of the most widely used is the k-means algorithm. In this paper, a genetic algorithm-based approach is presented. Data sets derived from experimental AE data are processed to highlight the contributions of the new algorithm. Its superiority over the k-means algorithm is demonstrated for several data sets, and especially when a cluster is significantly smaller than the others, or very far and thus behaves as a group of outliers or if the clusters have very different sizes. This method allows the better clustering of AE data even on complex data sets.

Can Silicon Retina Sensors be used for optical motion analysis in sports?

Currently optical motion analysis outside of clinical or laboratory setups is a tedious procedure as controlled light conditions, correct camera setup and diffcult post processing of video data are required to ensure a correct marker tracking. In this project we will for the first time investigate methods for optical motion analysis on the basis of data acquired by a novel type of biologically inspired vision sensor. These Silicon Retina sensors provide on-chip motion detection and an effcient, event based communication scheme, the address-event representation (AER), ideally suited for capturing, tracing and analyzing high speed motion yielding address event data (AE data). We believe that the approach is superior to the state-of-the-art motion capturing with high frame rate video cameras because it supplies continuous, frame-less motion information with low latency (Δt = 10ns) and high temporal resolution at low data rates. A simple movement (free fall) is analysed and data are evaluated against two different state-of-the-art motion analysis systems (infrared cameras (Vicon Bonita (240Hz)), full frame camera (Basler 602fc (300Hz)). Using Pearson's correlation coeffcient it is shown that the trajectories highly correlate between all three systems (r ≥ 0.999) but object tracking algorithms for AE data need further enhancement. Furthermore more complex movements were filmed and visually compared to frame based data. It is concluded that AE data could prove to be beneficial for certain motion analysis tasks although algorithms have to be developed to fully exploit the nature of the signal.

Acoustic Emission Characterization of Failure Modes in GFRP Laminates Under Mode I Delamination

In order to design structural components using composite materials a deep understanding of the material behaviour and its failure mechanisms is necessary. To create a better understanding of the initiation, growth and interaction of the different types of damage, damage monitoring during mechanical loading is very important. To this direction, AE is a powerful non destructive technique for real time monitoring of damage development in materials and structures which has been used successfully for the identification of damage mechanisms in composite structures under quasi static and dynamic-cycle loading. In this present work, pure resin plate and GFRP composite laminates with stacking sequence of [00]6, are fabricated using Hand lay-up method. During the layup a Teflon tape of width 45 mm is kept in the mid plane of the laminate which serves as an initiator for delamination during loading. As per ASTM STD D5528 01 DCB (Double Cantilever Beam) specimens are cut out from the laminates and are subjected to tensile test in the transverse direction along with acoustic emission monitoring. While loading, Markings are made on the sides of the specimen to track the crack front using a magnifying lens. Parametric analysis is performed on the AE data obtained during crack propagation to discriminate the failure modes. Fast Fourier Transform (FFT) enabled the calculation of frequency content of each damage mechanism. Further STFFT analysis is performed on a portion of the waveforms representing the dominant frequency content pertaining to each damage mechanism.

Abstract P6-11-01: Significant Efficacy in a Phase 2 Study of NKTR-102, a Novel Polymer Conjugate of Irinotecan, in Patients with Pre-Treated Metastatic Breast Cancer (MBC)

Abstract Background: NKTR-102, a topoisomerase 1 inhibitor-polymer conjugate with reduced peak concentrations and a continuous concentration profile, demonstrated evidence of anti-tumor activity in a broad range of tumors in phase 1 (Von Hoff; EORTC-NCI-AACR 2008, abstract #595), including three patients (pts) with triple negative breast cancer (one confirmed PR by RECIST, one 20% reduction in tumor burden and one 50%+ reduction in CA 27.29). We now report data of a randomized phase 2 study in pts with MBC following progression to taxane-based therapy. Methods: This open-label, phase 2 study enrolled pts to receive NKTR-102 intravenously (IV) at a target dose of 145 mg/m2. Eligible patients had progressive MBC following taxane therapy, ECOG PS 0-1, adequate renal, hepatic, and marrow function. The study randomized pts to receive NKTR-102 q14d or q21d; each schedule followed a two-stage Simon design to ensure adequate response rate prior to proceeding to full enrollment. The primary endpoint was objective response rate (ORR) by RECIST v1.0; secondary endpoints were safety and PFS. Results: 70 pts were randomized from Feb09 to Apr10 and treated (35 on each schedule); 20 patients remain active on study as of 14Jun2010. Median age was 55 (range 33-83); all but 1 pt was female. ECOG PS 0/1 equaled 40%/60%. Prior neo-adjuvant and adjuvant therapy had been administered in 15 and 39 patients respectively; 23 pts had received prior hormonal therapy. Median number of prior non-hormonal regimens for metastatic disease was 2 (range 0-4). All pts had received prior taxane therapy; 11 in the adjuvant setting and 60 in the metastatic setting). 45 pts had received only prior anthracycline/taxane therapy (“AT”); an additional 14 pts had received prior AT/capecitabine therapy (“ATC”). 42 were estrogen or progesterone receptor positive and 20 pts had triple negative disease. Best RECIST response rate (confirmed and unconfirmed) equaled 27% (9 pts; 2 awaiting confirmation) and 24% (8 pts; 4 awaiting confirmation) for the q14d and q21d schedules (n=33 and 34, respectively; 3 patients had not undergone first on-study CT exam). As ORR results were similar for each schedule, the combined best RECIST ORR for in AT/ATC pts equaled 23%/29%, respectively and in pts with triple negative disease, this equaled 25%. Common related Grade 3/4 toxicities (q14d/q21d schedules; n=67 pts with AE data available): diarrhea (15%/12%), neutropenia (15%/9%),fatigue (12%/3%), and dehydration (6%/3%). One pt in each schedule died due to septic shock and acute renal failure secondary to hypovolemia. Conclusion: Single-agent NKTR-102 demonstrates significant anti-tumor activity in pts with MBC, including pts having tumor progression after ATC-based therapy and pts with triple negative disease, with a tolerable toxicity profile. Mature ORR and median PFS will be available by December 2010. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P6-11-01.

The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery

The monitoring of progressive wear in gears using various non-destructive technologies as well as the use of advanced signal processing techniques upon the acquired recordings to the direction of more effective diagnostic schemes, is the scope of the present work. For this reason multi-hour tests were performed in healthy gears in a single-stage lab scale gearbox until they were seriously damaged. Three on-line monitoring techniques are implemented in the tests. Vibration and acoustic emission recordings in combination with data coming from oil debris monitoring (ODM) of the lubricating oil are utilized in order to assess the condition of the gears. A plethora of parameters/features were extracted from the acquired waveforms via conventional (in time and frequency domain) and non-conventional (wavelet-based) signal processing techniques. Data fusion was accomplished in the level of integration of the most representative among the extracted features from all three measurement technologies in a single data matrix. Principal component analysis (PCA) was utilized to reduce the dimensionality of the data matrix whereas independent component analysis (ICA) was further applied to identify the independent components among the data and correlate them to different damage modes of the gearbox. Finally heuristic rules based on characteristic values of the resulted independent components were set, realizing thus a health monitoring scheme for gearboxes.The integration of vibration, AE and ODM data increases the diagnostic capacity and reliability of the condition monitoring scheme concluding to very interesting results. The present work summarizes the joint efforts of two research groups towards a more reliable condition monitoring of rotating machinery and gearboxes specifically.

Detecting Corrosion of Steel Prestressing Strands Using Acoustic Emission


 
 
 America’s transportation infrastructure has been receiving intensive public and private attention in recent years, particularly highway bridges. Corrosion of reinforcement steel is a main durability issue especially for concrete structures present in coastal areas and in areas where de-icing salts are routinely used.Acoustic emission (AE) is a promising method for detecting corrosion in steel reinforced concrete members. This type of non-destructive testing (NDT) method primarily measures the magnitude of energy released within a material when physically strained. The expansive ferrous product resulting from corrosion induces pressure at the steel-concrete interface creating micro-cracks which can be detected by AE sensors. In this study, five concrete blocks with embedded prestressing steel strands were built and tested under accelerated corrosion conditions to evaluate possible correlations between AE activity and the onset and progression of corrosion. AE data along with half-cell potential measurements were recorded during the test to determine the stages and the overall deterioration process. Afterwards, the steel strands were removed from the specimens, cleaned and weighed; then the results were evaluated vis-à-vis Faraday’s law with respect to the degree of corrosion present in each block.
 
 

Relationship between higher rates of adverse events in deep brain stimulation using standardized prospective recording and patient outcomes

Adverse event (AE) rates for deep brain stimulation (DBS) are variable, due to various methodologies used for identifying, collecting, and reporting AEs. This lack of a prospective, standardized AE collection method is a shortcoming in the advancement of DBS. In this paper the authors disclose the standardized and prospectively recorded AE data from their institution, correlated with clinical outcome and quality of life (QOL) measures. All patients who underwent operations at the authors' institution for Parkinson disease (PD), essential tremor, dystonia, other tremor, and obsessive-compulsive disorder were included. Complications occurring intraoperatively or within the first 180 days following surgery were recorded, analyzed, and classified as mild, moderate, or severe, regardless of their perceived relationship to the procedure. The presence, frequency, and severity of AEs were compared with the following outcome measurements: postoperative change in the QOL scales (Medical Outcomes Study 36-Item Short-Form Survey, 39-Item PD Questionnaire); motor scales (Tremor Rating Scale, Unified Dystonia Rating Scale, Unified PD Rating Scale); and Patient Global Impression Scale (PGIS). Two hundred seventy DBS procedures were performed in 198 patients. Three hundred AEs were recorded in 146 (54.1%) of the 270 procedures, and the AEs were recorded in 119 (60.1%) of 198 patients. Of the 198 patients, the maximum severity of AEs was mild in 28 (14.1%), moderate in 35 (17.7%), and severe in 56 (28.3%). Of the 300 AEs, 102 (34.1%) of 299 were mild, 106 (35.5%) were moderate, and 91 (30.4%) were severe. The AEs were classified as probably not stimulation induced in 10 (3.4%) of 297, probably in 44 (14.9%), unclear for 89 (30%), and not applicable to stimulation in 154 (51.9%). Adverse events were also classified as probably related to surgery in 111 (37.2%) of 298, possibly related in 96 (32.2%), and probably not related to surgery in 91 (30.5%). There was no significant difference (p = 0.22) in QOL outcomes among patients who had no AEs compared with those who experienced mild, moderate, or severe AEs. There was no significant difference in QOL outcomes between patients who did not experience an AE compared with those who experienced any AE. There was no significant difference in the mean General PGIS score between patients without an AE versus those with any AE, as well as on the Symptom-Specific PGIS. Motor function outcomes did not vary between patients with or without AEs. For patients with PD with or without AEs, there was no significant difference in preoperative off-medicine Unified PD Rating Scale score and postoperative 6-month on-medication/on-stimulation change scores (p = 0.59). For patients with tremor there were no differences between those with or without AEs on the Tremor Rating Scale for motor function or activities of daily living. Patients with dystonia with and without AEs showed no differences in the Unified Dystonia Rating Scale. Prospectively and systematically recording AEs may result in higher AE rates, but this does not correlate with poorer QOL, motor function, or patient-oriented outcome scores.

Acoustic emission monitoring of bridges: Review and case studies

This paper gives a brief review of the acoustic emission technique and its applications to bridge health monitoring. Emphasis is given to the discussion of available techniques of AE data processing, both qualitative and quantitative. An assessment of the statistical quantitative analysis technique, intensity analysis, is illustrated through two case studies. This technique of damage quantification is applied to AE data collected from two genres of bridges in Louisiana: a prestressed concrete slab-on-girder bridge and a steel bridge with a concrete deck. Although there were limitations concerning the number and type of sensors used, much information was collected and useful inferences were made that may help better diagnose the health of bridges monitored in the future using this technique.

Damage Detection in Fibre Reinforced Ceramic and Metal Matrix Composites by Acoustic Emission

In this work damage micro-mechanisms of two different types of fibre reinforced composites are investigated by acoustic emission, AE. Ceramic based oxide fibre reinforced mullite matrix composite and metallic based SiC fibre reinforced titanium matrix composites exhibit different fracture mechanisms during loading and AE technique could pinpoint these damage mechanisms based on the AE responses detected simultaneously. The results show that in a ceramic matrix composite, the identification of fibre fracture and matrix cracking requires careful analysis of the AE data as both fibres and matrix break in brittle manner. Whereas the separation of fibre fracture from the ductile tearing of matrix ligaments could be easier in metallic based composites, such as titanium matrix composites.

Treatment of Acute Bleeds in Acquired Hemophilia: An Updated Analysis From the Hemophilia and Thrombosis Research Society (HTRS) Registry.

Treatment of Acute Bleeds in Acquired Hemophilia: An Updated Analysis From the Hemophilia and Thrombosis Research Society (HTRS) Registry.

Two Commonly Used Methods for Exposure—Adverse Events Analysis: Comparisons and Evaluations

The purpose of this study is to compare and evaluate logistic regression and time-to-event analysis, 2 commonly used methods for exposure-adverse event (AE) analyses. An AE data set selected from clinical trials is analyzed by both methods and the results are compared. The parameter estimates, odds ratios for logistic regression, and hazard ratios for time-to-event analysis for each AE are compared and further analyzed. In a data set involving 822 patients, 25 AEs are analyzed. A linear relationship is demonstrated between the parameter estimates from the 2 methods and between the odds ratios and hazard ratios. The small differences between the 2 analyses are related to the lower rate of the events and the weaker effects of the risk factors. Although the 2 methods can make predications for the risks, the severity, duration, and recurrence are not well defined. AE time profiles showing the onset, duration, and offset of AEs are important for risk assessment and management. Both analyses can provide information about exposure-AE relationship, and the results from the 2 analyses are consistent in most cases. One should not use the logistic model when length of follow-up varies because of biased estimates. The application of the 2 methods should be combined with AE profiling.

Geomagnetic signatures of auroral substorms preceded by pseudobreakups

The evolution of ten growth‐phase pseudobreakups and subsequent substorms, identified in Northern Hemisphere Polar UV images during winter 1998–1999, are compared to the AE index, the unified PC indices, and GOES B field data. Comparing substorm onset (auroral breakup) with GOES data and AE and PC indices, it is found that an exact onset determination from these parameters is in most cases not possible. The three weakest substorms leave no clear signatures in the auxiliary parameters. For the other events, the AE increase appears with a time delay of 5–15 min after onset. The PC indices increase, as expected, before the AE index. The time span between PC increase and onset varies widely (−26 to 5 min). A tail dipolarization is seen in GOES data with a time delay of 2–31 min after onset. The dipolarization delay at geosynchronous orbit appears because of the GOES displacement from the tail onset region. Using the mapped GOES distance from the auroral breakup region as an estimate of GOES displacement from the breakup source region, we find that the tail dipolarization region expands in average with an azimuthal speed of 0.22 MLT min−1 and an equatorward speed of 0.09° min−1. Pseudobreakups leave hardly any signature in AE or PC index data except in the four strongest substorm cases. In these cases, a bump appears in the PC indices during the pseudobreakup. A bump in geosynchronous B field data is found only in those two cases where GOES is located very close to the pseudobreakup tail source region.

A phase II study of paclitaxel and bevacizumab ± gemcitabine as first-line treatment for metastatic breast cancer (MBC): Interim safety results

1095 Background: Paclitaxel (P) + bevacizumab (B) is a regimen that has shown significant improvement in PFS in pts with MBC. We are currently conducting a randomized Phase II trial to examine the efficacy and safety of adding gemcitabine (G) to the PB doublet. Here, we report results of a planned interim safety analysis of the first 50 treated pts. Methods: This US, multicenter, randomized, superiority trial plans to enroll 180 women. Eligible pts have locally advanced or metastatic breast cancer, PS 0 or 1, and have not received prior cytotoxic therapy for metastatic disease. Prior adjuvant or neoadjuvant treatment with a taxane or endocrine therapy is allowed. Pts assigned to Arm A receive P 90 mg/m2 on Days 1, 8, and 15, followed by B 10 mg/kg on Days 1 and 15 of a 28-day cycle. Pts assigned to Arm B receive the same regimen plus G 1,500 mg/m2 on Days 1 and 15. The primary outcome measure is response rate, and safety is one of the secondary endpoints. Results: Between May 2006 and January 2007, fifty qualified women were treated (Caucasian 70%, Hispanic 14%, African 8%, East Asian 8%). Arm A pts (N=24): median age, 60 (range 37–75); ER status ±/unknown, 14/8/2; PR status ±/unknown, 11/11/2; PS 0/1, 14/10. Arm B patients (N=26): median age, 54 (range 40–80); ER status ±/unknown, 17/7/2; PR status ±/unknown, 14/10/2; PS 0/1, 16/10. There were no discontinuations due to AEs in Arm A. Two Arm B pts discontinued from study for treatment-related AEs (rash, proteinuria). One on-study death has occurred in an Arm B pt, unrelated to treatment. Drug exposure and AE data are summarized in the following table. Conclusions: Therapy with PB ± G does not appear to be associated with significant bleeding or thrombotic events. As expected, the addition of G to the PB doublet appears to increase the incidence of neutropenia in pts with MBC. Enrollment is complete and we expect disclosure of full efficacy and safety results in the near future. Arm A N=24 Arm B N=26 P B P B G Median cycles received # (range) 5 (2–8) 5 (2–8) 4.5 (1–10) 4.5 (1–10) 4.5 (1–10) Median dose intensity (%) 87.4 91.1 80.7 85.8 80.7 Arm A N=24 Arm B N=26 Hematologic events Grade 3 n (%) Grade 4 n (%) Grade 3 n (%) Grade 4 n (%) Neutropenia 3 (12.5) 2 (8.3) 6 (23.1) 1 (3.8) Febrile neutropenia 0 (0.0) 0 (0.0) 1 (3.8) 0 (0.0) Anemia 1 (4.2) 0 (0.0) 0 (0.0) 0 (0.0) Thrombocytopenia 0 (0.0) 0 (0.0) 1 (3.8) 0 (0.0) Nonhematologic events * Fatigue 3 (12.5) 0 (0.0) 4 (15.4) 0 (0.0) Anorexia 0 (0.0) 0 (0.0) 2 (7.7) 0 (0.0) Cellulitis 0 (0.0) 0 (0.0) 2 (7.7) 0 (0.0) Injection site infection 0 (0.0) 0 (0.0) 2 (7.7) 0 (0.0) Arm A N=24 Arm B N=26 Alopecia, bleeding and thrombotic events, all grades n (%) Grade 1 Grade 2 Grade 3 Grade 4 Grade 1 Grade 2 Grade 3 Grade 4 Alopecia 4 (16.7) 6 (25.0) NA NA 2 (7.7) 9 (34.6) NA NA Epistaxis 7 (29.2) 0 (0.0) 0 (0.0) 0 (0.0) 10 (38.5) 1 (3.8) 0 (0.0) 0 (0.0) Mouth hemorrhage 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 1 (3.8) 0 (0.0) 0 (0.0) 0 (0.0) Hemoptysis 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 1 (3.8) 0 (0.0) 0 (0.0) 0 (0.0) Pulmonary hemorrhage 1 (4.2) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) Urethral hemorrhage 0 (0.0) 1 (4.2) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) Hematuria 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 1 (3.8) 0 (0.0) 0 (0.0) 0 (0.0) Vaginal hemorrhage 0 (0.0) 1 (4.2) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) Hemorrhoidal hemorrhage 1 (4.2) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) Rectal hemorrhage 1 (4.2) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 1 (3.8) 0 (0.0) 0 (0.0) Deep vein thrombosis 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 1 (3.8) 0 (0.0) * Events occurring in ≥5% of patients. Author Disclosure Employment or Leadership Consultant or Advisory Role Stock Ownership Honoraria Research Expert Testimony Other Remuneration Eli Lilly AstraZeneca Oncology, Aventis Abraxis Oncology, AstraZeneca Oncology, Aventis, Eli Lilly, Genentech™ BioOncology Eli Lilly

Back propagation neural network prediction of failure strength of composite tensile specimens

In this research work, a feed forward back propagation neural network model was generated from acoustic emission cumulative counts data taken during loading of unidirectional carbon/epoxy tensile specimens. Only data collected up to 50% of ultimate load and its corresponding failure strength of 12 specimens were utilised for training the network out of 18 specimens tested, remaining six specimens data were utilised for testing the software. The network structured as 66-15-1 was able to give the prediction results with the worst case error of 4.98%. Fifteen neuron middle layer was mapping the pattern for failure of specimens well before from the AE data. This work indicates that it is possible to proof test the composites more sophisticated at lower loads (may be 50% of ultimate load) then are currently being tested (70% to 80% of ultimate load). Unintentional degradation of composite materials while proof testing could be thus minimised.

Analysis of damage mechanisms and associated acoustic emission in two SiC/[Si–B–C] composites exhibiting different tensile behaviours. Part I: Damage patterns and acoustic emission activity

The present work deals with two SiC/[Si–B–C] composites exhibiting different mechanical behaviours under tensile testing: low strain to failure for the one, high strain to failure for the other with similar ultimate strength. In the present Part I, mechanical hysteresis loops of stress–strain curves are analysed, and detailed cracking patterns are presented and quantified for both materials. The difference between both composites is explained by different values of the interfacial shear stress. Acoustic emission activities are compared as regards to the damage accumulation scenarios. In a companion paper [Moevus M, Godin N, Rouby D, R’Mili M, Reynaud P, Fantozzi G, et al. Analyse of damage mechanisms and associated acoustic emission in two SiC/[Si–B–C] [23] composites exhibiting different tensile curves. Part II: Unsupervised acoustic emission data clustering. Comp Sci Technol, in press], the AE data will be submitted to an unsupervised clustering procedure in order to distinguish the different damage mechanisms.

Fracture behavior and damage mechanisms identification of SiC/glass ceramic composites using AE monitoring

Large Scale Bridging in SiC/MAS-L (ceramic glass matrix) composites was investigated by using DEN specimens under tensile loading conditions with in situ Acoustic Emission monitoring. The AE data were successfully classified using Unsupervised Pattern Recognition Algorithms and the resulted clusters were correlated to the dominant damage mechanisms of the material. The evolution in time of the different damage mechanisms is feasible after the pattern recognition classification. Microscopic examination was used to correlate the clusters to the damage mechanism they correspond and thus to provide the failure mode identification based on AE data.

Embedded Code Processing for the On-Line Use of User-Developed AE Analysis Routines

In spite of the availability of very versatile analysis packages, such as VisualAE™, the large variety of test objects that can be successfully tested by AE sometimes calls for the use of an application dedicated analysis algorithm, especially if large numbers of similar or identical objects shall be tested. This can save a lot of time for data analysis and interpretation and reduce the requirements on operator qualification. Those analysis algorithms can be developed by AE-experts based on AE data from a number of test objects. Up to now, such a program had no access to real time data. The more such a dedicated analysis algorithm proves to be helpful and valuable for a specific application (e.g. testing small, buried LPG vessels), the higher the desire to use that algorithm also in real time (i.e. during data acquisition). This can simplify testing and improve test control e.g. by the application of higher sophisticated alarm criteria. With the new Embedded Code Processor (ECP) the user can develop and verify software algorithms of any complexity, that run within the environment of VisualAE™. This tremendously accelerates the implementation process. Any result within VisualAE can be input into the user-developed code, any new results can be calculated, and then used by VisualAE for filtering, data presentation and alarm raising, if desired in combination with the conventional results. This paper summarizes the structure of VisualAE in short and the intentions and possibilities of an Embedded Code Processor (ECP). ECP is already available and in use.

Development of an AE data management and analysis system

The use of acoustic emission testing in continuous monitoring applications presents the analyst with the problem of managing substantial volumes of generated data. To better administer this information, a system has been developed to catalogue, store and process AE data. Based on a relational database, it is independent of the software and hardware used to acquire the data. This paper describes the system and how it is used. Guidance is also given on how to optimise the database for performance, robustness and ease of data extraction. Finally, examples of its data mining capabilities are also presented.

Fatigue characteristics of foam core sandwich composites

In situ AE and post test microscopic analysis of quasi-static and flexural fatigue testing of sandwich composites indicated core damage to be the predominant failure activity while fiber rupture served as a precursor to catastrophic failure. Multiple crack initiation sites were observed under fatigue loading conditions in the vicinity of the notch tip. Both modes I and II cracking was observed in the core and along the interface between the core and the facesheets. Flexural fatigue life was found to be beyond a 500,000 cycle arbitrarily established cutoff below 75% of the ultimate static load. A statistical model based on experimental lifetime and corresponding AE data was developed to quantify the extent of damage; a reasonably good correlation with the experimental results has been obtained.

AE energy analysis on concrete bridge beams

Concrete bridges were built originally to have a cost-effective maintenance free life of 120 years, however both in Europe and the USA this has proved to the totally optimistic. Concrete bridges require major maintenance after twenty to thirty years in order to extend their life The Acoustic Emission technique has been successfully applied to monitor and provide information on the safety of concrete bridges, but the processing of the AE data is often not trivial. This paper proposes a method of AE analysis to assess concrete bridges based on the fact that the AE energy is one of the effective parameters to evaluate the damage of a concrete structure. Results from experiments on concrete beams with different design and loading configurations are presented. It is argued that in some cases the state of damage of bridge beams can be estimated from the AE energy analysis when approximately 45% of the ultimate load has been applied.