Acoustic emission detection using optical fibre sensors for aerospace applications

Structural Health Monitoring (SHM) ensures the structural health and safety of critical structures covering a wide range of application areas. This thesis presents novel, low-cost and good-performance fibre Bragg grating (FBG) based systems for detection of Acoustic Emission (AE) in aircraft structures, which is a part of SHM. Importantly a key aim, during the design of these systems, was to produce systems that were sufficiently small to install in an aircraft for lifetime monitoring. Two important techniques for monitoring high frequency AE that were developed as a part of this research were, Quadrature recombination technique and Active tracking technique. Active tracking technique was used extensively and was further developed to overcome the limitations that were observed while testing it at several test facilities and with different optical fibre sensors. This system was able to eliminate any low frequency spectrum shift due to environmental perturbation and keeps the sensor always working at optimum operation point. This is highly desirable in harsh industrial and operationally active environments. Experimental work carried out in the laboratory has proved that such systems can be used for high frequency detection and have capability to detect up to 600 kHz. However, the range of frequency depends upon the requirement and design of the interrogation system as the system can be altered accordingly for different applications. Several optical fibre configurations for wavelength detection were designed during the course of this work along with industrial partners. Fibre Bragg grating Fabry-Perot (FBG-FP) sensors have shown higher sensitivity and usability than the uniform FBGs to be used with such system. This was shown experimentally. The author is certain that further research will lead to development of a commercially marketable product and the use of active tracking systems can be extended in areas of healthcare, civil infrastructure monitoring etc. where it can be deployed. Finally, the AE detection system has been developed to aerospace requirements and was tested at NDT & Testing Technology test facility based at Airbus, Filton, UK on A350 testing panels.

Evaluation of Information Entropy from Acoustic Emission Waveforms as a Fatigue Damage Metric for Al7075-T6

Information entropy measured from acoustic emission (AE) waveforms is shown to be an indicator of fatigue damage in a high-strength aluminum alloy. Several tension-tension fatigue experiments were performed with dogbone samples of aluminum alloy, Al7075-T6, a commonly used material in aerospace structures. Unlike previous studies in which fatigue damage is simply measured based on visible crack growth, this work investigated fatigue damage prior to crack initiation through the use of instantaneous elastic modulus degradation. Three methods of measuring the AE information entropy, regarded as a direct measure of microstructural disorder, are proposed and compared with traditional damage-related AE features. Results show that one of the three entropy measurement methods appears to better assess damage than the traditional AE features, while the other two entropies have unique trends that can differentiate between small and large cracks.

Lack of magnetoacoustic emission in iron with 6.5% silicon

The phenomenon of magnetoacoustic emission (MAE) has been ascribed usually to one of two origins: either (1) motion of non-180 degrees domain walls or (2) creation or annihilation of domains. In this paper, we present strong evidence for the argument that the only origin for MAE is motion of non-180 degrees domain walls. The proof is evident as a result of measurements of zero MAE for a wide range of stress in the isotropic zero magnetostrictive polycrystalline alloy of iron with 6.5% silicon. We also explain why it was that the alternative origin was proposed and how the data in that same experiment can be reinterpreted to be consistent with the non-180 degrees wall motion origin. (C) 2008 Elsevier B.V. All rights reserved.

INVESTIGATION OF ACOUSTIC SIGNALS AS A TOOL FOR CHARACTERIZING SPOUTED BED DYNAMICS

The feasibility of characterizing the dynamics of a spouted bed based on acoustic emission (AE) signals is evaluated. Acoustic emission signals were measured in a semi-cylindrical Plexiglas column of diameter 150 mm and height 1000 mm with a conical base of internal angle 60 degrees and 25 mm inlet orifice diameter. Data were obtained for U/U(ms), from 0.3 to 2.0, static bed height from 250 to 500 mm, and glass beads of diameter 1.2 and 2.4 mm. AE signals reflected the effects of particle size and U/U(ms), but in general were insensitive to bed depth, even when there were drastic changes in spouting flow patterns. The results indicate that the AE signals were insensitive to the spouted bed hydrodynamics for the conditions studied. Overall, it appears that the AE analysis is unlikely to be a suitable technique for discriminating spouted bed flow regimes, at least for the range of frequencies and operating conditions investigated.

Akustisen emission anturin signaalin esikäsittelypiirin selvittäminen ja prototyypin toteuttaminen

Tämän työn tavoitteena oli selvittää ja toteuttaa esikäsittelypiirin prototyyppi akustisen emission anturin signaalille. Toteutettu esikäsittelypiiri toimii yksipuoleisella käyttöjännitteellä. Työssä käydään läpi esikäsittelypiirin suunnitteluun liittyvät vaiheet laskelmien ja simulaatioiden muodossa. Lisäksi työssä esitetään mittaustulokset esikäsittelypiirin toiminnasta.

Ultrasonic acoustic emissions in drought-stressed trees - more than signals from cavitation?

Ultrasonic acoustic emission (UAE) in trees is often related to collapsing water columns in the flow path as a result of tensions that are too strong (cavitation). However, in a decibel (dB) range below that associated with cavitation, a close relationship was found between UAE intensities and stem radius changes. • UAE was continuously recorded on the stems of mature field-grown trees of Scots pine (Pinus sylvestris) and pubescent oak (Quercus pubescens) at a dry inner-Alpine site in Switzerland over two seasons. The averaged 20-Hz records were related to microclimatic conditions in air and soil, sap-flow rates and stem-radius fluctuations de-trended for growth (ΔW). • Within a low-dB range (27 ± 1 dB), UAE regularly increased and decreased in a diurnal rhythm in parallel with ΔW on cloudy days and at night. These low-dB emissions were interrupted by UAE abruptly switching between the low-dB range and a high-dB range (36 ± 1 dB) on clear, sunny days, corresponding to the widely supported interpretation of UAE as sound from cavitations. • It is hypothesized that the low-dB signals in drought-stressed trees are caused by respiration and/or cambial growth as these physiological activities are tissue water-content dependent and have been shown to produce courses of CO2 efflux similar to our courses of ΔW and low-dB UAE.

Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.

Experimental analysis of wheel/workpiece dynamic interactions in grinding

The aim of this work is to study the wheel/workpiece dynamic interactions in high-speed grinding using vitrified CBN wheel and DTG (difficult to grind) work materials. This problem is typical in the grinding of engine valve heads. The influence of tangential force per abrasive grain was investigated as an important control variable for the determination of G ratio. Experiments were carried out to observe the influence of vibrations in the wheel wear. The measurements of acoustic emission (AE) and vibration signals helped in identifying the correlation between the dynamic interactions (produced by forced random excitation) and the wheel wear. The wheel regenerative chatter phenomenon was observed by using the wheel mapping technique. (c) 2008 CIRP.

Avaliação da integridade estrutural de uma prótese cimentada por emissão acústica

Mestrado em Engenharia Electrotécnica e de Computadores

Statistical similarity between the compression of a porous material and earthquakes

It has been long stated that there are profound analogies between fracture experiments and earthquakes; however, few works attempt a complete characterization of the parallelisms between these so separate phenomena. We study the Acoustic Emission events produced during the compression of Vycor (SiO&sub&2&/sub&). The Gutenberg-Richter law, the modified Omori's law, and the law of aftershock productivity hold for a minimum of 5 decades, are independent of the compression rate, and keep stationary for all the duration of the experiments. The waiting-time distribution fulfills a unified scaling law with a power-law exponent close to 2.45 for long times, which is explained in terms of the temporal variations of the activity rate.

Statistics of avalanches in martensitic transformations, II. Modelling

Using a scaling assumption, we propose a phenomenological model aimed to describe the joint probability distribution of two magnitudes A and T characterizing the spatial and temporal scales of a set of avalanches. The model also describes the correlation function of a sequence of such avalanches. As an example we study the joint distribution of amplitudes and durations of the acoustic emission signals observed in martensitic transformations [Vives et al., preceding paper, Phys. Rev. B 52, 12 644 (1995)].

Kinetics of martensitic transitions in Cu-Al-Mn under thermal cycling: Analysis at multiple length scales

In this paper we study the evolution of the kinetic features of the martensitic transition in a Cu-Al-Mn single crystal under thermal cycling. The use of several experimental techniques including optical microscopy, calorimetry, and acoustic emission, has enabled us to perform an analysis at multiple scales. In particular, we have focused on the analysis of avalanche events (associated with the nucleation and growth of martensitic domains), which occur during the transition. There are significant differences between the kinetics at large and small length scales. On the one hand, at small length scales, small avalanche events tend to sum to give new larger events in subsequent loops. On the other hand, at large length scales the large domains tend to split into smaller ones on thermal cycling. We suggest that such different behavior is the necessary ingredient that leads the system to the final critical state corresponding to a power-law distribution of avalanches.

Athermal character of structural phase transitions

The significance of thermal fluctuations in nucleation in structural first-order phase transitions has been examined. The prototypical case of martensitic transitions has been experimentally investigated by means of acoustic emission techniques. We propose a model based on the mean first-passage time to account for the experimental observations. Our study provides a unified framework to establish the conditions for isothermal and athermal transitions to be observed.

Driving rate effects in avalanche-mediated first-order phase transitions

We study the driving-rate and temperature dependence of the power-law exponents that characterize the avalanche distribution in first-order phase transitions. Measurements of acoustic emission in structural transitions in Cu-Zn-Al and Cu-Al-Ni are presented. We show how the observed behavior emerges within a general framework of competing time scales of avalanche relaxation, driving rate, and thermal fluctuations. We confirm our findings by numerical simulations of a prototype model.

Distributions of avalanches in martensitic transformations

An experimental study of the acoustic emission generated during a martensitic transformation is presented. A statistical analysis of the amplitude and lifetime of a large number of signals has revealed power-law behavior for both magnitudes. The exponents of these distributions have been evaluated and, through independent measurements of the statistical lifetime to amplitude dependence, we have checked the scaling relation between the exponents. Our results are discussed in terms of current ideas on avalanche dynamics.