Browsing by Author "Gagar, Daniel"

Now showing 1 - 4 of 4
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    Automated shot counter system for through-life support of target rifles
    (Elsevier, 2015-10-27) Gagar, Daniel; Hockley, Chris; Foote, Peter
    Competitive target shooting requires rifles with high levels of performance and small margins of error. Optimal performance of rifles in terms shot velocity can be expected over a period of use until an indeterminate but critical number of rounds has been fired when it will start to deteriorate. The rifle barrel must then be renewed. Accurate and reliable record-keeping of number of shots fired is therefore critical to minimise the through-life cost of owning a target rifle and also maintaining maximum performance. This can be most effectively done using an automated means for monitoring the number of rounds fired. In this paper the acoustic emission technique is used to monitor and identify shot rounds fired based solely on the features of Acoustic Emission (AE) signals for the first time. The results obtained from experiments showed unambiguous identification of shots fired and the capability to monitor degradation of the barrel as a function of number of shots fired.
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    Comparison of alternatives to amplitude thresholding for onset detection of acoustic emission signals
    (Elsevier, 2016-10-10) Bai, F.; Gagar, Daniel; Foote, Peter; Zhao, Yifan
    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 in an array is essential in performing localisation. Currently, this is determined using a fixed threshold which is particularly prone to errors when not set to optimal values. This paper presents three new methods for determining the onset of AE signals without the need for a predetermined threshold. The performance of the techniques is evaluated using AE signals generated during fatigue crack growth and compared to the established Akaike Information Criterion (AIC) and fixed threshold methods. It was found that the 1D location accuracy of the new methods was within the range of <1–7.1%<1–7.1% of the monitored region compared to 2.7% for the AIC method and a range of 1.8–9.4% for the conventional Fixed Threshold method at different threshold levels.
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    Development of probability of detection data for structural health monitoring damage detection techniques based on acoustic emission
    (Stanford University, 2013-12-12) Gagar, Daniel; Irving, Phil E.; Jennions, Ian K.; Foote, Peter; Read, Ian; McFeat, Jim
    Structural Health Monitoring (SHM) techniques have been developed as a cost effective alternative to currently adopted Non-Destructive Testing (NDT) methods which have well understood levels of performance. Quantitative performance assessment, as used in NDT, needs to be applied to SHM techniques to establish their performance levels as a basis for technique comparison and also as a requirement for practical aerospace application according to set regulations. One such measurand is Probability of Detection (POD). This paper reports experiments conducted to investigate the location accuracy of the Acoustic Emission (AE) system in monitoring events from HsuNielson and fatigue crack AE sources as a route to establish the POD of AE in SHM. It was found that fatigue crack tips could be located at 90% POD within 10 mm accuracy.
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    New methods for onset detection of acoustic emission signals
    (NDT.net, 2016-07-08) Gagar, Daniel; Bai, Fangliang; Zhao, Yifan; Foote, Peter
    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. The onset time of AE signals detected at different sensors in an array is used to determine their relative time difference of arrival which is essential in performing localisation of the signals’ originating source.Typically, this is done using a fixed threshold which is particularly prone to errors when not set to optimal values. This paper presents three new methods for determining the onset of AE signals without the need for a predetermined threshold. The performance of the techniques in terms of location accuracy is evaluated using AE signals generated during fatigue crack growth and compared to the established fixed threshold method. It was found that the mean absolute error in performing 1D location using the new methods was between 11.6 to 14.3 mm compared to a range of 19.3 to 37.2 mm for the conventional Fixed Threshold method at different threshold levels.