Development of Generic Methodology for Designing a Structural Health Monitoring Installation Based on the Acoustic Emission Technique

Gagar, Daniel; Martinez, Marcias; Foote, Peter

doi:10.1016/j.procir.2014.07.122

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…There are only a few studies that tried to develop such a MAPOD framework and only for specific SHM techniques mainly based on guided Lamb waves (GLW) [24,25] and bulk-wave ultrasonic sensors [26]. For acoustic emission (AE) analyses, modeling a real AE event is difficult due to its broadband nature [27]; therefore, only experimental approaches based on the Hsu-Nielsen source are used [28,29].…”

Section: Introductionmentioning

confidence: 99%

A Model-Assisted Probability of Detection Framework for Optical Fiber Sensors

Falcetelli

Yue

Rossi³

et al. 2023

Sensors

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Optical fiber sensors (OFSs) represent an efficient sensing solution in various structural health monitoring (SHM) applications. However, a well-defined methodology is still missing to quantify their damage detection performance, preventing their certification and full deployment in SHM. In a recent study, the authors proposed an experimental methodology to qualify distributed OFSs using the concept of probability of detection (POD). Nevertheless, POD curves require considerable testing, which is often not feasible. This study takes a step forward, presenting a model-assisted POD (MAPOD) approach for the first time applied to distributed OFSs (DOFSs). The new MAPOD framework applied to DOFSs is validated through previous experimental results, considering the mode I delamination monitoring of a double-cantilever beam (DCB) specimen under quasi-static loading conditions. The results show how strain transfer, loading conditions, human factors, interrogator resolution, and noise can alter the damage detection capabilities of DOFSs. This MAPOD approach represents a tool to study the effects of varying environmental and operational conditions on SHM systems based on DOFSs and for the design optimization of the monitoring system.

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Section: Introductionmentioning

confidence: 99%

A Model-Assisted Probability of Detection Framework for Optical Fiber Sensors

Falcetelli

Yue

Rossi³

et al. 2023

Sensors

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“…This enables advanced maintenance strategies, that is, condition-based maintenance, which leads to an increase in reliability of technical systems. 5 In this context, methods for non-destructive evaluation (NDE) are employed. In particular, acoustic emission (AE) technique, which is a passive, wave-propagation-based NDE method, gained attention for in situ monitoring recently.…”

Section: Introductionmentioning

confidence: 99%

Investigation of damage detectability in composites using frequency-based classification of acoustic emission measurements

Wirtz

Beganović

Söffker

2018

Structural Health Monitoring

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Advances in composite technology led to the substitution of conventional, metallic construction material by composites. However, the more widespread application of composites is currently restricted by complex fracture mechanisms, which are not well understood. One approach to overcome this challenge is structural health monitoring systems which provide a lot of information on the current system state as well as state of health in real time. In this context, reliability assessment of structural health monitoring systems is currently an open issue. The reliability of conventional nondestructive testing systems is evaluated, measured, and partly standardized using widely accepted methods such as the probability of detection rate. Frequently, the a 90|95 value, which is determined from the probability of detection curves, is used as a performance measure indicating the minimum damage size that is detected with a probability of 90% and 95% confidence. In contrast to non-destructive testing, structural health monitoring involves additional data analysis steps, that is, statistical pattern recognition, where the classification results are also subject to uncertainty. Because similar methods are not available, the reliability of structural health monitoring systems is usually not quantified. To investigate the influences on the classification performance, experiments were conducted. In particular, the effect of variable loading conditions and the evolution of damage over time are considered. To this end, acoustic emission measurements were performed, while the specimens of the composite material were subjected to different cyclic loading patterns. Here, acoustic emission refers to elastic stress waves in the ultrasound regime, which emerge from the structure on damage initiation and propagation. Furthermore, a frequency-based damage classification scheme for acoustic emission measurements is proposed. Time-frequency domain features are extracted from the measurement signals using shorttime Fourier transform. Classification is performed using support vector machine. Both choices serve as typical examples to discuss the effects which apply equally to other approaches. Experimental results presented in this article regarding fault diagnosis and discrimination of delamination, matrix crack, debonding, and fiber breakage in carbon-fiberreinforced polymer material show that good performance applying support vector machine could be achieved using 10fold cross validation. However, during model deployment, strong dependency of the classification reliability on loading conditions can be clearly stated, which could not be seen from the previous evaluation. Concluding from these results, it can be stated that the application of classifier-based structural health monitoring is more complex than generally assumed. The relations between the classification approaches, testing conditions, measurement devices, and filters have to be discussed with respect to the ability to provide reliable statements about the actual damage state.

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Structural health monitoring through acoustic emission

Nor

2018

Eco-Efficient Repair and Rehabilitation of Concrete Infrastructures

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Development of Generic Methodology for Designing a Structural Health Monitoring Installation Based on the Acoustic Emission Technique

Cited by 8 publications

References 12 publications

A Model-Assisted Probability of Detection Framework for Optical Fiber Sensors

A Model-Assisted Probability of Detection Framework for Optical Fiber Sensors

Investigation of damage detectability in composites using frequency-based classification of acoustic emission measurements

Structural health monitoring through acoustic emission

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