Comparative evaluation of in situ stress monitoring with Rayleigh waves

Hughes, James M.; Vidler, James; Ng, Ching‐Tai; Khanna, Aditya; Mohabuth, Munawwar; Rose, LR Francis; Kotousov, Andrei

doi:10.1177/1475921718798146

Cited by 23 publications

(15 citation statements)

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“…An impulse generator (A DECI, SE25P excited by a Model 600 Pulsar) and a pencil lead-break test were used to generate simulated AE signals. The signals from both AE sources were detected by the second AE sensor ≈1 ms after the first sensor had detected them [31,42,[81][82][83][84].…”

Section: Acoustic Emissionmentioning

confidence: 99%

Structural Health Monitoring Using Fibre Optic Acoustic Emission Sensors

Willberry

Papaelias

Fernando

2020

Sensors

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Acoustic emission (AE) is widely used for condition monitoring of critical components and structures. Conventional AE techniques employ wideband or resonant piezoelectric sensors to detect elastic stress waves propagating through various types of structural materials, including composites during damage evolution. Recent developments in fibre optic acoustic emission sensors (FOAES) have enabled new ways of detecting and monitoring damage evolution using AE. An optical fibre consists of a core with a high refractive index and a surrounding cladding. The buffer layer and outer jacket both act as protective polymer layers. Glass optical fibres can be used for manufacturing AE sensors of sufficiently small size to enable their embedding into fibre-reinforced polymer composite materials. The embedding process protects the FOAES against environmental stresses prolonging operational lifetime. The immunity of FOAES to electromagnetic interference makes this type of sensor attractive for condition monitoring purposes across a wide range of challenging operational environments. This paper provides an exhaustive review of recent developments on FOAES including their fundamental operational principles and key industrial applications.

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Section: Acoustic Emissionmentioning

confidence: 99%

Structural Health Monitoring Using Fibre Optic Acoustic Emission Sensors

Willberry

Papaelias

Fernando

2020

Sensors

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“…The waves are dispersed or attenuated when they encounter a flaw or a crack. The most common types of waves used for detecting cracks and delaminations include Lamb waves [12,13] and Rayleigh waves [14,15]. Embedded sensor-based SHM uses discrete sensors integrated within the structure to monitor its condition.…”

Section: Introductionmentioning

confidence: 99%

Inverse determination of delamination shape and size in self-sensing composites using electrical impedance tomography and genetic algorithms

Hassan

Tallman

2021

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2021

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Fiber-reinforced composites with nanofiller-modified polymer matrices have immense potential to improve the safety of high-risk engineering structures. These materials are intrinsically self-sensing because their electrical conductivity is affected by deformations and damage. This property, known as piezoresistivity, has been extensively leveraged for conductivity-based damage detection via electrical resistance change methods and tomographic imaging techniques such as electrical impedance tomography (EIT). Although these techniques are very effective at detecting the presence of damage, they suffer from an inability to provide precise information about damage shape, size, or mechanism. This is particularly detrimental for laminated composites which can suffer from complex failure modes, such as delaminations, that are difficult to detect. To that end, we herein propose a new technique for precisely determining damage shape and size in self-sensing composites. Our technique makes use of a genetic algorithm (GA) integrated with realistic physics-based damage models to recover precise damage shape from conductivity changes imaged via EIT. We experimentally validate this technique on carbon nanofiber (CNF)-modified glass fiber-reinforced polymer (GFRP) laminates by considering two specific damage mechanisms: through-holes and delaminations. Our results show that this novel technique can accurately reconstruct multiple through-holes with radii as small as 1.19 mm and delaminations caused by low velocity impacts. These findings illustrate that coupling piezoresistivity with conductivity-based spatial imaging techniques and physics-based inversion strategies can enable damage shaping capabilities in self-sensing composite structures.

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“…In the former case, which is not the focus of the present work, the most commonly used is the hole-drilling method [11][12][13]. Nondestructive evaluation for stress monitoring has been done more recently with the use of strain gauges [14], which are limited in resolution, and digital image correlation [15,16] which are not viable options for real-time continuous monitoring use, in contrast with the solutions based on ultrasonic information [17][18][19][20] that can be conveniently used for stress estimation as it involves cheap and simple equipment that may operate in a large frequency range depending on the load frequencyband characteristic.…”

Section: Introductionmentioning

confidence: 99%

Improved Stress Estimation with Machine Learning and Ultrasonic Guided Waves

2021

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Background Due to the acoustoelastic effect, ultrasonic guided waves have been used to estimate mechanical stress in a cheap and nondestructive fashion. Machine learning has been applied to map complex waveforms to stress estimates, though important aspects to construct and deploy real-time monitoring systems, such as accuracy and hardware consumption, to date, have not been concomitantly explored. Objective The goal of the present paper is to propose a data modeling methodology that optimizes accuracy and computational implementation, towards devising the best practices for real-time ultrasonic-based stress estimation. Methods We evaluate shallow and deep learning models with dimensionality reduction, which are compared to the most recent work showing overall better results for the approach presented herein both in terms of accuracy and hardware resources consumption. We generated a dataset to evaluate the models on a test rig with a plate measuring (i) ultrasonic guided waves excited by broadband signals and (ii) different stress conditions. The models are created and tested using a Monte-Carlo hold-out procedure with a repeated k-fold randomized hyperparameter search to evaluate their robustness in different stress conditions. ResultsWe dramatically improve the current state of the art by proposing and evaluating a more efficient model construction procedure. Results show that by using shallow models and principal component analysis, we were able to improve the model performance in terms of predictive power and inference hardware consumption. Specifically, we obtained an accuracy improvement of about 10% and hardware consumption used for inference smaller in three orders of magnitude when compared to the state-of-the-art reported with deep neural network models. Conclusions Results herein depicted impact the way practitioners may proceed for building efficient embedded predictive models for stress estimation using guided waves, enabling more precise, decentralized, and real-time nondestructive monitoring.

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Comparative evaluation of in situ stress monitoring with Rayleigh waves

Cited by 23 publications

References 50 publications

Structural Health Monitoring Using Fibre Optic Acoustic Emission Sensors

Structural Health Monitoring Using Fibre Optic Acoustic Emission Sensors

Inverse determination of delamination shape and size in self-sensing composites using electrical impedance tomography and genetic algorithms

Improved Stress Estimation with Machine Learning and Ultrasonic Guided Waves

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