Guided Wave Methods for Structural Health Monitoring

Giurgiutiu, Victor; Soutis, Constantinos

doi:10.1002/9780470686652.eae187

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…Guided waves (GW) are one of the most promising tools for structural health monitoring (SHM) of large plate-like structures and have attracted a great deal of interest from the research community [1,2]. GW-based techniques work on the principle that that the waves will be reflected by discontinuities in the medium (boundaries, damage).…”

Section: Introductionmentioning

confidence: 99%

A Study of Sensor Placement Optimization Problem for Guided Wave-Based Damage Detection

Soman

Kudela

Balasubramaniam

et al. 2019

Sensors

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Guided waves (GW) allow fast inspection of a large area and hence have attracted research interest from the structural health monitoring (SHM) community. Thus, GW-based SHM is ideal for thin structures such as plates, pipes, etc., and is finding applications in several fields like aerospace, automotive, wind energy, etc. The GW propagate along the surface of the sample and get reflected from discontinuities in the structure in the form of boundaries and damage. Through proper signal processing of the reflected waves based on their time of arrival, the damage can be detected and isolated. For complex structures, a higher number of sensors may be required, which increases the cost of the equipment, as well as the mass. Thus, there is an effort to reduce the number of sensors without compromising the quality of the monitoring achieved. It is of utmost importance that the entire structure can be investigated. Hence, it is necessary to optimize the locations of the sensors in order to maximize the coverage while limiting the number of sensors used. A genetic algorithm (GA)-based optimization strategy was proposed by the authors for use in a simple aluminum plate. This paper extends the optimization methodology for other shape plates and presents experimental, analytical, and numerical studies. The sensitivity studies have been carried out by changing the relative weights of the application demands and presented in the form of a Pareto front. The Pareto front allows comparison of the relative importance of the different application demands, and an appropriate choice can be made based on the information provided.

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Section: Introductionmentioning

confidence: 99%

A Study of Sensor Placement Optimization Problem for Guided Wave-Based Damage Detection

Soman

Kudela

Balasubramaniam

et al. 2019

Sensors

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“…A typical pitch–catch active-sensing diagnostic approach for hotspot SHM is to compare the difference (also referred to as scatter signal) between the receiving acousto-ultrasonic signal, in the form of a properly defined DI, from an actuator before and after the initiation of crack growth aiming at the detection of damage and estimation of its size (damage quantification). 1–6 As mentioned, a recent study by Janapati et al 10 showed that the dominating factors that influence the DI from one coupon to another within a nominally identical population are the sensor and actuator locations as well as the crack orientation for the same crack size. The sensor–actuator locations are defined relative to the target hotspot area under inspection.…”

Section: Methods Of Approachmentioning

confidence: 98%

“…1–9 The advantages of implementing SHM methods include the potential for improved safety and reliability, enhanced performance aligned with the actual structural state and potentially constrained within properly selected operating envelopes, increased resilience and sustainability, reduced maintenance and life-cycle cost, and ultimately the real-time structural awareness and the transition to condition-based maintenance. 1,4,6 Although SHM methods have shown high potential in providing real-time capabilities in damage detection, localization, and quantification tasks for real-life structures, one of the major challenges, similar to many other types of sensor-based systems facing real-life deployment, is still the lack of “trust” on the sensing data. The diagnostic error originating from sensing data needs to be quantifiable and validated through either experimental or analytical methods for the applied SHM approach.…”

Section: Introductionmentioning

confidence: 99%

Reliability of crack quantification via acousto-ultrasound active-sensing structural health monitoring using surface-mounted PZT actuators/sensors

Yadav

Mishra

Kopsaftopoulos

et al. 2020

Structural Health Monitoring

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This work presents the introduction and experimental investigation of an active-sensing acousto-ultrasound structural health monitoring approach for damage size quantification based on piezoelectric sensors/actuators mounted on multiple seemingly identical structural components. The objective of this work is to determine how reliable the damage diagnostics can be from one component to another similar (nominally identical) component using surface-mounted PZT (lead zirconate titanate) sensors/actuators, and also to evaluate how sensitive a sensor network configuration in terms of the number of sensors/actuators is with respect to its detection reliability. Extensive crack growth experiments on multiple identical coupons outfitted with the same sensor network configuration under cyclic loads were conducted to assess the damage quantification reliability from one coupon to another using the same diagnostic algorithm. The results of the study indicate that the crack size estimates obtained from the active-sensing structural health monitoring system can vary within the population of identical structural components (coupons), but the difference in quantifying damage among coupons decreases with the increase in the number of sensors and actuators used, that is, wave propagation paths. Furthermore, it is shown that the diagnostic results in terms of damage quantification converge with the increase in the number of sensors. The results of the study indicate that the diagnostic approach using a multi-path sensor network can reduce the damage quantification error from one component to another within a “hotspot” configuration (damage location is known or suspected a priori). Finally, the results of this study indicate that the more wave propagation paths used in the diagnostic active-sensing algorithm, the more reliable the damage quantification results are, provided that the same sensor network is used and installed at nominally identical locations for all coupons.

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“…In recent years, structural health monitoring (SHM) methodologies based on guided ultrasonic waves has attracted a great deal of interest, as they permit covering relatively large areas with a small number of sensors [ 1 ]. Lamb wave methods are sensitive to small damage, e.g., fatigue cracks in metallic structures or disbands and delamination in composite structures [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Damage Detection Using Lamb Wave-Based Phase-Sensitive OTDR and Support Vector Machines

Zahoor

Catalano

Vallifuoco

et al. 2023

Sensors

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In this paper, we propose and demonstrate a damage detection technique based on the automatic classification of the Lamb wave signals acquired on a metallic plate. In the reported experiments, Lamb waves are excited in an aluminum plate through a piezoelectric transducer glued onto the monitored structure. The response of the monitored structure is detected through a high-resolution phase-sensitive optical time-domain reflectometer (ϕ-OTDR). The presence and location of a small perturbation, induced by placing a lumped mass of 5 g on the plate, are determined by processing the optical fiber sensor data through support vector machine (SVM) classifiers trained with experimental data. The results show that the proposed method takes full advantage of the multipoint sensing nature of the ϕ-OTDR technology, resulting in accurate damage detection and localization.

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Guided Wave Methods for Structural Health Monitoring

Cited by 10 publications

References 12 publications

A Study of Sensor Placement Optimization Problem for Guided Wave-Based Damage Detection

A Study of Sensor Placement Optimization Problem for Guided Wave-Based Damage Detection

Reliability of crack quantification via acousto-ultrasound active-sensing structural health monitoring using surface-mounted PZT actuators/sensors

Automated Damage Detection Using Lamb Wave-Based Phase-Sensitive OTDR and Support Vector Machines

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