Optimization of sensor placement for guided waves based SHM using fiber Bragg grating sensors

Soman, Rohan; Wee, Junghyun; Peters, Kara; Ostachowicz, Wiesław

doi:10.1117/12.2558393

Cited by 5 publications

(9 citation statements)

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“…A similar directionality was observed for the PS-FBG sensor in the far-field region, but it is more complex for a shorter distance of propagation [114]. For the remote bonded configuration, experimental studies reveal a cos θ proportionality as opposed to the cos 2 θ proportionality for the direct bond [113], as seen in Figure 20. This is the same behavior as observed for sensors coupled through viscous bonding, where the transfer is through force rather than displacement [115].…”

Section: Directionalitysupporting

confidence: 65%

“…As a result, the axial strain in the FBG is proportional to cos 2 θ. Several researchers have experimentally investigated this, and confirmed the analytical expectations [94,113]. Example data for a FBG sensitivity to a S 0 Lamb wave at three different frequencies is shown in Figure 19.…”

Section: Directionalitymentioning

confidence: 58%

“…Optimization methods have been developed for piezo-based systems [175], however, these methods cannot be applied directly to optical fiber sensors due to their passive nature and directional sensitivity. Soman et al [113] developed an optimization system for FBG sensors based on genetic algorithm, which takes into consideration the directional sensitivity of the fibers. They later extended the work for the optimization of the actuators for a given location of the sensor [176,177].…”

Section: Conclusion and Future Trendsmentioning

confidence: 99%

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Optical Fiber Sensors for Ultrasonic Structural Health Monitoring: A Review

Soman

Wee

Peters

2021

Sensors

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Guided waves (GW) and acoustic emission (AE) -based structural health monitoring (SHM) have widespread applications in structures, as the monitoring of an entire structure is possible with a limited number of sensors. Optical fiber-based sensors offer several advantages, such as their low weight, small size, ability to be embedded, and immunity to electro-magnetic interference. Therefore, they have long been regarded as an ideal sensing solution for SHM. In this review, the different optical fiber technologies used for ultrasonic sensing are discussed in detail. Special attention has been given to the new developments in the use of FBG sensors for ultrasonic measurements, as they are the most promising and widely used of the sensors. The paper highlights the physics of the wave coupling to the optical fiber and explains the different phenomena such as directional sensitivity and directional coupling of the wave. Applications of the different sensors in real SHM applications have also been discussed. Finally, the review identifies the encouraging trends and future areas where the field is expected to develop.

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Section: Directionalitysupporting

confidence: 65%

Section: Directionalitymentioning

confidence: 58%

Section: Conclusion and Future Trendsmentioning

confidence: 99%

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Optical Fiber Sensors for Ultrasonic Structural Health Monitoring: A Review

Soman

Wee

Peters

2021

Sensors

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“…The most widely used ML algorithms in sensor optimizations are genetic algorithms (GA) in coordination with neural networks (NN), 61 as shown in Table 3. Different computational methodologies in optimal sensor placement along with the advantages and disadvantages of each algorithm are reviewed in Bigoni et al 62 and Soman et al 63 In practice, ML models are typically adapted to reposition the existing sensor locations to increase overall system performability in terms of damage detection. It offers simple, adaptable, and low-cost solutions over traditional approaches.…”

Section: Data Acquisitionmentioning

confidence: 99%

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Malekloo

Özer

AlHamaydeh

et al. 2021

Structural Health Monitoring

231

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Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past’s applicative inefficiencies and the emerging technologies of the future. In the age of the smart city, Internet of Things (IoT), and big data analytics, the complex nature of data-driven civil infrastructures monitoring frameworks has not been fully matured. Machine learning (ML) algorithms are thus providing the necessary tools to augment the capabilities of SHM systems and provide intelligent solutions for the challenges of the past. This article aims to clarify and review the ML frontiers involved in modern SHM systems. A detailed analysis of the ML pipelines is provided, and the in-demand methods and algorithms are summarized in augmentative tables and figures. Connecting the ubiquitous sensing and big data processing of critical information in infrastructures through the IoT paradigm is the future of SHM systems. In line with these digital advancements, considering the next-generation SHM and ML combinations, recent breakthroughs in (1) mobile device-assisted, (2) unmanned aerial vehicles, (3) virtual/augmented reality, and (4) digital twins are discussed at length. Finally, the current and future challenges and open research issues in SHM-ML conjunction are examined. The roadmap of utilizing emerging technologies within ML-engaged SHM is still in its infancy; thus, the article offers an outlook on the future of monitoring systems in assessing civil infrastructure integrity.

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“…For instance, for a 6-sensor system consisting of six PZT sensors there are potentially

= 15 sensor–actuator pairs, at the same time, for a sensor system with five actuators and one FBG sensor there are only five sensor–actuator pairs. Moreover, it has been shown in [ 14 , 15 , 16 ] that the response of FBG’s to incident GW is a function of cosine-squared of the angle between the axis of the FBG and the incident wave. Thus, the signal processing technique should incorporate these FBG imposed shortcomings into the damage detection algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Two-Step Guided Waves Based Damage Localization Technique Using Optical Fiber Sensors

Soman

Balasubramaniam

Golestani

et al. 2020

Sensors

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Structural health monitoring (SHM) systems help in reducing maintenance cost and avoiding catastrophic failure of the structure. As a result, they have been a focus of research for the past few decades. Ideally, the methods employed should be low cost and able to detect and localize small levels of damage reliably and accurately. This paper describes a guided waves (GW) based two-step technique for damage detection and localization using fiber Bragg grating (FBG) sensors. The FBG sensors offer benefits such as the ability to be embedded and multiplexed as well as being lightweight and insensitive to electric and magnetic fields, and they have long been seen as a promising solution for the GW measurements in structures. Unfortunately, in the conventional wavelength-based interrogation they have very low signal to noise ratio and as a result low sensitivity. Therefore, the FBG sensor is incorporated in the edge filtering configuration. The major challenges in the use of FBG sensors for GW-based detection are their directional sensitivity and passive nature. The passive nature leads to the reduction in the available actuator–sensor (AS) pairs while the directionality makes the signal processing a challenge. The proposed two-step methodology overcomes these shortcomings of FBG sensors. In the first step the amplitude weighted elliptical approach is used to identify the hotspots due to the inadequate number of AS pairs, the elliptical approach is not sufficient for damage localization. Therefore, in order to further localize the damage the edge reflection based ray-tracing approach is implemented in the second step. Through the two step method, the damage is accurately located. The paper provides the proof of concept of the proposed methodology on an aluminum plate with simulated damage. The results indicate, that indeed the two-step methodology allows accurate damage localization and overcomes the possibility of false detections.

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Optimization of sensor placement for guided waves based SHM using fiber Bragg grating sensors

Cited by 5 publications

References 0 publications

Optical Fiber Sensors for Ultrasonic Structural Health Monitoring: A Review

Optical Fiber Sensors for Ultrasonic Structural Health Monitoring: A Review

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

A Two-Step Guided Waves Based Damage Localization Technique Using Optical Fiber Sensors

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