Impact Localization and Severity Estimation on Composite Structure Using Fiber Bragg Grating Sensors by Least Square Support Vector Regression

Datta, Amitabha; Augustin, M. J.; Gupta, Neha; Viswamurthy, Sathyamangalam Ramanarayanan; Gaddikeri, Kotresh M.; Sundaram, Ramesh

doi:10.1109/jsen.2019.2901453

Cited by 59 publications

(20 citation statements)

References 13 publications

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“…Because of the good generalization ability of support vector machine (SVM), SVM has been widely used for forecasting [38]. However, the training process of SVM is a constrained quadratic programming problem.…”

Section: Least Squares Support Vector Machine(lssvm) Principlementioning

confidence: 99%

Improving the Reliability of Photovoltaic and Wind Power Storage Systems Using Least Squares Support Vector Machine Optimized by Improved Chicken Swarm Algorithm

Liu

Tseng

et al. 2019

Applied Sciences

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In photovoltaic and wind power storage systems, the reliability of the battery directly affects the overall reliability of the energy storage system. Failed batteries can seriously affect the stable operation of energy storage systems. This paper aims to improve the reliability of the storage systems by accurately predicting battery life and identifying failing batteries in time. The current prediction models mainly use artificial neural networks, Gaussian process regression and hybrid models. Although these models can achieve high prediction accuracy, the computational cost is high due to model complexity. Least squares support vector machine (LSSVM) is a computationally efficient alternative. Hence, this study combines the improved chicken swarm optimization algorithm (ICSO) and LSSVM into a hybrid ICSO-LSSVM model for the reliability of photovoltaic and wind power storage systems. The following are the contributions of this work. First, the optimal penalty parameter and kernel width are determined. Second, the chicken swarm optimization algorithm (CSO) is improved by introducing chaotic search behavior in the hen and an adaptive learning factor in the chicks. The performance of the ICSO algorithm is shown to be better than CSO using standard test problems. Third, the prediction accuracy of the three models is compared. For NMC1 battery, the predicted relative error of ICSO-LSSVM is 0.94%; for NMC2 battery, the relative error of ICSO-LSSVM is 1%. These findings show that the proposed model is suitable for predicting the failure of batteries in energy storage systems, which can improve preventive and predictive maintenance of such systems.

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Section: Least Squares Support Vector Machine(lssvm) Principlementioning

confidence: 99%

Improving the Reliability of Photovoltaic and Wind Power Storage Systems Using Least Squares Support Vector Machine Optimized by Improved Chicken Swarm Algorithm

Liu

Tseng

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In addition, indications are given of what research and development lines may be for advanced SHM systems. As already introduced in this paragraph, monitoring techniques that are based on UGW by piezoelectric transducers are among the most common and most developed, since they have a longer history [ 7 ] than SHM systems based on optical sensors, in particular Fiber Bragg Grating (FBG) sensors; for completeness, the evolution of state of the art for optoelectronic sensors is reported in [ 8 , 9 , 10 , 11 , 12 ], but it is not discussed further in this paper. Similarly, the evolution of piezoelectric materials for the construction of UGW sensors and transducers, the development of low-power consumption integrated electronic components and systems requires a continuous updating of research to provide new design methodologies and technologies to bring in the field the SHM systems.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

Capineri

Bulletti

2021

Sensors

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This review article is focused on the analysis of the state of the art of sensors for guided ultrasonic waves for the detection and localization of impacts for structural health monitoring (SHM). The recent developments in sensor technologies are then reported and discussed through the many references in recent scientific literature. The physical phenomena that are related to impact event and the related main physical quantities are then introduced to discuss their importance in the development of the hardware and software components for SHM systems. An important aspect of the article is the description of the different ultrasonic sensor technologies that are currently present in the literature and what advantages and disadvantages they could bring in relation to the various phenomena investigated. In this context, the analysis of the front-end electronics is deepened, the type of data transmission both in terms of wired and wireless technology and of online and offline signal processing. The integration aspects of sensors for the creation of networks with autonomous nodes with the possibility of powering through energy harvesting devices and the embedded processing capacity is also studied. Finally, the emerging sector of processing techniques using deep learning and artificial intelligence concludes the review by indicating the potential for the detection and autonomous characterization of the impacts.

show abstract

“…In addition, indications are given of what research and development lines may be for advanced SHM systems. As already introduced in this paragraph, monitoring techniques that are based on UGW by piezoelectric transducers are among the most common and most developed, since they have a longer history [7] than SHM systems based on optical sensors, in particular Fiber Bragg Grating (FBG) sensors; for completeness, the evolution of state of the art for optoelectronic sensors is reported in [8][9][10][11][12], but it is not discussed further in this paper. Similarly, the evolution of piezoelectric materials for the construction of UGW sensors and transducers, the development of low-power consumption integrated electronic components and systems requires a continuous updating of research to provide new design methodologies and technologies to bring in the field the SHM systems.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

Capineri¹,

Bulletti²

2021

Preprint

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This review article is focused on the analysis of the state of the art of sensors for guided 9 ultrasonic waves for the detection and localization of impacts, therefore of interest for the structural 10 health monitoring (SHM). The recent developments in sensor technologies are then reported and 11 discussed through the many references in recent scientific literature. The physical phenomena re-12 lated to impact event and the main physical quantities are then introduced to discuss their im-13 portance in the development of the hardware and software components for SHM systems. An im-14 portant aspect of the article is the description of the different ultrasonic sensor technologies cur-15 rently present in the literature and what advantages and disadvantages they could bring, in relation 16 to the various phenomena investigated. In this context, the analysis of the front-end electronics is 17 deepened, the type of data transmission both in terms of wired and wireless technology and in terms 18 of online and offline signal processing. The integration aspects of sensors for the creation of net-19 works with autonomous nodes with the possibility of powering through energy harvesting devices 20 and the embedded processing capacity is also studied. Finally, the emerging sector of processing 21 techniques using deep learning and artificial intelligence concludes the review by indicating the 22 potential for the detection and autonomous characterization of the impacts.

show abstract

Impact Localization and Severity Estimation on Composite Structure Using Fiber Bragg Grating Sensors by Least Square Support Vector Regression

Cited by 59 publications

References 13 publications

Improving the Reliability of Photovoltaic and Wind Power Storage Systems Using Least Squares Support Vector Machine Optimized by Improved Chicken Swarm Algorithm

Improving the Reliability of Photovoltaic and Wind Power Storage Systems Using Least Squares Support Vector Machine Optimized by Improved Chicken Swarm Algorithm

Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

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