Experimental research on damage detecting in composite materials with FBG sensors under low frequency cycling

Zhu, Qi; Xu, Cheng; Yang, Guobiao

doi:10.1016/j.ijfatigue.2017.03.034

Cited by 15 publications

(5 citation statements)

References 9 publications

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“…The proposed technique performed better in 70.7% of the cases than the P-Inv-based technique. Zhu et al 80 investigated the damage detection capability of FBG sensors in composite materials under low-frequency cycling. FBG-embedded composite cantilever beam was used in this study for both undamaged and damaged scenarios.…”

Section: Strain-based Methodsmentioning

confidence: 99%

Sensory methods and machine learning based damage identification of fibre-reinforced composite structures: An introductory review

Bandara

Herath

Epaarachchi

2022

Journal of Reinforced Plastics and Composites

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Fibre-reinforced composite materials are extensively used for manufacturing critical engineering components in diverse applications, which demands intelligent and reliable structural health monitoring (SHM) schemes to prevent catastrophic failures associated with composite structures. Composite materials have complex failure mechanisms, and it is essential to employ reliable SHM methods with high accuracy to detect damages at the incipient stage. Although there are several SHM technologies available, no single strategy is impeccable for tackling all damage types due to the incredibly complex failure mechanisms of the composite materials. Machine learning (ML) methods are frequently integrated to compensate for the limitations of the traditional SHM methods. This paper presents the state-of-the-art sensory methods and deep learning (DL) techniques while emphasizing the future directions for the engineering and scientific community interested in developing novel SHM systems for fibre-reinforced polymer composite structures intended for civil, aerospace, automotive, marine, oil and gas exploration industries.

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Section: Strain-based Methodsmentioning

confidence: 99%

Sensory methods and machine learning based damage identification of fibre-reinforced composite structures: An introductory review

Bandara

Herath

Epaarachchi

2022

Journal of Reinforced Plastics and Composites

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“…If the component S i (t) is orthogonal, then the second term to the right of Formula ( 5) should be 0, global orthogonal exponent is obtained in Formula ( 6). Then the orthogonal exponent of any two components S k , S j can be obtained from Formula (7). The components of each order are orthogonal, and because each component is decomposed from the original data, the whitening EMD algorithm has better self-adaptability.…”

Section: Low Velocity Impact Feature Extraction Algorithmmentioning

confidence: 99%

“…The strength of composite board with variable thickness is usually higher than that of common composite board, but the signal analysis under dynamic load is more complicated. However, the real-time monitoring of composite board with variable thickness of impact signal can early warning the structural low velocity impact damage [7]. In recent years, scholars from all over the world have made some research on the structure of variable thickness of composite materials.…”

Section: Introductionmentioning

confidence: 99%

Low Velocity Impact Energy Monitoring and Recognition of Composite Laminates with Variable Thickness Based on Optical Fiber Sensor Network

Guan

Zhu

2021

Applied Sciences

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At present, most of the research on low velocity impact of composite laminates focuses on load location and damage assessment. To provide further early warnings about structural impact damage, impact energy can be monitored and identified. For high strength composite laminates with variable thickness, in order to further accurately evaluate the impact energy, it is necessary to adopt more suitable dynamic load signal analysis and impact energy identification methods. Therefore, a new low velocity impact monitoring and identification method for composite plates with variable thickness is proposed. All impact sample signals collected by optical fiber sensor network are decomposed by whitening Empirical Mode Decomposition (EMD); the energy feature set is established according to the impact energy eigenvalue of sample signal; according to the first order component of signal decomposition, the thickness coefficient is determined and the energy feature set is modified to evaluate the actual impact energy. Meanwhile, combined with optical fiber sensing and signal processing technology, an impact energy monitoring system has been established, and the low velocity impact monitoring and identification experiments of composite laminates with variable thickness were carried out. The proposed energy identification method successfully identified 1–3 J impact energy with an average error of 4.82%, and the average error of large thickness area with low sensitivity was significantly reduced from 13.25% to 5.67%. The results show that the thickness coefficient correction method based on whitening EMD can evaluate the low velocity impact energy more accurately, and the thickness coefficient correction step significantly improves the recognition performance.

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“…(2) Any shape distortion during fabrication, such as springbacks or warpages, could be detected and characterized. (3) Damage produced during handling can be identified before assembly, reducing time and costs of quality control (Shrestha et al, 2015; Zhu et al, 2017). (4) Monitoring of strain level that can be useful to UAS operators and autopilots for controlling that safety thresholds are not overpassed during aggressive maneuvers or operations.…”

Section: The Uasmentioning

confidence: 99%

Fiber Bragg grating application to study an unmanned aerial system composite wing

Gutiérrez

Fernández

Galvín

et al. 2019

Journal of Intelligent Material Systems and Structures

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Structural health monitoring consists of structural integrity assessment by means of data acquisition and analysis from on-board sensors. Fiber Bragg grating–based monitoring is increasingly attracting the scientific community working on structural health monitoring due to its multiple advantages such as electromagnetic immunity, negligible weight and size, and multiplexing availability. However, the integration of fiber optics within a structure still requires new procedures and signal treatment techniques for increasing technology reliability and exploiting its full potential. In this article, five embedded Fiber Bragg grating sensors are installed in an unmanned aerial system wing for correlating operational conditions with structural strain in real time. Sensor locations are determined by a finite element model accounting for manufacturing limitations of the fiber line. The developed Fiber Bragg grating system and processing techniques are used in static and dynamic tests showing the capacities of this powerful technology. The assessment includes deflection shape estimation, strain cycles counting, audible and visual strain alarms, aileron control based on strain levels, and structural resonance response detection.

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Experimental research on damage detecting in composite materials with FBG sensors under low frequency cycling

Cited by 15 publications

References 9 publications

Sensory methods and machine learning based damage identification of fibre-reinforced composite structures: An introductory review

Sensory methods and machine learning based damage identification of fibre-reinforced composite structures: An introductory review

Low Velocity Impact Energy Monitoring and Recognition of Composite Laminates with Variable Thickness Based on Optical Fiber Sensor Network

Fiber Bragg grating application to study an unmanned aerial system composite wing

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