Improved static and dynamic FBG sensor system for real-time monitoring of composite structures
Glass-fibre reinforced polymer (GFRP) composite materials certainly have the undeniable favour over conventional metallic materials, notably in light weight to high strength ratio. However, these composite materials are prone to sudden catastrophic damage that requires the structural health monitori...
| Summary: | Glass-fibre reinforced polymer (GFRP) composite materials certainly have the undeniable favour over conventional metallic materials, notably in light weight to high strength ratio. However, these composite materials are prone to sudden catastrophic damage that requires the structural health monitoring (SHM). FBG sensor has shown a great potential in embedding and integrating with the composite materials, performing real-time monitoring of the structural condition. However, a critical review on the current FBG based real-time monitoring system initiates that many attempts and intentions are still needed to bring the present monitoring system to a fully matured readiness level. The main problems are the drawbacks in static and dynamic strain sensing monitoring assessment. Error in desired readings due to variations in output voltage and spectrum illustration for static strain interpretation are the drawbacks in static strain sensing. On the other hand, due to the presence of noise in the signal spectrum, the estimation of time of arrival (TOA) through peak detection is pin-pointed as the drawback in dynamic strain sensing. Thus, the designation of this research study is to improve the current FBG based real-time monitoring system with the use of certain functions and algorithms, that are the instant mesh-grid function, voltage normalization algorithm, CC-LSL algorithm, and FFT function. Two specimens have been fabricated namely composite plate and composite beam which are based on hand lay-up lamination method. FBG sensors are embedded in both the structures. For improvement in static strain measurement, both the specimens are being subjected to load induced. As a results, the mesh-grid function utilized is capable of meshing any sizes and shapes of a structure, and display the deflection of the structure in an interactive way of artificial representation. The voltage normalization algorithm has reduced the output voltage variations from 26 data/minute to 17 data/minute with the elimination of pre-calibration each time before use. For the improvement in dynamic strain sensing, impact localization are being carried out on the beam at certain points. As a results, the merging of cross-correlation approach with linear source location technique (CC-LSL) has estimated the impact location close to the actual hit location with the largest relative error at only 2.47 %. The comparison of frequency spectrum between FBG sensor and AE sensor shows an identical profile with the percentage error of less than 10 %. The validation of frequency spectrums from FBG sensor and AE sensor with Abaqus FEA simulation shows that the frequency spectrums captured by FBG sensor are more sensitive to the normal mode wave propagation of the structure compared to AE sensor. Overall, the static and dynamic sensitivity of the FBG sensor was recorded at 1.21 pm/με with maximum capturing frequency of 5 kHz. From the conclusion of the study, it is truly believed that with this reputable sensing system, it is is one step closer to achieving the key concept of smart structure. |
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