A data-driven approach of load monitoring on laminated composite plates using support vector machine

Y. S. Gwon; H. Fekrmandi

doi:10.1117/12.2305840

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27 March 2018 A data-driven approach of load monitoring on laminated composite plates using support vector machine

Y. S. Gwon, H. Fekrmandi

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Proceedings Volume 10602, Smart Structures and NDE for Industry 4.0; 1060206 (2018) https://doi.org/10.1117/12.2305840
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2018, Denver, Colorado, United States

Abstract

In this study, the surface response to excitation method (SuRE) is investigated using a data-driven method for load monitoring on a laminated composite plate structure. The SuRE method is an emerging approach in ultrasonic wavebased structural health monitoring (SHM) field. In this method, a range of high-frequency, surface-guided waves are excited on the structure using piezoceramic elements. The waves propagate on the structure and interact with internal or surface damages. Initially, a baseline data of the intact structure is created by measuring the frequency transfer function between the excitation and sensing point. The integrity of structure is evaluated by monitoring changes in the frequency spectrums. The SuRE method has effectively been used for a variety of SHM applications including the detection of loose bolts, delamination in composite structures, internal corrosion in pipelines, and load and impact monitoring. Data obtained using the SuRE method was used for identifying the location of the applied load on a laminated composite plate using Support Vector Machine (SVM). A set of two piezoelectric elements were attached on the surface of the plate. A sweep excitation (150-250 kHz) generated surface-guided waves, and the transmitted waves were monitored at the sensory positions. The reference data set was measured simultaneously from the sensors. The plate was subjected to static loads while health monitoring data was being captured using the SuRE method. The confusion matrix indicated that the model classified correctly with up to 99.8% accuracy.

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Y. S. Gwon and H. Fekrmandi "A data-driven approach of load monitoring on laminated composite plates using support vector machine", Proc. SPIE 10602, Smart Structures and NDE for Industry 4.0, 1060206 (27 March 2018); https://doi.org/10.1117/12.2305840

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