Mr. Yunseok Gwon Profile

Yunseok Gwon

at South Dakota School of Mines and Technology

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Publications (2)

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Proceedings Article | 27 March 2018 Presentation + Paper

Reliability of surface response to excitation method for data-driven prognostics using Gaussian process regression

H. Fekrmandi, Y. S. Gwon

Proceedings Volume 10600, 106002R (2018) https://doi.org/10.1117/12.2304475

KEYWORDS: Composites, General packet radio service, Structural health monitoring, Sensors, Reliability, Calibration, Data modeling, Ultrasonics, Electromagnetic coupling, Data analysis

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In this study, the surface response to excitation method (SuRE) is investigated using a data-driven method for diagnostics and prognostics of applied load on a plate structure. The SuRE method is an emerging approach in ultrasonic wave-based structural health monitoring (SHM). In this method, high-frequency, surface-guided waves are excited on the structure using piezoceramic elements. The waves propagate and interact with internal or surface damages on the structure. State of heath is evaluated by monitoring changes in the measured frequency transfer functions. Reliability and computational efficiency of the SuRE method has been verified for several diagnostic and structural health monitoring applications. In this paper, the effectiveness of the SuRE method for prognostics and health management (PHM) of a composite plate under applied load is studied. Two piezoelectric elements are attached on the surface of a carbon fiber reinforced polymer (CFRP) composite plate. Sweep excitation-generated (150-250 kHz), surface-guided waves and the transmitted waves were monitored at the sensory position. The reference data set comprised of characteristic transfer functions was generated. SHM data using the SuRE method was captured for eleven locations of applied load between the sensor and exciter. Four data-driven prognostic models, using Gaussian Processes Regression (GPR), were qualified by interval-averaging features extracted from the spectrums and predicted the location of load. During this study, a new approach based on SuRE method is proposed for identifying the location of applied load on a composite and the optimum parameters of the study were evaluated to enhance the performance of GPR identified the optimum parameters number of SuRE method and selected features for most accurate predictions.

Proceedings Article | 27 March 2018 Presentation + Paper

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

Y. S. Gwon, H. Fekrmandi

Proceedings Volume 10602, 1060206 (2018) https://doi.org/10.1117/12.2305840

KEYWORDS: Composites, Sensors, Structural health monitoring, Machine learning, Data modeling, Data processing, Data acquisition, Signal generators, Ultrasonics, Wave propagation

Read 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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