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Open AccessArticle

Artificial Intelligence-Based Bolt Loosening Diagnosis Using Deep Learning Algorithms for Laser Ultrasonic Wave Propagation Data

1
Department of Civil, Architecture and Environmental System Engineering, Sungkyunkwan University, Suwon 16419, Korea
2
Department of Safety Engineering, Dongguk University-Gyeongju, Gyeongju 38066, Korea
3
School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, Suwon 16419, Korea
4
Technical Research Center, Smart Inside Co., Ltd., Suwon 16419, Korea
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(18), 5329; https://doi.org/10.3390/s20185329
Received: 11 August 2020 / Revised: 12 September 2020 / Accepted: 14 September 2020 / Published: 17 September 2020
The application of deep learning (DL) algorithms to non-destructive evaluation (NDE) is now becoming one of the most attractive topics in this field. As a contribution to such research, this study aims to investigate the application of DL algorithms for detecting and estimating the looseness in bolted joints using a laser ultrasonic technique. This research was conducted based on a hypothesis regarding the relationship between the true contact area of the bolt head-plate and the guided wave energy lost while the ultrasonic waves pass through it. First, a Q-switched Nd:YAG pulsed laser and an acoustic emission sensor were used as exciting and sensing ultrasonic signals, respectively. Then, a 3D full-field ultrasonic data set was created using an ultrasonic wave propagation imaging (UWPI) process, after which several signal processing techniques were applied to generate the processed data. By using a deep convolutional neural network (DCNN) with a VGG-like architecture based regression model, the estimated error was calculated to compare the performance of a DCNN on different processed data set. The proposed approach was also compared with a K-nearest neighbor, support vector regression, and deep artificial neural network for regression to demonstrate its robustness. Consequently, it was found that the proposed approach shows potential for the incorporation of laser-generated ultrasound and DL algorithms. In addition, the signal processing technique has been shown to have an important impact on the DL performance for automatic looseness estimation. View Full-Text
Keywords: acoustic emission; digital signal processing; laser applications; machine learning; structural shapes; waves acoustic emission; digital signal processing; laser applications; machine learning; structural shapes; waves
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MDPI and ACS Style

Tran, D.Q.; Kim, J.-W.; Tola, K.D.; Kim, W.; Park, S. Artificial Intelligence-Based Bolt Loosening Diagnosis Using Deep Learning Algorithms for Laser Ultrasonic Wave Propagation Data. Sensors 2020, 20, 5329.

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