Abstract
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.
Highlights
This study introduced the applicability of deep learning (DL) algorithms and non-contact laser scanning in the estimation of the looseness
A deep convolutional neural network (DCNN) and several signal processing techniques were applied to obtain a better image of the DL performance
The results were compared with conventional regression models, k-nearest neighbor (kNN), support vector regression (SVR), and deep artificial neural network (DNN)
Summary
As one of the most well-known connection methods, bolt connections are widely used for connecting components to structures because of their simplicity of maintenance disassembly.bolted joints may operate under various conditions during service life, such as humidity, high temperature and cyclic loads, which may reduce the preload of the bolts, cause structural instability, or even contribute to a catastrophic accident and failure of the entire structure [1].effective monitoring and diagnosis of the bolt connections are necessary to ensure that structures are safe and reliable.Sensors 2020, 20, 5329; doi:10.3390/s20185329 www.mdpi.com/journal/sensors many academic works have explored different methods of detecting bolt loosening, including vibration-based measurements [2,3], electro-mechanical impedance methods [4,5,6,7,8], electrical conductivity techniques [9], ultrasonic-based measurements [10,11,12,13,14,15,16,17,18,19,20], and vision-based methods [21,22,23,24,25,26], much less research has been done on the applications of machine learning (ML)or deep learning (DL) algorithms in this field. Effective monitoring and diagnosis of the bolt connections are necessary to ensure that structures are safe and reliable. Many academic works have explored different methods of detecting bolt loosening, including vibration-based measurements [2,3], electro-mechanical impedance methods [4,5,6,7,8], electrical conductivity techniques [9], ultrasonic-based measurements [10,11,12,13,14,15,16,17,18,19,20], and vision-based methods [21,22,23,24,25,26], much less research has been done on the applications of machine learning (ML). ML and DL have become the breakthrough tools, in the field of computer vision, to overcome the limitations of conventional structural health monitoring (SHM) and non-destructive evaluation (NDE).
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