Bolt-loosening identification of bolt connections by vision image-based technique

Self Cite

In this study, we investigate a novel idea of using synthetic images of bolts which are generated from a graphical model to train a deep learning model for loosened bolt detection. Firstly, a framework for bolt-loosening detection using image-based deep learning and computer graphics is proposed. Next, the feasibility of the proposed framework is demonstrated through the bolt-loosening monitoring of a lab-scaled bolted joint model. For practicality, the proposed idea is evaluated on the real-scale bolted connections of a historical truss bridge in Danang, Vietnam. The results show that the deep learning model trained by the synthesized images can achieve accurate bolt recognitions and looseness detections. The proposed methodology could help to reduce the time and cost associated with the collection of high-quality training data and further accelerate the applicability of vision-based deep learning models trained on synthetic data in practice.

Section: Homography-based Perspective Rectificationmentioning

confidence: 99%

“…Recently, several research groups have put their efforts into developing vision-based approaches for loosened bolt assessment [ 23 , 24 , 25 , 26 , 27 , 28 ]. A pioneered vision-based approach was developed by Park et al [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model

Pham

Kim

et al. 2020

Self Cite

“…Several vision-based assessment methods for real-time bolt looseness detection have been proposed [122,123,124]. Nguyen et al [125] proposed a vision-based algorithm to identify bolt-looseness in steel structure bolted flange connections. A similar vision-based monitoring technique for detection of bolted joints looseness in wind turbine tower structures was proposed by Park et al [126] which can be adopted to pipeline monitoring in a fairly straightforward fashion.…”

Section: Visual/ Biological Leak Detection Methodsmentioning

confidence: 99%

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

Adegboye

Fung

Karnik

2019

280

144

Pipelines are widely used for the transportation of hydrocarbon fluids over millions of miles all over the world. The structures of the pipelines are designed to withstand several environmental loading conditions to ensure safe and reliable distribution from point of production to the shore or distribution depot. However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature. Incidents of pipeline failure can result in serious ecological disasters, human casualties and financial loss. In order to avoid such menace and maintain safe and reliable pipeline infrastructure, substantial research efforts have been devoted to implementing pipeline leak detection and localisation using different approaches. This paper discusses pipeline leakage detection technologies and summarises the state-of-the-art achievements. Different leakage detection and localisation in pipeline systems are reviewed and their strengths and weaknesses are highlighted. Comparative performance analysis is performed to provide a guide in determining which leak detection method is appropriate for particular operating settings. In addition, research gaps and open issues for development of reliable pipeline leakage detection systems are discussed.

“…Although 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. Recently, ML and DL have become the breakthrough tools, particularly in the field of computer vision, to overcome the limitations of conventional structural health monitoring (SHM) and non-destructive evaluation (NDE).…”

Section: Introductionmentioning

confidence: 99%

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

Tran

Kim

Tola

et al. 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.