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

Abstract

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.

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert–Huang Transform and Deep Learning

“…In [ 46 ], Oliveira et al proposed a novel electromechanical impedance SHM solution by combining electromechanical impedance piezoelectricity (EMI-PZT), the high accuracy of proposed method is guaranteed by CNN-based feature extraction which include several banks of filters. In [ 47 ], a graphic model and CNN-based bolt loosening SHM method was proposed by Pham et al The proposed method performed a CNN-based bolt detection algorithm and used the Hough transfer-based method to estimate the bolt angle, this method proposed a convenient strategy to monitoring the bolt structures just using images sampled by a camera.…”

“…In FD applications, because raw data is often sampled in one-dimensional (1-D) format, researchers have turned to feature extraction operations that construct 2-D features for addressing FD problems using CNNs, such as sliding window [ 38 , 39 ], short time Fourier transform (STFT) [ 40 ], discrete wavelet transform (DWT) [ 41 , 42 ], and Hilbert–Huang transform (HHT) [ 43 , 44 ]. Structure health monitoring (SHM) is becoming a research hotspot in which CNN is applied and several methods have been proposed in the field of SHM combining CNN to solve mechanical system SHM problems [ 45 , 46 , 47 ].…”

Percussion‐based bolt looseness identification using vibration‐guided sound reconstruction

“…Besides, vision-based methods offer unique advantages for effective inspection of bolt groups. The rotation angle of bolt head [16][17][18][19][20] and the exposed screw from the contact surface [21][22][23][24] can be regarded as looseness indicators to discriminate bolt conditions. However, visual methods are suitable to quickly determine a bolt with remarkable looseness but are not capable of quantifying bolt looseness degrees, especially for the early looseness.…”

Condition Assessment of Joints in Steel Truss Bridges Using a Probabilistic Neural Network and Finite Element Model Updating

“…While having a clear theoretical basis and being easy to apply, the direct methods have in practice low accuracy, which favors the indirect methods. The indirect methods generally comprise the impedance-based, vibration-based, ultrasonicbased, and vision-based approaches [7,8]. It has become increasingly popular to detect flaws by using vibration-based methods as a global approach in both academic research and practical applications.…”