Detection of loosening angle for mark bolted joints with computer vision and geometric imaging

Deng, Xinjian; Liu, Jianhua; Gong, Honghan; Huang, Jiayu

doi:10.1016/j.autcon.2022.104517

Cited by 19 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They performed experimental analysis on a flange joint to exhibit its accuracy in detecting rotation angle. Deng et al 167 integrated computer vision and geometric imaging theory for automatic looseness detection in bolted joints. Initially, they utilized a keypoint-RCNN to detect key points and establish a region of interest (ROI).…”

Section: Mechanical Properties Ofmentioning

confidence: 99%

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Chelimilla

Chinthapenta

Kali

et al. 2023

Structural Health Monitoring

View full text Add to dashboard Cite

The integrity of bolted joints is still a challenging problem owing to the gross or localized slip at the interfacial surfaces of the joints when subjected to external disturbances such as vibrations. This slip escalates the interfacial movement and, thus, leads to a decrease in preload levels, that is, looseness of the bolted assemblies. In the last decade, modal analysis, wave propagation, and percussion methods were traditionally used to detect looseness in bolted connections. With an increase in computational power, machine learning algorithms such as neural networks, random forests, decision trees, and support vector machines complemented the traditional methods in accurate looseness estimation. Subsequently, this integration paved the path for real-time health monitoring of bolted joints. This paper summarizes recent investigations on looseness detection in bolted assemblies based on traditional methods and machine learning algorithms. The working principle, advantages, challenges, and applications of the aforementioned methods are also detailed in this paper. Apart from these investigations, the latest studies on the Internet of Things-based health monitoring of structures are also reviewed to explore their adaptability in remote monitoring of bolted connections for damage detection in the future.

show abstract

Section: Mechanical Properties Ofmentioning

confidence: 99%

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Chelimilla

Chinthapenta

Kali

et al. 2023

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…This method can identify bolts with a looseness of only 2°. Deng et al 20 determined the mark ellipse by identifying five key points of the bolt. Furthermore, the reference and rotation mark points are determined by identifying the intersections of the bar marks and ellipse.…”

Section: Introductionmentioning

confidence: 99%

Vision‐based robust missing and loosened bolt detection for splice plate joints

Yao,

Chen,

Liu

et al. 2024

Structural Design Tall Build

View full text Add to dashboard Cite

SummaryVision‐based bolt defect detection methods based on feature changes have been reported. However, the robustness of key feature extraction and bolt detection requires improvement. This paper proposes a robust missing and loose bolt defect detection approach. The key features—reference points for perspective correction and the straight lines of the bolt edges—are extracted from the masks obtained by semantic segmentation models. The true and false bolt discrimination approach based on the mask shape can help improve bolt object detection accuracy. Overlapping between the bolt bounding boxes in the reference and detection images indicates missing bolts. The rotation angles reveal loosened bolts. The proposed approach was tested on fabricated bolted joint specimens and a steel railway bridge. The results suggest that these improvements ensure defect detection accuracy, with a miss rate of only 1% for missing bolt detection. Moreover, a loosened bolt with only 3° rotation is successfully detected. This approach has promising potential applicability in automatically detecting bolt defects in large steel structures.

show abstract

“…Huynh et al 37 detected and cut the bolt area through convolutional neural network, and then identified the bolt loosening angle based on Hough transform. Deng et al 38 based on computer vision and geometric imaging theory to detect the marked bolt loosening angle, including three modules: bolt positioning, key point identification, and loosening angle calculation. All in all, the advantages of computer vision methods in cost, application scenarios and future development have been demonstrated in existing research.…”

Section: Introductionmentioning

confidence: 99%

Bolt looseness detection based on Canny edge detection algorithm

Song

Cui

et al. 2023

Concurrency and Computation

View full text Add to dashboard Cite

SummaryAiming at current serious problems that the bolt looseness can cause heavy damages and the manual bolt looseness detection is ineffective, this study presents a bolt looseness detection method based on Canny edge detector. First, HSV (hue, saturation, value) color space is used to extract the area of mark bar. Then, after expanding, eroding and median filtering the image, Canny edge detector is used to segment the marker area to extract its edge. Last, the fitted slope of edge is used to judge whether the bolt is loose. The experimental results show that the proposed method has F1 score of 88.37%, contributing to the automated detection of bolt looseness.

show abstract

Detection of loosening angle for mark bolted joints with computer vision and geometric imaging

Cited by 19 publications

References 15 publications

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Vision‐based robust missing and loosened bolt detection for splice plate joints

Bolt looseness detection based on Canny edge detection algorithm

Contact Info

Product

Resources

About