ArticleSensors (Basel, Switzerland)2020
Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Physics-guided resampling and asymmetric fusion for ultra-high-frequency bolt-loosening detection.Scientific reports · 2026Article
- An image dataset for use in detecting unwanted bolt rotation.Data in brief · 2025Article
- Automated Structural Bolt Micro Looseness Monitoring Method Using Deep Learning.Sensors (Basel, Switzerland) · 2024Article
- Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends.Sensors (Basel, Switzerland) · 2023Review
- Diagnostics of Bolted Joints in Vibrating Screens Based on a Multi-Body Dynamical Model.Materials (Basel, Switzerland) · 2023Article
- Detection of Missing Bolts for Engineering Structures in Natural Environment Using Machine Vision and Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Bolt Installation Defect Detection Based on a Multi-Sensor Method.Sensors (Basel, Switzerland) · 2023Article
- Scattered Train Bolt Point Cloud Segmentation Based on Hierarchical Multi-Scale Feature Learning.Sensors (Basel, Switzerland) · 2023Article
- Vision-Based Detection of Bolt Loosening Using YOLOv5.Sensors (Basel, Switzerland) · 2022Article
- Corroded Bolt Identification Using Mask Region-Based Deep Learning Trained on Synthesized Data.Sensors (Basel, Switzerland) · 2022Article
- Vibration-Based Loosening Detection of a Multi-Bolt Structure Using Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2022Article
- Correction: Pham et al. Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model.Sensors (Basel, Switzerland) · 2021Article
- Vibration-Based Diagnostics of Radial Clearances and Bolts Loosening in the Bearing Supports of the Heavy-Duty Gearboxes.Sensors (Basel, Switzerland) · 2020Article
- Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert-Huang Transform and Deep Learning.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.