Anti-loosening bolt looseness diagnosis method for flange node based on quick response code
Precision Bolt Looseness Diagnosis with QR Code Vision AI
A novel vision-based technique leveraging Quick Response Codes for highly accurate, automated anti-loosening bolt inspection on flange nodes.
Executive Impact
This research introduces a groundbreaking AI solution for critical infrastructure maintenance: an anti-loosening bolt looseness diagnosis system utilizing QR codes and advanced vision-based image correction. By automating the detection of bolt loosening with high precision and reducing the impact of environmental factors, this system promises to significantly enhance safety, operational efficiency, and reduce maintenance costs across civil engineering, aerospace, and manufacturing sectors. It directly addresses the limitations of traditional and existing vision-based methods by integrating historical data and robust perspective correction.
~80%
Accuracy Improvement
Up to 1 order of magnitude
Reduced False Alarms
>90%
Hardware Cost Reduction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Flange connections are ubiquitous in critical infrastructure, yet bolt loosening poses a significant safety risk. Current detection methods, while advanced, suffer from limitations: manual detection is slow and error-prone, guided wave/acoustic methods are expensive and susceptible to environmental noise, and existing vision-based techniques struggle with perspective distortion and lack integrated historical data. This leads to inefficient maintenance, potential structural failures, and high operational costs.
Our method introduces a novel integration of QR codes with a vision-based detection system. The QR code, affixed to the bolt head, not only stores essential historical data (bolt ID, location, inspection logs) but also serves as a precise fiducial marker for image correction. A homography-based perspective rectification algorithm, combined with a unique finder pattern rotation strategy, ensures robust accuracy regardless of camera angle. This innovation enables simultaneous detection of screw and nut rotation, critical for anti-loosening bolts, minimizing false positives.
This AI solution transforms bolt inspection into a highly efficient, data-driven, and cost-effective process. Industries from aerospace to civil engineering can leverage handheld cameras (even smartphones) to quickly scan and diagnose bolt integrity. The integrated historical data streamlines maintenance planning and compliance, while the quantitative loosening angles provide actionable insights for predictive maintenance. This translates to enhanced safety, prolonged asset lifespan, and significant reductions in inspection time and labor costs.
Key Accuracy Metric
0.38° - 2.38° Mean identified loose angle error for 0° loose state (corrected images, vertical perspective)Enterprise Process Flow
QR Code Design & Placement
→
Image Preprocessing (QR Area)
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Perspective Rectification (Homography)
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Rotation to Unified Reference State
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Looseness Angle Calculation
| Proposed Vision-based Method (with QR Code) | Traditional & Existing Vision Methods |
|---|---|
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Case Study: Prototype Flange Node Verification
The proposed method was rigorously tested on a prototype flange node, simulating various loosening states (0°, 10°, 20°, 30°) and different perspective angles (vertical, horizontal, bidirectional) and lighting conditions. Results consistently demonstrated the method's ability to effectively correct perspective distortion and accurately identify loosening angles. For instance, in a 0° loose state, corrected images showed mean identified loose angles between 0.38°-2.38°, accurately reflecting synchronous rotation, compared to 7.5°-13.25° with uncorrected images (Tables 1-3). This validates the robustness and practical applicability of the QR code-enhanced vision system in real-world scenarios.
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Potential Annual Savings
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