MISSING BOLT DETECTION ON A CONVEYOR BELT

Volume: 10    Issue: 1

Year of Publication: 2024

Pages: (18-22)

Authors: Hemalatha S, Devadharshini C, KavyaSruthi S S, HafidWaqar Ahmed S, Sudharma K V

Download Full Text (PDF)

Abstract

As an essential component of conveyor systems, ensuring the integrity of conveyor belts is vital for safe and efficient operations. Missing bolts can cause equipment failure, operational delays, and safety hazards. Traditional manual inspections are labor-intensive, inefficient, and prone to errors, especially in harsh environments like heavy dust or poor lighting. This paper introduces a missing bolt detection system using YOLOv8, a cutting-edge deep learning algorithm. YOLOv8\"s advanced features enable the accurate detection of missing bolts on conveyor components. The model is trained with annotated images captured under challenging conditions, including dust, low light, and partial obstructions. The system analyzes real-time video or images from industrial cameras to locate missing bolts. Detected issues are marked immediately, allowing timely corrective actions. Field tests show high accuracy and reliability, even in complex conditions. This solution significantly reduces risks associated with missing bolts and enhances maintenance efficiency. It ensures conveyor system safety and operational reliability in demanding industrial environments.

References

1. Algoryx. Agx dynamics. https://www.algoryx.se/agx-dynamics/, 2024. [Accessed 2024-05-21].

Keywords

Missing bolts,YOLOv8, Conveyor belt, real-time detection.