Artificial Intelligence Embedded Image Process-Based Fatigue Life Determination on Wire Ropes Subjected to Bending Loads

In lift systems, the repeated running back-and-forth movement of steel wire ropes over sheaves induces fatigue failure, which is a critical safety concern. Accurately assessing the fatigue life of hoisting ropes is an important issue for the reliability of lift systems. Certain main factors influencing fatigue life include rope structure, the sheave-to-rope diameter ratio (D/d), operational speed, and applied tensile force. It is expected that the fatigue occurs primarily through wire fractures in outer strands. The quantity of these fractures dictates when the rope should be replaced. However, quantifying these fractures without halting operations poses significant challenges in terms of downtime and budgeting. This study introduces an innovative approach that employs image processing enhanced by Artificial Intelligence within a fatigue testing setup. Utilising high-speed cameras, the system aims to detect the evaluation of fatigue failure. Overall, this research combines cutting-edge technology to enhance fatigue testing methodologies.