Imagery Dataset for Remaining Useful Life Estimation of Synthetic Fibre Ropes

Synthetic fibre ropes (SFRs) are critical for offshore cranes, wind turbine installations, and heavy-lift operations, where sudden failure can cause catastrophic safety incidents and costly downtime. Despite growing interest in data-driven condition monitoring, no public image dataset captured the complete rope degradation lifecycle—until now. A team of researchers from Aalborg University has released approximately 34,700 high-resolution images of eleven Dyneema SK75/78 high-modulus polyethylene (HMPE) rope samples. Each sample was subjected to cyclic fatigue on a sheave-bend test stand at seven distinct axial load levels ranging from 60 kN to 280 kN, with fatigue lifetimes spanning 695 to 8,340 cycles.

After every fixed number of sheave cycles (an inspection burst), ten images were taken at different cross-sectional positions along the rope, providing spatially representative sampling of surface degradation from start to failure. Every image is annotated with the elapsed cycle count, enabling direct computation of RUL for any rope in the sequence. This dataset is designed for machine learning tasks including RUL regression, damage progression modeling, anomaly detection, and load-conditioned prognostics. As a benchmark resource, it opens the door for vision-based condition monitoring algorithms that could predict rope failure before it happens, improving safety and reducing downtime in heavy industry.