Imagery Dataset for Remaining Useful Life Estimation of Synthetic Fibre Ropes

TL;DR

This paper introduces a comprehensive high-resolution image dataset capturing the degradation of synthetic fibre ropes under cyclic fatigue, supporting machine learning tasks for RUL estimation and damage monitoring.

Contribution

It provides the first publicly available image dataset of SFRs' complete degradation lifecycle under controlled fatigue loading, enabling advanced ML-based condition monitoring.

Findings

Dataset includes 34,700 images of 11 rope samples

Images are annotated with cycle counts for RUL computation

Supports ML tasks like damage modeling and anomaly detection

Abstract

Remaining useful life (RUL) estimation of synthetic fibre ropes (SFRs) is critical for safe operation in offshore-crane, wind turbine installation, and heavy-load handling applications, where rope failure can result in catastrophic safety incidents and costly downtime. Despite growing research interest in data-driven condition monitoring, there is no publicly available image dataset that captures the complete degradation lifecycle of SFRs under controlled cyclic fatigue loading. To address this gap, we present a novel image dataset comprising approximately 34,700 high-resolution images of eleven Dyneema SK75/78 high-modulus polyethylene (HMPE) rope samples subjected to cyclic fatigue on a sheave-bend test stand at seven distinct axial load levels ranging from 60 kN to 280 kN. Ropes were loaded until mechanical failure, with fatigue lifetimes ranging from 695 cycles to 8,340 cycles. After every fixed number of sheave cycles (an inspection burst), ten images were captured at different cross-sectional positions along the rope, providing spatially representative sampling of surface degradation throughout the rope's entire service life. The images obtained from each load are annotated with the corresponding elapsed cycle count, enabling a direct computation of RUL for any rope in the sequence. This dataset aims to support a broad range of machine learning (ML) tasks including RUL regression, damage progression modelling, anomaly detection, and load-conditioned prognostics. The dataset is intended to serve as a benchmark resource for the development and comparison of vision-based condition monitoring (CM) and prognostics algorithms for SFRs.