RUL Estimation

RUL: Remaining Useful Life

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4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Imagery Dataset for Remaining Useful Life Estimation of Synthetic Fibre Ropes
May 5, 2026Anju Rani, Daniel Ortiz-Arroyo, Petar DurdevicRUL Estimation Predictive Maintenance
Department of Energy Aalborg University Esbjerg, Denmark 6700
A Novel Preprocessing-Driven Approach to Remaining Useful Life (RUL) Prediction Using Temporal Convolutional Networks (TCN)
May 4, 2026Florent Imbert, Tosin Adewumi, Hui HanRUL Estimation Predictive Maintenance
Machine Learning Group · Lulea University of Technology · Lulea, Sweden
Remaining Useful Life Estimation for Turbofan Engines: A Comparative Study of Classical, CNN, and LSTM Approaches
Apr 29, 2026Astitva Goel, Samarth Galchar, Sumit KanuRUL Estimation Predictive Maintenance
Khoury College of Computer Science, Northeastern University
A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management
Apr 28, 2026Athanasios Koukosias, Vasileios Tzanidakis, Sotiris Athanasiou +1RUL Estimation Predictive Maintenance
Department of Informatics and Telecommunications, University of Thessaly, 3rd km Old National Road, Lamia, 35100 GR, Greece · Sunlight Group, Thivaidos 22, Kifisia, Athens, 14564, Athens
Heterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analytics
Apr 21, 2026Yuhan Hu, Xiaolei FangRUL Estimation Predictive Maintenance
Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA
Continuous ageing trajectory representations for knee-aware lifetime prediction of lithium-ion batteries across heterogeneous dataset
Apr 17, 2026Agnieszka Pregowska, Stefan MarynowiczRUL Estimation Domain Generalization

May 5, 2026, cs.CVJ/K move · Enter open · S save

Imagery Dataset for Remaining Useful Life Estimation of Synthetic Fibre Ropes

Anju Rani, Daniel Ortiz-Arroyo, Petar Durdevic

Department of Energy Aalborg University Esbjerg, Denmark 6700

RUL Estimation Predictive Maintenance Structural Health Monitoring

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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.

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Latest papers 36

Imagery Dataset for Remaining Useful Life Estimation of Synthetic Fibre Ropes

A Novel Preprocessing-Driven Approach to Remaining Useful Life (RUL) Prediction Using Temporal Convolutional Networks (TCN)

Remaining Useful Life Estimation for Turbofan Engines: A Comparative Study of Classical, CNN, and LSTM Approaches

A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management

Heterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analytics

Continuous ageing trajectory representations for knee-aware lifetime prediction of lithium-ion batteries across heterogeneous dataset