Timely asset maintenance remains a critical challenge in Industry 4.0 environments. Predictive Maintenance aims to anticipate failures and estimate Remaining Useful Life (RUL), enabling cost reduction and minimizing production downtime. However, real-world industrial scenarios are often characterized by noisy telemetry...
Remaining useful life (RUL) prediction of rolling bearings is essential for condition-based maintenance and reliability management of rotating machinery. To improve degradation representation and temporal modeling ability, this paper proposes a bearing RUL prediction method based on multiscale time-frequency features a...
This study proposes a machine learning-based predictive maintenance framework for machine failure prediction and fault diagnosis using the AI4I 2020 Predictive Maintenance Dataset, and identified torque, torque–speed ratio, and tool wear as the most influential predictors of machine failure.
A hybrid methodology for classifying degradation stages and estimating a relative RUL-related degradation indicator for bearings is proposed by integrating synthetic data modeling, feature selection, and a combined unsupervised–supervised learning approach, offering a reliable and scalable solution for predictive maint...
This paper focuses on the prediction of Remaining Useful Life (RUL) for turbofan engines in the context of Predictive Maintenance (PdM) in Industry 4.0. The study is based on the NASA C-MAPSS dataset and focuses on the development of a predictive stage, including data preprocessing, feature engineering, normalisation,...
The Predictive Maintenance Fault Network (PdM-FaultNet) is the combination of the Enhanced Wombat Optimization Algorithm (EWOA) for the feature selection and Dual Quantum-inspired Denoising Autoencoder Transformer (DQDAT) for the predictive modeling.
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