Jul 2026· Eksploatacja I Niezawodnosc-maintenance and Reliability· 0 citations· 45 references
TL;DR
A Composite Health Index (CHI) is developed to transform multi-motor sensor data into an interpretable machine-level degradation indicator and is used to train ensemble machine learning models including Random Forest, Extra Trees, and XGBoost.
Abstract
Predictive maintenance improves reliability and reduces downtime in modern manufacturing systems. However, many studies rely on laboratory datasets or single-component monitoring, limiting their applicability to complex industrial environments. This study proposes a predictive maintenance framework for a multi-pass wire drawing machine using vibration and motor current signals from a real industrial production line. A Composite Health Index (CHI) is developed to transform multi-motor sensor data into an interpretable machine-level degradation indicator. The extracted features are used to train ensemble machine learning models including Random Forest, Extra Trees, and XGBoost, whose outputs are combined through a weighted hybrid ensemble model. A decision-layer mechanism with smoothing and temporal filtering is applied to reduce false alarms while preserving detection capability. Experimental results show that the model achieves a recall of 0.90 and an F1-score of 0.75, demonstrating its effectiveness for industrial applications.
The results show that vibration-based machine learning can support robust, near-real-time fault identification in mechanical manufacturing environments and highlights the importance of chronological validation, feature engineering over multiple time windows, and the trade-off between predictive performance and deployment efficiency.
P. Malega, J. Kováč, Róbert Munkáči et al.· Applied Sciences· 0 citations
The results show that the proposed Condition Monitoring (CM) approach significantly reduces resource waste and prevents costly downtime, offering a practical and scalable asset management model for industrial applications.
A. Oner, Meral Bayraktar· Italian National Conference...· 0 citations
A machine-learning-based system for fault identification and classification based on vibration, slip ratio, and belt-tension measurements obtained from a controlled experimental setup demonstrates machine learning’s potential for scaled implementation in industrial predictive maintenance settings and establishes its applicability for belt-pulley systems’ real-time health evaluation.
A. Sivathanu· Proceedings of the Instituti...· 0 citations
An end-to-end predictive maintenance system is proposed for high stress mechanical drivetrain and rotating machinery and the architecture proposed combines an industrial Internet of Things edge sensory network and hybrid machine learning and deep learning pipelines.
Ashish Kumar, Md Mohtab Alam, N. Priya et al.· International journal of com...· 0 citations
The rapid digital transformation of industrial manufacturing has introduced digital twin (DT) technology as a cornerstone for intelligent maintenance systems within Industry 4.0 and Industry 5.0 environments. This study presents an empirical investigation of digital twin–based predictive maintenance (PdM) using machine learning algorithms on real-world industrial sensor data. The research evaluates the performance of Random Forest, Gradient Boosting, Support Vector Machine, and Artificial Neural Networks in predicting equipment failures and optimizing maintenance strategies. A dataset of over 10,000 machine operation records, including temperature, vibration, pressure, and operational cycles, was analyzed to assess predictive accuracy and operational impact. Results indicate that Random Forest achieved the highest predictive accuracy (92.4%), while digital twin integration reduced unplanned machine downtime by approximately 28% compared to reactive maintenance approaches. The study highlights vibration and temperature as the most critical indicators of machine failure, demonstrating the importance of sensor-driven monitoring in predictive maintenance. Findings further show that digital twin–enabled predictive maintenance supports proactive maintenance planning, human-centered decision-making, and operational efficiency, bridging the gap between Industry 4.0 automation and Industry 5.0 human–AI collaboration.
Ifebadiofu Matthew Okoro, Perseverance Omoh Agbi· International journal of res...· 0 citations