RESEARCH PAPER
Machine Learning-Based Predictive Maintenance of a Wire Drawing Machine Using Vibration and Motor Current Signals
1
Erciyes University, Turkey
2
Recep Tayyip Erdogan ÜniversitY, Turkey
A – Conceptualization; B – Methodology; C – Software; D – Validation; E – Formal analysis; F – Investigation; G – Resources; H – Data curation; I – Writing – original draft; J – Writing – review & editing; K – Visualization; L – Supervision; M – Project administration; N – Funding acquisition
Submission date: 2026-03-11
Final revision date: 2026-05-07
Acceptance date: 2026-06-25
Online publication date: 2026-07-29
KEYWORDS
Predictive maintenanceEnsemble learningVibration diagnosticsMulti-sensor monitoringMotor current analysisComposite Health Index
TOPICS
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.
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