Predictive AI-Based Maintenance Model for Slurry Pumps Using ISO 10816-3 Vibration Standard
DOI:
https://doi.org/10.23055/ijietap.2026.33.2.11421Keywords:
Slurry Pump, Predictive Maintenance, ISO 10816-3, XGBoost, LSTM, Random ForestAbstract
This study proposes a predictive maintenance model for slurry pumps used in the mold flux drying process by integrating the ISO 10816-3 vibration standard with AI-based machine learning algorithms. Real-world sensor data on vibration, current, pressure, revolutions per minute (RPM), and temperature are collected and processed for training and evaluating AI models based on eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Random Forest (RF) algorithms. The vibration zones defined by the ISO 10816-3 standard are employed as ground truth labels to enhance the interpretability and reliability of the predictive maintenance models. Among the AI models, the XGBoost-based model demonstrates the best predictive performance. A hybrid diagnostic system is developed by integrating the ISO-defined vibration thresholds with the XGBoost algorithm, which provides effective early warning alerts for potential failures in real time.
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