Predictive Maintenance of Mining Centrifuges Using Machine Learning and Deep Learning Models
Received: 27 September 2025 | Revised: 30 October 2025 and 11 November 2025 | Accepted: 15 November 2025 | Online: 9 February 2026
Corresponding author: Doaa Ahmad Alqaraleh
Abstract
This study facilitates the achievement of Sustainable Development Goals (SDG 9: Industry, Innovation, and Infrastructure and SDG 13: Climate Action) using intelligent maintenance solutions. Achieving operational and environmental sustainability in the mining sector poses significant challenges that require innovative solutions to enhance equipment efficiency and minimize unforeseen failures. The current research formulates a predictive maintenance system is utilizing Artificial Intelligence (AI) and Machine Learning (ML) techniques, based on the analysis of sensor data from industrial centrifuges. Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Logistic Regression (LR), and XGBoost are evaluated. XGBoost demonstrated exceptional proficiency in managing time-series data, achieving the highest accuracy of 99.80%, followed by LSTM. These results underscore the potential of ML techniques to optimize predictive maintenance, reduce energy and resource consumption, enhance operational efficiency, and promote sustainable industrial practices.
Keywords:
predictive maintenance, machine learning, deep learning, industrial centrifuge, mining industry, sustainable development goals, industrial innovationDownloads
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Copyright (c) 2025 Doaa Ahmad Alqaraleh, Sami Salama Hussen Hajjaj, Hassan Mohamed, Mohd Radzi Aridi, Mohd Zafri Bin Baharuddin
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