i-manager Publications

Artificial Intelligence Tools for Preventive Maintenance in Gold Processing Mills: A Review

Ruvimbo Victoria Makuwaza*, Munyaradzi Innocent Mupona**, Donald Museka***

*-*** Department of Industrial and Manufacturing Engineering, Harare Institute of Technology, Harare, Zimbabwe.

Periodicity:October - December'2025

Abstract

The integration of Artificial Intelligence (AI) into gold processing operations has significantly transformed maintenance strategies by minimizing unplanned downtime and improving operational efficiency. Traditional maintenance approaches, such as reactive and scheduled maintenance, often lead to either unexpected failures or excessive servicing. AI-driven predictive maintenance offers a proactive, data-driven alternative that leverages real-time monitoring and advanced analytics to forecast equipment failures and optimize maintenance schedules. Gold processing mills, which operate under harsh and variable conditions, require intelligent maintenance systems to manage equipment like crushers, ball mills, and classifiers effectively. This review explores how AI technologies—such as machine learning, deep learning, reinforcement learning, and natural language processing—are applied in predictive maintenance within gold processing contexts. It synthesizes methodologies from existing literature, identifies common themes, highlights gaps, and discusses the transition from preventive to predictive maintenance. The findings aim to guide both researchers and practitioners in implementing AI-based maintenance strategies that enhance equipment reliability, reduce costs, and support data-driven decision-making in the mining industry.

Keywords

Artificial Intelligence, Predictive Maintenance, Gold Processing Mills, Machine Learning, Internet of Things, Digital Twin, Computer Vision

How to Cite this Article?

Makuwaza, R. V., Mupona, M. I., and Museka, D. (2025). Artificial Intelligence Tools for Preventive Maintenance in Gold Processing Mills: A Review. i-manager’s Journal on Future Engineering & Technology, 21(1), 19-34.

References

[1]. Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on Explainable artificial intelligence (XAI). IEEE Access, 6, 52138-52160.

[2]. Adimulam, T., Bhoyar, M., & Reddy, P. (2019). AI-Driven predictive maintenance in IoT-Enabled industrial systems. Iconic Research and Engineering Journals, 2(11), 398- 410.

[3]. Ahmed, I., Jeon, G., & Piccialli, F. (2022). From artificial intelligence to explainable artificial intelligence in industry 4.0: A survey on what, how, and where. IEEE Transactions on Industrial Informatics, 18(8), 5031-5042.

[4]. Aikin, A. R. (2021). Predictive maintenance best practices: Best practice strategies involve reducing maintenance costs and improving equipment performance. Plant Engineering, 75(5), 26-32.

[5]. Amruthnath, N., & Gupta, T. (2018a). Fault class prediction in unsupervised learning using model-based clustering approach. In 2018 International Conference on Information and Computer Technologies (ICICT) (pp. 5-12).IEEE.

[6]. Amruthnath, N., & Gupta, T. (2018b). A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance. In 2018 5th International Conference on Industrial Engineering and Applications (ICIEA) (pp. 355-361). IEEE.

[7]. AVEVA. (2024). Predictive Maintenance: Using AI to Prevent Equipment Failures.

[8]. Bokrantz, J., & Skoogh, A. (2023). Adoption patterns and performance implications of Smart Maintenance. International Journal of Production Economics, 256, 108746.

[9]. Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. D. P., Basto, J. P., & Alcalá, S. G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024.

[10]. Daily, J., & Peterson, J. (2016). Predictive maintenance: How big data analysis can improve maintenance. In Supply Chain Integration Challenges in Commercial Aerospace: A Comprehensive Perspective on the Aviation Value Chain (pp. 267-278). Springer International Publishing.

[11]. Davenport, T. H., & Bean, R. (2018). Big companies are embracing analytics, but most still don't have a data- driven culture. Harvard Business Review, 6, 1-4.

[12]. Demidova, L. A. (2020). Recurrent neural networks' configurations in the predictive maintenance problems. In IOP Conference Series: Materials Science and Engineering, 714 (1), 012005. IOP Publishing.

[13]. Feng, M., & Li, Y. (2022). Predictive maintenance decision making based on reinforcement learning in multistage production systems. IEEE Access, 10, 18910- 18921.

[14]. Ferreira, L., Pilastri, A., Romano, F., & Cortez, P. (2022). Using supervised and one-class automated machine learning for predictive maintenance. Applied Soft Computing, 131, 109820.

[15]. Geeks for Geeks. (2025). What is Reinforcement Learning?

[16]. Holdsworth, J., & Scapicchio, M. (n.d.). What is Deep Learning? IBM Think.

[17]. Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017). What do we need to build explainable AI systems for the medical domain? arXiv preprint arXiv:1712.09923.

[18]. Hu, L., Nguyen, N. T., Tao, W., Leu, M. C., Liu, X. F., Shahriar, M. R., & Al Sunny, S. N. (2018). Modeling of cloud-based digital twins for smart manufacturing with MT connect. Procedia Manufacturing, 26, 1193-1203.

[19]. IBM. (n.d.). What is Machine Learning.

[20]. Jiang, Y., Yin, S., Li, K., Luo, H., & Kaynak, O. (2021). Industrial applications of digital twins. Philosophical Transactions of the Royal Society A, 379(2207), 20200360.

[21]. Josh. (2024). Heavy Vehicle Inspection.

[22]. Juarez, M. G., Botti, V. J., & Giret, A. S. (2021). Digital twins: Review and challenges. Journal of Computing and Information Science in Engineering, 21(3), 030802.

[23]. Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237-285.

[24]. Kamat, P., & Sugandhi, R. (2020). Anomaly detection for predictive maintenance in industry 4.0-A survey. In E3S Web of Conferences, 170, 02007. EDP Sciences.

[25]. Koprinkova-Hristova, P. (2013). Reinforcement learning for predictive maintenance of industrial plants. Information Technology and Control, 11(1), 21-28.

[26]. Kruczek, P., Gomolla, N., Hebda-Sobkowicz, J., Michalak, A., Sliwinski, P., Wodecki, J., & Zimroz, R. (2019). Predictive maintenance of mining machines using advanced data analysis system based on the cloud technology. In Proceedings of the 27th International Symposium on Mine Planning and Equipment Selection- MPES 2018 (pp. 459-470). Springer International Publishing.

[27]. Lee, W. J., Wu, H., Yun, H., Kim, H., Jun, M. B., & Sutherland, J. W. (2019). Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data. Procedia Cirp, 80, 506-511.

[28]. Lin, S. L. (2021). Application of machine learning to a medium Gaussian support vector machine in the diagnosis of motor bearing faults. Electronics, 10(18), 2266.

[29]. Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1-10.

[30]. Mahato, A. (2024). AI based PDM in manufacturing industry 4.0: A bibliographic review. International Journal of Engineering Research & Technology (IJERT), 13 (4), 1-10.

[31]. Mahesh, B. (2020). Machine learning algorithms – A review. International Journal of Science and Research (IJSR), 9(1), 381–386.

[32]. Martínez-Arellano, G., Terrazas, G., & Ratchev, S. (2019). Tool wear classification using time series imaging and deep learning. The International Journal of Advanced Manufacturing Technology, 104(9), 3647-3662.

[33]. Masani, K. I., Oza, P., & Agrawal, S. (2019). Predictive maintenance and monitoring of industrial machine using machine learning. Scalable Computing: Practice and Experience, 20(4), 663-668.

[34]. May, M. C., Neidhöfer, J., Körner, T., Schäfer, L., & Lanza, G. (2022). Applying natural language processing in manufacturing. Procedia CIRP, 115, 184-189.

[35]. McKinsey & Company. (2016). The Need to Lead in Data and Analytics. McKinsey & Company.

[36]. Mihai, S., Yaqoob, M., Hung, D. V., Davis, W., Towakel, P., Raza, M., & Nguyen, H. X. (2022). Digital twins: A survey on enabling technologies, challenges, trends and future prospects. IEEE Communications Surveys & Tutorials, 24(4), 2255-2291.

[37]. Miller Magazine. (2018). Mill Maintenance Methods.

[38]. Mining Technology. (2024). Predictive Maintenance and the Rise of AI in Mining.

[39]. Molnar, S. (2020). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable.

[40]. Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2022). Design and development of an edge-computing platform towards 5G technology adoption for improving equipment predictive maintenance. Procedia Computer Science, 200, 611-619.

[41]. Nanoprecise. (2025). Predictive Maintenance in Mining Sector.

[42]. Ogunfowora, O., & Najjaran, H. (2023). Reinforcement and deep reinforcement learning-based solutions for machine maintenance planning, scheduling policies, and optimization. Journal of Manufacturing Systems, 70, 244-263.

[43]. Okpala, C., Chikwendu, U., & Onyeka, N. C. (2025). Artificial intelligence- driven total productive maintenance: The future of maintenance in smart factories. International Journal of Engineering Research and Development, 21(1), 68-74.

[44]. Ong, K. S. H., Niyato, D., & Yuen, C. (2020). Predictive maintenance for edge-based sensor networks: A deep reinforcement learning approach. In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT) (pp. 1-6). IEEE.

[45]. Osisanwo, F. Y., Akinsola, J. A., & Ojo, J. O. (2017). Supervised machine learning algorithms: Classification and comparison. International Journal of Computer Trends and Technology (IJCTT), 48(3), 128–138.

[46]. Pagano, D. (2023). A predictive maintenance model using long short-term memory neural networks and Bayesian inference. Decision Analytics Journal, 6, 100174.

[47]. Paredes, J., Chávez, D., Isa-Jara, R., & Vargas, D. (2025). A hybrid machine learning algorithm approach to predictive maintenance tasks: A comparison with machine learning algorithms. Results in Engineering, 26, 105137.

[48]. Provencher, M. (2020). A Guide to Predictive Maintenance for the Smart Mine. Retrieved fro.

[49]. Ray, S. (2019). A quick review of machine learning algorithms. In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (Comitcon) (pp. 35-39). IEEE.

[50]. Razor Labs. (2022). AI Enabled Predictive Maintenance in Mining.

[51]. Robatto Simard, S., Gamache, M., & Doyon-Poulin, P. (2023). Current practices for preventive maintenance and expectations for predictive maintenance in East- Canadian mines. Mining, 3(1), 26-53.

[52]. Sensemore. (2024). First Step in AI-based Predictive Maintenance.

[53]. Sharma, J., Lal Mittal, M., Soni, G., & Keprate, A. (2024). Explainable Artificial Intelligence (XAI) approaches in predictive maintenance: A review. Recent Patents on Engineering, 18(5), 18-26.

[54]. Turner, C. J., Emmanouilidis, C., Tomiyama, T., Tiwari, A., & Roy, R. (2019). Intelligent decision support for maintenance: An overview and future trends. International Journal of Computer Integrated Manufacturing, 32(10), 936-959.

[55]. Vishnu, C. R., & Regikumar, V. (2016). Reliability based maintenance strategy selection in process plants: A case study. Procedia Technology, 25, 1080-1087.

[56]. Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144-156.

[57]. Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19.