Application of artificial intelligence algorithms in PLCs for industrial predictive maintenance: a literature review
Keywords:
PLC, Artificial Intelligence, Literature Review, Prisma guidelines
Abstract
This paper reviews how artificial intelligence algorithms are being used in conjunction with PLCs to perform predictive maintenance in industry. In recent years, many companies have begun to generate large amounts of data and connect more sensors, making it possible to analyze machine behavior in greater detail. AI helps detect faults before they occur and better plan interventions. An orderly process was followed for the review. Information was searched for only in Scopus, between 2020 and 2025. Keywords related to PLCs, maintenance, and AI algorithms were used. The articles were then filtered according to their importance and whether they actually provided useful results. In the end, 18 studies that met the criteria were selected. Some authors use edge computing to run models close to the equipment. Others prefer to send the data to the cloud. The use of IoT and IIoT to connect sensors and monitoring platforms also appears quite frequently. In almost all cases, AI improves fault detection, reduces unplanned downtime, and helps make faster decisions.
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References
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[18] R. Mennilli, L. Mazza, and A. Mura, “Integrating Machine Learning for Predictive Maintenance on Resource-Constrained PLCs: A Feasibility Study,” Sensors, vol. 25, no. 2, 2025, doi: 10.3390/s25020537.
[19] C. Balada, M. Bondorf, S. Ahmed, M. Zdrallek, and A. Dengel, “Leveraging the Potential of Novel Data in Power Line Communication of Electricity Grids,” IEEE Access, vol. 13, pp. 71662–71672, 2025, doi: 10.1109/ACCESS.2025.3560811.
[20] R. Wang et al., “PLC based laser scanning system for conveyor belt surface monitoring,” Sci Rep, vol. 14, no. 1, 2024, doi: 10.1038/s41598-024-78985-0.
[21] R. Nagy, F. Horvat, and S. Fischer, “Innovative Approaches in Railway Management: Leveraging Big Data and Artificial Intelligence for Predictive Maintenance of Track Geometry,” Tehnicki Vjesnik, vol. 31, no. 4, pp. 1245–1259, 2024, doi: 10.17559/TV-20240420001479.
[22] A. Paszkiewicz, G. Piecuch, T. Żabiński, M. Bolanowski, M. Salach, and D. Rączka, “Estimation of Tool Life in the Milling Process—Testing Regression Models,” Sensors, vol. 23, no. 23, 2023, doi: 10.3390/s23239346.
[23] R. Mohan, J. P. Preetha Roselyn, and R. A. Uthra, “LSTM based artificial intelligence predictive maintenance technique for availability rate and OEE improvement in a TPM implementing plant through Industry 4.0 transformation,” J Qual Maint Eng, vol. 29, no. 4, pp. 763–798, 2023, doi: 10.1108/JQME-07-2022-0041.
[24] P. Fic, A. Czornik, and P. Rosikowski, “Anomaly Detection for Hydraulic Power Units—A Case Study,” Future Internet, vol. 15, no. 6, 2023, doi: 10.3390/fi15060206.
[25] M. Koraei, J. M. Cebrian Gonzalez, and M. Jahre, “Near-optimal multi-accelerator architectures for predictive maintenance at the edge,” Future Generation Computer Systems, vol. 140, pp. 331–343, 2023, doi: 10.1016/j.future.2022.10.030.
[26] I. T. Franco and R. M. de Figueiredo, “Predictive Maintenance: An Embedded System Approach,” Journal of Control, Automation and Electrical Systems, vol. 34, no. 1, pp. 60–72, 2023, doi: 10.1007/s40313-022-00949-4.
[27] E. Alladio et al., “The ‘DOLPHINS’ Project: A Low-Cost Real-Time Multivariate Process Control From Large Sensor Arrays Providing Sparse Binary Data,” Front Chem, vol. 9, 2021, doi: 10.3389/fchem.2021.734132.
[28] S. Baek, “System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems,” International Journal of Advanced Manufacturing Technology, vol. 113, no. 3–4, pp. 955–966, 2021, doi: 10.1007/s00170-021-06652-z.
[29] T. Eppinger, G. Longwell, P. Mas, K. Goodheart, U. Badiali, and R. Aglave, “Increase food production efficiency using the executable Digital Twin (xDT),” Chem Eng Trans, vol. 87, pp. 37–42, 2021, doi: 10.3303/CET2187007.
[2] J. E. Naranjo, G. Caiza, R. Velastegui, M. Castro, A. Alarcon-Ortiz, and M. V. Garcia, “A Scoping Review of Pipeline Maintenance Methodologies Based on Industry 4.0,” Sustainability, vol. 14, no. 24, p. 16723, Dec. 2022, doi: 10.3390/su142416723.
[3] J. Q. Yang, S. Zhou, D. Van Le, D. Ho, and R. Tan, “Improving Quality Control with Industrial AIoT at HP Factories: Experiences and Learned Lessons,” in 2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), IEEE, Jul. 2021, pp. 1–9. doi: 10.1109/SECON52354.2021.9491592.
[4] A. Madamanchi, F. Rabbi, A. M. Sokolov, and N. U. I. Hossain, “A Machine Learning-Based Corrosion Level Prediction in the Oil and Gas Industry,” in ICIMP 2024, Basel Switzerland: MDPI, Oct. 2024, p. 38. doi: 10.3390/engproc2024076038.
[5] S. Medisetti, V. Y. Kotiboyina, and A. M. Gollapudi, “Modelling and simulation of a PLC based automatic stamping and inspection system incorporated with a pick and place pneumatic suction cup using Factory I/O,” 2024, p. 020049. doi: 10.1063/5.0213847.
[6] S. Mainali and C. Li, “A robotic fish processing line enhanced by machine learning,” Aquac Eng, vol. 108, p. 102481, Mar. 2025, doi: 10.1016/j.aquaeng.2024.102481.
[7] G. Wu, Z. Chen, and J. Dang, Intelligent Bridge Maintenance and Management. Singapore: Springer Nature Singapore, 2024. doi: 10.1007/978-981-97-3827-4.
[8] W. Istiono and A. N. Wira Pratama, “Innovative virtual reality solutions for technical training in heavy construction equipment repair and maintenance,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 37, no. 1, p. 627, Jan. 2025, doi: 10.11591/ijeecs.v37.i1.pp627-635.
[9] J. E. Naranjo, D. G. Sanchez, A. Robalino-Lopez, P. Robalino-Lopez, A. Alarcon-Ortiz, and M. V. Garcia, “A Scoping Review on Virtual Reality-Based Industrial Training,” Applied Sciences, vol. 10, no. 22, p. 8224, Nov. 2020, doi: 10.3390/app10228224.
[10] Z. Farshadfar, T. Mucha, and K. Tanskanen, “Leveraging Machine Learning for Advancing Circular Supply Chains: A Systematic Literature Review,” Logistics, vol. 8, no. 4, p. 108, Oct. 2024, doi: 10.3390/logistics8040108.
[11] L. A. Cárdenas-Robledo, Ó. Hernández-Uribe, C. Reta, and J. A. Cantoral-Ceballos, “Extended reality applications in industry 4.0. – A systematic literature review,” Telematics and Informatics, vol. 73, p. 101863, Sep. 2022, doi: 10.1016/j.tele.2022.101863.
[12] H. J. Lee, H. S. Jo, M. G. Na, and C. H. Kim, “AI-based condition monitoring of photocouplers to enhance the maintenance strategy for digital reactor protection systems,” Nuclear Engineering and Technology, vol. 58, no. 2, 2025, doi: 10.1016/j.net.2025.103958.
[13] Z. L. Mayaluri, A. K. Naik, R. Samantaray, A. Rath, and G. Panda, “A Computational Intelligence Framework for Industry 4.0-based Intelligent Motion Control using AI-Integrated PLCs,” J Sci Ind Res (India), vol. 84, no. 9, pp. 945–956, 2025, doi: 10.56042/jsir.v84i9.18846.
[14] S. Abd-Elhaleem, A. Zanfal, and M. Hamdy, “Predictive maintenance based on IIoT and machine learning aligned with industry 4.0: a case study in waste-water treatment plant,” Neural Comput Appl, vol. 37, no. 24, pp. 20383–20407, 2025, doi: 10.1007/s00521-025-11463-4.
[15] O. Otuagoma Smith, E. Obuseh Emmanuel, O. Oyubu Akpovi, O. Antony, E. O. Jonathan, and T. B. Ronald, “Assessment of Conveyor Telemetry System in a Manufacturing Firm: A Case Study of a Brewery in Eastern Nigeria,” NIPES - Journal of Science and Technology Research, vol. 7, no. 2, pp. 44–58, 2025, doi: 10.37933/nipes/7.2.2025.3.
[16] S. S. Chaudhari, K. S. Bhole, and S. B. Rane, “Industrial Automation and Data Processing Techniques in IoT-Based Digital Twin Design for Thermal Equipment: A case study,” Journal of The Institution of Engineers (India): Series C, vol. 106, no. 2, pp. 553–569, 2025, doi: 10.1007/s40032-025-01164-1.
[17] L. Körösi and S. Kajan, “Overview of implementation principles of artificial intelligence methods in industrial control systems,” Journal of Electrical Engineering, vol. 76, no. 1, pp. 99–105, 2025, doi: 10.2478/jee-2025-0010.
[18] R. Mennilli, L. Mazza, and A. Mura, “Integrating Machine Learning for Predictive Maintenance on Resource-Constrained PLCs: A Feasibility Study,” Sensors, vol. 25, no. 2, 2025, doi: 10.3390/s25020537.
[19] C. Balada, M. Bondorf, S. Ahmed, M. Zdrallek, and A. Dengel, “Leveraging the Potential of Novel Data in Power Line Communication of Electricity Grids,” IEEE Access, vol. 13, pp. 71662–71672, 2025, doi: 10.1109/ACCESS.2025.3560811.
[20] R. Wang et al., “PLC based laser scanning system for conveyor belt surface monitoring,” Sci Rep, vol. 14, no. 1, 2024, doi: 10.1038/s41598-024-78985-0.
[21] R. Nagy, F. Horvat, and S. Fischer, “Innovative Approaches in Railway Management: Leveraging Big Data and Artificial Intelligence for Predictive Maintenance of Track Geometry,” Tehnicki Vjesnik, vol. 31, no. 4, pp. 1245–1259, 2024, doi: 10.17559/TV-20240420001479.
[22] A. Paszkiewicz, G. Piecuch, T. Żabiński, M. Bolanowski, M. Salach, and D. Rączka, “Estimation of Tool Life in the Milling Process—Testing Regression Models,” Sensors, vol. 23, no. 23, 2023, doi: 10.3390/s23239346.
[23] R. Mohan, J. P. Preetha Roselyn, and R. A. Uthra, “LSTM based artificial intelligence predictive maintenance technique for availability rate and OEE improvement in a TPM implementing plant through Industry 4.0 transformation,” J Qual Maint Eng, vol. 29, no. 4, pp. 763–798, 2023, doi: 10.1108/JQME-07-2022-0041.
[24] P. Fic, A. Czornik, and P. Rosikowski, “Anomaly Detection for Hydraulic Power Units—A Case Study,” Future Internet, vol. 15, no. 6, 2023, doi: 10.3390/fi15060206.
[25] M. Koraei, J. M. Cebrian Gonzalez, and M. Jahre, “Near-optimal multi-accelerator architectures for predictive maintenance at the edge,” Future Generation Computer Systems, vol. 140, pp. 331–343, 2023, doi: 10.1016/j.future.2022.10.030.
[26] I. T. Franco and R. M. de Figueiredo, “Predictive Maintenance: An Embedded System Approach,” Journal of Control, Automation and Electrical Systems, vol. 34, no. 1, pp. 60–72, 2023, doi: 10.1007/s40313-022-00949-4.
[27] E. Alladio et al., “The ‘DOLPHINS’ Project: A Low-Cost Real-Time Multivariate Process Control From Large Sensor Arrays Providing Sparse Binary Data,” Front Chem, vol. 9, 2021, doi: 10.3389/fchem.2021.734132.
[28] S. Baek, “System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems,” International Journal of Advanced Manufacturing Technology, vol. 113, no. 3–4, pp. 955–966, 2021, doi: 10.1007/s00170-021-06652-z.
[29] T. Eppinger, G. Longwell, P. Mas, K. Goodheart, U. Badiali, and R. Aglave, “Increase food production efficiency using the executable Digital Twin (xDT),” Chem Eng Trans, vol. 87, pp. 37–42, 2021, doi: 10.3303/CET2187007.
Published
2026-01-15
How to Cite
Tumalli Naranjo, G. R. (2026). Application of artificial intelligence algorithms in PLCs for industrial predictive maintenance: a literature review. Ciencias De La Ingeniería Y Aplicadas, 10(1), 82 - 103. https://doi.org/10.61236/ciya.v10i1.1236
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