Aplicación de algoritmos de inteligencia artificial en PLC para mantenimiento predictivo industrial: una revisión de literatura
Palabras clave:
PLC, Inteligencia Artificial, Revisión de literatura, guías Prisma
Resumen
Este trabajo revisa cómo se están usando algoritmos de inteligencia artificial junto con los PLC para hacer mantenimiento predictivo en la industria. En los últimos años, muchas empresas han empezado a generar grandes cantidades de datos y a conectar más sensores, así que ahora es posible analizar el comportamiento de las máquinas con más detalle. La IA ayuda a detectar fallas antes de que ocurran y a planificar mejor las intervenciones. Para la revisión se siguió un proceso ordenado. Se buscó información solo en Scopus, entre 2020 y 2025. Se usaron palabras clave relacionadas con PLC, mantenimiento y algoritmos de IA. Después se filtraron los artículos según su importancia y si realmente aportaban resultados útiles. Al final se seleccionaron 18 estudios que cumplían con los criterios. Algunos autores usan computación en el borde para que los modelos corran cerca del equipo. Otros prefieren enviar los datos a la nube. También aparece bastante el uso de IoT y IIoT para conectar sensores y plataformas de monitoreo. En casi todos los casos, la IA mejora la detección de fallas, reduce paradas no planificadas y ayuda a tomar decisiones más rápidas.
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[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.
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[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.
[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.
Publicado
2026-01-15
Cómo citar
Tumalli Naranjo, G. R. (2026). Aplicación de algoritmos de inteligencia artificial en PLC para mantenimiento predictivo industrial: una revisión de literatura. Ciencias De La Ingeniería Y Aplicadas, 10(1), 82 - 103. https://doi.org/10.61236/ciya.v10i1.1236
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Derechos de autor 2026 Ciencias de la Ingeniería y Aplicadas
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