IoT-Based Predictive Maintenance for AC Motors in Water Treatment Plants Using Multi-Sensor Data and LSTM Networks with GAN Augmentation
DOI:
https://doi.org/10.21831/elinvo.v10i2.89410Keywords:
IoT, Predictive maintenance, Water treatment plant, AC motor, LSTM, GAN augmentationAbstract
AC motors are critical assets in water treatment plants because they operate continuously to drive key processes. Reactive or schedule-based maintenance can miss early degradation and increase the risk of unplanned downtime. This study presents a field implementation of an Internet of Things (IoT)-based predictive maintenance system in a WTP. The system integrates vibration, temperature, and rotational speed (RPM) sensors with a cloud-based IoT pipeline for real-time data acquisition. Operational data were collected for 30 days from a single motor unit and analyzed using Random Forest and Long Short-Term Memory models. To address limited abnormal-event data, Generative Adversarial Network (GAN)-based augmentation was applied during training. The results show that LSTM performed more consistently than Random Forest; after augmentation, the F1-score improved from 0.92 to 0.95. The monitoring data also captured warning-level changes during operation, including vibration up to 3.9 mm/s, temperature up to 95 °C, and rotational speed dropping to around 1420 RPM, which may indicate abnormal operating conditions requiring inspection. Given the single-unit scope and short duration, the findings are reported as an initial implementation case study. Nevertheless, the work demonstrates the feasibility of a low-cost IoT-based monitoring and prediction framework to support maintenance decisions in WTP operations.
References
[1] S. Eslamian, Ed., Handbook of Engineering Hydrology. CRC Press, 2014. doi: 10.1201/b16766.
[2] E. E. Greenwood et al., “Mapping safe drinking water use in low- and middle-income countries,” Science (1979), vol. 385, no. 6710, pp. 784–790, Aug. 2024, doi: 10.1126/science.adh9578.
[3] F. Sun, M. Chen, and J. Chen, “Integrated Management of Source Water Quantity and Quality for Human Health in a Changing World,” in Encyclopedia of Environmental Health, Elsevier, 2011, pp. 254–265. doi: 10.1016/B978-0-444-52272-6.00286-5.
[4] H. M. Forhad et al., “IoT based real-time water quality monitoring system in water treatment plants (WTPs),” Heliyon, vol. 10, no. 23, p. e40746, Dec. 2024, doi: 10.1016/j.heliyon.2024.e40746.
[5] Y. Liu et al., “Exploring Intelligent Feedback Systems for Student Engagement in Digitized Physical Education Learning through Machine Learning,” in Proceeding of the 2024 International Conference on Artificial Intelligence and Future Education, New York, NY, USA: ACM, Nov. 2024, pp. 114–118. doi: 10.1145/3708394.3708415.
[6] N. Mumtaz, T. Izhar, J. Izhar, and S. A. Ahmad, “Empowering water security: the synergy of automation and IoT integration,” in Computational Automation for Water Security, Elsevier, 2025, pp. 135–156. doi: 10.1016/B978-0-443-33321-7.00010-X.
[7] T. Jomjaiekachorn, T. Anuwongpinit, and B. Purahong, “IoT-based Water Quality Monitoring Station and Forecasting System with Machine Learning,” in 2025 13th International Electrical Engineering Congress (iEECON), IEEE, Mar. 2025, pp. 1–6. doi: 10.1109/iEECON64081.2025.10987795.
[8] M. S. Miralam, “Towards Developing an AI Random Forest Model Approach Adopted for Sustainable Food Supply Chain under Big Data,” Journal of Applied Data Sciences, vol. 6, no. 2, pp. 1252–1263, May 2025, doi: 10.47738/jads.v6i2.680.
[9] T. König, A. M. Ramananda, F. Wagner, and M. Kley, “A LSTM-GAN Algorithm for Synthetic Data Generation of Time Series Data for Condition Monitoring,” Procedia Comput Sci, vol. 246, pp. 1508–1517, 2024, doi: 10.1016/j.procs.2024.09.602.
[10] L. Y. K. Wang, J. C. H. Loo, Y. X. Wong, and Y. Y. Lai, “IoT Based Wastewater Temperature, pH And Turbidity Monitoring with Data Analytics and Visualisation,” in Proceedings of the 2024 the 12th International Conference on Information Technology (ICIT), New York, NY, USA: ACM, Dec. 2024, pp. 213–218. doi: 10.1145/3718391.3718428.
[11] M. K. Goyal, Haripriya, and A. P. Singh, “Real-Time Data Streams: The Future of Predictive Maintenance in Manufacturing,” in 2023 IEEE International Conference on ICT in Business Industry & Government (ICTBIG), IEEE, Dec. 2023, pp. 1–7. doi: 10.1109/ICTBIG59752.2023.10456061.
[12] R. Rulinawaty, A. Andriyansah, A. Santosa, S. Fadillah, A. Karyana, and Y. Efendi, “Management model of food big data for national food security policy,” in AIP Conference Proceedings, R. T., N. null, N. A.A., S. D.A.N., and R. R., Eds., American Institute of Physics, 2024. doi: 10.1063/5.0225794.
[13] S. C. M. Sundararajan et al., “IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks,” Sci Rep, vol. 15, no. 1, 2025, doi: 10.1038/s41598-024-84943-7.
[14] A. E. Alprol, A. T. Mansour, M. E. E.-D. Ibrahim, and M. Ashour, “Artificial Intelligence Technologies Revolutionizing Wastewater Treatment: Current Trends and Future Prospective,” Water (Switzerland), vol. 16, no. 2, 2024, doi: 10.3390/w16020314.
[15] S. Das, K. R. Khondakar, H. Mazumdar, A. Kaushik, and Y. K. Mishra, “AI and IoT: Supported Sixth Generation Sensing for Water Quality Assessment to Empower Sustainable Ecosystems,” ACS ES&T Water, vol. 5, no. 2, pp. 490–510, Feb. 2025, doi: 10.1021/acsestwater.4c00360.
[16] A. Ishtaiwi, M. J. Suárez-Barón, A. Elhilo, and M. A. Rajab, “Framework for Addressing Agricultural Water Scarcity Using Big Data and IoT,” in 2025 1st International Conference on Computational Intelligence Approaches and Applications, ICCIAA 2025 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2025. doi: 10.1109/ICCIAA65327.2025.11013506.
[17] C. Anitha, S. Tandon, M. Vamsikrishna, M. Arunmozhi, M. Chauhan, and R. Bajaj, “Role of IoT Intelligence System & Big Data Management to Control Flood Data,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 15, pp. 455–463, 2024, [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187489527&partnerID=40&md5=3897427dd4c56e447716a7a6837bddbe
[18] T. Jomjaiekachorn, T. Anuwongpinit, and B. Purahong, “IoT-based Water Quality Monitoring Station and Forecasting System with Machine Learning,” in Proceedings - iEECON 2025: 2025 13th International Electrical Engineering Congress: Carbon Neutrality: Challenges and Solutions Based on Sustainable Power of Nature, Institute of Electrical and Electronics Engineers Inc., 2025. doi: 10.1109/iEECON64081.2025.10987795.
[19] T. Miller et al., “Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data,” Electronics (Switzerland), vol. 14, no. 4, 2025, doi: 10.3390/electronics14040696.
[20] A. D. Frayudha and T. Mulyono, “Predictive Analytics with IoT: Research Trends, Methods, and Architectures Using Systematic Literature Review,” International Journal of Advanced Engineering and Management Research, vol. 08, no. 06, pp. 25–37, 2023, doi: 10.51505/ijaemr.2023.8603.
[21] D. Kapoor, D. Gupta, and M. Uppal, “IoT- driven Predictive Maintenance for Enhanced Reliability in Industrial Applications,” in 2024 International Conference on Intelligent & Innovative Practices in Engineering & Management (IIPEM), IEEE, Nov. 2024, pp. 1–6. doi: 10.1109/IIPEM62726.2024.10925824.
[22] M. A. T. Alrubei and H. S. Mogheer, “Analysis and development of RMS measurement system,” Radioelectronics. Nanosystems. Information Technologies., vol. 17, no. 3, pp. 285–294, Jun. 2025, doi: 10.17725/j.rensit.2025.17.285.
[23] P. M. Hasugian, H. D. Hutahaean, B. Sinaga, Sriadhi, and S. Silaban, “Review the Utilization of Big Data and K-Means Algorithm in Supporting The Determination of Village Status As Support To The Ministry of Village PDTT,” J Phys Conf Ser, vol. 1811, no. 1, p. 012063, Mar. 2021, doi: 10.1088/1742-6596/1811/1/012063.
[24] M. K. Goyal, Haripriya, and A. P. Singh, “Real-Time Data Streams: The Future of Predictive Maintenance in Manufacturing,” in 3rd IEEE International Conference on ICT in Business Industry and Government Ictbig 2023, 2023. doi: 10.1109/ICTBIG59752.2023.10456061.
[25] A. D. Frayudha and H. Agung, “Power BI and SQL Server Dashboard Data for Monitor Transportation Presence Employees at PT PON Gresik,” MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology), vol. 16, no. 1, pp. 19–23, Mar. 2024, doi: 10.18860/mat.v16i1.24149.
[26] P. M. Hasugian, H. D. Hutahaean, B. Sinaga, Sriadhi, and S. Silaban, “Review the Utilization of Big Data and K-Means Algorithm in Supporting the Determination of Village Status As Support to the Ministry of Village PDTT,” in Journal of Physics: Conference Series, 2021. doi: 10.1088/1742-6596/1811/1/012063.
[27] T. Miller et al., “Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data,” Electronics (Basel), vol. 14, no. 4, p. 696, Feb. 2025, doi: 10.3390/electronics14040696.
Downloads
Published
How to Cite
Issue
Section
Citation Check
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The article published in ELINVO became ELINVO's right in publication.
This work by ELINVO is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
