Development and Evaluation of a Multi-Sensor IoT System for Real-Time Condition Monitoring and Fault Classification of an Industrial Electric Motor

Industrial electric motors require continuous condition monitoring because developing electrical, thermal, and mechanical abnormalities can lead to unplanned shutdowns and equipment damage. This study developed and evaluated a multi-sensor Internet of Things system for real-time condition monitoring and fault classification of an industrial electric motor driving a fire-hydrant pump under field operating conditions. An ESP32 edge device was integrated with an MLX90614 infrared temperature sensor, an MPU6050 three-axis accelerometer, PZEM-004T electrical measurement modules, and an optical rotational-speed sensor. The system acquired temperature, vibration, phase voltage, current, frequency, power, power factor, and rotational-speed measurements. The acquired data were transmitted through authenticated Message Queuing Telemetry Transport communication to Node-RED for processing and routing, stored in InfluxDB Cloud, and presented through Grafana dashboards for real-time monitoring, trend analysis, and alert generation. Field measurements were combined with systematically labelled electrical, thermal, and mechanical fault-condition records to develop the classification dataset. Four recurrent deep-learning architectures—Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Gated Recurrent Unit, and attention-enhanced Gated Recurrent Unit—were trained and comparatively evaluated using multimodal time-series sequences. The LSTM model achieved a test accuracy of 99.95%, while the GRU, Bi-GRU, and GRU-Attention models each achieved 99.97%. A hybrid decision mechanism further compared model predictions with predefined operational thresholds to identify inconsistent classifications and provide an independent condition check. The findings demonstrated that the developed system supported continuous field monitoring, reliable cloud-based data handling, and accurate classification of motor operating conditions. The system provides a practical basis for data-driven fault detection and predictive maintenance decision support for industrial electric motors.

Industrial Electric Motor, Condition Monitoring, Internet Of Things, Fault Classification, ESP32, Recurrent Neural Network, Predictive Maintenance

1. T. Abbasi, K. Lim, and K. Yam, “Predictive Maintenance of Oil and Gas Equipment using Recurrent Neural Network,” IOP Conf. Ser. Mater. Sci. Eng., vol. 495, 2019, [Online]. Available: https://api.semanticscholar.org/CorpusId:216694755 [Google Scholar] [Crossref]

2. M. Obaid and Q. Ahmed, “A STRUCTURED RELIABILITY AND MAINTAINABILITY ASSESSMENT MODEL: AN APPLICATION TO HIGH VOLTAGE MOTORS,” The Journal of Engineering Research [TJER], 2023, [Online]. Available: https://api.semanticscholar.org/CorpusId:257993458 [Google Scholar] [Crossref]

3. K. Barrera-Llanga, J. Burriel-Valencia, Á. Sapena-Bañó, and J. Martínez-Román, “Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis,” Sensors (Basel), vol. 25, 2025, [Online]. Available: https://api.semanticscholar.org/CorpusId:275717757 [Google Scholar] [Crossref]

4. S. Patil, Ambika, Laxmi, Hema, and Aishwarya, “IoT-PHMNet: A Real-Time Prognostics and Health Management Framework for Industrial Motor Systems,” Int. J. Res. Appl. Sci. Eng. Technol., 2026, [Online]. Available: https://api.semanticscholar.org/CorpusId:289459444 [Google Scholar] [Crossref]

5. C. Zachariades and V. Xavier, “A Review of Artificial Intelligence Techniques in Fault Diagnosis of Electric Machines,” Sensors (Basel), vol. 25, 2025, [Online]. Available: https://pdfs.semanticscholar.org/1418/6ab49e3ae2d8d3850df52f0e45d56e7888fe.pdf [Google Scholar] [Crossref]

6. M. Şen and M. Mutluer, “A Review of BLDC Motors: Types, Application, Failure Modes and Detection,” Energies (Basel)., 2025, [Online]. Available: https://pdfs.semanticscholar.org/5bfd/d171de73efd0e7746cc227d9f0d06066e6dd.pdf [Google Scholar] [Crossref]

7. S. Misra et al., “Fault Detection in Induction Motor Using Time Domain and Spectral Imaging-Based Transfer Learning Approach on Vibration Data,” Sensors (Basel), vol. 22, 2022, [Online]. Available: https://pdfs.semanticscholar.org/faf0/0a55312dcd512b18719970fa75f2b4b1bf39.pdf [Google Scholar] [Crossref]

8. B. H. Bahgat, E. A. Elhay, and M. M. Elkholy, “Advanced fault detection technique of three phase induction motor: comprehensive review,” Discover Electronics, vol. 1, 2024, [Online]. Available: https://doi.org/10.1007/s44291-024-00012-3 [Google Scholar] [Crossref]

9. D. Mine, C. Amuzuvi, and H. Warden, “Failure Prediction of Induction Motors: A Case Study using CSLGH900/6-214, 5.8 MW, 11 kV/3ph/50 Hz Sag Mill Motor at Goldfields, Damang Mine,” Global Journal of Research In Engineering, pp. 7–28, 2020, [Online]. Available: https://api.semanticscholar.org/CorpusId:218551061 [Google Scholar] [Crossref]

10. T. Mutsvangwa and D. T. Makusha, “Development of an AI Based AC Motor Fault Prediction Model,” International Journal on Science and Technology, 2025, [Online]. Available: https://api.semanticscholar.org/CorpusId:279315147 [Google Scholar] [Crossref]

11. U. Khan, D. Cheng, F. Setti, F. Fummi, M. Cristani, and L. Capogrosso, “A Comprehensive Survey on Deep Learning-based Predictive Maintenance,” ACM Transactions on Embedded Computing Systems, vol. 25, pp. 1–43, 2025, [Online]. Available: https://api.semanticscholar.org/CorpusId:278069227 [Google Scholar] [Crossref]

12. D. Anaba, A. Jason, Kess-Momoh, and S. Ayodeji, “Innovative maintenance strategies for industrial equipment: A review of current practices and future directions,” Open Access Research Journal of Science and Technology, 2024, [Online]. Available: https://api.semanticscholar.org/CorpusId:271193342 [Google Scholar] [Crossref]

13. M. Moleda, B. Małysiak-Mrozek, W. Ding, V. Sunderam, and D. Mrozek, “From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry,” Sensors (Basel), vol. 23, 2023, [Online]. Available: https://api.semanticscholar.org/CorpusId:259588499 [Google Scholar] [Crossref]

14. S. Apeiranthitis, P. Zacharia, A. Chatzopoulos, and M. Papoutsidakis, “Predictive Maintenance of Machinery with Rotating Parts Using Convolutional Neural Networks,” Electronics (Basel)., 2024, [Online]. Available: https://api.semanticscholar.org/CorpusId:267122647 [Google Scholar] [Crossref]

15. R. Rode, B. P. Kurpatwar, P. Patil, M. J. Gitay, N. Pawar, and A. P. Ghule, “Research and experimental analysis of intelligent predictive maintenance for industrial machinery using AI-based analytical techniques,” Aposta: Revista de Ciencias Sociales, 2026, [Online]. Available: https://api.semanticscholar.org/CorpusId:289244561 [Google Scholar] [Crossref]

16. T. Lukito, R. Herlianti, M. Mayanti, and L. H. Kusumah, “Implementation of predictive maintenance in various Industry: A Review,” TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika, 2025, [Online]. Available: https://api.semanticscholar.org/CorpusId:276028877 [Google Scholar] [Crossref]

17. D. Patel and P. Kalgutkar, “Predictive Maintenance for Industrial Equipment Using Machine Learning,” International Journal of Advanced Research in Science, Communication and Technology, 2024, [Online]. Available: https://doi.org/10.48175/ijarsct-19379 [Google Scholar] [Crossref]

18. B. Paul and J. Manohar, “A COMPREHENSIVE SURVEY ON REAL TIME INDUCTION MOTOR FAILURE DIAGNOSIS AND ANALYSIS,” ASEAN Engineering Journal, 2025, [Online]. Available: https://api.semanticscholar.org/CorpusId:276826200 [Google Scholar] [Crossref]

19. J. J. Saucedo-Dorantes, M. Delgado-Prieto, J. V. Pérez, and D. A. Elvira-Ortiz, “Rotating Electromechanical System Dataset for Condition Monitoring,” Sci. Data, vol. 13, 2026, [Online]. Available: https://api.semanticscholar.org/CorpusId:287538027 [Google Scholar] [Crossref]

20. O. Yadav and G. L. Pahuja, “Bearing Health Assessment Using Time Domain Analysis of Vibration Signal,” International Journal of Image, Graphics and Signal Processing, 2020, [Online]. Available: https://api.semanticscholar.org/CorpusId:258350537 [Google Scholar] [Crossref]

21. Ms. S. Sangole, “Motor Fault Detection Using Vibration and Current,” Int. J. Res. Appl. Sci. Eng. Technol., 2026, [Online]. Available: https://api.semanticscholar.org/CorpusId:286862932 [Google Scholar] [Crossref]

22. T. Cao and W. Hu, “Multi-Sensor Fusion Techniques for Monitoring Emissions and Health of New Energy Vehicle Powertrains,” Journal of Environmental & Earth Sciences, 2026, [Online]. Available: https://api.semanticscholar.org/CorpusId:291094206 [Google Scholar] [Crossref]

23. S. K. M. Gangadhara, K. A. Neelegowda, A. A. Chikkalingaiah, and N. Chikkaguddaiah, “Intelligent Railway Wagon Health Assessment Using IoT Sensors and Predictive Analytics for Safety-Critical Applications,” IoT, 2026, [Online]. Available: https://api.semanticscholar.org/CorpusId:287190330 [Google Scholar] [Crossref]

24. A. Madan, W. Jiang, Y. Wang, Y. Yang, J. Hao, and P. Shum, “Wall Thinning Monitoring in Boiler U-Bends: A Review and Future Prospects with Fiber Optic Sensing,” Micromachines (Basel)., vol. 17, 2026, [Online]. Available: https://api.semanticscholar.org/CorpusId:287945417 [Google Scholar] [Crossref]

25. M. Ammann and P. Bereuter, “Interactive Dashboards for Multi-Modal Data Analysis: Insights from Rail Construction Projects,” Proceedings of the ICA, 2025, [Online]. Available: https://api.semanticscholar.org/CorpusId:282294137 [Google Scholar] [Crossref]

26. N. Chbaik, A. Khiat, A. Bahnasse, and H. Ouajji, “Blockchain-Assisted IoT Wireless Framework for Equipment Monitoring in Smart Supply Chain: A Focus on Temperature and Humidity Sensing,” IEEE Access, vol. 12, pp. 117504–117522, 2024, [Online]. Available: https://api.semanticscholar.org/CorpusId:272014424 [Google Scholar] [Crossref]

27. M. Zennaro, E. Pietrosemoli, A. Soltobaev, T. Sultanov, and B. Moldobekov, “From mountains to data: low-cost weather stations in Kyrgyzstan’s challenging terrain,” Frontiers Commun. Networks, vol. 6, 2025, [Online]. Available: https://api.semanticscholar.org/CorpusId:278262669 [Google Scholar] [Crossref]

28. N. R. Tey, V. Velisavljevic, V. Dyo, M. Khodadadzadeh, J. Newton, and B. Newton, “Exploring Dataset Availability and Methods for Enhanced Automated Fault Detection in Induction Motors,” IEEE Access, vol. 14, pp. 76990–77013, 2026, [Online]. Available: https://api.semanticscholar.org/CorpusId:287176135 [Google Scholar] [Crossref]

29. M. Şen and M. Mutluer, “A Review of BLDC Motors: Types, Application, Failure Modes and Detection,” Energies (Basel)., 2025, [Online]. Available: https://api.semanticscholar.org/CorpusId:283816726 [Google Scholar] [Crossref]

30. S. Ali, A. Khizhik, S. Svirin, A. S. Ryzhikov, and D. Derkach, “Learning to Hear Broken Motors: Signature-Guided Data Augmentation for Induction-Motor Diagnostics,” Eng. Appl. Artif. Intell., vol. 170, p. 114137, 2025, [Online]. Available: https://api.semanticscholar.org/CorpusId:279260666 [Google Scholar] [Crossref]

31. G. Manivannan, Harsha, L. S, and K. N, “Comparative Performance Analysis of Bat Algorithm Optimized Support Vector Machine and Hybrid CNN-BiLSTM for Vibration Signals Based Electrical Motor Fault Detection,” 2026 4th International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), pp. 186–192, 2026, [Online]. Available: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11559312 [Google Scholar] [Crossref]

32. N. N. S. Torres et al., “Fault Diagnosis in Internal Combustion Engines Using Artificial Intelligence Predictive Models,” Applied System Innovation, 2025, [Online]. Available: https://api.semanticscholar.org/CorpusId:281754968 [Google Scholar] [Crossref]

33. J. H. Lee, M. Hong, and K. M. Park, “Sensorless Collision Detection and Classification in Collaborative Robots Using Stacked GRU Networks,” Actuators, 2026, [Online]. Available: https://api.semanticscholar.org/CorpusId:287213403 [Google Scholar] [Crossref]

34. M. Yousuf, T. Alsuwian, A. A. Amin, S. Fareed, and M. Hamza, “IoT-based health monitoring and fault detection of industrial AC induction motor for efficient predictive maintenance,” Measurement and Control, vol. 57, no. 8, pp. 1146–1160, Aug. 2024, doi: 10.1177/00202940241231473. [Google Scholar] [Crossref]

35. Z. Long et al., “Motor fault diagnosis using attention mechanism and improved adaboost driven by multi-sensor information,” Measurement, vol. 170, p. 108718, 2021, doi: https://doi.org/10.1016/j.measurement.2020.108718. [Google Scholar] [Crossref]

36. O. Kullu and E. Cinar, “A Deep-Learning-Based Multi-Modal Sensor Fusion Approach for Detection of Equipment Faults,” Machines, vol. 10, no. 11, 2022, doi: 10.3390/machines10111105. [Google Scholar] [Crossref]

37. M. Abdelmaksoud, M. Torki, M. El-Habrouk, and M. Elgeneidy, “Convolutional-neural-network-based multi-signals fault diagnosis of induction motor using single and multi-channels datasets,” Alexandria Engineering Journal, vol. 73, pp. 231–248, 2023, doi: https://doi.org/10.1016/j.aej.2023.04.053. [Google Scholar] [Crossref]

38. S. Sobhi, M. Reshadi, N. Zarft, A. Terheide, and S. Dick, “Condition Monitoring and Fault Detection in Small Induction Motors Using Machine Learning Algorithms,” Information, vol. 14, no. 6, 2023, doi: 10.3390/info14060329. [Google Scholar] [Crossref]

39. J. Vanga et al., “Fault classification of three phase induction motors using Bi-LSTM networks,” Journal of Electrical Systems and Information Technology, vol. 10, no. 1, May 2023, doi: 10.1186/s43067-023-00098-x. [Google Scholar] [Crossref]

40. T.-T. Vo, M.-K. Liu, and M.-Q. Tran, “Harnessing attention mechanisms in a comprehensive deep learning approach for induction motor fault diagnosis using raw electrical signals,” Eng. Appl. Artif. Intell., vol. 129, p. 107643, 2024, doi: https://doi.org/10.1016/j.engappai.2023.107643. [Google Scholar] [Crossref]

• The Impact Of UI/UX Design on User Trust and Task Completion in Civic Tech Platforms
• Solar Cell Photovoltaic Model Shell Sp 75
• Development of an Intelligent Traffic Management System to Address Visibility Obstruction at Urban Intersections: A Case Study of Ibadan Metropolis
• Optimum Placement of Facts Devices on an Interconnected Power Systems Using Particle Swarm Optimisation Technique
• Assessing Construction Transformation and Implication on Future Production Flow System