Signal monitoring system and control of three-phase electric motors with IoT integration for industrial applications
Keywords:
Predictive maintenance. Industry 4.0. Electrical machines.Abstract
This study presents the development of a signal monitoring and control system for induction motors, integrating IoT through the MQTT protocol. The integration of conventional industrial systems with new IoT technologies was pursued during the development process to optimize motor maintenance. The adopted methodology involved designing a printed circuit board for data acquisition, programming embedded code, and implementing a Node-RED interface for remote control. Bench tests validated the system under real conditions, demonstrating its compatibility with legacy inverters and redundant communication via MODBUS. Compared to other studies in the literature, the proposed system stands out for its flexibility, enabling the modernization of industrial processes with minimal structural intervention.
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ABOUZEID, A. F. et al. Control strategies for induction motors in railway traction applications. Energies, Basileia, v. 13, n. 3, p. 700, 2020.
AKTAS, M. et al. Direct torque control versus indirect field-oriented control of induction motors for electric vehicle applications. Engineering Science and Technology, Amsterdã, v. 23, n. 5, p. 1134–1143, 2020.
CIANCETTA, F. et al. A low-cost IoT sensors network for monitoring three-phase induction motor mechanical power adopting an indirect measuring method. Sensors, Basileia, v. 21, n. 3, p. 754, 2021.
EMBONG, A.; ASBOLLAH, L.; HAMID, S. A. Empowering industrial automation labs with IoT: A case study on real-time monitoring and control of induction motors using Siemens PLC and Node-RED. Journal of Mechanical Engineering and Sciences, Pahang, v. 18, n. 2, p. 10004–10016, 2024.
ICHPAS, W. H.; NÚÑEZ, J. C. IoT platform-based control module for remote monitoring of low-power three-phase motors. In: 2023 6TH ASIA CONFERENCE ON ENERGY AND ELECTRICAL ENGINEERING (ACEEE), 6., 2023, Chengdu. Anais [...]. Piscataway: IEEE, 2023. p. 301-305
IOANNIDES, M. G. et al. Design and operation of internet of things-based monitoring control system for induction machines. Energies, Basileia, v. 16, n. 7, p. 3049, 2023.
IYER, R.; SHARMA, A. IoT based home automation system with pattern recognition. International Journal of Recent Technology and Engineering, Bhopal, v. 8, n. 2, p. 3925–3929, 2019.
DEMIR, E.; KORKMAZ, H. A novel monitoring dashboard and hardware implementation simplifying the remote access in industry. In: 2023 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR INDUSTRY 4.0 & IOT, 1., 2023, Brescia. Anais [...]. Piscataway: IEEE, 2023. p. 399-403.
MAMATHA, G.; THEJASWI, A. H. Induction motor condition monitoring and controlling based on IoT. International Journal of Research in Engineering, Science and Management, Nelore, v. 4, n. 9, p. 220–225, 2021.
NUNES, W. R. B. M. et al. 3ph high efficiency induction motors with IFOC applied to a wheelchair by joystick. IEEE Latin America Transactions, Piscataway, v. 14, n. 5, p. 2041–2051, 2016.
OTHMAN, S. A.; MOHAMMED, J. A. K.; MOHAMMED, F. M. Variable speed drives in electric elevator systems: A review. In: 3RD INTERNATIONAL SCIENTIFIC CONFERENCE OF ENGINEERING SCIENCES AND ADVANCES TECHNOLOGIES (IICESAT), 3., 2021, Hila. Anais [...]. Bristol: IOP Publishing, 2021. v. 1973, n. 1, p. 012028.
PEETERS, C.; ANTONI, J.; HELSEN, J. Blind filters based on envelope spectrum sparsity indicators for bearing and gear vibration-based condition monitoring. Mechanical Systems and Signal Processing, Amsterdã, v. 138, n. 1, p. 106556, 2020.
PRAJAPATI, A.; BECHTEL, J.; GANESAN, S. Condition based maintenance: a survey. Journal of Quality in Maintenance Engineering, Leeds, v. 18, n. 4, p. 384-400, 2012.
PRINCE et al. Development of energy efficient drive for ventilation system using recurrent neural network. Neural Computing and Applications, Heidelberg, v. 33, n. 14, p. 8659–8668, 2021.
RAI, P.; REHMAN, M. Esp32 based smart surveillance system. In: 2019 2ND INTERNATIONAL CONFERENCE ON COMPUTING, MATHEMATICS AND ENGINEERING TECHNOLOGIES (ICOMET), 2., 2019, Sukkur. Anais [...]. Piscataway: IEEE, 2019. p. 1–3.
SANTOS, J. F. dos et al. Desenvolvimento de um sistema IIoT para monitoramento
de corrente e temperatura para motores elétricos. In: CONGRESSO BRASILEIRO DE AUTOMÁTICA-CBA, 24., 2022, Fortaleza. Anais [...]. Fortaleza: SBA. 2022. v. 3, n. 1.
ŞEN, M.; KUL, B. Iot-based wireless induction motor monitoring. In: XXVI INTERNATIONAL SCIENTIFIC CONFERENCE ELECTRONICS (ET), 26., 2017, Sozopol. Anais [...]. Sozopol: Technical University of Sofia, 2017. p. 1–5.
SHEHZAD, Z. A. et al. IoT & ML-based parameter monitoring of 3-φ induction motors for industrial application. In: 2023 INTERNATIONAL CONFERENCE ON EMERGING POWER TECHNOLOGIES (ICEPT), 2., 2023, Topi. Anais [...]. Piscataway: IEEE, 2023. p. 1-5.
SHUKLA, A. et al. Monitoring of single-phase induction motor through IoT using esp32 module. Journal of Sensors, Hoboken, v. 2022, n. 1, p. 8933442, 2022.
SHUKLA, S. et al. A new analytical MPPT-based induction motor drive for solar PV water pumping system with battery backup. IEEE Transactions on Industrial Electronics, Piscataway, v. 69, n. 6, p. 5768–5781, 2021.
TOULAN, Mohamed; NAFEH, Abdelnasser; ARAFA, Shawky. Improvement of induction motors reliability in fertilizers plants using IoT and enterprise resource planning. In: INTERNATIONAL TELECOMMUNICATIONS CONFERENCE (ITC-EGYPT), 4., 2024, Cairo. Anais [...]. Piscataway: IEEE, 2024. p. 487-492.
VANITHA, N.; SUTHANTHIRA, A.; KARTHIKEYAN, R.; RAMANI, T.; MEENAKSHI; SUJATHA, S. IoT enhanced induction motor drive in electric vehicle propulsion using field oriented control. In: 2023 7TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMMUNICATION AND AEROSPACE TECHNOLOGY (ICECA), 7., 2023, Coimbatore. Anais [...]. Piscataway: IEEE, 2023. p. 288-292.
WANG, B. et al. Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery. Neurocomputing, Amsterdã, v. 379, n. 1, p. 117–129, 2020.
YOUSUF, M. et al. IoT-based health monitoring and fault detection of industrial AC induction motor for efficient predictive maintenance. Measurement and Control, Londres, v. 57, n. 8, p. 00202940241231473, 2024.
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