Aug 2026· Bulletin of Electrical Engineering and Informatics· 0 citations· 37 references
TL;DR
This research presents an internet of things (IoT-based) system for real-time monitoring and control of a three-phase induction motor that enables continuous monitoring, early fault detection, and predictive maintenance, thereby improving overall operational efficiency and reducing downtime.
Abstract
AC motors, particularly induction motors, remain the most widely used machines in industrial applications due to their simplicity, robustness, and efficiency. Given their critical role, continuous monitoring and regulation of induction motor parameters are essential to ensure reliability and prevent unexpected failures. This research presents an internet of things (IoT-based) system for real-time monitoring and control of a three-phase induction motor. Various sensors are employed to measure key parameters such as motor temperature, current, and voltage, with the collected data transmitted to a processing unit and displayed on a server for remote access. To enhance fault resilience, the system integrates both automatic and manual control mechanisms for starting or stopping motors under abnormal conditions. The proposed approach enables continuous monitoring, early fault detection, and predictive maintenance, thereby improving overall operational efficiency and reducing downtime. Induction motors, first introduced by Nikola Tesla, account for over 50% of global electricity consumption and are deployed in nearly 90% of industrial operations. Their dominance stems from inherent advantages, including being self-starting, cost-effective, reliable, and maintenance-friendly, as well as offering a strong power factor, compact design, and high efficiency. By leveraging IoT technology, this work bridges the gap between traditional motor operation and modern Industry 4.0 practices, providing a scalable solution for smart industrial environments.
The significance of induction motors lies in their robustness, simplicity of design, durability, and minimum
maintenance, which makes them essential components in industry. Nevertheless, constant exposure to electrical, thermal, and
mechanical stresses results in problems like overheating, winding deterioration, bearing damage, rotor imbalance, and
vibrations. Without timely identification of these issues, they lead to breakdowns, delays, and additional expenses. The suggested
innovation refers to a smart embedded system to monitor the operational state of an induction motor and analyze fault levels in
real time. It measures the parameters of temperature, currents, and vibrations that reflect motor conditions. These measurements
are carried out with sensors and analyzed by means of a microcontroller through comparison with predefined thresholds,
resulting in fault recognition and classification. Existing systems lack remote monitoring and instant alerts. To address these
gaps, the proposed system provides local monitoring along with IoT-based remote transmission. A buzzer/LED alert mechanism
ensures immediate notification during critical conditions.
C. R, Byra Reddy G R, Chaithra S et al.· International Journal for Re...· 0 citations
Energy supply is constantly becoming unstable, inefficient, and expensive due to the exorbitant cost of imported smart meters, which prevents their widespread use. The design and execution of a low-cost, locally adjustable smart energy monitoring and load control system for residential and small-scale industrial applications are presented in this paper. To provide real-time monitoring and intelligent load control, the system combines an Arduino-based Internet of Things platform with voltage and current sensors, relay modules, and cloud-based visualization. Over the course of 30 days, the prototype was installed in three homes with various load profiles after being calibrated against standard meters. Through a mobile dashboard, the solution effectively reduced wasteful consumption, enhanced user awareness, and automated load prioritization during periods of high demand. The obtained energy reductions are competitive with global standards while maintaining noticeably reduced costs through local hardware sourcing. Although Wi-Fi instability and sporadic resets following power surges were issues, user comments validated the ease of use. The results show that low-cost IoT-based devices can improve energy efficiency, cut expenses.
W. Bello, Miriam I. Chukwuma- Uchegbu, A. Abdul et al.· BIMA JOURNAL OF SCIENCE AND...· 0 citations
Induction motors account for approximately 40–45% of global industrial electricity consumption. Unplanned downtime due to stator faults, bearing failures, and rotor imbalances costs manufacturing enterprises an estimated $15–20 billion annually. This paper presents a complete Internet of Things (IoT) based predictive maintenance system for real-time condition monitoring of three-phase induction motors. The proposed system integrates low-cost wireless vibration sensors, thermal imaging modules, and current signature analyzers connected via a LoRaWAN gateway to a cloud-based analytics platform. A two-year study was conducted across 48 industrial motors (5.5–250 kW) operating in a German automotive parts factory. Using machine learning algorithms (random forest and long short-term memory networks), the system achieved 94.7% accuracy in fault prediction with an average warning time of 312 hours before critical failure. The results demonstrate a 67% reduction in unplanned downtime, a 53% decrease in maintenance costs, and a payback period of 1.2 years. The proposed solution prevents 82 tonnes of CO₂ equivalent per facility per year by reducing production stoppages and avoiding premature motor replacement. This scalable approach can be implemented across various industrial sectors.
A. Zemtsov· Journal of Electrical Engine...· 0 citations
Industrial machinery operating in manufacturing and process environments is frequently subjected to adverse operating conditions such as excessive temperature rise, abnormal current consumption, and mechanical vibrations, which may lead to performance degradation, unexpected failures, production losses, and safety hazards. To address these challenges, this paper presents the design and implementation of an Internet of Things (IoT)-enabled real-time machine health monitoring and protection system based on the ESP32 microcontroller platform. The proposed system integrates a DHT11 sensor for temperature and humidity monitoring, an ACS712 Hall-effect sensor for current measurement, and an MPU9250 inertial measurement unit (IMU) for vibration analysis. Sensor data are continuously acquired, processed, and transmitted through Wi-Fi to a cloud-based Firebase Realtime Database, enabling remote access and centralized monitoring. A responsive web dashboard hosted on GitHub Pages provides real-time visualization of machine operating parameters, status indicators, and fault notifications. To enhance operational safety and equipment reliability, threshold-based fault detection algorithms are implemented to identify abnormal operating conditions. When predefined critical limits are exceeded, the ESP32 automatically initiates protective actions by disconnecting the machine through a relay module, activating a visual alarm, and updating the fault status on the cloud platform. The dashboard additionally supports bidirectional communication, allowing authorized operators to remotely restart the machine, while a local push-button interface enables manual system recovery. Furthermore, the developed platform incorporates a browser-based logging mechanism that records timestamped sensor measurements, machine status transitions, fault events, and downloadable CSV trend data for maintenance analysis and performance evaluation. Experimental validation demonstrates reliable real-time monitoring with a data refresh interval of approximately 3 s, accurate threshold-based fault detection, dependable cloud connectivity, and effective remote supervisory control. The proposed solution offers a low-cost, scalable, and practical framework for predictive maintenance and industrial equipment condition monitoring in smart manufacturing environments.
Sudharshana, Kratika V Ulman, Kishan K Kulal et al.· 2026 International Conferenc...· 0 citations
The Sudden equipment failures are common in industrial systems, which means more downtime and higher maintenance costs. A Smart AI-Integrated Predictive Maintenance and Condition Monitoring System is proposed to solve this problem. It will allow for real-time monitoring and early fault detection in industrial machines. The system uses an Arduino Uno (ATmega328P) microcontroller that is connected to several sensors, such as voltage, current, temperature (LM35), vibration, proximity, and MPU6050 sensors, to collect important operational data. The ESP8266 NodeMCU Wi-Fi module sends the processed data to an IoT cloud platform so that it can be monitored and analyzed from afar. There is also an I2C display developed in for real-time viewing on position. The proposed system uses AI-based analysis to find problems and assume when equipment might break down, which enables maintenance be performed on time. By using a hardware prototype to evaluate shows that the system reliably monitor in real time and make accurate predictions, which cuts down on downtime and makes the system work better. The Integration of AI and IoT technologies makes predictive maintenance in modern industrial settings cheaper, more flexible, and smarter.
V. R. Kumar, P. Poornima, M. Abinesh et al.· International Conference on...· 0 citations
In the modern industrial sector, monitoring the condition of induction motors is the main key to maintaining efficiency and reliability. Internet of Things (IoT) technology in the monitoring system can be done in real-time, to provide convenience in monitoring induction performance. Haiwell Cloud SCADA-based monitoring system to analyze the behavior of induction motors in real-time voltage, current, speed, frequency, and temperature. This system integrates a tension control method using a magnetic powder brake to simulate changes in the load dynamically. The results show that at a low torque of 0 – 6 Nm, the induction motor operates stably at a speed close to synchronous with a low stator current. However, at torques above 8 Nm, there is a significant voltage drop, and the stator current increases four times compared to the nominal current, and there is a temperature increase of up to 35oC, which represents an increase in power losses. This research offers a combination of IoT-based monitoring and tension control methods. IoT-based monitoring systems help users easily monitor the performance of induction motors in real-time and anticipate potential problems before failures occur.