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Open Access
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Sanket Arun Adswe, Aditya Ambhure, Sanket Adsule, Jishan Mujawar, S.R. Rohile
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- Student, Student, Student, Student, Professor Department of Electronics and Electrical Engineering, Sinhgad College of Engineering, Pune, Department of Electronics and Electrical Engineering, Sinhgad College of Engineering, Pune, Department of Electronics and Electrical Engineering, Sinhgad College of Engineering, Pune, Department of Electronics and Electrical Engineering, Sinhgad College of Engineering, Pune, Department of Electronics and Electrical Engineering, Sinhgad College of Engineering, Pune Maharashtra, Maharashtra, Maharashtra, Maharashtra, Maharashtra India, India, India, India, India
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Abstract
nThe induction motor failure detection system proposed in this research is based on the Internet of Things and uses an ESP32, vibration, current, temperature, IR, and Blynk app. An Internet of Things (IoT)-based fault detection system uses sensors to monitor the motor’s condition and transmits the information to a cloud-based platform. The cloud-based software may then analyze the data to identify issues and alert maintenance personnel.An overview of an Internet of Things (IoT)-based fault detection system for induction motors is provided in this article. It makes use of the ESP32 microcontroller, vibration, current, temperature, and IR sensors to measure speed as well as the Blynk app to issue alerts and track health. The motor’s vibration, current, temperature, and speed are all continuously monitored by the system. Through the Blynk app, the user receives an alert from the system if any of these parameters go above a predetermined threshold. After that, the user can take corrective measures to avoid a motor failure.
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Keywords: IoT, ESP32, induction motor fault detection, vibration sensor, current sensor, temperature sensor, IR sensor, Blynk app.
n[if 424 equals=”Regular Issue”][This article belongs to Recent Trends in Sensor Research & Technology(rtsrt)]
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References
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- Khan, M. A., Khan, N. U., Iqbal, J., & Ahad, M. F. (2019). Machine Induction Motor Fault Detection Using Cloud Computing and Machine Learning. IEEE Transactions on Industrial Electronics, 67(5), 4144-4154.
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- Tran MQ, Amer M, Abdelaziz AY, Dai HJ, Liu MK, Elsisi M. Robust fault recognition and correction scheme for induction motors using an effective IoT with deep learning approach. Measurement. 2023 Feb 15;207:112398.
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| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | May 5, 2024 | |
| Accepted | May 15, 2024 | |
| Published | May 28, 2024 |
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