IoT-based health monitoring and fault detection of industrial AC induction motor for efficient predictive maintenance
| Autor: | Muhammad Yousuf, Turki Alsuwian, Arslan Ahmed Amin, Sanwal Fareed, Muhammad Hamza |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Measurement + Control, Vol 57 (2024) |
| Druh dokumentu: | article |
| ISSN: | 0020-2940 00202940 |
| DOI: | 10.1177/00202940241231473 |
| Popis: | This research paper presents the development and implementation of an integrated condition monitoring and fault detection system for AC induction motors using a combination of sensors, GSM communication, and a cloud-based Internet of Things (IoT) platform. The proposed system aims to enhance industrial motors’ operational reliability and efficiency by providing real-time data monitoring and early fault detection. The key components of the system include temperature, vibration, current, voltage, and speed sensors, which are strategically placed to gather critical motor performance data. These sensors feed data to an Arduino-based control unit responsible for sensor data acquisition and processing. To ensure timely response to anomalies, the system is equipped with an alarm system and GSM alerts, which notify designated personnel in case of abnormal motor behavior. Moreover, the paper incorporates remote monitoring capabilities, enabling users to access motor health data and real-time status from a distance. Historical data is also stored for analysis and comparison through the integration of a cloud-based Blynk-IoT platform. Additionally, the system facilitates RPM control and utilizes relay modules for seamless motor control and protection. The proposed system was tested and validated using Proteus for circuit diagram simulation and Arduino for sensor coding. The results demonstrate its effectiveness in detecting abnormal motor behavior and its potential to prevent catastrophic failures by enabling predictive maintenance. The proposed system successfully detects and displays abnormalities in important parameters like vibration, temperature, speed, three-phase currents, and voltages with 99% accuracy. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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