A Low-cost redundant IOT current monitoring system with fault and drift detection for industrial IIOT applications.
Manisekaran Sujatha, Ganeshram (2026)
2026
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202604136285
https://urn.fi/URN:NBN:fi:amk-202604136285
Tiivistelmä
This thesis explores the design and implementation of an affordable IoT-based current monitoring system that offers enhanced reliability and accuracy. In industrial settings, precise current measurement is essential for monitoring equipment and preventing unexpected failures. However, systems relying on a single sensor are often prone to faults and measurement drift, which can reduce reliability. Therefore, this work investigates whether a redundant sensing approach can improve measurement accuracy and overall system performance.
The proposed system is a redundant sensing setup with fault detection and failover, where three ACS712 Hall-effect current sensors are connected to an Arduino Uno for data collection and edge processing. The sensor data is analyzed using voting logic along with fault and drift detection mechanisms to ensure reliable output. A Raspberry Pi acts as an IoT gateway, transmitting the processed data to a web-based dashboard for real-time visualization and logging. Experimental tests were conducted under both normal and faulty conditions to evaluate system performance using metrics such as measurement error and system availability.
The results demonstrate that redundancy improves measurement stability and increases fault tolerance compared to a single-sensor system. Faulty sensor readings can be detected and excluded, allowing the system to produce consistent output. Overall, the developed system provides a practical and low-cost solution for industrial current monitoring and can be applied in predictive maintenance scenarios.
The proposed system is a redundant sensing setup with fault detection and failover, where three ACS712 Hall-effect current sensors are connected to an Arduino Uno for data collection and edge processing. The sensor data is analyzed using voting logic along with fault and drift detection mechanisms to ensure reliable output. A Raspberry Pi acts as an IoT gateway, transmitting the processed data to a web-based dashboard for real-time visualization and logging. Experimental tests were conducted under both normal and faulty conditions to evaluate system performance using metrics such as measurement error and system availability.
The results demonstrate that redundancy improves measurement stability and increases fault tolerance compared to a single-sensor system. Faulty sensor readings can be detected and excluded, allowing the system to produce consistent output. Overall, the developed system provides a practical and low-cost solution for industrial current monitoring and can be applied in predictive maintenance scenarios.