IoT-Based Embedded Device Development and RealTime Analysis for Unexpected Operations of Motors

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Publication Details

Author listTeerapat Riyota; Panuthat Boonpramuk; Santi Nuratch

Publication year2022

Start page28

End page33

Number of pages6

URLhttps://ieeexplore.ieee.org/document/10048607


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Abstract

In most industrial automation systems, all machines and equipment must operate smoothly and continuously to maintain the system performance. Therefore, all kinds of unexpected failures of the system cannot be accepted. For this reason, the maintenance processes are the essential operations performed periodically to avoid system failures. Of course, this operation takes time and resources, but it cannot be avoided. It is known that most automation systems compose of electrical motors. They are the core devices for robots and other movable machines. Each motor produces meaningful parameters such as temperature,
vibration, speed, noise, and power consumption. This paper presents the design and development techniques of the IoT-based embedded device that can be used for real-time monitoring and analysis of the unexpected operations of motors. The proposed device continuously senses and analyzes motor parameters to monitor and predict unexpected operations. We focus on temperature and unbalanced phenomenal measurement of the motors. The unbalanced parameter is implicitly measured from the speed changes using the time measurement of the sensing signals. The proposed device also supports wireless real-time data exchange to the server. The device and server exchange their data using the WebSockets protocol. The data sent to the server can be used for storage, processing, and monitoring. In this work, we also develop a web-based application that allows users to control and monitor the parameters of motors in real-time. The experimental results show that the proposed method can detect the unbalance operations and temperature of the motors at different operations. Therefore, the proposed system can be used in real-world applications that require abnormality detection, abnormal condition warning, and prediction of mechanical devices.


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Last updated on 2023-03-10 at 07:37