The Design and Development of an Internet of Things-Based Condition Monitoring System for Industrial Rotating Machines
In general, the industries utilize more rotating machines and the efficient functioning of these machines is vital for the smooth operation of industrial processes. Further, the detection and identification of motor issues in a timely manner is crucial to prevent unexpected downtime and expensive re...
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Published in: | Engineering proceedings Vol. 58; no. 1; p. 71 |
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Main Authors: | , , , , |
Format: | Journal Article |
Language: | English |
Published: |
MDPI AG
01-11-2023
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Subjects: | |
Online Access: | Get full text |
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Summary: | In general, the industries utilize more rotating machines and the efficient functioning of these machines is vital for the smooth operation of industrial processes. Further, the detection and identification of motor issues in a timely manner is crucial to prevent unexpected downtime and expensive repairs. In this work, a novel approach is proposed to monitor and assess the condition of motors in real-time by analyzing the environmental parameters using sensors which are capable of measuring temperature and humidity, to gather data about the operating environment of motors in industrial settings. Also, by continuously monitoring these environmental factors, deviations from optimal conditions can be detected, allowing for proactive maintenance actions to be taken. The proposed system consists of a network of temperature and humidity sensors strategically placed in proximity to the motors being monitored. Further, these sensors collect temperature and humidity data at regular intervals and transmit them to an Internet of Things (IoT) cloud platform. Finally, the data are analyzed using a fuzzy logic decision-making algorithm and are compared against predefined threshold values to determine if the motor is operating within acceptable conditions. This work appears to be of high industry relevance since automated notifications or alerts are to be sent to maintenance personnel when abnormal conditions are detected. |
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ISSN: | 2673-4591 |
DOI: | 10.3390/ecsa-10-16240 |