A Real-Time Condition Monitoring and Maintenance Management System for Low Voltage Industrial Motors Using Internet-of-Things

Maintenance and sound operating industrial equipment are critical for any manufacturing company. Standardization of the manufacturing infrastructure and establishment of a systematic maintenance program is essential for this process. However, condition monitoring must also be an integral part of a smart manufacturing program that seeks to improve and optimize the operational efficiency of production systems. Absence of this type of data-driven observations and mindset in manufacturing decision making, may result in safety risks, missing critical signs, or occurrence of unexpected repairs that could bring the equipment to a halt. Moreover, routine maintenance may short the actual useful life of some equipment and when scheduled maintenance shutdowns occur too often, they increase downtime and cost. This paper presents the design and development of a real-time condition monitoring system for managing industrial low voltage motors using internet-of-things. The system can record and monitor vibration and temperature conditions of an industrial motor and transmit the data through a wireless network to a data logging center. The current prototype was developed using open source software and hardware and can successfully identify abnormal motor conditions from sensor input values that exceed predefined setpoints. When a motor is approaching an abnormal condition, the system changes state and informs the user through a mobile alert. The motor management system requires the user to perform an RFID enforced inspection. The alarm system remains active until authorized personnel visits the equipment and scans the dedicated RFID batch. Furthermore, the motor management system can be accessed remotely to allow the user to visualize the current condition of the motor. Finally, we present a pilot experiment that was conducted to test the condition monitoring capabilities of the prototype and we discuss future steps in order to further develop the current system to a smart predictive maintenance system capable of detecting and predicting specific motor faults. (C) 2020 The Authors. Published by Elsevier B. V.