With the emergence of Industry 4.0, technologies such as data acquisition, the Internet of Things (IOT), machine learning, and Artificial Intelligence (AI) on automated equipment have become indispensable to industrial factories in engineering education. This study explored motor data acquisition and proposed a new prediction value by applying machine learning technology and AI through data acquisition. Through the accumulation of models and data, models are created and fed back to the equipment. This method can strengthen the capacity of maintenance engineers when running diagnostics as engineers can perform maintenance in advance. This study developed machine learning and AI to acquire, predict and diagnose motor data by collecting motor data through a programmable logic controller (PLC) and an intelligence power monitor. The proposed machine learning and AI utilized the Microsoft Visual Studio system while integrating the PLC and the intelligence power monitor for communication. A predictive analysis was conducted through machine learning and AI to obtain the predicted value which can be used to prompt maintenance engineers to perform early equipment maintenance for motor diagnostics and predictions. The proposed technology are applicable to the development of curriculum for engineering education.

Authors:
Wen-Jye Shyr, National Changhua University of Education, Taiwan
Chun-Yuan Chang, National Changhua University of Education, Taiwan
Chia-Hsun Kuo, National Changhua University of Education, Taiwan

About the Presenter(s)
Professor Wen-Jye Shyr is a University Professor/Principal Lecturer at National Changhua University of Education in Taiwan

See this presentation on the full schedule – Monday Schedule

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