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An Industrial-Grade Cyber-Physical Platform for Introducing Machine Learning Concepts

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thesis
posted on 29.07.2021, 01:31 by Dylan James ImbusDylan James Imbus
Industry 4.0 holds many promises for manufacturers; however, a shortage of qualified employees has prevented a swift adoption of the revolution's new technologies. Engineer and Economist Klaus Schwab argues Education 4.0 is the key to addressing the employee shortage and preparing future generations for the shifting labor market. To support Education 4.0, classes must allow students to engage emerging technologies that help bridge Operational Technology (OT) and Informational Technology (IT). The thesis detailed an educational laboratory that demonstrates the application of data analytics (an IT tool) and optimize the performance of a cyber-physical system composed of industrial (OT) components. The lab experience focuses on a disc's controlled positioning (levitating) using a PLC-based PID controller and a VFD. The activity requires students to capture data of a moving discs, create a machine learning function representing the disc's movement, and use the machine learning function for classification and PID optimization problems. A comparative analysis of a PID cycle ensures a regressions model accurately represents the physical model using measurements including peak-overshoot, rise time, settling time, and the flight plots' Means of their Squared Error. Further, the study examines multiple ML models each built using various features to identify the systems relevant and redundant data.

History

Degree Type

Master of Science in Industrial Technology

Department

Technology

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Grant Richards

Additional Committee Member 2

Huachao Mao

Additional Committee Member 3

Gaurav Nanda

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