Implementation and Evaluation of a Predictive Maintenance Course Utilizing Machine Learning

Abstract

This paper explores a course designed to instruct students on project-based machine learning in predictive maintenance. A class of nine students was instructed to predict the remaining useful life of simulated turbofan units using various analysis techniques and machine learning models. Student performance was evaluated with a self-efficacy survey conducted on the first and last day of the course. Participants began with low self-efficacy in knowledge and skill domains, but high attitudes regarding ML. By the end of the course, knowledge and skills saw a significant increase in score, with attitudes remaining constant. This course provides insight into the gains in ML knowledge and skills for non-CS students, as well as a pedagogical example that engineering and engineering technology instructors can employ to incorporate ML content into their courses. Data is presented to show that engineering students can develop practical ML skills for engineering applications.