Hands-On Predictive Maintenance Kit for Manufacturing Education: An Accessible Experiential Learning Approach
- Jun 24
- 2 min read
Ibrahim El Khatib, Manuel Ivan Vea, Marcelo Montemayor Cavazos, Russel Bradley, Erick Ramirez-Cedillo , Brian W. Anthony.
Predictive maintenance (PdM) is a cornerstone of smart manufacturing systems due to its role in identifying potential machine failures before they occur, which allows strategic maintenance scheduling, maximizing operational uptime. Despite its importance in industry, traditional engineering education still lacks the tools required to give students a hands-on PdM experience. Current educational methods rely on mock PdM datasets, which disconnect students from the practical educational experiences built on sensor implementation, real-time data acquisition, and analytics using Machine Learning (ML). To bridge this gap, this research focused on the development of a low-cost educational PdM Add-On kit for FrED, a low-cost educational desktop-scale manufacturing system, developed at MIT, based on industrial fiber draw that serves as an experimental platform for data collection and analysis. The kit, consisting of a modular sensor suite and companion software, makes hands-on PdM education more accessible through realistic and interactive applications. This work aims to demonstrate that FrED, equipped with the PdM Add-On kit, provides an effective and scalable platform for data-centric PdM education. The kit was deployed in a classroom environment, where students collected process data, applied preprocessing and feature extraction methods, and developed ML models. The augmented FrED platform enabled students to engage with the full PdM workflow using a setup that mimics a real-world industrial scenario while growing their advanced analytics skill set. To validate the pedagogical effectiveness of this intervention, the study employed a mixed-methods assessment framework grounded in Self-Determination Theory (SDT). Student outcomes were evaluated using three distinct instruments: a technical Concept Inventory (CI) to measure objective learning gains, the Perceived Competence Scale (PCS) to quantify shifts in self-efficacy, and the Intrinsic Motivation Inventory (IMI) to assess student engagement and intrinsic motivation. Moreover, reflective questions were used to understand the student learning changes shown in the quantitative instruments.
Conference:
2026 ASEE Annual Conference & Exposition
DOI
Coming soon...
Publication Date
2026-6-24
Available online (up to 31 July 2026) https://nemo.asee.org/public/conferences/374/papers/52851/view Open source kit and scripts:
Comments