Teaching AI-Integrated Engineering Through Project-Based Learning: A Case Study on Deep Learning for Solar PV Fault Detection

Authors

  • Bhupendra Dhirhi Student, Department of Mechanical Engineering, Government Engineering College, Raipur, Chhattisgarh
  • Gajendra Yadav Student, Department of Mechanical Engineering, Government Engineering College, Raipur, Chhattisgarh
  • Animesh Verma Student, Department of Mechanical Engineering, Government Engineering College, Raipur, Chhattisgarh
  • Ajay Tripathi Professor, Department of Mechanical Engineering, Government Engineering College, Raipur, Chhattisgarh
  • Govind Sahu Assistant Professor, Department of Mechanical Engineering, Government Engineering College, Raipur, Chhattisgarh

DOI:

https://doi.org/10.16920/jeet/2026/v39is4/26115

Keywords:

Engineering education, project-based learning, artificial intelligence, deep learning, solar PV fault detection, YOLOv8s, Mask R-CNN, thermal infrared imaging, undergraduate curriculum, competency development.

Abstract

Engineering education faces growing pressure to equip graduates with competencies spanning artificial intelligence (AI), renewable energy systems, and professional engineering practice. This paper presents a project-based learning (PBL) case study in which final-year undergraduate mechanical engineering students designed, trained, evaluated, and deployed two complementary deep learning models for automated fault detection in solar photovoltaic (PV) panels. The student team developed (1) a YOLOv8s object detection model for thermal infrared image analysis, achieving a mean Average Precision (mAP@0.5) of 98.51% across five fault categories, and (2) a Mask R-CNN instance segmentation model for visible-light panel analysis, achieving overall detection accuracy above 90% across six fault categories. Image data were acquired via UAV drone from a 50 MW operational solar plant at Charoda, Bhilai, grounding the learning experience in authentic industrial practice. This paper analyses the pedagogical design, student learning outcomes, disciplinary competencies developed, and implementation challenges, and offers a transferable framework for engineering educators seeking to embed AI project work within undergraduate curricula.

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Published

2026-06-30

How to Cite

Dhirhi, B., Yadav, G., Verma, A., Tripathi, A., & Sahu, G. (2026). Teaching AI-Integrated Engineering Through Project-Based Learning: A Case Study on Deep Learning for Solar PV Fault Detection. Journal of Engineering Education Transformations, 39(4), 1–8. https://doi.org/10.16920/jeet/2026/v39is4/26115

Issue

Volume 39, Special Issue 4, June 2026

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC

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