Supervisor
Taufique Ahmed
Programme
HDIP in Data Analytics for Business
Subject
Computer Science
Abstract
Unplanned equipment downtime is a significant challenge in manufacturing, resulting in substantial productivity losses and operational costs. Predictive maintenance, enabled by machine learning and big data analytics, offers an opportunity to identify potential equipment failures before they occur and improve maintenance efficiency. This report extends a previous capstone project that applied Random Forest and Logistic Regression to the AI4I 2020 Predictive Maintenance Dataset. The current study expands the analysis by incorporating XGBoost, systematic hyperparameter optimisation, cross-validation, and SHAP-based model interpretability. In addition, SWOT and PESTLE analyses, alongside a legal and ethical assessment, examine the broader strategic and responsible implementation of AI-driven predictive maintenance in industry. The study evaluates the models' ability to predict equipment failures while considering their practical value, interpretability, and implications for responsible AI adoption in manufacturing.
Date of Award
2026
Full Publication Date
2026
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
de Andrade Batista, P.
(2026) Predictive Maintenance for Manufacturing Equipment CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.151