Exploring Machine Learning Algorithms for Predictive Maintenance in Manufacturing Industries

Shiva Boraiah, Suprith (2023) Exploring Machine Learning Algorithms for Predictive Maintenance in Manufacturing Industries. Masters thesis, Dublin, National College of Ireland.

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Abstract

Investigating the potential advantages of predictive maintenance using Machine Learning (ML) algorithms for the industrial sector's process efficiency and reliability is the main objective of this study. The purpose of this study was to evaluate feature engineering and selection strategies for improving model performance by comparing several ML algorithms. The primary goal of the research was to find out how the unpredictability of predictive maintenance models affects business choices. It was discovered that the most effective algorithms for failure prediction are Multi-Layer Perceptrons (MLP), which achieved an accuracy of up to 99.4 percent. The other top technique is Random Forests (RF). Data preparation is essential for improving algorithmic predictions, as the study also showed. Nevertheless, limitations related to data quality and the intelligibility of the models were recognized. To enhance maintenance systems' predictive capabilities in production settings, future studies should integrate data in real-time and apply advanced hybrid models.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Moldovan, Arghir-Nicolae UNSPECIFIED
Uncontrolled Keywords:	Predictive Maintenance; Machine Learning; MLP; Random Forest Classifier; Feature Engineering; Hybrid Modelling; Support Vector Machine
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Manufacturing Industry
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	22 May 2025 16:15
Last Modified:	22 May 2025 16:15
URI:	https://norma.ncirl.ie/id/eprint/7612

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