Proactive Equipment Maintenance in Manufacturing: Leveraging Modern Deep Learning for Fault Prediction

Dabbakuti, Sasi Venkata Krishna (2024) Proactive Equipment Maintenance in Manufacturing: Leveraging Modern Deep Learning for Fault Prediction. Masters thesis, Dublin, National College of Ireland.

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Abstract

The advent of Industry 4.0 has revolutionized manufacturing, integrating advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and data analytics into production processes. This research focuses on lever- aging modern deep learning models for fault prediction in manufacturing equipment, aiming to transition from reactive to proactive maintenance strategies. The study utilizes a comprehensive dataset from Kaggle, containing historical maintenance records and operational metrics. Various machine learning and deep learning models, including Random Forest, Logistic Regression, Decision Trees, and Neural Networks, were implemented and evaluated for their predictive capabilities. The Neural Network model emerged as the most effective, achieving the highest accuracy of 76.56%, with a strong recall for predicting equipment failures. The Decision Tree model also showed robust performance with an accuracy of 73.44%, particularly excelling in predicting non-failures. The Random Forest and Logistic Regression models, while effective, demonstrated slightly lower accuracies of 71.88% and 70.31%, respectively. These findings highlight the potential of deep learning models, especially Neural Networks, in enhancing predictive maintenance systems. The study underscores the importance of data preprocessing, feature extraction, and model evaluation in developing robust predictive maintenance systems. It also emphasizes the need for advanced ensemble techniques, real-time data processing, and the integration of edge computing for practical applications. Future work will explore these areas to further enhance the accuracy, reliability, and scalability of predictive maintenance systems, contributing to the ongoing digital transformation in the manufacturing sector. By adopting these advanced predictive models, manufacturing organizations can achieve significant improvements in operational efficiency, reduce maintenance costs, and enhance equipment reliability.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Haque, Rejwanul UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks > Internet of things 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 Artificial Intelligence
Depositing User:	Ciara O'Brien
Date Deposited:	18 Jun 2025 11:17
Last Modified:	18 Jun 2025 11:17
URI:	https://norma.ncirl.ie/id/eprint/7907

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