Thesis Abstract

**Abstract
** The application of machine learning for predictive maintenance in industrial systems has gained significant attention in recent years due to its potential to revolutionize maintenance practices and enhance operational efficiency. This thesis explores the use of machine learning algorithms to predict equipment failures in industrial settings, with a focus on improving maintenance strategies and minimizing downtime. The research methodology involves a comprehensive literature review to understand the current state-of-the-art in predictive maintenance techniques and machine learning algorithms. The findings from the literature review provide insights into the various approaches and challenges associated with implementing predictive maintenance in industrial systems. The research methodology includes data collection from real-world industrial systems, preprocessing and feature engineering, model selection, training, and evaluation. The study evaluates the performance of different machine learning algorithms such as decision trees, random forests, support vector machines, and neural networks in predicting equipment failures. The results of the study demonstrate the effectiveness of machine learning in predicting equipment failures with high accuracy and reliability. The discussion of findings highlights the importance of feature selection, model tuning, and data quality in achieving optimal predictive maintenance outcomes. The thesis concludes with a summary of the key findings, implications for industrial practitioners, and recommendations for future research in the field of predictive maintenance using machine learning. Overall, this thesis contributes to the body of knowledge on the application of machine learning for predictive maintenance in industrial systems. The research findings provide valuable insights for industrial practitioners seeking to leverage advanced analytics and machine learning techniques to optimize maintenance strategies and improve operational efficiency. The study underscores the significance of predictive maintenance in enhancing equipment reliability, reducing maintenance costs, and maximizing overall equipment effectiveness in industrial settings.

Thesis Overview