Topic: Machine Learning for Predictive Maintenance in Industrial Systems

Thesis Abstract

Abstract
This thesis explores the application of machine learning techniques for predictive maintenance in industrial systems. Predictive maintenance aims to predict equipment failures before they occur, thus reducing downtime and maintenance costs. The use of machine learning algorithms offers a data-driven approach to analyzing historical maintenance data, sensor data, and other relevant information to predict when maintenance is required. The research begins with an introduction that provides an overview of the motivation behind predictive maintenance and the importance of utilizing machine learning in industrial systems. The background of the study delves into the current state of predictive maintenance practices and the limitations of traditional maintenance approaches. The problem statement highlights the challenges faced by industries in maintaining equipment reliability and the potential benefits of predictive maintenance. The objectives of the study are to develop machine learning models that can accurately predict equipment failures, optimize maintenance schedules, and minimize downtime. The limitations of the study are discussed to provide a clear understanding of the potential constraints and challenges that may arise during the research process. The scope of the study outlines the specific industrial systems and datasets that will be used for analysis. The significance of the study lies in its potential to revolutionize maintenance practices in industrial settings by enabling proactive and cost-effective maintenance strategies. The structure of the thesis details the organization of the research, from the introduction to the conclusion, providing a roadmap for readers to navigate through the study. Definitions of key terms are provided to clarify the terminology used throughout the thesis. The literature review in Chapter Two examines existing research and industry practices related to predictive maintenance and machine learning algorithms. Key concepts and methodologies are explored to provide a comprehensive understanding of the current landscape of predictive maintenance in industrial systems. Chapter Three focuses on the research methodology, detailing the data collection process, feature selection, model development, and evaluation techniques. The chapter also discusses the validation and testing of the machine learning models to ensure their accuracy and reliability. In Chapter Four, the findings of the research are presented and discussed in detail. The performance of the machine learning models in predicting equipment failures and optimizing maintenance schedules is analyzed, along with the implications of the findings for industrial applications. Chapter Five concludes the thesis by summarizing the key findings, discussing the contributions of the research, and providing recommendations for future work in the field of predictive maintenance using machine learning. The conclusion highlights the potential impact of this research on improving maintenance practices and operational efficiency in industrial systems.