Thesis Abstract

Abstract
This thesis explores the application of machine learning techniques to enhance predictive maintenance in Industrial Internet of Things (IIoT) systems. The rise of IIoT technologies has revolutionized industrial operations by enabling the collection of vast amounts of data from interconnected devices. However, managing and maintaining these systems efficiently poses significant challenges. Predictive maintenance aims to address these challenges by leveraging data analytics to predict equipment failures before they occur, thereby minimizing downtime and reducing maintenance costs. Chapter 1 provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The chapter concludes with a definition of key terms to provide a clear understanding of the research context. Chapter 2 consists of a comprehensive literature review that examines existing research on predictive maintenance, machine learning algorithms, and IIoT systems. The review highlights the importance of predictive maintenance in industrial settings and discusses the role of machine learning in optimizing maintenance strategies. Chapter 3 outlines the research methodology employed in this study, detailing the data collection process, feature selection, model development, and evaluation metrics. The chapter also discusses the implementation of machine learning algorithms for predictive maintenance in IIoT systems. Chapter 4 presents a detailed discussion of the findings obtained from the application of machine learning techniques to predictive maintenance in IIoT systems. The chapter analyzes the performance of different machine learning models in predicting equipment failures and evaluates the effectiveness of these models in improving maintenance practices. Chapter 5 serves as the conclusion and summary of the thesis, highlighting the key findings, contributions, and implications of the research. The chapter also discusses future research directions and recommendations for implementing predictive maintenance solutions in industrial IoT environments. In summary, this thesis contributes to the field of predictive maintenance by demonstrating the efficacy of machine learning algorithms in enhancing maintenance practices in industrial IoT systems. By leveraging data-driven insights, organizations can proactively address equipment failures, optimize maintenance schedules, and improve operational efficiency in the era of Industry 4.0.

Thesis Overview