Thesis Abstract

Abstract
This thesis explores the application of Artificial Intelligence (AI) for predictive maintenance in industrial machinery, aiming to enhance the efficiency and reliability of maintenance practices. The research investigates the potential of AI technologies, such as machine learning algorithms and predictive analytics, in predicting equipment failures before they occur, thus enabling proactive maintenance strategies. The study focuses on the development and implementation of AI-based predictive maintenance systems in industrial settings, with a specific emphasis on optimizing machinery performance, reducing downtime, and minimizing maintenance costs. The introduction provides an overview of the significance of predictive maintenance in the industrial sector and the challenges associated with traditional reactive maintenance approaches. The background of the study discusses the evolution of maintenance strategies and the emergence of AI technologies as a promising solution for predictive maintenance. The problem statement highlights the limitations of current maintenance practices and the need for more proactive and data-driven approaches to equipment maintenance. The objectives of the study include assessing the effectiveness of AI in predicting machinery failures, developing predictive maintenance models using machine learning techniques, and evaluating the impact of AI-based maintenance strategies on equipment performance. The study also outlines the limitations and scope of the research, emphasizing the focus on specific industrial machinery and the challenges associated with data availability and quality. The literature review provides a comprehensive analysis of existing research on AI applications in predictive maintenance, highlighting the various machine learning algorithms and predictive analytics techniques used in industrial settings. The chapter explores the benefits and challenges of AI-based maintenance systems, as well as the key factors influencing the successful implementation of predictive maintenance strategies. The research methodology chapter outlines the research design, data collection methods, and analytical techniques used in the study. The methodology includes the development of predictive maintenance models, data preprocessing, feature selection, model training, and evaluation of model performance. The chapter also discusses the validation of the AI models through case studies and real-world industrial applications. The discussion of findings chapter presents the results of the study, including the performance of the developed predictive maintenance models, the accuracy of failure predictions, and the impact of AI-based maintenance strategies on equipment reliability and maintenance costs. The chapter also discusses the practical implications of the research findings and provides recommendations for the implementation of AI-driven predictive maintenance systems in industrial settings. In conclusion, this thesis demonstrates the potential of AI technologies for predictive maintenance in industrial machinery, highlighting the benefits of proactive maintenance strategies in improving equipment performance and reducing maintenance costs. The study contributes to the growing body of research on AI applications in the industrial sector and provides valuable insights for practitioners and researchers interested in implementing AI-based maintenance solutions.

Thesis Overview