Application of Machine Learning for Predictive Maintenance in Industrial IoT Systems

Thesis Abstract

Abstract
The utilization of Machine Learning (ML) techniques in Industrial Internet of Things (IIoT) systems has gained significant attention in recent years, particularly in the domain of predictive maintenance. This thesis focuses on the "Application of Machine Learning for Predictive Maintenance in Industrial IoT Systems" to improve the efficiency and reliability of industrial operations. The primary objective of this research is to develop and evaluate ML models that can accurately predict equipment failures before they occur, thereby enabling proactive maintenance strategies and minimizing costly downtime. The thesis begins with an in-depth exploration of the background of the study, highlighting the significance of predictive maintenance in the context of IIoT systems. The problem statement underscores the challenges faced by traditional maintenance approaches and the potential benefits of integrating ML algorithms for predictive maintenance. The objectives of the study are outlined to guide the research process, followed by a discussion of the limitations and scope of the research. Chapter two presents a comprehensive literature review that covers ten key areas related to ML applications in predictive maintenance, including data collection techniques, feature engineering, model selection, and performance evaluation metrics. The review synthesizes existing research findings and identifies gaps in the current literature, providing a solid theoretical foundation for the subsequent chapters. Chapter three details the research methodology employed in this study, encompassing eight key elements such as data collection methods, feature selection techniques, model training and evaluation processes, and validation strategies. The methodology is designed to ensure the reliability and validity of the study results, allowing for a systematic investigation of ML-based predictive maintenance techniques in IIoT environments. Chapter four presents an extensive discussion of the research findings, including the performance evaluation of various ML models in predicting equipment failures. The analysis of results highlights the strengths and limitations of different algorithms, providing insights into the effectiveness of ML for predictive maintenance applications in industrial settings. Finally, chapter five offers a conclusion and summary of the project thesis, encapsulating the key findings, implications, and recommendations for future research. The study contributes to the growing body of knowledge on ML-driven predictive maintenance in IIoT systems, offering valuable insights for practitioners and researchers seeking to enhance the efficiency and reliability of industrial operations through data-driven approaches. In conclusion, the "Application of Machine Learning for Predictive Maintenance in Industrial IoT Systems" represents a significant step towards advancing predictive maintenance practices in industrial settings, leveraging the power of ML algorithms to optimize equipment performance and minimize maintenance costs.