Thesis Abstract

Abstract
This thesis explores the application of machine learning algorithms for predictive maintenance in industrial Internet of Things (IoT) systems. The industrial sector is increasingly adopting IoT technologies to improve operational efficiency, reduce downtime, and enhance overall productivity. Predictive maintenance plays a crucial role in this context by enabling proactive equipment maintenance based on real-time data analytics. Machine learning algorithms, with their ability to analyze large volumes of data and identify patterns, offer a promising approach to predict equipment failures and optimize maintenance schedules. The research begins with a comprehensive review of the literature on predictive maintenance, machine learning algorithms, and IoT systems in the industrial domain. The study investigates how various machine learning techniques, such as supervised learning, unsupervised learning, and deep learning, can be leveraged for predictive maintenance tasks. Additionally, the research examines the challenges and limitations associated with implementing predictive maintenance in industrial IoT systems, including data quality issues, model interpretability, and scalability concerns. The methodology chapter outlines the research approach, data collection methods, and evaluation criteria for assessing the performance of machine learning algorithms in predicting equipment failures. The research methodology involves collecting historical sensor data from industrial equipment, preprocessing the data, training machine learning models, and evaluating the predictive accuracy of the models using metrics such as precision, recall, and F1 score. The findings chapter presents a detailed analysis of the experimental results, highlighting the performance of different machine learning algorithms in predicting equipment failures. The discussion covers the strengths and weaknesses of the algorithms, the impact of hyperparameter tuning on model performance, and the implications of the findings for predictive maintenance strategies in industrial IoT systems. Finally, the conclusion summarizes the key findings of the research and provides recommendations for future work in this area. The study underscores the potential of machine learning algorithms to enhance predictive maintenance practices in industrial IoT systems and emphasizes the importance of data quality, feature engineering, and model interpretability in developing effective predictive maintenance solutions. Overall, this thesis contributes to the growing body of knowledge on applying machine learning algorithms for predictive maintenance in industrial IoT systems, offering insights into the challenges, opportunities, and best practices for leveraging data-driven approaches to optimize equipment maintenance and improve operational efficiency in industrial settings.

Thesis Overview