Thesis Abstract

Abstract
This thesis explores the application of machine learning techniques for predictive maintenance in industrial Internet of Things (IoT) systems. The implementation of predictive maintenance using machine learning algorithms has gained significant attention in recent years due to its potential to improve operational efficiency, reduce downtime, and optimize maintenance schedules in industrial settings. This research aims to investigate the effectiveness of machine learning models in predicting equipment failures before they occur, based on real-time data collected from IoT devices. The study begins with a comprehensive review of existing literature on predictive maintenance, machine learning algorithms, and IoT systems in industrial environments. The research methodology section outlines the data collection process, feature selection, model training, and evaluation criteria for assessing the performance of the predictive maintenance system. The findings from the analysis of the data collected will be discussed in detail, highlighting the accuracy, reliability, and efficiency of the machine learning models in predicting equipment failures. The results of this study will provide valuable insights into the practical application of machine learning for predictive maintenance in industrial IoT systems. The implications of implementing predictive maintenance using machine learning algorithms include cost savings, improved asset management, and enhanced overall equipment effectiveness. The limitations and challenges encountered during the research process will also be addressed, along with recommendations for future research in this area. Overall, this thesis contributes to the growing body of knowledge on the integration of machine learning and IoT technologies for predictive maintenance in industrial settings. The findings of this research have the potential to revolutionize maintenance practices in various industries, leading to more proactive and data-driven approaches to equipment maintenance and reliability management.

Thesis Overview

The project titled "Applying Machine Learning for Predictive Maintenance in Industrial IoT Systems" focuses on leveraging machine learning techniques to enhance the predictive maintenance processes in Industrial Internet of Things (IoT) systems. Predictive maintenance is a proactive approach that aims to predict equipment failures before they occur, allowing for timely maintenance and minimizing downtime. In the context of Industrial IoT systems, which involve interconnected devices and sensors in industrial settings, the potential benefits of predictive maintenance are significant in terms of cost savings, operational efficiency, and equipment longevity. The research aims to address the challenges associated with traditional maintenance practices in industrial settings, such as scheduled maintenance based on predefined intervals or reactive maintenance in response to failures. By harnessing machine learning algorithms and data collected from IoT devices, the project seeks to develop predictive maintenance models that can accurately forecast equipment failures and recommend maintenance actions based on real-time data analysis. Key components of the research include a comprehensive literature review to explore existing approaches to predictive maintenance, machine learning techniques, and IoT technologies. The research methodology will involve data collection from industrial IoT systems, preprocessing and feature engineering of the data, model selection and training, and evaluation of the predictive maintenance models. The project will also investigate the implications of implementing predictive maintenance in industrial environments, including the potential challenges and limitations. The significance of this research lies in its potential to revolutionize maintenance practices in industrial settings by enabling predictive and proactive maintenance strategies. By incorporating machine learning into the maintenance process, organizations can optimize their operations, reduce downtime, lower maintenance costs, and improve overall equipment reliability. The findings of this research are expected to contribute to the growing body of knowledge on predictive maintenance in IoT systems and provide practical insights for implementing such systems in industrial applications.