Predictive Maintenance Model of Rotating Machinery Using AI

Abstract

The primary objective of this thesis is to formulate a predictive maintenance framework capable of effectively monitoring and analysing the operational status of diverse rotating machinery. This framework is designed to promptly alert operational personnel regarding the deterioration of machine components, enabling them to anticipate maintenance requirements and plan accordingly. In contemporary industrial contexts, the practice of condition-based maintenance has gained widespread adoption, particularly in the realm of rotating machinery. Employing an array of sensors and programmable logic controllers, diverse signals encompassing parameters such as vibration, temperature, speed, and pressure are systematically captured. Nonetheless, the conclusive assessment of machine health frequently relies on the expertise of rotating machinery engineers, who scrutinize trends and provide recommendations for component inspections to ascertain instances of degradation. Industry paradigms are rapidly evolving, echoing the principles of Industry 4.0, which champion the creation of fully automated industrial ecosystems through the symbiotic integration of machine learning and the Internet of Things (IoT). The adept utilization of refined machine learning algorithms empowers proficient decision-making processes, enabling proactive maintenance planning well in advance. This confluence of advanced technologies heralds a transformative era in industrial practices, ushering in unprecedented efficiency and foresight in maintenance strategies. This thesis is dedicated to the intricate analysis of diverse maintenance methodologies, accentuating the juxtaposition between condition-based monitoring and predictive maintenance strategies. The narrative meticulously explores the distinctive attributes, advantages, and limitations inherent to each approach. A crucial focal point of this research is the development of a predictive maintenance model using AI, fuelled by insights gained from open data sources to train machine learning algorithms. This innovative approach seeks to advance the understanding and application of predictive maintenance within the broader landscape of artificial intelligence-driven maintenance practices.