Title: Utilizing Advanced Analytics and Machine Learning for Predictive Maintenance of Industrial Equipment
Authors: Thompson Odion Igunma, Emmanuel Augustine Etukudoh
Volume: 9
Issue: 4
Pages: 439-449
Publication Date: 2025/04/28
Abstract:
Predictive Maintenance (PdM) leverages advanced analytics and machine learning to foresee equipment failures and optimize maintenance schedules, providing significant operational efficiency, cost-effectiveness, and reliability benefits. This paper explores the historical development of maintenance strategies, key concepts in PdM, and the role of advanced analytics and machine learning in transforming industrial maintenance. Key technologies like machine learning algorithms, data analytics platforms, and IoT integration are discussed alongside emerging trends like digital twins, edge computing, and 5G technology. The benefits of PdM, including reduced maintenance costs, extended equipment lifespan, and improved safety, are weighed against challenges such as data quality, computational resources, and resistance to change. The paper also highlights future directions, including the impact of AI advancements, the growing use of prescriptive maintenance, and potential research areas like data privacy, model scalability, and sustainability. Addressing these challenges and leveraging emerging technologies will be crucial for the continued evolution and success of PdM in industrial settings.