Comprehensive Analysis of IoT with Artificial Intelligence to Predictive Maintenance Optimization for Indian Shipbuilding

Abstract

The extensive review of the literature evaluation on predictive maintenance (PdM) in this work focuses on system designs, goals, and methodologies. In the business world, any equipment or system failures or unscheduled downtime would negatively affect or stop an organization's key operations, possibly incurring heavy fines and irreparable reputational damage. Traditional maintenance methods now in use are plagued by a variety of limitations and preconceptions, including expensive preventive maintenance costs, insufficient or incorrect mathematical deterioration procedures, and manual feature extraction. The PdM maintenance framework is suggested as a new method of maintenance framework to prevent any damage only after the analytical analysis shows specific malfunctions or breakdowns, which is in line with the growth of digital building and the advancement of the Internet of Things (IoT), and Artificial Intelligence (AI), and so on. We also present an overview of the three main types of fault diagnosis and prognosis methods used in PdM mechanisms: scientific, conventional Machine Learning (ML), and deep learning (DL). While offering a thorough assessment of DL-dependent techniques, we make a quick overview of the knowledge-based and conventional ML-dependent strategies used in various components or systems. Eventually, significant possibilities for further study are discussed.