The application of artificial intelligence, combined with advanced data gathering, processing, and analytics, has revolutionized industrial operations and elevated predictive maintenance in Industry 4.0 to a new level. Cutting-edge big data analytics platforms, cloud computing, and IoT-enabled enhancements in data collection have made predictive machine learning models accessible, cost-effective, and feasible for industrial applications. The chapter illustrates the importance of a well-structured data architecture and details steps from data collection and pre-processing to training and deploying machine learning models. Integrating real-time data streams with historical data allows for a comprehensive view of equipment health, enabling timely and accurate maintenance decisions. These enhancements have improved accuracy and increased effectiveness in several central aspects. The key techniques discussed include supervised learning and unsupervised learning, deep neural networks, and time series forecasting. In this chapter, such developments are shown for the aerospace, manufacturing, and transportation industries. The chapter deals with issues like data collection, streaming, storing and processing large amounts of data, and construction of more sophisticated models based on contemporary AI and ML algorithms and, therefore, provides development towards enhancing predictive maintenance in the era of Industry 4.0.
Keywords: Cloud computing, Data streaming, Deep neural networks, Edge computing, ETL processes, IoT, Supervised learning, Time series analysis, Unsupervised learning.
