In Industry 4.0, “intelligent factories” collect and analyze data to keep tabs on the production process. Machine learning, data mining, and other statistical AI technologies can identify and forecast possible manufacturing procedure abnormalities, improving productivity and dependability. Nevertheless, the information retrieved from manufacturing information is sometimes presented in a complex structure due to the heterogeneous nature of the data. This puts up the semantic gap problem, which is shorthand for the reality that various production systems are incompatible. In addition, a unified knowledge model of physical assets and the ability to think in real time about analytical activities are essential for automating the decision-making process of Computerized Physical Systems (CPS), which are growing more data-intensive. Using symbolic AI in predictive maintenance could be a promising solution to these problems. Through numerous examinations, predictive upkeep offers a comprehensive review of the identification, localization, and identification of malfunctions in associated machinery. RAMI4.0 provides a structure to analyze the several initiatives that comprise Industry 4.0. The hierarchical structure, functional classification, and product life cycle are all encompassed. The Corporate Data Space, currently known as the International Data Space, is an online database that allows for the safe transfer and simple linking of data between corporate ecosystems using shared standards and governance frameworks. It guarantees data owners' online privacy while laying the groundwork for developing and using intelligent services and novel business procedures. In light of Industry 4.0, this article investigates potential ways to bolster maintenance prediction. Data exchange between businesses with varying security needs and the subsequent modularization of relevant functions are outcomes of implementing the RAMI 4.0 architecture, which facilitates predictive maintenance utilizing the FIWARE framework.
Keywords: AI, Computerized Physical System, Industry 4.0, Machine learning, Predictive Maintenance.
