Abstract
Today data analytics is vital for companies willing to extrapolate information from their assets to support asset-related decisions. Information is relevant but not enough to exploit the potentials hidden in domain-related knowledge. The focus of this paper is predictive maintenance, herein knowledge is relevant to support the design of a Prognostics and Health Management (PHM) process to achieve a reliable decision-making. In this scope, the paper builds on the presumption that data analytics can be empowered by semantic data modelling to conceptualise and formalize data before the application of any kind of advanced algorithm implementing a data-driven approach. Thus, this research aims at proposing a semantic data model that guides the data analytics by revealing data characteristics and inter-relationships and guarantees completeness to finally support the PHM process. A data-driven approach, joining semantic data modelling and analytics, is proven through examples taken from the controlled environment of the Industry 4.0 Laboratory of the School of Management of Politecnico di Milano.