According to the practical requirements of the factory of the future, this project combines the recent advancements in different domains to develop an explainable intelligent maintenance system (X-IMS) that enables both self-monitoring and decision-making support functionalities for connected manufacturing systems. The developed solution should allow automated construction of effective and interpretable health indicators for system continuous self-monitoring. They also integrate explainable intelligent algorithms for fault detection, diagnostic, and/or prognostics at the system level. Furthermore, embedded maintenance decision optimization algorithms, that can handle prediction uncertainties, component dependencies, and impacts of multiple maintenance activities will be developed. The optimal decision process obtained by the proposed intelligent algorithms should be explicitly conveyed to managers and therefore enable them to understand, trust, and effectively deploy the developed solution in practice. The performance of the algorithms developed in this project will be verified and highlighted by real industrial applications.