Due the increase of demand for the optimization of productive processes and the use of information at industries. Based on this, it has been highlighted some initiatives that are being adopted by manufacturing, such as Predictive Maintenance, focused on increasing the useful life of equipment to get a more flexible production. The predictive maintenance has the purpose of, with the support of relevant data collected through smart sensors, constructing a diagnostic which allows any maintenance staff to comprehend a machine's health and plan possible interventions in the production line. However, it has been noticed that there are numerous challenges in implementing this concept in corporations due to unmeasured technical, organizational and cultural factors. The objective of this article is to evaluate the capability of the Industry 4.0 technologies in relation to the functional requirements according to the PoC (Proof of Concept) of Predictive Maintenance within the automotive industry for the construction of a Referential Architecture. It was developed through the association of attributes, functional requirements and applicable technologies supported by multi-criteria analytical decision-making methods (MCADMM). The RAMI 4.0 framework was used to structure the layers of strategic implementation of the initiatives belonging to Industry 4.0 (I4.0). Specialists were consulted, through a Survey, to collaborate on the evaluation of the relational framework between functional requirements and applicable technologies. The results presented which are the most significant technologies at each strategic level of RAMI 4.0, thus characterizing a Referential Model for Predictive Maintenance initiative in the automotive sector.