# PrimaVera: Synergising Predictive Maintenance
## 5 Methodology
### Demonstrators  
Besides case studies the project will also work on three closely interlinked large scale field demonstrators that are aligned with the application domains of the industrial partners. The work on these demonstrators will span the entire duration of the project and will integrate knowledge, insights, methods and models developed during the project. The three demonstrators are:1. Health assessment and prognostics tool for _infrastructure_ related equipment (PLC, e-drive). This demonstrator tool will implement the data collection, diagnostic and prognostic methods into a practical software tool that enables an asset owner to assess the system's health and predict future failures. 2. Planning and maintenance tool to optimise service logistics (_high-tech_). This demonstrator tool will implement the maintenance and supply chain optimisation methods into a user-friendly software tool. 3. Digital twin to support ship maintenance fed by real-time sensor data (_maritime_). The digital twin is a computer model representing the physical components and functions of (part of) a real ship, as well as its degradation behaviour. By feeding sensor data from the real ship into its digital twin, the actual status can be used as starting point for the simulation of various scenarios. User-friendly visualisation capabilities then enable to present the present and future status of on-board critical systems, which will support maintenance decision making. These demonstrators will also function as an important tool to evaluate the applicability and usability of the proposed process model. Implementation of Demonstrators  
During the proposal phase it was already recognised that the work on the three industrial demonstrators is a substantial task which can only be achieved by using existing tools and making it a common goal, requiring a continuously collaboration between all the partners throughout the whole project. The DevOps approach is highly suited to meet these requirements as cross-functional teams work on continuous operational feature deliveries [124]. To manage the amount of work, one generic framework is built on which the three demonstrators will be executed. The framework will consist of a separate building block for each of the five stages presented in Section 4.1, i.e., Data acquisition; Data processing and diagnostics; Prognostics; Maintenance and logistics optimisation and Decisions, forming a complete predictive maintenance cycle. The building blocks will contain the new methodologies developed in each of these five stages as well as existing methodologies. The five stages correspond to the first five work packages in the project. To implement the framework, each of the five work packages is responsible for delivering their own building block. A separate sixth work package is defined for the integration of all the building blocks in the final framework. To guarantee the integration of the various building blocks, at the beginning of the project already a deliverable with the definition of the building blocks content and their interfaces is defined with input from all the partners and used for further discussion and refined throughout the project. To enhance the quality and functionality of the framework, it will be delivered in three stages throughout the project: a functional prototype, a working prototype and the final version. These are separate deliverables already foreseen in the proposal stage, to which all the partners will contribute. Similarly, the three demonstrators to which the framework will be applied are being defined from the start of the project. All the involved industrial partners have been visited by the various project partners as a mutual effort, and had led to the three demonstrators mentioned above. Next, the demonstrators will be examined in more detail, for instance for the available sensor data, its quality, and current methods applied by the industrial partners.