# PrimaVera: Synergising Predictive Maintenance
## 1 Introduction  
Predictive maintenance is the ability to use data-driven analytics to optimise the upkeep of capital equipment [1]. Predictive maintenance bridges the gap between condition-based maintenance and corrective maintenance and is enabled by the advent of Industry 4.0 [2]. Value is created by transforming the collected data from intelligent systems into predictions about the system's health, so that maintenance can be done exactly when and where needed. Estimates of the impact of predictive maintenance vary widely, but in general the return of investment is deemed to be favourable [3]. Despite the favourable return on investment, implementation of predictive maintenance in practice is still limited in many industries [4, 5].  
Furthermore, predictive maintenance is a key enabling technology for servitisation in smart industries. Servitisation is an emerging trend [6] in which organisations and citizens no longer own their assets, but rather lease their services: companies buy hours on production machineries with aguaranteed throughput; people lease a car rather than buying one. As a consequence, servitisation mandates constant availability at a low cost, prescriptive (personalised) service, and full digitisation and automation of service provision.  
Although the prospective benefits of predictive maintenance are tremendous, realising the envisioned benefits is far from trivial. While many core building blocks of predictive maintenance (such as sensor technology, failure prediction methods, and optimisation techniques) exist, current solutions focus on individual steps in the predictive maintenance cycle, and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks into an effective and efficient framework that supports optimal maintenance and asset management in a complex arena. The PrimaVera project picks up this challenge through a multidisciplinary team providing exactly these experises.  
This paper highlights two major elements of the project to synergise predictive maintenance. First, the challenges hindering the successful application of predictive maintenance have been identified and are presented in this paper. Secondly, this paper introduces a generic predictive maintenance process model which provides a structured approach for deploying new predictive maintenance solutions.  
The PrimaVera project includes leading industrial partners from three major sectors of the Dutch economy: infrastructure, high-tech and maritime. The project has been awarded a grant of five million euros in funding from the Dutch Research Council (NWO) and co-funding from the participating consortium members. The project has a duration of five years.  
The rest of this paper is organised as follows. Section 2 presents the state of the art of each of the six elements compromising predictive maintenance. Based on the state of the art, the PrimaVera project has identified four obstacles to overcome in order to utilise predictive maintenance to its full potential. These obstacles are described in Section 3, Section 4 outlines the generic process model to tackle these obstacles. Furthermore, Section 4 describes the research approach taken for each step of the process model. Section 5 details the methodology used during the project together with a brief overview of the intended demonstrators. Section 6 details the constituents of the consortium and finally, the last section contains the conclusion.