From the capacity of production depending on machine tool availability, it becomes a day-today concern for maintenance operator and production manager. Each stop in production constitutes a loss of earning that should be anticipated to allow an efficient planning of maintenance operation and to avoid unscheduled production downtime. Condition-based maintenance solutions at a machine level provide the operator with tools that support monitoring of the machine's behaviour and highlight behaviour changes. However, machine tools as large engineering systems have multiple subsystems and equipment of different natures (electrical, mechanical, hydraulics, electronics, etc.) following different fault rates and modes in which behaviour may differ from specific phases of their life cycle due to events and maintenance history. Supporting proactive maintenance at a fleet level provides summarized information and means of comparison and investigation for decision-making.

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Twin-Control concept combines the development of holistic simulation models with the knowledge of the performance of the real machines and processes. To deal with this second part, a data monitoring infrastructure must be implemented so that required information is acquired, managed and analyzed properly. The approach used in Twin-Control consists in the installation of a local monitoring hardware that acquires internal variables of the machine, collects data of additional sensors and uploads all data to a cloud platform [1]. ARTIS Genior modular is used for the local monitoring, and PREDICT's KASEM ® is used as cloud platform for data analysis. A fleet-level data analysis will be performed by integrating all the information coming from the different machines. This chapter is structured as follows. After a brief introduction, an overview of the equipment to be monitored and integrated in Twin-Control is presented. The T. Fuertjes (B)

Maintenance is a research field that has recently been gaining importance in business and where the study and development of monitoring and predictive technologies has been very active, as the role of these technologies is key in enabling predict and prevent maintenance strategies. Moreover, by means of monitoring features of processes and components, an impact in lifecycle value can be achieved. However, challenges remain in structuring the condition monitoring offer and the technological platform due, in particular, to the variety of potential domains of application, the characteristics of the existing information and the final goals of the monitoring activities. These challenges may impact in the deployment time of a condition monitoring solution. In order to limit these challenges, a methodology for fast deployment of condition monitoring and a technological service platform is presented. The methodology has been obtained from research and analysis of several use cases in the context of product-service systems. The focus is on methodological and technological results, which are presented in a general manner such that they can be applicable to the deployment of condition monitoring and services in various domains. Finally, application of the methodology is presented in two different scenarios.