The developed database provides a good overview of and assessment to the state-of-play of predictive maintenance models, techniques and platforms, covering a broad spectrum of platforms that involve several kinds of PM models and techniques. D1.1 has identified technical elements for specifications and design decisions of UPTIME platform architecture, business cases and business model.
The database is accessible here .
D1.3a provides the first iteration of UPTIME Predictive Maintenance (technical) Data Model and the patterns specified for industrial/factories data acquisition, harmonisation and processing., to define the related data sources for maintenance.
D1.4 has identified the state-of-the-art of predictive maintenance algorithms related to diagnosis, prognosis, decision making, in order to define how the algorithms are implemented in the UPTIME solution. The used methodology is very helpful to establish a common understanding across the different disciplines in the project with respect to diagnosis, prognosis and decision-making.
D2.1 provides the first draft of the UPTIME conceptual architecture and a set of functional/high level specifications in accordance to the UPTIME vision. It describes the concept of predictive maintenance, the technological pillars of UPTIME (i.e. Industry 4.0, IoT and Big Data, Proactive Computing), the existing baseline tools available in the consortium (i.e. USG, preInO, PANDDA, DRIFT, SeaBAR) and the requirements of the engineering process.
D3.2 provides the first prototype of the diagnosis and prognosis components of the UPTIME Platform: UPTIME-DETECT, -PREDICT & -ANALYZE components. The requirements towards the diagnosis and prognosis framework from the UPTIME business cases and the UPTIME architecture are addressed. A new component UPTIME_ANALYZE has been introduced to leverage manufacturer’s legacy data and operational data related to maintenance.
D3.4 provides the first prototype of the data aggregation and visualisation component of the UPTIME Platform: UPTIME_VISUALIZE component. The requirements towards the user interface from the UPTIME business cases and the UPTIME architecture are addressed.
D3.5 provides the first prototype of the data-driven FMECA component of the UPTIME Platform: UPTIME_DECIDE component. The requirements towards the FMECA mechanisms from the UPTIME business cases and the UPTIME architecture are addressed.
D4.1 provides comprehensive definition of the FFT business case, including as-is and to-be businesses process and identification of FFT stakeholders/business, system and technical requirements. In the context of FFT’s participation in the UPTIME project, the objective is to demonstrate a solution to many of the challenges arising from maintaining the transportation jig by implementing and using the UPTIME framework and information system. The goal within the use case is to attain comprehensive, continuous and up to date data on the condition of the mobile asset, automatically process this information to obtain actionable information and make it available to the affected stakeholders in different forms to enable them to optimise their workflows.
D5.1 provides comprehensive definition of the Whirlpool Business case, including as-is and to-be business processes and identification of Whirlpool stakeholders/business, system and technical requirements. The scope of D5.1 is to report the work performed in the context of T5.1 “Definition of the Whirlpool Business Case” and T5.2 “Requirements and Whirlpool System Conceptualisation” activities, providing the outline and the plan of the WHR business case in the white goods / home appliances industry towards the demonstration of the UPTIME predictive maintenance framework.
D6.1 provides comprehensive description of the MAILLIS Business case, including as-is and to-be business processes and identification of MAILLIS stakeholders/business, system and technical requirements.This deliverable presents the current state‐of‐the‐art modelling approaches and the to‐be situation in the context of MAILLIS Business Case. It is also aligned with the other UPTIME pilots (WP4 and WP5).
D7.1 documents the planned activities of T7.1, T7.2 and T7.3 over the project duration. It describes detailed plans on project scientific and industry dissemination and communication activities, concept of the community building & management as well as its partner programme, and showcases events to demonstrate UPTIME to potential future users. This includes the identification of target group and the communication channels to pass these messages to the target groups.
D8.1 presents an overview of the predictive maintenance market in Europe in order to understand its ecosystem and value chain, identify its drivers and opportunities. It describes the undertaken competitive analysis to identify where UPTIME stands towards competition and its key differentiators as well as the UPTIME value proposition that introduces UPTIME added value with respect to the market needs and competition. A market receptivity analysis with a feedback about UPTIME Value proposition and a market prioritization analysis that identifies the most promising market segments for the go-to-market strategy are also elaborated. In the end, a business opportunity assessment that evaluates with concrete figures the size of the market within reach of UPTIME and a strategic analysis that combines all the aspects depicted above to guide the exploitation strategy are presented.