Z-BRE4K will provide a modular solution for predictive maintenance that is highly customizable. Therefore, the different modules of the solution cane be sold as stand alone products or can be combined depending on the users needs. Z-BRE4K solution can be applied to both new machines and old machines that were not designed to be equipped with predictive maintenance solutions.

Flexible human-like dual arm robots in cooperation with human workers ultimately aims to enable manufacturing of highly customised products, which is the main objective of the project. 

The UPTIME solution will combine and extend existing predictive maintenance tools and services (USG/BIBA, preIno/BIBA, PANDDA /ICCS, SeaBAR/Pumacy, and DRIFT/RINA-C) and will define the way for its implementation in a systematic and unified way with the aim to fully exploit the advancements in ICT and maintenance management by examining the potential of big data in an e-maintenance infrastructure.

Extended version of USG will implement the Signal Processing phase; extended version of preInO will address the Diagnosis and the Prognosis phases; extended version of PANDDA will deal with Maintenance Decision Making; extended version of SeaBAR will address the Maintenance and Operational Actions Implementation; and extended version of DRIFT will deal with data-driven FMECA.

Each extended UPTIME service will incorporate real-time data-driven information processing technologies and algorithms so that the integrated system is able to cover complete scenarios and fulfil the needs of the manufacturing companies participating in the consortium. The aforementioned tools will interact when necessary to the manufacturing company’s system (e.g. ERP, MES) in order to exchange data and information for scheduling production, quality and logistics activities together with maintenance activities (e.g. by using the production plan of the ERP system). Through the Continuous Improvement mechanism, UPTIME will be able to continuous learn with the aim to update and improve Diagnosis (Detect), Prognosis (Predict) and Maintenance Decision Making (Decide) phases by gathering actions-related and/ or failure-related sensor data (Act).  

UPTIME aims to deliver novel e-maintenance services and tools to support the daily work of maintenance engineers as well as the overall maintenance management with the aim to optimize in-service efficiency. UPTIME solution consists of extended e-maintenance services and tool, which will incorporate novel methods and algorithms for addressing the phases of the UPTIME framework and conclude in a novel predictive maintenance solution covering the whole prognostic lifecycle.

UPTIME will be able to be applied in the context of the production process of any manufacturing company regardless their processes, products and physical model used. It will take advantage of predictive maintenance management, industrial IoT and big data, as well as proactive computing and the e-maintenance concept in order to reframe predictive maintenance strategy and to create a unified information system in alignment to the new predictive maintenance strategy framework and to Gartner’s 4 levels of data analytics maturity.

UPTIME will be applicable at the level of component, machine and production system, depending on the placement of sensors throughout the production lifecycle and the data availability in the manufacturing company’s systems (e.g. Enterprise Resources Planning- ERP, Manufacturing Execution System- MES). Within UPTIME, there will be interactions between the various e-maintenance services and the e-operations data and information from the manufacturing companies’ systems in order to synchronise maintenance with production, quality and logistics management. The results of the UPTIME solution will be evaluated by the manufacturing companies participating in the consortium and will be demonstrated in manufacturing companies beyond the consortium.

Through its unique bundle of a predictive maintenance management framework and an integrated platform, UPTIME will prevent critical asset failure, deploy resources more cost-effectively, maximize equipment uptime and enhance operations, while accelerating the quality and supply chain processes. By better diagnosing, anticipating and acting upon the asset performance and product quality, the UPTIME novel predictive maintenance framework is expected to increase in-service efficiency by at least 10% in the 3 pilot cases through combined KPIs measurements.

By investigating and demonstrating the applicability of the UPTIME predictive maintenance system in three pilot cases in different manufacturing sectors, UPTIME will contribute to a more widespread adoption of predictive maintenance and demonstrate more accurate, secure and trustworthy techniques at component, machine and system level.