UPTIME will seek to reframe predictive maintenance strategy by proposing a unified framework and to create an associated unified information system in alignment to the aforementioned framework. Therefore, UPTIME will extend and unify new digital, e-maintenance services and tools in order to exploit the full potential of a predictive maintenance strategy with the UPTIME solution, will deploy and validate the UPTIME solution in the manufacturing companies participating in the UPTIME consortium and will diffuse the UPTIME solution in the manufacturing community. UPTIME will enable manufacturing companies having installed sensors to fully exploit the availability of huge amounts of data with respect to the implementation of a predictive maintenance strategy. Moreover, production, quality and logistics operations driven by predictive maintenance will benefit from UPTIME. UPTIME will enable manufacturing companies to reach Gartner's level 4 of data analytics maturity (optimized decision-making) in order to improve physically-based models and to synchronise maintenance with quality management, production planning and logistics options. In this way, it will optimize in-service efficiency through reduced failure rates and downtime due to repair, unplanned plant/production system outages and extension of component life. Moreover, it will contribute to increased accident mitigation capability since it will be able to avoid crucial breakdown with significant consequences. Consequently, UPTIME will exploit the full potential of predictive maintenance management and its interactions with other industrial operations by investigating a unified methodology and by implementing a unified information system addressing the predictive maintenance strategy.
|Number of participants:||11|
|Total budget - EC contribution:||6248367 Euro - 4847836 Euro|
|Call topic:||Novel design and predictive maintenance technologies for increased operating life of production systems (FoF.2017.09)|
|Instrument:||Innovation Action (IA)|
Significant innovations, use case requirements and lessons learned - (No information available)
Added value and impact - (7) view
UPTIME will provide a unified predictive maintenance management framework and a smart predictive maintenance information system covering the whole prognostic lifecycle. The UPTIME solution will be applicable to any production system incorporating sensors and will be based on real-time reliability-related (prognostic) information in order to reduce the equipment downtime and malfunctions with the aim to produce high-quality products with optimized losses. It will utilize sensors for measuring various parameters of the production process, provide diagnostic outcomes, i.e. the current equipment health state, generate predictions about future equipment behaviour, and recommend optimal actions at optimal times. It will also incorporate a continuous improvement mechanism for continuous learning of Diagnosis, Prognosis and Maintenance Decision Making phases triggered by sensor data during maintenance and other operational actions implementation. The elimination of unexpected failures will lead to an increased level of safety in the workplace and to improved overall operations efficiency.
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.
- Short/mid-term impact: improvement of financial results of industrial companies and their competitiveness thanks to better operational performance (optimized maintenance and production)
- Mid/long term impact: the improved competitiveness will help to reconquer some market shares and then reinforce an offensive marketing strategy (low margin segments could be reprioritized)
The economic impact of UPTIME is the most important one and can be seen at 2 levels :
Technologies and enablers - (No information available)
Digitalisation pathways - (No information available)
ICT performance characteristics - (No information available)
Standards, standardisation and regulation - (No information available)
Business model aspects - (No information available)
- No published items
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