TOPIC : Novel design and predictive maintenance technologies for increased operating life of production systems
|Publication date:||14 October 2015|
|Types of action:||IA Innovation action|
|DeadlineModel: Opening date:||single-stage 20 September 2016||Deadline:||19 January 2017 17:00:00|
|Time Zone : (Brussels time)|
Topic DescriptionSpecific Challenge:
The elevated complexity and costs of production assets combined with the requirements for high-quality manufactured products necessitate novel design and reliability-based maintenance approaches that are able to provide the required levels of availability, maintainability, quality, safety while considering the system as a whole and throughout the production lifecycle.
Analysis of operational parameters and in-service behaviour, self-learning features and condition prediction mechanisms could contribute to improve smart predictive maintenance systems capable to integrate information from many different sources and of various types, in order to more accurately estimate the process performances and the remaining useful life. That will lead to a more efficient management, reconfiguration and re-use of assets and resources, avoiding false alarms and unforeseen failures which lower operators' confidence in such systems.Scope:
The aim would be to design optimal maintainability solutions into production systems to improve operating life at maximised performance and reduce costs by carrying out maintenance activities at the most optimised time before failure occurs, thus minimising the degree of intervention required and maximising the system availability.
More trustworthy predictive maintenance and cause-and-effect analysis techniques should be developed to aggregate and interpret data captured from production systems and effectively share the massive amount of information between users. Measurements of a range of parameters at the level of components, machines and production systems should be carried out to provide data for building trend reference models for prediction of equipment condition, to improve physically-based models and to synchronise maintenance with production planning and logistics options. The dependability of the techniques would be demonstrated for a range of components and machines.
While the focus will be on demonstrating the design approaches and maintenance technologies, R&D activities supporting the integration and scale-up are expected as well.
Demonstration activities should address all of the following areas:
- Methodologies and tools for improved maintainability and increased operating life of production systems.
- Methodologies and tools to schedule maintenance activities together with production activities.
- Predictive maintenance solutions, combined with integrated quality-maintenance methods and tools, as well as failure modes, effects, and criticality analysis (FMECA) techniques, that effectively share information among different data sources in a secure way. Exploitation of networks of Smart Objects Technologies is an option.
- Versatility, in order to make solutions transferable to different industrial sectors.
- The project must include two complex demonstrators in real industrial settings to represent a clear added value.
In order to ensure the industrial relevance and impact of the demonstration effort, the active participation of industrial partners, including SMEs, represents an added value to the activities.
Activities are expected to focus on Technology Readiness Levels 5 to 7 and to be centred around TRL6.
This topic addresses cross-KET activities.
This topic is particularly suitable for SMEs, as well as for international cooperation.
The Commission considers that proposals requesting a contribution from the EU between EUR 4 and 6 million would allow this specific challenge to be addressed appropriately. Nonetheless, this does not preclude submission and selection of proposals requesting other amounts.Expected Impact:
The developed new technologies should lead to a significant impact in the following terms:
- 10% increased in-service efficiency through reduced failure rates, downtime due to repair, unplanned plant/production system outages and extension of component life.
- More widespread adoption of predictive maintenance as a result of the demonstration of more accurate, secure and trustworthy techniques at component, machine and system level
- Increased accident mitigation capability.
Proposals should include a business case and exploitation strategy, as outlined in the Introduction to the LEIT part of this Work Programme.Cross-cutting Priorities:
Topic conditions and documents
Please read carefully all provisions below before the preparation of your application.
LIST OF COUNTRIES and APPLICABLE RULES FOR FUNDING
described in part A of the General Annexes of the General Work Programme.
Note also that a number of non-EU/non-Associated Countries that are not automatically eligible for funding have made specific provisions for making funding available for their participants in Horizon 2020 projects (follow the links to Australia, Brazil, Canada, China, Hong Kong &Macau, India, Japan, Republic of Korea, Mexico, Russia, Taiwan).
- ELIGIBILITY and ADMISSIBILITY CONDITIONS
described in part B and C of the General Annexes of the General Work Programme.
Proposal page limits and layout: Please refer to Part B of the standard proposal template.
3.1 Evaluation criteria and procedure, scoring and threshold:
The criteria, scoring and threshold are described in General Annex H of the work programme.
The following exceptions apply:
For single-stage and second-stage evaluations, the threshold for the criteria Excellence and Impact will be 4. The overall threshold, applying to the sum of the three individual scores, will be 12.
The procedure for setting a priority order for proposals with the same score is given in General Annex H of the work programme. The following exceptions apply:
Under 3 (a)
Proposals are first ranked in separate lists according to the topics against which they were submitted (‘topic ranked lists’). When comparing ex aequo proposals from different topics, proposals having a higher position in their respective 'topic ranked list' will be considered to have a higher priority in the overall ranked list.
Under 3 (b)
For all topics and types of action, the prioritisation will be done first on the basis of the score for Impact, and then on that for Excellence.
3.2 Submission and evaluation process: Guide to the submission and evaluation process
- INDICATIVE TIMETABLE for EVALUATION and GRANT AGREEMENT
Information on the outcome of single-stage evaluation: maximum 5 months from the deadline for submission.
Signature of grant agreements: maximum 8 months from the deadline for submission.
- PROVISIONS, PROPOSAL TEMPLATES and EVALUATION FORMS
for the type of action under this topic
Specific provisions and funding rates
Standard proposal template
Standard evaluation form
H2020 General MGA -Multi-Beneficiary
Annotated Grant Agreement
- ADDITIONAL PROVISIONS
Horizon 2020 budget flexibility
Technology readiness levels (TRL) – where a topic description refers to TRL, these definitions apply.
Financial support to Third Parties – where a topic description foresees financial support to Third Parties, these provisions apply.
- OPEN ACCESS
Open access must be granted to all scientific publications resulting from Horizon 2020 actions.
Where relevant, proposals should also provide information on how the participants will manage the research data generated and/or collected during the project, such as details on what types of data the project will generate, whether and how this data will be exploited or made accessible for verification and re-use, and how it will be curated and preserved.
Open access to research data
The Open Research Data Pilot has been extended to cover all Horizon 2020 topics for which the submission is opened on 26 July 2016 or later. Projects funded under this topic will therefore by default provide open access to the research data they generate, except if they decide to opt-out under the conditions described in annex L of the Work Programme. Projects can opt-out at any stage, that is both before and after the grant signature.
Note that the evaluation phase proposals will not be evaluated more favourably because they plan to open or share their data, and will not be penalised for opting out.
Open research data sharing applies to the data needed to validate the results presented in scientific publications. Additionally, projects can choose to make other data available open access and need to describe their approach in a Data Management Plan.
- Projects need to create a Data Management Plan (DMP), except if they opt-out of making their research data open access. A first version of the DMP must be provided as an early deliverable within six months of the project and should be updated during the project as appropriate. The Commission already provides guidance documents, including a template for DMPs.
- Eligibility of costs: costs related to data management and data sharing are eligible for reimbursement during the project duration.
The legal requirements for projects participating in this pilot are in the article 29.3 of the Model Grant Agreement.
- ADDITIONAL DOCUMENTS
Legal basis: Horizon 2020 - Regulation of Establishment
Legal basis: Horizon 2020 Rules for Participation
Legal basis: Horizon 2020 Specific Programme
H2020 Work Programme 2016-17: Introduction
H2020 Work Programme 2016-17: Introduction to Leadership in enabling and industrial technologies (LEITs)
H2020 Work Programme 2016-17: Nanotechnologies, advanced materials, advanced manufacturing and processing, biotechnology
H2020 Work Programme 2016-17: Cross-cutting activities (Focus Areas)
H2020 Work Programme 2016-17: Dissemination, Exploitation and Evaluation
H2020 Work Programme 2016-17: General Annexes
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