Cheaper and more powerful sensors, together with big data analytics, offer an unprecedented opportunity to track machine-tool performance and health condition. However, manufacturers only spend 15% of their total maintenance costs on predictive (vs reactive or preventative) maintenance.
The project will deploy and test a predictive cognitive maintenance decision-support system able to identify and localize damage, assess damage severity, predict damage evolution, assess remaining asset life, reduce the probability of false alarms, provide more accurate failure detection, issue notices to conduct preventive maintenance actions and ultimately increase in-service efficiency of machines by at least 10%. The platform includes 4 modules:
- A data acquisition module leveraging external sensors as well as sensors directly embedded in the machine tool components,
- An artificial intelligence module combining physical models, statistical models and machine-learning algorithms able to track individual health condition and supporting a large range of assets and dynamic operating conditions,
- A secure integration module connecting the platform to production planning and maintenance systems via a private cloud and providing additional safety, self-healing and self-learning capabilities and
- A human interface module including production dashboards and augmented reality interfaces for facilitating maintenance tasks.
The consortium includes 3 end-user factories, 3 machine-tool suppliers, 1 leading component supplier, 4 innovative SMEs, 3 research organizations and 3 academic institutions. Together, we will validate the platform in a broad spectrum of real-life industrial scenarios (low volume, high volume and continuous manufacturing). We will also demonstrate the direct impact of the platform on maintainability, availability, work safety and costs in order to document the results in detailed business cases for widespread industry dissemination and exploitation.
| Web resources: |
http://www.precom-project.eu
https://cordis.europa.eu/project/id/768575 |
| Start date: | 01-11-2017 |
| End date: | 28-02-2021 |
| Total budget - Public funding: | 7 263 332,00 Euro - 6 146 402,00 Euro |
| Twitter: | @PreCoM_Project |
Original description
Cheaper and more powerful sensors, together with big data analytics, offer an unprecedented opportunity to track machine-tool performance and health condition. However, manufacturers only spend 15% of their total maintenance costs on predictive (vs reactive or preventative) maintenance.The project will deploy and test a predictive cognitive maintenance decision-support system able to identify and localize damage, assess damage severity, predict damage evolution, assess remaining asset life, reduce the probability of false alarms, provide more accurate failure detection, issue notices to conduct preventive maintenance actions and ultimately increase in-service efficiency of machines by at least 10%.
The platform includes 4 modules: 1) a data acquisition module leveraging external sensors as well as sensors directly embedded in the machine tool components, 2) an artificial intelligence module combining physical models, statistical models and machine-learning algorithms able to track individual health condition and supporting a large range of assets and dynamic operating conditions, 3) a secure integration module connecting the platform to production planning and maintenance systems via a private cloud and providing additional safety, self-healing and self-learning capabilities and 4) a human interface module including production dashboards and augmented reality interfaces for facilitating maintenance tasks.
The consortium includes 3 end-user factories, 3 machine-tool suppliers, 1 leading component supplier, 4 innovative SMEs, 3 research organizations and 3 academic institutions. Together, we will validate the platform in a broad spectrum of real-life industrial scenarios (low volume, high volume and continuous manufacturing). We will also demonstrate the direct impact of the platform on maintainability, availability, work safety and costs in order to document the results in detailed business cases for widespread industry dissemination and exploitation.
Status
CLOSEDCall topic
FOF-09-2017Update Date
27-10-2022The Predictive Cognitive Maintenance Decision Support System (PreCoM) enables its users to detect damages, estimate damage severity, predict damage development, follow up, optimize maintenance (for reducing unnecessary stoppages) and get recommendations (on what, why, where, how and when to perform maintenance). PreCoM is a cloud-based smart PdM system using vibration as a condition monitoring parameter. Some accelerometers for measuring vibration (of both rotating and nonrotating components), as well as other sensors (i.e. for temperature), have been installed in machines’ significant components (i.e. components whose failures either expensive or dangerous). Over 20 hardware and software modules (common to all considered and equivalent use cases) are integrated into a single automatic and digitised system that gathers, stores, processes and securely sends data, providing recommendations necessary for planning and optimizing maintenance and manufacturing schedules. The PreCoM system includes loops and sub-systems for data acquisition, data/sensor quality control, predictive algorithm, scheduling algorithm, follow up tool, self-healing ability for specific problems, and end-user information interface.
To develop and apply statistical models for supporting PdM, it is always crucial to have as much as possible failure data, which is not easy to find in the companies’ databases. Furthermore, advancing and integrating different technologies in a single automatic and digitised smart PdM system is a challenge that requires close collaboration between research and industry players.
Project clusters are groups of projects that cooperate by organising events, generating joint papers, etc...
The Foresee Cluster Roadmap document includes section 5 on ‘Standardization aspects of Predictive Maintenance’ and ANNEX I ‘Standards application in ForeSee projects’:
- Standardisation Overview
- Views on Maintenance standards and Predictive Maintenance
- Maintenance terminology
- Evaluation of Standards
- Future Activities
PreCoM impact is introduced below as an average of the impact achieved in the three uses cases: Sakana, Spinea and Goma Campus, based on the components monitored by PreCoM:
- Reduced downtime by about: 88%
- Maintainability (MTTR) improved by about: 24%
- Reduced supervision time in the training for new technicians by about: 76%
- Increased machine overall equipment effectiveness (OEE) by about: 5%
- Applying PdM by company personnel has been increased. From previous level 0 to about 80%
- Energy is rationalized by reduction equivalent to about: 16%
- Material loss is reduced by about: 16%
- Application of PreCoM versus work safety: No accidents previously as well as during PreCoM
- Reduced maintenance hours by about: 16%
- The saving in maintenance hours (at minimum) is about: 92 hours per year
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| LINNEAS UNIVERSITY (Coördinator)
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| Bosch Rexroth GmbH
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| COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES (CEA)
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| CONSORCIO INSTITUTO TECNOLOXICO MATEMATICA INDUSTRIAL ITMATI
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| E-MAINTENANCE SWEDEN AB
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| GOMA CAMPS SOCIEDAD ANONIMA
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| IDEKO
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| LANTIER SL
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| OVERBECK GMBH
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| PARAGON S.A.
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| SAKANA, SOCIEDAD COOPERATIVA
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| SAVVY DATA SYSTEMS SL
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| SPINEA SRO
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| Soraluce S.Coop
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| TECHNISCHE UNIVERSITAET CHEMNITZ