webinar presentation July 20

1. ForeSee is a cluster of six projects, which have
received funding from the European Union’s
Horizon 2020 and Horizon Europe research
and innovation programme
European Cluster for Predictive
Operations in Manufacturing
Standards in action: needs and benefits
from R&I projects' point of view
UPTIME Unified Predictive Maintenance System
Yves Keraron, President ISADEUS ISADEUS

2. Agenda
• Overview of the UPTIME project
• Standardization activities
• Lessons learnt
• Thoughts on future developments
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3. Overview of the UPTIME Project - Organization
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 Grant EU H2020 - FOF-09-2017
Novel design and
predictive maintenance
technologies for
increased operating life
of product systems
 Duration 42 Months
01.09.2017 – 28.02.2021
 Partners 11 from 5 EU countries
 Objective Unified predictive
maintenance system
 Solution UPTIME Platform
6 components
 Use Cases White goods, steel industry,
production systems

4. The UPTIME platform
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 SENSE for data acquisition:
All your assets, connected.
 DETECT and PREDICT for streaming data analytics:
Act before faults occur.
 ANALYZE for batch data analytics:
Drill down into your data – get real insights.
 DECIDE for proactive decision making:
Let AI help you make the best
maintenance decisions.
 VISUALISE for visual analytics:
See what matters in your data.
 FMECA for risk assessment:
Improve your asset’s performance with
data-driven risk analysis.
Needs for standards for the interoperability of the various components and
for the integration
with the legacy information systems

5. Impacts and Benefits
• Standardized approach to a
Predictive Maintenance
• Modular approach that broadens
potential production equipment that
can benefit from UPTIME and
accelerates deployment
• Solution which can address
other problems than Predictive
Maintenance (quality, monitoring, AR
etc.)
• Payback within 2 years expected –
UPTIME facilitates increased
productivity and cost effectiveness
through transparency, traceability,
lower maintenance and repair costs,
higher machine availability and better
quality of products
• Condition monitoring allows insight
into rollers’ bearings level of wear
• Prediction of the time when Milling
Rollers should be replaced
• Increased efficiency of maintenance
operations
• Reduced coordination, management
and reporting overhead
• More attractive to customer due to
more transparent (and on-demand)
reporting, risk reduction and
reduction of costly breakdowns
• Flexible deployment and good level
of integration (data acquisition,
handling and access/analysis)
• Training algorithms in a new
product line
• Enterprise System Integration
• Sensorization of assets in a
wear-intensive environment
• Training of personnel
• Mobile assets
• Digitalization of data collection
• Multi-stakeholder processes
Benefits
/
advantages
Efforts
Typical
impacts(1)
OEE(2) increase :
5-10%
MTBF(3) increase :
20-30%
Total Cost of Maint. reduction :
10-20%
(1) Figures depend on the use case, evaluation ongoing
(2) OEE : Overall Equipment Efficiency
(3) MTBF : Mean Time between Failures
(4) MTTR : Mean Time to Repair
MTTR (4) reduction :
10-20%

6. Why the UPTIME Platform?
The UPTIME platform
• Puts the users at the center
• Combines best of the breed technology
components
• Is open, modular and evolutionary at a low
cost
• Combines Models and AI Analytics
• Uses advanced technologies for
visualization
• Implements a Digital Twin which is
extensible with other technologies
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Yves Keraron - ISADEUS
Use of existing standards – Business or technological ones and
adaptation or production of new ones is key to achieve this goal

7. Standardization – Industry competitiveness –
Innovation in a digital environment
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UPTIME innovation
• Most of the best of the breed components are open source
and as such play a role in the standardization process in
industry
• Computation of legacy data, structured/non structured data,
IoT, edge computing
Standardization strategy
• Identification of existing standards and questionnaire to see
how they were known by the partners
• Focus on the functional standards for preventive
maintenance and on the standards to exchange between
components
• MIMOSA for the exchange of data with legacy systems
• Efforts on a common vocabulary by analyzing the definitions
of maintenance terms in standards

8. Industry competiveness
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• Standardized approach to a
Predictive Maintenance
• Modular approach that broadens
potential production equipment that
can benefit from UPTIME and
accelerates deployment
• Solution which can address
other problems than Predictive
Maintenance (quality, monitoring, AR
etc.)
• Payback within 2 years expected –
UPTIME facilitates increased
productivity and cost effectiveness
through transparency, traceability,
lower maintenance and repair costs,
higher machine availability and better
quality of products
• Condition monitoring allows insight
into rollers’ bearings level of wear
• Prediction of the time when Milling
Rollers should be replaced
• Increased efficiency of maintenance
operations
• Reduced coordination, management
and reporting overhead
• More attractive to customer due to
more transparent (and on-demand)
reporting, risk reduction and
reduction of costly breakdowns
• Flexible deployment and good level
of integration (data acquisition,
handling and access/analysis)
• Training algorithms in a new
product line
• Enterprise System Integration
• Sensorization of assets in a
wear-intensive environment
• Training of personnel
• Mobile assets
• Digitalization of data collection
• Multi-stakeholder processes
Benefits
/
advantages
Efforts
Typical
impacts(1)
OEE(2) increase :
5-10%
MTBF(3) increase :
20-30%
Total Cost of Maint. reduction :
10-20%
MTTR (4) reduction :
10-20%

9. Standards radar chart
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Adopted
Candidate
Track
Color code
Transverse standard
Domain/discipline
standard
Technological
standard
Smart
Manufacturing
Reference
Model
ISO
13374
OWL
Monitor
external
development
Adopt
Existing
standards
Ecosystem
(e.g. Foresee Cluster
here UPTIME)
Development
Participate in
external
Development
(Lead activity, team member,
influence std policy)
MIMOSA
OSA-CBM
ISO
15926
ISO
14224
SHACL
JSON
OPC-UA
EN
13306
Predictive
Maintenance
Ontology
IEC 60812
FMECA
ISO-IEC DIS
21838
IEEE
802.15.4
MQTT
EN 17007

10. Main lessons learnt
Possible improvements in the way forward
• Some standards have been successfully implemented : ISO 13374, IEC
60812, AMQP (ISO:IEC 19464:2014), MQTT (ISO/IEC 20922:2016).
• Availability of FMECA modules adaptable to various contexts for
instance through future Asset Administration Shell specifications wil
help in the future
• Use of trans-domain reference designation standards as ISO/IEC
81346 series to breakdown industrial assets according different
aspects
• Difficulties to address semantics aspect for instance with ontologies ;
efforts to harmonize the vocabulary used in different standards EN
13306, EN 17007, IEC 60813, ISO 14224, …
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11. Thoughts for future developments
• Focus on the relationships:
• Whole-Part relationships, Hierarchy of composition
• Class-Sub-Class relationships, Hierarchy of classification
• Non-hierarchical relationships
• Model based standards towards SMART standards:
https://www.iso.org/smart
• Model based System Engineering mindset : common methodology for
diverse systems
• Efforts on the formal aspects of n-modeling : Applied Category Theory to
be explored
7/21/2022 Presenter - Company 11

12. European Cluster for Predictive
Operations in Manufacturing
www.foresee-cluster.eu
Thank you!