The document presents key performance indicators (KPIs) for energy-efficient asset management in a factory automation testbed, developed by researchers from Tampere University of Technology. It outlines the testbed setup, defines KPIs, and discusses the use of an event processing language for real-time data management and retrieval. Future work involves setting performance targets and developing alarm mechanisms for monitoring KPI thresholds.

1. Key Performance Indicators for Energy
Efficient Asset Management in a
Factory Automation Testbed
Date: July, 2012 Conference: 2012 IEEE International
Linked to: eSONIA Conference on Industrial Informatics
Title of the paper: Key Performance
Indicators for Energy Efficient Asset
Management in a Factory Automation
Testbed
Authors: Bin Zhang, Corina Postelnicu,
Jose Luis Martinez Lastra
Contact information
Tampere University of Technology,
FAST Laboratory,
If you would like to receive a reprint of
P.O. Box 600,
the original paper, please contact us
FIN-33101 Tampere,
Finland
Email: fast@tut.fi
www.tut.fi/fast

2. Key Performance Indicators
for Energy Efficient Asset Management
in a Factory Automation Testbed.
Bin Zhang
Corina Postelnicu
Jose L. Martinez Lastra
Tampere University of Technology, Finland
ARTEMIS eSONIA project (Embedded Service Oriented Monitoring, Diagnostics and Control: Towards the
Asset Aware and Self Recovery Factory)

3. Outline
1. Background. Testbed.
2. Key Performance Indicators. Event Processing Language
rules
3. Examples
4. Conclusions

4. 1. Background. Testbed
FASTORY

5. Production

6. Production
3 parts
Frame Screen Keyboard
3 MODELS

7. Production
3 COLOURS

8. Production
+ +
+ +
+ +
+ +
= 729 product variants

9. Manufacturing cell
embedded controllers to control
the devices using web services
Each cell has 3 controllers
Energy
consumption

10. 3. Event Processing Language rules for
KPI retrieval
Esper: a Java based component developed for rapid
development of applications which require processing of
large volumes of events in real time.
A database stores data and manipulates it according to
incoming queries
 Esper
responds according to incoming events
stores queries in an engine
 Event Processing Language (EPL) syntax
 Rely on CAMX IPC-254x and IPC255x
standards

11. select a.cellId as cellId, a.dateTime
Cell ID,
as dateTime, b.AWATTHR as energy,
energy use
a.palletId as palletId
when the
from pattern [ every
robot starts
a=EquipmentChangeState
to execute,
(currentState="READY-
Energy use, pallet ID and
Energy PROCESSING-EXECUTING") ->
states of time stamp
consumption b=EnergyMeter(cellId=a.cellId)]
Unit energy consumption working cells,
for producing select a.cellId as cellId, a.dateTime
cell ID and Cell ID,
one product as dateTime, b.AWATTHR as energy,
pallet ID energy use
a.palletId as palletId
when the
from pattern [ every
robot stops to
a=EquipmentChangeState
execute,
(previousState="READY-
pallet ID and
PROCESSING-EXECUTING") ->
time stamp
b=EnergyMeter(cellId=a.cellId)]
select a.cellId as cellId, a.dateTime
Cell ID,
as dateTime, b.AWATTHR as energy,
energy use
a.processType as processType
when the
from pattern [ every
robot starts
a=EquipmentChangeState
to execute,
(currentState="READY-
process type
PROCESSING-EXECUTING") ->
Energy use, and time
b=EnergyMeter(cellId=a.cellId AND
Energy states of stamp
processType = a.processType)]
Process energy consumption consumption working cells,
select a.cellId as cellId, a.dateTime
per process type of process Cell ID,
as dateTime, b.AWATTHR as energy,
and cell ID energy use
a.processType as processType
when the
from pattern [ every
robot stops to
a=EquipmentChangeState
execute,
(previousState="READY-
process type
PROCESSING-EXECUTING") ->
and time
b=EnergyMeter(cellId=a.cellId AND
stamp
processType=a.processType)]

12. Other KPI discussed in the paper
Unit production time
Unit processing time
Production shutdowns
Cell Production Rate
Cell energy consumption
Unit energy consumption
MTTR
MTTF
MTBF
Frame quality rate, Keyboard quality rate, Screen quality
rate, Overall quality rate
Total energy consumption
Line Production Rate

13. 4. Summary
 calculation of KPI values at runtime
 EPL rules running on Esper engine
 KPI management system implemented as a web
application with MVC design pattern.
Future work:
 targets set for the selected indicators, so that overall
performance is improved continuously;
 alarm mechanisms designed to visually announce out-of-
preset-boundaries behavior;

14. Thank you!
bin.zhang@tut.fi
corina.postelnicu@tut.fi

15. 2. Implementation architecture
Web services discovery via
request from a web browser
 initiation of the WS4D
framework  a client is
created in this framework for
receiving information from
web services within the line.

16. Background
8 step iterative model for design of Key Performance Indicators