Embed Size (px)
DESCRIPTION
This paper presents an approach using power consumption to detect system deterioration (misalignment of conveyors) Power consumption data are correlated with workload of the conveyor system. Real time data coming from a real factory automation testbed are input to SVM for classification. The output is compared with the output of a rule-based engine.
Citation preview
Embedded Service Oriented Diagnostics based on Energy
Consumption Data
11/04/2023Embedded Service Oriented Diagnostics
based on Energy Consumption Data1
•Date: September, 2012•Linked to: eSONIA
Contact information
Tampere University of Technology,
FAST Laboratory,
P.O. Box 600,
FIN-33101 Tampere,
Finland
Email: [email protected]
www.tut.fi/fast
Conference: 2012 IEEE International Conference on Information and Automation for Sustainability
Title of the paper: Embedded Service Oriented Diagnostics based on Energy Consumption Data
Authors: Corina Postelnicu,
Navid Khajehzadeh,
Jose Luis Martinez Lastra
If you would like to receive a reprint of the original paper, please contact us
Embedded Service Oriented Diagnostics based on Energy
Consumption Data
ICIAfS 2012, Beijing, China
27-29.9.2012
ARTEMIS eSONIA project (Embedded Service Oriented Monitoring, Diagnostics and Control: Towards the Asset Aware and Self Recovery Factory)
Corina Postelnicu
Navid Khajehzadeh
Jose L. Martinez Lastra
Presenter: Bin Zhang
Factory Automation Systems and Technologies
Tampere University of Technology, Finland
Outline
1.Introduction
2.Testbed
3.Implementation– Data collection– Support Vector Machine– Validation
4.Failure detection model
5.Conclusions and future work
11/04/2023Embedded Service Oriented Diagnostics
based on Energy Consumption Data3
Introduction
– Predictive maintenance techniques Passive: measuring data (vibration, temperature,
etc), then comparing with normal values Active: injecting test signals, then monitoring
responses
11/04/2023Embedded Service Oriented Diagnostics
based on Energy Consumption Data4
Unexpected failures
Financial losses & accidents
Introduction
– Quantification: threshold settings by running the equipments until failure occurs
– Assumption: the measured parameters should not be influenced by other parameters
– Limitation: suitable for processing workstations, not transportation devices (parameters are influenced by workload)
This paper associates the workload on a conveyor system to the power consumption information for failure detection.
11/04/2023Embedded Service Oriented Diagnostics
based on Energy Consumption Data5
Testbed
11/04/2023Embedded Service Oriented Diagnostics
based on Energy Consumption Data6
Testbed
11/04/2023Embedded Service Oriented Diagnostics
based on Energy Consumption Data7
Embedded controllers to publish the device information as web services
Each cell has 4 controllers
En
erg
y
con
sum
pti
on
Implementation: Data collection
11/04/2023Embedded Service Oriented Diagnostics
based on Energy Consumption Data8
Item Transfer InCell 5
Item Transfer outCell 5
Item Transfer InCell 6
Cell 5 Cell 6
1. Energy consumption 2. Workload
Implementation: Data collection
11/04/2023Embedded Service Oriented Diagnostics
based on Energy Consumption Data9
1. Correlation of bypass conveyor power consumption (watt) and number of pallets (0-5)
2. Power consumption of the conveyor system(watt, 1 or 2 pallets)
Class 1: 0-1 pallet
Class 2: 2 or more pallets
Implementation: Support Vector Machine
11/04/2023Embedded Service Oriented Diagnostics
based on Energy Consumption Data10
1.Support Vector Machine (SVM)• A classifier to provide a boundary to divide a
dataset into two classes.
2.Least Square Support Vector Machine (LS-SVM)• Classification is done using linear equations
instead of a burdensome Quadratic equation.• 70% to 80% of data are used for learning and the
rest for validation.
Implementation: Validation
11/04/2023Embedded Service Oriented Diagnostics
based on Energy Consumption Data11
Accuracy is computed by comparing the LS-SVM result against the rule-based engine, which shows an error percentage of 5.56%
The 2 classes identified by the rule-based engine
The 2 classes identified by LS-SVM
Failure detection model
11/04/2023Embedded Service Oriented Diagnostics
based on Energy Consumption Data12
Conclusions and future work
This paper presents an approach using power consumption to detect system deterioration (misalignment of conveyors)• Power consumption data are correlated with
workload of the conveyor system.• Real time data coming from a real factory
automation testbed are input to SVM for classification.
• The output is compared with the output of a rule-based engine.
11/04/2023Embedded Service Oriented Diagnostics
based on Energy Consumption Data13
Conclusions and future work
Future work• Bring more parameters for analysis, i.e. vibration
and temperature
11/04/2023Embedded Service Oriented Diagnostics
based on Energy Consumption Data14
11/04/2023Embedded Service Oriented Diagnostics
based on Energy Consumption Data15
Thank you!
ARTEMIS eSONIA project (Embedded Service Oriented Monitoring, Diagnostics and Control: Towards the Asset Aware and Self Recovery Factory)
