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Responding to your environment
Predictive Analytics for Maintenance UREASON | PPA Conference February 2017
UREASON
Active in: Process Industry, Telecom, Smart Grid, Smart Cities
since 2001
Vast Experience: Big Data, IoT, AI, Fault Management,
Predictive Analytics, Predictive Maintenance
A.I. Technology House
Proven track record with customers in wide variety of industry
– general theme: reason over large volumes of data to reduce
business uncertainty
Known as Innovator – from Concept to Feasibility and Roll-out
Main offices in the Netherlands (Delft) and the UK
(Maidenhead), sales offices in France and Germany
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We Entered Into the World of Real-Time Predictive Analytics
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Revolution(s)
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http://pages.experts-exchange.com/processing-power-compared/
Revolution(s)
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1,00E-06
1,00E-05
1,00E-04
1,00E-03
1,00E-02
1,00E-01
1,00E+00
1,00E+01
1,00E+02
1,00E+03
1,00E+04
1,00E+05
1,00E+06
1,00E+07
1,00E+08
1,00E+09
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
Mem
ory
Pri
ce (
$/M
B)
Year
Historical Cost of Computer Memory and Storage
Flip-Flops
Core
ICs on boards
SIMMs
DIMMs
Big Drives
Floppy Drives
Small Drives
Flash Memory
SSD
1,00E-06
1,00E-05
1,00E-04
1,00E-03
1,00E-02
1,00E-01
1,00E+00
1,00E+01
1,00E+02
1,00E+03
1,00E+04
1,00E+05
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
Pri
ce (
$/M
B)
Year
Disk Drive Cost with TIme
Floppy Disk Drives Mainframe Drives
Small Disk Drives
Flash Memory
http://www.jcmit.com/memoryprice.htm
Revolution(s)
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World Economic Forum, January 2015, Industrial Internet of Things: Unleashing the Potential of Connected Products and Services
“If you can’t measure it, you can’t manage it.”
‘New’ Business Models Appearing
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Source TechWorld
New Models
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Predictive Maintenance
Predictive Maintenance:
Maintenance upon indication.
Indication is based on predictive
models that have been trained on
datasets that capture:
- Asset condition
- Asset usage
- Assets failure modes
- Asset failures
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Predictive Maintenance:
• Reduces size and scale of repairs
• Reduces downtime
• Increases accountability
• Reduces number of repairs
• Increases asset useful lifetime
• Increases throughput
• Increases quality of output
• Reduces investments
• Lowers overall maintenance costs
through better use of labor and
materials
Predictive Maintenance Programs
Identify the critical assets (critical to operations)
- Total Loss of Production
- Partial Loss of Production
- Negligible Impact
Balance the investment in PM programs on TMC
- Use failure data already available
- Improve asset data management
Keep in mind that often the effects (financial benefits) of
Predictive Maintenance programs or often not seen in 1st years
of program but successive years!!
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ADVANCE
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Ingredients Needed for Predictive Maintenance
Technical
• Historical Data - Data Historians DCS/SCADA, EAMS
Systems (Maximo and alike)
• Classification of failures – Asset failed on ‘time/date’, failure is
type ‘x’ in addition to Common Failure Mode info
• Analytical Tools/Models to train predictive models –>
UREASON Platform
• Streaming Data - Asset vibration, temperature, load, …
• Real-Time Monitoring System –> UREASON Platform
Organizational
• Start small
• Project champion
• Management Support
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Typical Steps– Predictive Maintenance Programs
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5
Updates &
Tuning (3)
4
Model
Deployment
3C
Model
Validation
3B
Model
Training
3A
Feature
Selection
2B
Data
Preparation
2A
Data
Understanding
1
Business
Understanding
Heavy end-user involvement
Knowledge Intensive Heavy end-user involvement
Data Collection Data Science
Process: ~ 2 - 4 Weeks – Feasible YES/NO
Based on results of the POC and business adoption possible rollout plan can be established
Example: Asset availability, Industrial Turbines, Energy Sector
Use Case : Condition based monitoring across all Turbines
• Comparison with training data, showed furnace was 50˚C
above average
• Operators ran routine maintenance checks, but showed NO
ISSUES.
• Business decided not to ignore analytics
• Furnaces were taken offline and early maintenance
procedures instigated
• Maintenance found faults with Combustion units, Rotating
blades, etc.
• Resulting saving accounted for £5 million over one year.
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Predictive Models
Feature and failure data determine to what extend supervised
learning models can be used.
In the PmP/APM domain ANNs provide value as (analogue)
‘soft sensors’ that describe complex data relationships. As such
they can be used to highlight suspect anomalies – e.g.
prediction of bearing temperature/axle vibration versus
measured.
In the PmP/APM domain SVM provide excellent value for
asset/process failure prediction. In cases failure data is absent,
or insufficiently available Unsupervised learning route is an
option left to find structure in the inputs.
UREASON prefers to combine condition/asset data with event
data to create hybrid probabilistic failure models (see image)
that include CEP and ML techniques.
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Source: http://ureason.com/espcep-predictive-maintenance/
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Responding to your environment
Contact
UREASON International BV
Drie Akersstraat 11
2611 JR, Delft
The Netherlands
Telephone:
General: +31 85 273 49 20
Fax: +31 85 273 49 29
Email:
General: [email protected]
Support: [email protected]