Predictive Maintenance with R
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Predictive Maintenance with R
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• About eoda
• Predictive Maintenance
• Predictive Maintenance with R
• Results as a Service
Agenda
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About eoda
• an interdisciplinary team of data scientists, engineers, economists
and social scientists,
• founded 2010 in Kassel (Germany),
• specialized in analyzing structured and unstructured data,
• integrated portfolio for solving analytical problems,
• with a focus on „R“.
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Consulting
Software
Solution
Training
eoda portfolio
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Predictive Maintenance
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The requirements on maintenance
International competition
Shorter product life cycles
Faster technological leaps
More complex business processes
Shift from product to service
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Evolution of Maintenance Concepts
Reactive or Breakdown Maintenance
Preventive or Periodic Maintenance
Condition-based Maintenance
Unplanned production shutdowns
Inefficient use of resources
Simple rules Not very precise
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Predictive Maintenance as an extension of condition-based maintenance
represents the informatization of production processes. With
intelligent IT-based production systems Predictive Maintenance
represents one important step on the path towards the development of a
Smart Factory in industrial production.
Predictive Maintenance
The future of maintenance
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Predictive Maintenance Example – Gearbox Bearing damage in wind farm
• Reactive Maintenance
• Cost for a replacement of the bearing $ 250.000
• Cran costs $ 150.000
• Power generation / Revenue losses $ 26.000
$ 426.000
Source: http://www.wwindea.org/
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Predictive Maintenance Example – Gearbox Bearing damage in wind farm
• Predictive Maintenance
Use of acceleration sensors, oil particle counters and weather forecast modules,
plus reliable evaluation of the data
Early detection of the damage at the gearbox bearing
• Repair instead of exchange of the bearing $ 30.000 < $ 250.000
• Lower cran costs $ 75.000 < $ 150.000
• Power generation / Revenue losses $ 2.000 < $ 26.000
$ 107.000 < $ 426.000
Source: http://www.wwindea.org/
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Predictive Maintenance Potential factors
50 % Reduction of maintenance costs
50 % Reduction of machine damage
50 % Reduction of machine downtime
20 % Increase in machine lifetime
20 % Increase in productivity
25 % - 60% Profit growth Source: Barber, Steve & Goldbeck, P.: “Die Vorteile einer vorwärtsgerichteten Handlungsweise mit vorbeugenden und vorausschauenden Wartungstools und –strategien – konkrete Beispiele und Fallstudien.”
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Predictive Maintenance
Time
Data collection
Data management
Data analysis
Planning of
maintenance
Maintenance
Business Value
Workflow
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Predictive Maintenance Data Collection and Management
Environmental Data
Sensor-based Machine Data
Production indicators
Different types of data
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Predictive Maintenance Data analysis
Datascience know-how
Requirements of the market
Domain Expertise
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Predictive Maintenance Data analysis
Source: David Smith
Data Scientists
Power User
Business User
Service People
Different user types with different comepetence level
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Predictive Maintenance with R
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Predictive Maintenance with R Advantages
• Features
• The features that come with R (without additional investment) are incomparable
• R in the software stack
• R can be integrated into all the layers of an analysis or reporting architecture
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Predictive Maintenance with R Advantages
• Features
• The features that come with R (without additional investment) are incomparable
• R in the software stack
• R can be integrated into all the layers of an analysis or reporting architecture
C Prototyping Implementation
R directly on the machine
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Predictive Maintenance with R Advantages
• Features
• The features that come with R (without additional investment) are incomparable
• R in the software stack
• R can be integrated into all the layers of an analysis or reporting architecture
• Investment protection
• The involvement of the scientific community and large companies support the development
and acceptance of R
• Quality
• R offers high reliability and uses the latest statistical methods
• Costs
• R is Open Source and there are no license costs
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Data Collection and Management
Environmental Data
Sensor-based Machine Data
Production indicators
Example of use: Different types of data at different times
Predictive Maintenance with R
Time Density
7:30 15,3
8:30 16,1
9:30 15,7
10:30 15,5
11:30 16,0
12:30 15,9
Time Pressure
7:00 235
8:00 239
9:00 240
10:00 228
11:00 231
12:00 233
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Data Collection and Management
Environmental Data
Sensor-based Machine Data
Production indicators
Predictive Maintenance with R
Time Density
7:30 15,3
8:30 16,1
9:30 15,7
10:30 15,5
11:30 16,0
12:30 15,9
Time Pressure
7:00 235
8:00 239
9:00 240
10:00 228
11:00 231
12:00 233
Big Data Model based interpolation Density Density
15,4
16,0
15,7
15,4
15,8
16,1
Example of use: Different types of data at different times
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Data analysis
Source: David Smith
Data Scientists
Power User
Business User
Service People
Predictive Maintenance with R
The comeptence level disappear with R
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Predictive Maintenance with R Results as a Service
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Data
Analysis
Web based Front End
Predictive Maintenance with R Results as a Service eoda Service Platform
API Interactive Web App
R-Scripts
…
Administration Authentication
(LDAP) User-, Role-
Management Session
Management
…
Public data
sources
Internal
data Machine
data
Java Script
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eoda GmbH
Ludwig-Erhard-Straße 8
34131 Kassel
Germany
+49 (0) 561/202724-40
www.eoda.de
http://blog.eoda.de
https://service.eoda.de/
http://twitter.com/datennutzen
https://www.facebook.com/datenwissennutzen
Thank you for your attention For more information Whitepaper: Predictive Maintenance with R
www.eoda.de
Results as a Service eoda Service Platform
https://service.eoda.de/