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Predictive Maintenance with R
•About eoda 
•Predictive Maintenance 
•Predictive Maintenance with R 
•Results as a Service 
Agenda
About eoda 
•an interdisciplinary team of data scientists, engineers, economists and social scientists, 
•founded 2010 in ...
Consulting 
Software 
Solution 
Training 
eoda portfolio
Predictive Maintenance
The requirements on maintenance 
International competition 
Shorter product life cycles 
Faster technological leaps 
More ...
Evolution of Maintenance Concepts 
Reactive or Breakdown Maintenance 
Preventive or Periodic Maintenance 
Condition-based ...
Predictive Maintenance as an extension of condition-based maintenance represents the informatization of production process...
Predictive Maintenance 
Example – Gearbox Bearing damage in wind farm 
•Reactive Maintenance 
•Cost for a replacement of t...
Predictive Maintenance 
Example – Gearbox Bearing damage in wind farm 
•Predictive Maintenance 
Use of acceleration sensor...
Predictive Maintenance 
Potential factors 
50 % Reduction of maintenance costs 
50 % Reduction of machine damage 
50 % Red...
Predictive Maintenance 
Time 
Data collection 
Data management 
Data analysis 
Planning of maintenance 
Maintenance 
Busin...
Predictive Maintenance Data Collection and Management 
Environmental Data 
Sensor-based Machine Data 
Production indicator...
Predictive Maintenance 
Data analysis 
Datascience 
know-how 
Requirements of the market 
Domain 
Expertise
Predictive Maintenance 
Data analysis 
Source: David Smith 
Data Scientists 
Power User 
Business User 
Service People 
Di...
Predictive Maintenance with R
Predictive Maintenance with R 
Advantages 
•Features 
•The features that come with R (without additional investment) are i...
Predictive Maintenance with R 
Advantages 
•Features 
•The features that come with R (without additional investment) are i...
Predictive Maintenance with R 
Advantages 
•Features 
•The features that come with R (without additional investment) are i...
Data Collection and Management 
Environmental Data 
Sensor-based Machine Data 
Production indicators 
Example of use: Diff...
Data Collection and Management 
Environmental Data 
Sensor-based Machine Data 
Production indicators 
Predictive Maintenan...
Data analysis 
Source: David Smith 
Data Scientists 
Power User 
Business User 
Service People 
Predictive Maintenance wit...
Predictive Maintenance with R 
Results as a Service
Data 
Analysis 
Web based Front End 
Predictive Maintenance with R 
Results as a Service eoda Service Platform 
API 
Inter...
eoda GmbH Ludwig-Erhard-Straße 8 34131 Kassel Germany +49 (0) 561/202724-40 www.eoda.de http://blog.eoda.de https://servic...
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Predictive Maintenance with R

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International competition, shorter product life cycles and faster technological leaps forward – these are only a few of the challenges the production of a company is facing in the 21st century. In order to survive in an environment like this, resource-efficient and secure planning of production processes are necessary to guarantee a consistent and high quality output. Unforeseeable machine failures as well as performance drops or deterioration in quality because of defective system components can lead to shortness of supplies which will eventually weaken the market position of the entire organization.

To meet these requirements organizations are increasingly focusing on the improvement of maintenance, repair and operations of their machinery. In the previous years, the industry shifted their focus away from only reactive repair mechanisms towards the predictive coordination of machine maintenance.

Predictive Maintenance falls under the category of the future of maintenance developments. Originally developed in the course of the “Industrie 4.0” high-tech strategy of the German government, today Predictive Maintenance represents the informatization of production processes - intelligent IT-based production systems on the path towards a Smart Factory. Through the generation and analysis of different machine data, the predictive power of the state of industrial plants is not only enhanced, but also provides the basis for an improved planning certainty as well as the efficient planning of repair and maintenance work.

Published in: Technology

Predictive Maintenance with R

  1. 1. Predictive Maintenance with R
  2. 2. •About eoda •Predictive Maintenance •Predictive Maintenance with R •Results as a Service Agenda
  3. 3. 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“.
  4. 4. Consulting Software Solution Training eoda portfolio
  5. 5. Predictive Maintenance
  6. 6. The requirements on maintenance International competition Shorter product life cycles Faster technological leaps More complex business processes Shift from product to service
  7. 7. 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
  8. 8. 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
  9. 9. 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/
  10. 10. 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/
  11. 11. 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.”
  12. 12. Predictive Maintenance Time Data collection Data management Data analysis Planning of maintenance Maintenance Business Value Workflow
  13. 13. Predictive Maintenance Data Collection and Management Environmental Data Sensor-based Machine Data Production indicators Different types of data
  14. 14. Predictive Maintenance Data analysis Datascience know-how Requirements of the market Domain Expertise
  15. 15. Predictive Maintenance Data analysis Source: David Smith Data Scientists Power User Business User Service People Different user types with different comepetence level
  16. 16. Predictive Maintenance with R
  17. 17. 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
  18. 18. 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
  19. 19. 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
  20. 20. 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
  21. 21. 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 Density interpolation 15,4 16,0 15,7 15,4 15,8 16,1 Example of use: Different types of data at different times
  22. 22. Data analysis Source: David Smith Data Scientists Power User Business User Service People Predictive Maintenance with R The comeptence level disappear with R
  23. 23. Predictive Maintenance with R Results as a Service
  24. 24. 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
  25. 25. 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 info@eoda.de 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/

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