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.

1. Predictive Maintenance with R

2. •About eoda
•Predictive Maintenance
•Predictive Maintenance with R
•Results as a Service
Agenda

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. Consulting
Software
Solution
Training
eoda portfolio

5. Predictive Maintenance

6. The requirements on maintenance
International competition
Shorter product life cycles
Faster technological leaps
More complex business processes
Shift from product to service

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. 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. 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. 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. 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. Predictive Maintenance
Time
Data collection
Data management
Data analysis
Planning of maintenance
Maintenance
Business Value
Workflow

13. Predictive Maintenance Data Collection and Management
Environmental Data
Sensor-based Machine Data
Production indicators
Different types of data

14. Predictive Maintenance
Data analysis
Datascience
know-how
Requirements of the market
Domain
Expertise

15. Predictive Maintenance
Data analysis
Source: David Smith
Data Scientists
Power User
Business User
Service People
Different user types with different comepetence level

16. Predictive Maintenance with R

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. 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. 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. 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. 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. Data analysis
Source: David Smith
Data Scientists
Power User
Business User
Service People
Predictive Maintenance with R
The comeptence level disappear with R

23. Predictive Maintenance with R
Results as a Service

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

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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/