predictive maintenance with r
DESCRIPTION
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.TRANSCRIPT
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 Kassel (Germany),
• specialized in analyzing structured and unstructured data,
• integrated portfolio for solving analytical problems,
• with a focus on „R“.
Consulting
Software
Solution
Training
eoda portfolio
Predictive Maintenance
The requirements on maintenance
International competition
Shorter product life cycles
Faster technological leaps
More complex business processes
Shift from product to service
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
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
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/
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/
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.”
Predictive Maintenance
Time
Data collection
Data management
Data analysis
Planning of
maintenance
Maintenance
Business Value
Workflow
Predictive Maintenance Data Collection and Management
Environmental Data
Sensor-based Machine Data
Production indicators
Different types of data
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
Different user types with different comepetence level
Predictive Maintenance with R
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
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
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
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
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
Data analysis
Source: David Smith
Data Scientists
Power User
Business User
Service People
Predictive Maintenance with R
The comeptence level disappear with R
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 Interactive Web App
R-Scripts
…
Administration Authentication
(LDAP) User-, Role-
Management Session
Management
…
Public data
sources
Internal
data Machine
data
Java Script
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/