Predictive Maintenance with R
Oliver Bracht – CEO
info@eoda.de
Agenda
• About eoda
• Predictive Maintenance
• Predictive Maintenance with R
• Best practice
Oliver Bracht – CEO
info@eoda.de
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“.
Oliver Bracht – CEO
info@eoda.de
eoda portfolio
Consulting
Software
Solution
Training
Oliver Bracht – CEO
info@eoda.de
Predictive Maintenance
Oliver Bracht – CEO
info@eoda.de
The past of maintenance
Reactive or Breakdown Maintenance
Preventive or Periodic Maintenance
Condition-based Maintenance
Oliver Bracht – CEO
info@eoda.de
The past of maintenance
Unplanned
Reactive or production
Breakdownshutdowns
Maintenance
Inefficient
Preventiveuse
or Periodic
of resources
Maintenance
High
Condition-based
monitoring costs
Maintenance
Oliver Bracht – CEO
info@eoda.de
The requirements on maintenance
International competition
Shorter product life cycles
New business processes
Faster technological leaps
The future of maintenance
Predictive Maintenance
Oliver Bracht – CEO
info@eoda.de
Predictive Maintenance
Definition
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.
Oliver Bracht – CEO
info@eoda.de
Predictive Maintenance
Potential
Analytic
know-how
Domain
Expertise
Requirements
of the market
Oliver Bracht – CEO
info@eoda.de
Predictive Maintenance
Workflow
Business Value
Maintenance
Planning of
maintenance
Data analysis
Data management
Data collection
Time
Oliver Bracht – CEO
info@eoda.de
Predictive Maintenance
Production indicators
Data Collection and Management
Big Data
Environmental Data
Sensor-based Machine Data
Oliver Bracht – CEO
info@eoda.de
Predictive Maintenance
Data analysis
Data Scientists
Power User
Administrative User
Consumer
Source: David Smith
Oliver Bracht – CEO
info@eoda.de
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/
Oliver Bracht – CEO
info@eoda.de
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/
Oliver Bracht – CEO
info@eoda.de
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.”
Oliver Bracht – CEO
info@eoda.de
Predictive Maintenance w
Oliver Bracht – CEO
info@eoda.de
Predictive Maintenance with R Advantages
•
Features
•
•
The features that come with R (without additional investment) are incomparable
R in the software stack
Oliver Bracht – CEO
info@eoda.de
Predictive Maintenance with R Advantages
•
Features
•
•
The features that come with R (without additional investment) are incomparable
R in the analytic stack
•
R can be integrated into all the layers of an analysis or reporting architecture
 Integration into an existing IT environment
 Forecast on the machine
Prototyping
C
Implementation
R directly on the machine
Oliver Bracht – CEO
info@eoda.de
Predictive Maintenance with R Advantages
•
Features
•
•
R in the analytic stack
•
•
The involvement of the scientific community and large companies support the development
and acceptance of R
Quality
•
•
R can be integrated into all the layers of an analysis or reporting architecture
Investment protection
•
•
The features that come with R (without additional investment) are incomparable
R offers high reliability and uses the latest statistical methods
Costs
•
R is Open Source and there are no license costs
Oliver Bracht – CEO
info@eoda.de
Predictive Maintenance with R Data Collection and Management
Production indicators
Big Data
Environmental Data
Sensor-based Machine Data
Oliver Bracht – CEO
info@eoda.de
Predictive Maintenance with R Data Collection and Management
Different types of data at different times
Production indicators
Big Data
Environmental Data
Time
Pressure
Time
Density
7:00
235
7:30
15,3
8:00
239
8:30
16,1
9:00
240
10:00 228
9:30 15,7
Sensor-based Machine Data
10:30 15,5
11:00 231
11:30 16,0
12:00 233
12:30 15,9
Oliver Bracht – CEO
info@eoda.de
Predictive Maintenance with R Data Collection and Management
Different types of data at different times
Environmental Data
Big
ModelData
based interpolation
Production indicators
Time
Pressure Density
7:00
235
8:00
9:00
Time
Density
15,4
7:30
15,3
239
16,0
8:30
16,1
240
15,7
10:00 228
15,4
9:30 15,7
Sensor-based Machine Data
10:30 15,5
11:00 231
15,8
12:00 233
16,1
Smart Data
11:30 16,0
12:30 15,9
Oliver Bracht – CEO
info@eoda.de
Predictive Maintenance with R
Data analysis
Data Scientists
Power User
User
Consumer
Source: David Smith
Oliver Bracht – CEO
info@eoda.de
Predictive Maintenance with R
Best Practice
Oliver Bracht – CEO
info@eoda.de
Predictive Maintenance with R
Best practice
Web based Front End
API
Authentication
(LDAP)
Administration
RScripts
Analysis
Internal
data
Java
Script
User-, RoleManagement
…
…
Data
Public data
sources
Interactive
Web App
Machine
data
Oliver Bracht – CEO
info@eoda.de
Session
Management
Thank you for your attention
For more information
Whitepaper:
Predictive
Maintenance with R
www.eoda.de
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Oliver Bracht – CEO
info@eoda.de