Predictive maintenance is an extremely promising maintenance strategy, but implementation often turns out to be way more complicated than expected. A lot of attempts to implement predictive maintenance strand at the same departments as where they were initiated. The key towards successful implementation of predictive maintenance is to combine the knowledge of all departments in making decisions. In this presentation we start by explaining, based on the subject of sensor selection, why involving your entire organization is so important. Afterwards we give advice on how to implement predictive maintenance, give examples based on the Strukton Worksphere case and discuss how to get your entire organization on board.

1. Why predictive maintenance should be a
combined effort
Wouter Verbeek, ISN Conference
November 15, 2016

2. 2
Contents
What I will tell you today
2
1
3
4
Why so many predictive maintenance projects fail
How to do it right
The Strukton Worksphere case
Discussion

3. 1 Why so many predictive
maintenance projects fail

4. Being a Very Hungry Caterpillar
Concluding after driving more than
an hour that you’ve taken the
wrong road
Why so many predictive maintenance projects fail
The main reasons
4

5. • A data-driven approach requires large amounts of relevant data
Only computer power required
• In reality: often not that much data
• In that case a lot of human effort and knowledge is required
− making failure mode, effect and criticality analyses (FMECA)
− performing feature extraction
− ….
• Lot of companies don’t realize this and do not allocate
enough resources
 end up without predictive maintenance and without budget
5
Why so many predictive maintenance projects fail
Being a Very Hungry Catepillar

6. 6
Why so many predictive maintenance projects fail
Concluding after driving more than an hour that you’ve taken the wrong road
Install
sensors
Gather
data
Select assets and
develop algorithms
Create business
model
Implement predictive
maintenance
Statisticians
Mechanics
Business Development
When noticed a wrong decission, it can’t be changed anymore

7. 3 How to do it right

8. Involve everyone from start
Focus
Work agile
8
How to do it right
The most important lessons

9. Questions at start of project:
• For which assets might predictive
maintenance be a profitable strategy?
• Which failures for the selected assets
can be detected beforehand?
• Which physical phenomena are related
to the failures we want to predict?
• How much do these sensors cost and
is the business case profitable?
• How often do we have to measure and
with what accuracy?
• How can these sensors be connected
to our systems?
9
How to do it right
Predictive maintenance requires your entire company directly at the beginning
Cartoon by C.W. Miller

10. Questions at start of project:
• For which assets might predictive
maintenance be a profitable strategy?
• Which failures for the selected assets
can be detected beforehand?
• Which physical phenomena are related
to the failures we want to predict?
• How much do these sensors cost and
is the business case profitable?
• How often do we have to measure and
with what accuracy?
• How can these sensors be connected
to our systems?
10
How to do it right
Predictive maintenance requires your entire company directly at the beginning
People involved:
• Business development
• Mechanics, Engineers
• Mechanics, Engineers
• Business Development, Engineers
• Mechanics, Statisticians, IT
• IT, Mechanics, Engineers, Statisticians
Cartoon by C.W. Miller

11. Questions at start of project:
• For which assets might predictive
maintenance be a profitable strategy?
• Which failures for the selected assets
can be detected beforehand?
• Which physical phenomena are related
to the failures we want to predict?
• How much do these sensors cost and
is the business case profitable?
• How often do we have to measure and
with what accuracy?
• How can these sensors be connected
to our systems?
11
How to do it right
Predictive maintenance requires your entire company directly at the beginning
People involved:
• Business development
• Mechanics, Engineers
• Mechanics, Engineers
• Business Development, Engineers
• Mechanics, Statisticians, IT
• IT, Mechanics, Engineers, Statisticians
Cartoon by C.W. Miller

12. • Take the required time and effort for each asset
• Think big, but start small
− Two or three pilot projects
− One type of asset per pilot project
− A few failure modes to detect
• End up with one working predictive maintenance project, instead of being
half way ten
12
How to do it right
Focus!

13. 13
How to do it right
Organizing predictive maintenance requires immediate feedback
• Iterate  Create minimum viable products to get feedback early
• The outcomes of the pilot projects are uncertain and largely unknown  do not
specify too much beforehand
• Lean startup methodology fits predictive maintenance well
Build
MeasureLearn

14. 4 The Strukton Worksphere case

15. • Designs and builds utility buildings and installs and maintains technical
installations in buildings (manages 4,4 million m2 in the Netherlands)
• Sensors of all assets in a building are connected to central monitoring system
Strukton PULSE
• Uses insights in current functioning of assets, the comfort in a building and the
energy consumption
• Although sensor information is available, Strukton Worksphere does not yet
perform predictive maintenance
− No predictive analytics
− No link with operational planning
− No business case for predictive maintenance
The Strukton Worksphere case
Situation
15

16. • Started with identifying strengths and
weaknesses related to predictive
maintenance within the organization
(Quickscan)
• Workshop with IT, Business
Development, Datamanagement and
operation managers of the regions
together
− Identified key issues all together
− Developed roadmaps for seven
subjects (ranging from HR to data)
using two multidisciplenary teams
• Next step:
− Identify pilot projects and set-up
teams
The Strukton Worksphere case
Approach
16

17. 5 Conclusions and discussion

18. 18
Conclusions and discussion
To take home
The reasons why predictive maintenance
projects fail
How to do it right
Being a Very
Hungry Caterpillar
Concluding after
driving more than
an hour that you’ve
taken the wrong
road
Involve everyone
from start
Focus
Work agile

19. A Extra slides

20. Condition-
monitoring
methods
Model-
based
Physical
modeling
Knowledge-
based
methods
Expert
systems
Fuzzy logic
Data-driven
Statistical
methods
Classical
statistical
methods
Bayesian
methods
Artificial
intelligence
Support
Vector
Machines
Neural
networks
Neuro-fuzzy
systems
20
Extra slides
Condition-monitoring methods

21. 21
Extra slides
Different kinds of sensor information
Tiedo Tinga and Richard Loendersloot, Aligning PHM, SHM and CBM by understanding the physical system failure behaviour, European Conference of the PHM Society, 2014
Platform / systemUsage Remaining life
Local loads
Service life /
Damage
accumulation
Failure model
PrognosticsStructural model
Usage
monitoring
Load
monitoring
Condition
monitoring