Why predictive maintenance should be a combined effort
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Transcript of Why predictive maintenance should be a combined effort
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Why predictive maintenance should be a combined effort
Wouter Verbeek, ISN Conference November 15, 2016
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Contents
What I will tell you today
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Why so many predictive maintenance projects fail
How to do it right
The Strukton Worksphere case
Discussion
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1 Why so many predictive maintenance projects fail
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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
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• 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
Why so many predictive maintenance projects fail
Being a Very Hungry Catepillar
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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
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3 How to do it right
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Involve everyone from start
Focus
Work agile
How to do it right
The most important lessons
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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?
How to do it right
Predictive maintenance requires your entire company directly at the beginning
Cartoon by C.W. Miller
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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?
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
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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?
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
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• 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
How to do it right
Focus!
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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
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4 The Strukton Worksphere case
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• 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
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• 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
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5 Conclusions and discussion
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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
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A Extra slides
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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
Extra slides
Condition-monitoring methods
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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 loadsService life /
Damage accumulation
Failure model
PrognosticsStructural model
Usage monitoring
Load monitoring
Condition monitoring