why predictive maintenance should be a combined effort

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

1 Why so many predictive maintenance projects fail

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

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

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?

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

4 The Strukton Worksphere case

• 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

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