Predictive Maintenance why and how - Primavera Project · 2020. 8. 1. · Predictive Maintenance ... process industry costs hundreds of thousands of euros • An hour of downtime

Prof. dr. ir. Tiedo Tinga

Life Cycle Management

[email protected]

Nederlandse Defensie AcademieNetherlands Defence Academy

Predictive Maintenance – why and how ?

Dynamics based Maintenance

[email protected]

utwente.nl/time

PrimaVera colloquium

3-4-2020PrimaVera colloquium

Introduction - Tiedo Tinga

• Education– MSc Applied Physics / Materials Science at Groningen University

– PDEng in Materials Technology at Groningen / Delft University

– PhD Mechanics of Materials at Eindhoven University

• Positions– University of Twente Prof. Dynamics based Maintenance (0.15)

– Netherlands Defence Academy Prof. Life Cycle Management

› Collaborating with RNLN on Smart Maintenance road map

– Past: Scientist at National Aerospace Laboratory NLR

2


Outline

• Basic concepts & motivation

• Why Smart Maintenance ?

• Challenges

• Physics of failure

• Case studies

3

MAINTENANCE BASICS

3-4-2020PrimaVera colloquium4


Maintenance – intro + definition

• Each system will eventually fail– High costs (loss of production, damage, repairs)– System not available (power supply, military)– Safety issues (health, environment)

• Prevent these failures through proper maintenance

• Maintenance

‘the combination of all technical, administrative and managerial actions during the life cycle of an item intended to retain it in, or restore it to, a state in which it can perform the required function’

[European standard EN13306]

5


Costs of Maintenance

6

(Van Dongen, 2011)


Costs of (no) maintenance

• A day of downtime in the process industry costs hundreds of thousands of euros

• An hour of downtime in semiconductor manufacturing costs tens of thousands of euros

• An hour of downtime of a military system costs ???

7


Military systems

Challenging Life Cycle Management– 20-30 yrs in use sustainment costs >

initial investment

– Highly technological and complex

– Variable operational conditions

– High requirements for availability

Requires smart approach to LCM

Maintenance is important

Predictive maintenance even better !

8


Other application fields

9

Maintenance policies

10

PredictiveMaintenance


Condition based maintenance

• Based on Condition Monitoring / SHM– By measuring the condition, the optimal moment for

maintenance can be determined– Requirements

› Measurable degradation› Accurate sensor› Accessibility

• Predictive maintenance– By predicting (calculating) the condition, the optimal moment

for maintenance can be determined– Requirements

› Accurate model› For varying conditions: monitoring of usage / loads

11


Dynamic vs. static

• Static intervals (scheduled)– Fixed during design– Future operational conditions still unknown– Conservative intervals often too short

• Dynamic maintenance (condition-based)– Length of interval set during operational phase– Based on measured condition, usage or loads– Knowledge on failure mechanisms required (quantitative

relation)– Advantages:

› Limited spoiling of remaining life = efficient› Prevention of failures at severe usage = effective

12


Diagnosis vs. Prognosis

• Condition monitoring assess the present condition

• Need for determining moment for maintenance

• Two options:– wait for indication of failure / degradation (diagnostic)

» often based on certain threshold value with safetyfactor

– predict remaining life (prognostic)» from every state prediction of expected maintenance » prediction improves when reaching end of life» based on assumed usage

less risk / better planning !

13


Just-in-time Maintenance

• Health & Condition monitoring Determine actual condition with sensors / measurements condition / performance monitoring

Predictions based on trends / extrapolation

– Reaction time often short P-F interval

– Extrapolation inaccurate at varying usage

+Present condition always accurately known

• Predictive Maintenance & Prognostics Calculation of (remaining) life time based on model or

experience (statistics)

Measured or assumed usage profile required

–Only certainty at failure, before: actual condition unknown

+Varying usage can be accounted for

+Good model enables predictions far into future (planning !)

14


P-F interval

Time

Conditio

n degradation starts

upcoming failure can be detected

(functional) failure actually occurs

P

F

P-F interval

15

3-4-2020

Prognostic approaches

• Experience-based (traditional)– Estimate future usage (OEM)

often conservative

– Collected data

not always available (registration, PM)

– Experience from past

Not always representative

• Model-based– Model of physical failure mechanism

– Input from monitored usage / loads

Always representative, takes large effort

• Data-driven– Derive relations from big data sets (e.g. sensors)

Sometimes unexpected relations, but is black box

not always representative

16 PrimaVera colloquium


WHY SMART MAINTENANCE ?



Changes in Maintenance

• Maintenance was (is) conservative field– Keep using what is working properly (for decades)

– Largely based on experience

• Several changes in last 5-10 years– Importance of maintenance

› Lot of impact on production process / system availability

› Not only costs, can also make money

» Performance based contracts

› Life cycle costs / total cost of ownership

– Technology push

› Sensors

› IoT

› Data

• Awareness that maintenance can / must bedone smarter !

19


The maintenance challenge

• Preventive maintenance length of service

intervals

• Balance between– costs

» spare parts, repairs, man hours

» not too early !

– reliability / availability

» no unexpected failures

» not too late !

• Optimal solution– on-condition maintenance (just-in-time)

– both efficient (costs) and effective (no failures)

20


What is Smart Maintenance ?

• Predictive Maintenance making failures

predictable– Big data, data analytics, sensoring, AI

– Prognostics failure modelling

• But also– Use of 3D printing for spare parts / repairs

– Use of AR / VR for training and support of technicians

– New sensors / monitoring techniques

– Use of apps for failure / maintenance registrations

– Support / automation of Root Cause Analyses

– Optimization of maintenance intervals / inventory levels

– Smart outsourcing / servitization

21

CHALLENGES / EXPERIENCE


PrimaVera colloquium

Challenges in Predictive Maintenance

1. System vs. component level

2. Critical part selection

3. Predictive modelling / prognostics

4. Monitoring / data collection

5. Data analysis

6. Model validation

3-4-202023


1. System vs. component

Diesel engine

• Liner / ring

• Valves

• Bearing

• Many others …

Radar

• PCB’s

• Bearing

• Many others …

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2. Critical part selection

• Creating set of filters to select most critical parts / failure modes / subsets of data

• 3 filters– Quick initial classification (RCM, degrader, 4-quadrant)

– Showstoppers + detailed feasibility

Tiddens, 2017

25


3. Preventive Maintenance approach selection

• Selecting most suitable approach– RCM only supports highest level

• Should fit with ambition level and data availability

Tiddens, 2014-2017

26


Fitting ambition to data (or v.v.)

Tiddens, 2014-2017

A specific system under specified

conditions

A specific system in a specific

environment

An average system in a specific

environmentA generic system or fleet level prediction

Individual system under historically

average conditions

VMechanism-

Based

IIIStressor-

Based

IExperience-

based

IIReliability

Statistics

IIIStressor-

Based

IVDegradation

-Based

VMechanism-

Based

1: High

quality historical

data

x

1: Low

quality historical

data

x

1: High quality

historical data

x

1: High

quality historical

data

x

2Usage

monitoring

3Load

monitoring

4Condition

monitoring

5Health

monitoring

2Usage

monitoring

3Load

monitoring

4Condition

monitoring

5Health

monitoring

OR

OR

AND

OR AND

AND

IIIStressor-

Based

IIReliability

Statistics

AL 5AL 1 AL 2 AL 3 AL 4

27


Decision diagram

• Selecting the proper approach in specific situations– What is requirement / ambition level ?

– Which data / knowledge is available ?

– Is the business case positive ?

Tiddens, 2014 – 2017

Is there a positive business

case (for the selected type)?

Conduct Maintenance

Technique

N

Is expert knowledge available?

Is high-quality historical data available?

Is historical data, usage data and stressor data available?

Is historical data, AND condition or health data

available?

Is usage OR stressor data, AND condition OR health

data available?

What type of data is available?

What type of prognosis is required?

Can you collect this data?

2A. Can you improve with a

lower type?

2B. Is there other data available?

Y

Y

Y

Y

Y

N

N

N

N

N

N

Y

Y

Y

Y

Infeasible

N

N

Reliability Statistics

Experience-based

Stressor-based

Degradation-based

Mechanism-based

Decision Pull

Start

Technology Push

Inidvidual monitoring?

Future conditions similar to current (historical) conditions?

Varying operational conditions?

Y

Usage differences?

N

Y

Y

N

N

N

Y

AL 3

AL 1

AL 2

AL 4

AL 5

28


4. Monitoring / data collection

• Which sensor(s) to use ? At which location ?

• 2 approaches:1. Collect all available data and start analyzing

• Data often appears to be non-specific (e.g. SCADA)

• Often large amounts of data, difficult to analyse

2. Determine which data is relevant and necessary, and then start monitoring

• Amount of data limited, right parameters available

• Only useful data set after certain period of monitoring (~years ?)

• So combination would be optimal– Selection of relevant parameters remains crucial:

› Critical parts

› Root cause analysis failure mechanisms / loads

29


5. Data analysis

• Structured storage of data not always organized

• Making data usable takes a lot of time: accessible, complete, valid, format, etc.

• Also data-driven methods require domain knowledge + sufficient numbers of examples of ‘bad behavior’

combining data / model + CM !

• Data-driven prognostics might be too ambitious (yet), but low-hanging fruit is there:

– diagnostics

– verification of usage / mission profiles

– check on data quality

30


6. Validation of predictions

• Prediction only acceptable / useful after validation

Required:

1. Sufficient number of actual failures • Hard to achieve for critical systems

2. Proper registration of failures • Time / age, failure mode,….

• Registration system not always properly organized

3. Proper registration of usage history• How has this component exactly been used since installation ?

• Requires proper registration, configuration management, …

31


PHYSICS OF FAILURE



Balance

• Load versus load-carrying capacity

Load types

Mechanical

Thermal

Chemical

Electrical

Cosmic

Capacity

Load

Primary load Secondary load

Design

UsageUsage

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Failure mechanisms

• Static overload

• Deformation

• Fatigue

• Creep

• Wear

• Melting

• Thermal degradation

• Electric failures

• Corrosion

• Radiative failures

• Complete overview:

35


Knowledge on failure (mechanisms) can be used …

• before failures occur– Identify critical components FMECA

– Predict time to failure determine optimal maintenance

intervals

– Develop efficient condition monitoring smart sensoring

• after failure has occurred– Why did component fail ?

– How can future failures be prevented ?

– Root Cause Analysis

• when a fraction of a (larger) population has failed– Quantify failure behaviour

– Find Relevant Failure Parameter (RFP)

Application in (smart) maintenance

36


Model-based: relating usage to lifetime

Failure

model

Zoom in to the level of the

physical failure mechanism

UsagePlatform /

systemRemaining life

Local LoadsService life /

Damage accumul.

thermal / fluid /

structural model

Usage monitoring

Load monitoring Condition monitoring

Prognostics

37

CASE STUDIES



• HUMS system available for monitoring– Usage flight hours, landings, conditions, etc.

– Health mainly vibrations

• Maintenance primarely related to flight hours

• Identified critical components (Pareto + CMMS)– Cost drivers

– Availability killers

• Determined failure mechanism + governing loads

NH-90 helicopter prognostics

39

Heerink, 2013


• Landing gear shock absorber is critical

• Time to failure not correlating to FH

• Develop prognostic method

NH-90 helicopter prognostics (2)

40


• Mechanism: wear of seal(oil leakage)

• Relevant Failure Parameter: travelled distance # landings + weight

NH-90 helicopter prognostics (3)

41

i iV k Fs


Prognostics diesel engine parts

42

0

0,002

0,004

0,006

0,008

0,01

0,012

0,014

0,016

Cu

mu

lati

ve W

ear

(mm

)

PS.Cum.W CE.Cum.W

SB.Cum.W SB.Cum.New

0

5E-09

1E-08

1,5E-08

2E-08

2,5E-08

0

2000

4000

6000

8000

10000

Tran

sit

Wea

ther

Har

bo

r

Op

erat

ion

s

Tota

l ho

urs

[h

]

total time PS CE SB SB new

Physical model Actual usage Degradation rates

Amoiralis, Duplex 2015-2017


Service life PCBs in radar system

43

Physical model Actual usage Degradation rates

Politis, Ten Zeldam, 2015-2017


• Many (early) failures in gearbox, shafts, generators

• Have loads been incorporated properly in (standard) design calculations ?

Wind turbine power train

44

Rommel 2018-2020


• Variations in rotor behaviour affect the bearings

Bearing loads and life time

45

Rommel 2018-2020


• Also models for– Connecting coupling (misalignment)

– Gearbox stages (planetary & wheel-pinion)

– Transformers (heat generation, degradation rate)

– Effect of convertors / grid instability (harmonics)

Quantification of loads / life reduction !

• Can be used to– Quantify / compare relaibility of different WTs

– Predict when maintenance is needed

› Now relative, after validation also absolute

› Important for planning / clustering (@sea)

– Improve the design

› Select most robust gears

› No connecting couplings

Complete power train

46


CV-90 dynamic maintenance

• Critical parts selected using ‘degrader analysis’ (FMECA)

– cost drivers / performance killers

track, track pads, engine, thermal camera

• Defines usage profiles– task user

– operational context location

47


CV-90 dynamic maintenance

• Usage specified

• Severity of usage (track pads) Expert opinion !


Calculation for track pads

• Usage parameter: equivalent kilometers

– Usage profile with 800 ‘real’ km

– load factor usage profile = 1432 / 800 = 1.79

Hybrid approach for rail degradation


• Head checks due to rolling contact fatigue

Meghoe, Jamshidi, 2019

Hybrid approach for rail degradation (2)


• Evolution of cracks EC / US measurements


Conclusions

• Large potential in prediction of failures

– Higher availability, lower costs

– Additional benefits in logistic process / safety

• Development and validation of methods requireslarge effort

– Create & validate models

– Component vs system level

– Data – quantity and quality

• Combination(s) of condition monitoring & predictions and data-driven & model-basedapproach is recommended


• Check our publications on – https://www.utwente.nl/en/et/ms3/research-

chairs/dbm/publications/

– https://research.utwente.nl/en/persons/tiedo-tinga

Further reading

53

https://www.utwente.nl/en/et/ms3/research-chairs/dbm/publications/

https://research.utwente.nl/en/persons/tiedo-tinga


Documents

Predictive Maintenance why and how - Primavera Project · 2020. 8. 1. · Predictive Maintenance ... process industry costs hundreds of thousands of euros • An hour of downtime