Data driven predictive maintenance strategy - … · Data driven predictive maintenance strategy...

Data driven predictive

maintenance strategy

IEA Wind Task 33 Workshop

Dr Robin Elliott, Senior Consultant

23rd September 2015

© Copyright 2015Slide 2CONFIDENTIAL

Romax Technology

• Gearbox and drivetrain specialists

• Established in 1989

• Approx. 220 employees globally, 120

in UK, 12 offices worldwide

• Work in a range of industries

o Wind energy

o Automotive, heavy industry,

marine, aerospace


Romax Technology

InSight health management solutions, predictive

maintenance software and services, independent engineering, due

diligence

Drivetrain and gearbox design, worldwide no. 1

independent wind turbine gearbox designer; 39 certified gearbox

designs to date

Drivetrain simulation, RomaxWIND virtual product

development environment; dynamic analysis and bearing

simulation

Engineering consultancy and fleet services, inspections,

analysis, certification support, failure investigation


InSight – enabling predictive maintenance


Engineering track record

• Romax has assessed the performance

and health of over 10 GW of wind

turbines across 4 continents

• Over 40% of the UK offshore fleet is

currently monitored by Romax

• Acciona, Bonus, Clipper, De Wind,

Gamesa, GE, Mitsubishi, NEG-Micon,

Nordex, Senvion, Siemens, Sinovel,

Suzlon, Vestas, etc.


Contents

• Combining data sources in a single software platform to enable

predictive maintenance

• Maximising up-front data analysis to aid inspections and

maintenance

• Collecting consistent failure data through standardised

inspection routines and reporting

• Other examples of usage of reliability data


Combining data sources in a single software platform to

enable predictive maintenance


Fleet Monitor software

• Hardware-independent wind turbine monitoring solution


CMS (Bently Nevada,

Commtest, SMP, etc.)

Wind farm 1

(e.g. Siemens, Vestas, etc.)

Romax server

‘Hardware independent’ condition monitoring

Romax monitoring

service

Fleet MonitorTM

software

Wind farm 2 (e.g.

GE, Gamesa, Clipper, etc.)

Database or

site server

Database or

site server

CMS (Gram & Juhl

TCM, B&K Vibro, etc.)


Assessing machinery health using vibration, inspection

and oil analysis

Increasing

vibration

trend triggers

inspectionHigh Fe content

in oil analysis

report

Inspection

report logged in

Fleet Monitor


Maximising up-front data analysis to aid inspections and

maintenance


Up-front data analysis

Determine likely targets

before inspections:

• Detailed SCADA and CMS

analysis

• Detailed review of

previous failure types and

timescales

• Issues often fixed with

gearbox modifications –

which are fitted?


‘Punch list’ for inspections and maintenance


Collecting consistent failure data through standardised

inspection routines and reporting


Collecting consistent failure dataStandardised inspection routines and reporting

At what point has a

component ‘failed’?

• Consistent, objective criteria

• Consistently reported

• Romax have developed an

app for phones / tablets

• Directs and simplifies

inspections


Collecting consistent failure dataStandardised inspection routines and reporting

• Detailed failure data helps

direct inspections

o E.g. if planetary bearings are

prone to cracking, inspect more

of each race

o Spend more time where

necessary

• Results immediately sent back

to Romax

o Serial issues can quickly be

identified


Other examples of usage of reliability data


Failure data example – main bearing failure

• CMS has detected a bearing issue – do I need to replace it? And when?

• Micropitting is often the first step in the failure of the bearing

• The debris generated leads to macropitting and a run-away surface fatigue condition

• Deterioration can be slowed, e.g. by grease flushing



• Typical main bearing failure modes detected by CMS:

Severe outer race macropitting and cracking

Roller macropitting

Severe roller damage Inner race macropitting



• Typical main bearing fault development over a long time period:

First Romax Alarm

8.5 months

Bearing

Replaced

Main

Beari

ng

Healt

h I

nd

ex

Date

• Recording the damage

level over time enables

statistical estimates of

remaining life



• Time from fault detection by CMS to main

bearing failure ranges from ~2 months to

~2 years

• P50 time to failure for this particular

bearing type and failure mode is ~1 year

from fault detection

• Correlation of failure data with CMS data

improves maintenance planning – can

reduce the cost of major replacements!


Failure data example - due diligence

• Turbine technology review

o Reduce uncertainty in availability,

power production, failure rates and

O&M cost forecasts

o Turbine historical performance –

benchmarking against global portfolio

o O&M costs and mitigation strategies

o Reliability forecasts, Weibull analysis

o Review of operational agreements,

warranty risks


Failure data example - due diligence

• Site specific/turbine specific

O&M cost forecasts

• Analysis of cost scenarios –

P90, P50, O&M service

providers, equipment

upgrades, etc.

• Financial model review

• Analysis of power production,

availability and downtime

• Independent Engineering

support for contract

negotiations


Asset life extension

• Life extension for 20+ years

• Evaluating asset’s value

approaching the end of useful life

• Operational expenditures vs. power

generation scenarios

• Life extension strategy

• ‘Repower’ decision support

(expected revenue, operational

statistics)

Loads• Designed/predicted loads

• Measured loads

Simulation• Tuned models to predict

asset’s useful life

Cost impact

calculation

• O&M cost

forecast

• Revenue

forecast


Summary

• Romax is an ISP to turbine manufacturers, wind farm owners, operators

and financial sector

• Good reliability data is essential to reduce CoE through:

oMore robust design

oReduced cost of Operations and Maintenance through predictive methodologies

oBetter service contracts

oReduced insurance premiums

oLife extension

Data driven predictive maintenance strategy - … · Data driven predictive maintenance strategy...

Documents

Transcript of Data driven predictive maintenance strategy - … · Data driven predictive maintenance strategy...