Combined Rotor and Drive Train Monitoring System for Predictive Maintenance Sheduling

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Combined Rotor and Drive Train Monitoring System
for Predictive Maintenance Sheduling

• Phil Rhead
• Business Development Manager

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Offshore Wind- Operation and Maintenance

• Turbines are rapidly increasing in size
• Offshore turbines are getting installed in
  increasing numbers
• Installations are occuring in more complex
  locations
• Deeper, more remote waters
• Cost of inspection and maintenance is high due
  to
• Difficult access
• Remote, weather dependant, big structures
• Time and assets required
• Service teams, vessels

• Reliable condition monitoring systems must play a
  big part of offshore wind Operations and
  Maintenance Strategy to keep costs down

Image courtesy of REpower Systems AG
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Condition Monitoring- As it is today!

• A large number of parameters are monitored on
  modern wind turbines related to turbine health,
• Drive train vibrations
• Bearings, Gearbox, Driveshaft
• Generator oil condition
• Water contamination, particulates
• Pitch motor torques / pressures
• Wind and machine parameters,
• yaw angles, pitch angle, temperature, power

Images courtesy of SKF, ISET, Gram and Juhl
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Condition Monitoring - But !!!!!

• All parameters are measurements of the effect or
  result of degradation or damage!
• None of these provide any specific information
  about reasons for the damage occuring in the
  first place!
• Industrial gearbox / drivetrain loads can be
  characterised by minor speed and load variations
  ideal conditions for a long and trouble free
  life
• Problems with Wind Turbine gearboxes and
  drivetrains are frequently reported!
• The cause must be related to the Wind Input Loads
  and the wind turbine implementation!
• Instantaneous load variations
• Fluctuating load pattern
• Frequent peak loads
• Rotor torque and axial thrust forces
• Dynamic misalignment of drivetrain

Image courtesy of earthscan.co.uk
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Rotor Condition Monitoring - Utilising Blade
Load Data

• Insensys fibre optic sensors are now being fitted
  to production turbines and are accepted as the
  best sensor solution for blade load measurement
  for Independent Pitch Control (IPC) applications

Sensor Arrays installed in the blade ( 4 per
blade)
OEM-1030 Measurement unit located in turbine hub
Optical Interconnection Cables
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Rotor Condition Monitoring - Utilising Blade
Load Data

• Measuring information from the blades can reveal
  a great deal of additional information about the
  performance of the turbine that can not be gained
  from conventional SHM techniques
• Blade Condition
• Rotor Performance
• Drivetrain Loads
• Insensys has developed algorithms to provide
  additional condition monitoring from blade load
  information that is complimentary to existing
  information
• Data can be logged directly or linked to an
  existing Condition Monitoring System enabling
  direct correlation between cause and effect

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Rotor Condition Monitoring - Available
Information From Blades

• Blade parameters include
• Strain
• Bending moments
• Load histories and extreme loads
• Accumulative fatigue and residual lifetime
• Resonant frequencies debond and delamination,
  icing and damage
• Rotor parameters include
• Rotor imbalance
• Detection of fouling
• Rotor speed
• Aerodynamic input power (efficiency and
  comparison between machines)
• Thrust forces on tower
• Drive train parameters include
• Yaw and tilt moments
• Resultant load vector
• Drive torque and impulses

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Rotor Condition Monitoring - Bending Moment
Example

• Large volumes of data collected over the turbines
  life
• The last thing operators need is even more data
  to review and analyse!
• Data is reduced for analysis, display and
  interpretation
• 10 minute blocks of data are processed to a few
  summary values
• Statistical analysis of all raw and derived
  measurements performed
• Max, min and averages

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Rotor Condition Monitoring- Blade Fatigue and
Lifetime Calculation

• Rainflow counting of stress time histories
• Fatigue calculated at 4 locations around the root
  of each blade
• Cumulative fatigue, damage rate and residual
  lifetime are continually updated
• Alarms generated for
• High damage rate events
• Low residual lifetime
• To improve maintenance and inspection scheduling

Edgewise
Flapwise
Combined
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Rotor Condition Monitoring - Rotor Imbalance
Detection

• Blade loads can be resolved to determine
• Drive torque
• Tilt moment
• Yaw moment
• Resultant offset load
• Frequency analysis provides indication of rotor
  imbalance
• Mass imbalance
• (ice or damage)
• Aerodynamic imbalance
• (fouling)

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Rotor Condition Monitoring- Input Power and
Lightning

• Input Power
• Calculated in Insensys instrument from
• Rotor speed and Drive torque
• Combined with generated power can provide
  efficiency of turbine
• Compared with other turbines can identify
  variability in machine performance
• Lightning Strike Detection
• Detect frequency and intensity of strikes
• Benefits include safer turbine operation, lower
  maintenance costs and warranty claim validation

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Rotor Condition Monitoring - SKF Integration

• Load Sensor Installed in blades
• Insensys Measurement Unit installed in Hub
• Load information processed in hub and transferred
  to monitoring unit
• Data transferred to user via web interface

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Rotor Condition Monitoring - SKF Integration

• Insensys and SKF systems retrofitted into an
  existing machine
• Wireless data connection established between
  Insensys and SKF systems
• Total of 32 Rotor parameters logged by SKF
  systems
• Bending moments, fatigue, torques, offset loads
• Full set of 92 parameters logged by Insensys
  system
• Pitch angle, strain data, temperature....
• Realtime condition monitoring via SKF system
• Event driven alarms generated in iMU
• Based on threshold values stored in iMU
• Can correlate blade and rotor loads with changes
  in other turbine parameters in a single piece of
  software

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Rotor Condition Monitoring - Summary

• Insensys fibre optic instrumentation is a proven,
  reliable technology for blade load measurement in
  Wind Turbines
• The benefits of using blade loads sensors for
  turbine control applications are already well
  understood
• Systems are being deployed in commercial turbines
• Significant additional benefit can be achieved by
  monitoring the blades loads and correlating the
  data with the data from the drivetrain monitoring
  system
• Enables structural health of blades, rotor and
  drivetrain to be tracked together
• Direct correlation between the input and output

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Thank you for listening!www.insensys.cominfo_at_in
sensys.comThanks to SKF - Harry Timmermann -
Fredrik Sundquist