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Day-to-day condition monitoring for a large fleet of wind turbines

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This webinar describes some of the challenges faced when monitoring a large fleet of wind turbines. Factors such as different turbine and gearbox types, different condition monitoring systems (CMS), geographically dispersed sites and variations in maintenance practice all make the job of a monitoring engineer a difficult task. Romax utilize in-house software called InSight Fleet Monitor to provide condition monitoring services for over 2 GW of assets globally. Using a single software platform enables the CMS engineers to effectively monitor a huge number of wind turbines efficiently.

This webinar uses some recent examples and case studies to demonstrate fleet-wide condition monitoring in practice. Examples focus on main bearing and gearbox fault detection and, most importantly for the operator, methods for predicting the remaining useful life or ‘time to repair’ for key components.

View this webinar to learn:
-How condition monitoring can be effectively rolled out for large, disparate fleets of wind turbines.
-Valuable insights from recent examples in the field, particularly relating to gearbox and main bearing faults.
-Predicting ‘time to repair’ for major components.

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Day-to-day condition monitoring for a large fleet of wind turbines

  1. 1. Day-to-day Condition Monitoring For a Large Fleet of WindTurbines
  2. 2. Before We Start q  This webinar will be available at www.windpowerengineering.com & email q  Q&A at the end of the presentation q  Hashtag for this webinar: #WindWebinar
  3. 3. Moderator Presenters Paul Dvorak Windpower Engineering & Development Sam Wharton Romax Technology John Coultate Romax Technology
  4. 4. Day-to-day condition monitoring for a large fleet of wind turbines Dr John Coultate, Head of Monitoring and O&M Consultancy Dr Samuel Wharton, Condition Monitoring Engineer February 19th 2015
  5. 5. Contents 1.  Introduction to Romax Technology 2.  ‘Condition Monitoring 101’ 3.  Challenges faced monitoring a large fleet of wind turbines 4.  Practical examples - main bearing and gearbox fault detection and workflow
  6. 6. •  Gearbox and drivetrain specialists •  Established in 1989 •  Approx. 250 employees globally, 120 in UK, 12 offices worldwide •  Work in a range of industries o  Automotive, Off-road, Marine, Aerospace o  Wind energy Romax Technology
  7. 7. Track record in condition monitoring •  Romax has assessed the performance and health of over 5GW of wind turbines globally •  Romax provides a monitoring service for turbines worldwide, including over 40% of the UK offshore fleet
  8. 8. Monitoring Service Example Project Centrica •  Three  UK  offshore  wind  farms:  Lincs,  Lynn  and  Inner  Dowsing •  129  x  3.6  MW  turbines •  Vibration  monitoring  service  delivered  by  Romax  using  Fleet   Monitor  software •  Regular  health  assessment  incorporating  SCADA  analysis
  9. 9. InSight
  10. 10. Condition monitoring
  11. 11. Condition monitoring 101 •  Why install CMS? •  The business case is complex with four main sources of return: 1.  Catastrophic failures can be avoided •  CMS catches faults developing and enables more up-tower repairs. •  E.g. High speed shaft and generator bearing faults are reliably detected before a critical failure occurs. Damaged components are replaced up- tower without a large crane. 2.  Crane costs are minimised by combining operations •  Early detection of faults means that crane operations can be combined for multiple turbines rather than reacting to one-off failures.
  12. 12. Condition monitoring 101 •  Why install CMS? 3.  Downtime reduced •  Pro-active maintenance - spare parts and crane are ordered before a failure occurs. 4.  Improved annual energy production •  Early detection using CMS means turbines with faults can be de-rated and run through high wind periods before scheduled repair.
  13. 13. Example return from CMS – main bearing replacement •  Significant benefits to predicting main bearing failure and scheduling multiple simultaneous repairs: Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct •  Main  bearing  fault   detected  on  one  1.5   MW  turbine •  Continue  running   turbine  during  windy   season.   •  Calculate  optimal  de-­‐‑ rating  if  necessary •  Main  bearing   fault  detected   on  another   turbine •  Continue   running  both   turbines  while   repairs  are   scheduled •  Crane  and  spare   parts  are  ordered   for  2x  turbines •  Repair  both   turbines   simultaneously   during  low  wind   season Total cost saving for this single operation ~ $310k on two turbines Main  sources  of  ROI: 1.  Reduced  crane  cost 2.  Reduced  downtime/   increased  power  production
  14. 14. Challenges faced monitoring a large fleet of wind turbines
  15. 15. CMS  (Bently  Nevada,   Commtest,  SMP,  etc.) Wind  farm  1 (e.g.  Siemens,  Vestas,  etc.) Challenge #1 – Too many different type of wind turbine and CMS Wind  farm  2  (e.g.   GE,  Gamesa,  Clipper,  etc.) CMS-­‐‑specific   database  and   software CMS  (Gram  &  Juhl   TCM,  B&K  Vibro,  etc.) CMS-­‐‑specific   database  and   software •  Monitoring engineers can be overwhelmed by different pieces of software and data •  Difficult to make consistent decisions •  Often lots of staff requiredMonitoring   engineer(s)
  16. 16. CMS  (Bently  Nevada,   Commtest,  SMP,  etc.) Wind  farm  1 (e.g.  Siemens,  Vestas,  etc.) Romax  server ‘Hardware independent’ condition monitoring architecture 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.)
  17. 17. Challenge #2 – Keeping track of faults and alarms from 100s/1000s of turbines •  ‘Workflow’ is a major concern. •  Above a certain fleet size, keeping track of faults and alarms is difficult. Too many alarms is a problem. •  Concise routine reports with top findings, red/amber/green classifications and recommendations:
  18. 18. Challenge #3 – Effectively incorporating SCADA analysis and other data •  SCADA analysis is well established for power production reporting; power curve analysis; wind resource assessment, etc. •  SCADA reporting generally well implemented for NERC compliance •  Not well utilized for reliability analysis
  19. 19. Condition monitoring tools •  Good condition monitoring software should be able to: o  Handle data from multiple CMS vendors. o  Easily switch between different configurations (multiple gearbox variants, turbine types, power ratings) o  Provide useful alarms that accurately indicate fault progression. o  Ideally: Be a portal for allowing operators and monitoring engineers to keep track of data from multiple sources to aid fault diagnosis and maintenance planning. Insight  Fleet  MonitorTM  Software
  20. 20. Condition monitoring tools Raw  Vibration  Data Time Processing (FFT..etc) Component  Specific  Alarm Turbine   Drivetrain Operating Conditions   Drivetrain   Info  +   Experience
  21. 21. Condition monitoring tools – Gearbox Information •  Condition monitoring software needs to have built-in information on every gearbox/drivetrain variant operating in the fleet. Gearbox1 Gearbox2 Raw  Vibration  Data Time  (sec) Time  (sec) Frequency  (Hz) Frequency  (Hz) Frequency   Transform   (FFT) Frequency   Transform   (FFT) Frequency  Spectra Gearbox1 frequency  table Gearbox2 frequency  table
  22. 22. Condition monitoring tools – Operating Conditions •  Condition monitoring software needs to have access to the operating conditions of a wind turbine at the point of time the measurement was taken (i.e. Active Power and Shaft Speeds) HSS  Shaft   at  20  RPM HSS  Shaft   at  25  RPM All  power  classes Near  rated  power Peak  Amplitude  Trending Amplitude
  23. 23. •  Fault peak tracking is a very useful technique for detecting the onset of faults, but can often be poor indicators of advanced damage •  In Fleet Monitor we can easily combine multiple measurements and trends into one more powerful indicator of fault progression – The Romax Health Index. Condition monitoring tools- Trending Combine  multiple     parameters   in  Fleet  Monitor Romax   Health Index Peak Amplitude Trending
  24. 24. Condition monitoring tools - Alarm Setting •  Two types of alarm threshold: o  Manual – Alarm thresholds are chosen based on guidelines or experience by monitoring engineer. •  Don’t require historical data •  Sometimes are not very sensitive o  Automatic – Alarm thresholds are set based on a fitted distribution to the data. •  Require historical data •  Can be very sensitive
  25. 25. Condition monitoring tools – SCADA data Vibration SCADA-­‐‑based Temperature Time Fleet  Monitor
  26. 26. Practical examples
  27. 27. CMS case study 1 – main bearing faults •  Typical main bearing failure modes detected by CMS: Severe  outer  race  macropiaing  and  cracking Roller  macropiaing Severe  roller  damage Inner  race  macropiaing
  28. 28. CMS case study 1 – main bearing faults •  Typical main bearing fault development over a long time period: First  Romax  Alarm 8.5  months This  turbine  had  a  damaged  front  main  bearing.  Indentation  marks  recorded  on  rollers  and  inner  race. Bearing  Replaced Main  Bearing  Health  Index Date
  29. 29. CMS case study 1 – main bearing faults •  Main bearing fault that developed in 30 days: This  turbine  had  a  damaged  front  main  bearing.  There  were  indentation  marks  on  the  inner  ring. Romax  Alarm Increased  grease   flushing  regime  to   prolong  life Bearing  Replaced Over  4  months  power   production  after  first   alarm >4  months Main  Bearing  Health  Index
  30. 30. CMS case study 2 – gear tooth faults •  Typical gear failure modes detected by CMS: Root  bending  overstress Tooth  fatigue  crack
  31. 31. CMS case study 2 – gear tooth faultsGear  Health  Index 1st  CMS  Alarm Turbine  Stopped Turbine  started  without  replacement Replacement  of   High  Speed  Shaft 4  Hours 2nd  CMS  Alarm
  32. 32. CMS case study 3 – planetary stage faults •  Typical planetary stage failure modes detected by CMS: Tooth  fatigue  crack Severe  roller  macropiaing Planet  bearing  inner  race   macropiaing
  33. 33. CMS case study 3 – planetary stage faults •  Analysis of historical data: •  Planetary gear stage failed catastrophically •  OEM did not detect the fault •  Romax analysis detected the fault over 3 months before failure Health  index Romax  Alarm  >3  months Turbine  with  2nd  stage   ring  gear  fault Healthy   turbine
  34. 34. CMS software case study – HSS bearing fault •  HSS Bearing Yellow (warning) Alarm triggered by Romax Bearing Health Index trend. Alarm  triggered
  35. 35. CMS software case study – HSS bearing fault •  HSS Bearing Yellow (warning) Alarm triggered by Romax Bearing Health Index trend. •  Alarm is investigated by monitoring engineer using vibration analysis toolbox. •  Clear fault frequencies associated with specific HSS bearing fault. •  Report sent to farm operator recommending inspection and oil sample analysis in next six months plus continued monitoring. Alarm  triggered HSS  Bearing  Fault  Frequency  Harmonic   1 HSS  Bearing  Fault  Frequency  Harmonic   2 Fault  frequencies
  36. 36. CMS software case study – HSS bearing fault •  Health index trend continues to increase. •  Inspection carried out by Romax engineers confirms damage to bearing. •  Oil analysis shows high Fe content. •  Operator keeps track of reports. •  Operator stores oil analysis results. •  Replacement scheduled. High  Fe
  37. 37. CMS software case study – HSS bearing fault •  Bearing health index triggers red (critical) alarm. •  Exception report sent to operator.
  38. 38. CMS software case study – HSS bearing fault •  Bearing health index triggers red (critical) alarm. •  Exception report sent to operator. •  Replacement carried out •  Maintenance record updated in Fleet Monitor. •  Post-replacement Health Index trend drops to new baseline level. •  Alarm threshold to be lowered.
  39. 39. Remaining useful life
  40. 40. 3y+ 2y 1y Event Condition Life  Model Inspect Vibration What is a remaining useful life model?
  41. 41. Predictive life models •  For many years, predictive life models have been used for maintenance scheduling: o  Aerospace; power production; helicopters; etc. •  Some pitfalls to avoid: o  You can’t just simply use a model from a different industry for a wind turbine o  You can’t rely on a computer simulation to mimic complex wind turbine failures •  Romax are pioneering a predictive life model approach for wind turbines.
  42. 42. Life Model Benefits •  Life models allow effective long term maintenance planning by: o  Ranking components for wear levels over time o  Working in conjunction with existing systems and processes (CMS, particle counters, inspections) •  A predictive maintenance strategy can greatly reduce future operating costs
  43. 43. Summary and Conclusions
  44. 44. Summary and conclusions •  Scaling up a condition monitoring operation poses some difficult challenges: o  Hardware independent monitoring o  Building an expert team o  ‘Workflow’ – keeping track of faults and alarms o  Predicting failures •  Romax deliver specialist software and services to solve these problems.
  45. 45. Questions? Paul Dvorak Windpower Engineering & Development pdvorak@wtwhmedia.com Twitter: @windpower_eng Sam Wharton Romax Technology Samuel.wharton@romaxtech.com John Coultate Romax Technology john.coultate@romaxtech.com
  46. 46. Thank You q  This webinar will be available at www.windpowerengineering.com & email q  Tweet with hashtag #WindWebinar q  Connect with Windpower Engineering & Development q  Discuss this on the EngineeringExchange.com

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