Big Data in Wind Turbine Condition Monitoring (Prof. Jan Helsen) at the BigDataEurope Workshop, Amsterdam, Novermber 2017

1. Prof Jan Helsen
Vrije Universiteit Brussel/OWI-lab
Visiting Scholar MIT
jan.helsen@owi-lab.be
Leveraging physics with big-data
28.02.17 Amsterdam
www.owi-lab.be

2. § Set-up in 2010 as a new application lab coordinated by Sirris to support wind energy RD&I activities
§ Partnership with the 4 Flemish universities dealing with (offshore) wind energy research:
VUB, UGent, Uantwerpen, KU Leuven
§ Range of unique test & monitoring infrastructures (large climate chamber / measurement equipment /…)
§ Focus on wind energy in harsh environments: offshore wind and cold climates
§ New cluster working (IBN-cluster) since 2017
Introduction OWI-Lab: Belgian RD&I center for wind energy
www.offshoreenergycluster.be
www.owi-lab.be
Cluster & Platform
support – Initiation of RD&I

3. Introduction OWI-Lab: Belgian RD&I center for wind energy
Climatic test lab
= Environmental testing
of wind turbine components
(Offshore) field
testing & measurements
www.offshoreenergycluster.be
www.owi-lab.be
DATA
§ CAPEX
reduction
§ OPEX
reduction
§ RISK
reduction

4. Why big data?
Image: NCTA
• Exponential rise in the number of devices connected to internet
• Wide coverage of high speed internet connections available
• More and more embedded sensors at lower cost
• Different sources of information are becoming available

5. Status
Log
Data
Sensor
Data
Stream
Status
Log
Data
Sensor
Data
Stream
Farm 1
Status
Log
Data
Sensor
Data
Stream
Status
Log
Data
Sensor
Data
Stream
Farm 2

6. Big Data Sources
SCADA
1sec
Status
Codes
Dedicated
Measure-
ments
CMS
SCADA
10min

7. Not just the numbers matter, the conclusion
counts
High Quality
Actionable Data
Data Stream
Data Stream
Data Stream
Data Stream
Data Stream
Data Stream
Data Stream
Data Stream
Analytics

8. Data Driven Design
Validation

9. All Comes Down To
Probability/Risk Of Failure

10. Need for deeper understanding of
critical phenomena
&
Risk Minimization

11. Field-data enabled monitoring & design
Re-design based on what can be learned
from the information acquired during
long-term measurements
• What goes wrong(tonalities, failures)?
• Its consequences
• Its criticality
Design based
on simulation
Model validation
with laboratory
tests
Model validation
with field tests
Re-design based on
long-term
behavior
F. Vanhollebeke. “Dynamic analysis of a wind turbine gearbox: Towards prediction of mechanical
tonalities”. In: (2015). PhD thesis, K.U.Leuven.
Gearbox Picture Reproduced from:

12. Turbine Reliability: Understand the failure
Mode
Grid Loss Event
www.owi-lab.be

13. Unveil The Fingerprint Of Failure/Flaws In Your Design

14. Introducti
on
• Failure modes extracted from failure data:
• Example: NREL Failure database
• HSS Bearings
• WEC failures
• Failure mode not completely understood
• Slip is possible influencer
Context

15. Introducti
on
• Bearing slip:
• Roller slip
• Cage slip
• Widely believed to be playing important role in bearing failure
Torque
RPM
!
Source: Timken
Load Zone
Acceleration +
deceleration
of rollers
Context

16. Introducti
on
• Drivetrain torsional resonance:
• Rotational motion of rotor and generator inertia
• About drivetrain stiffness
Context

17. • 750kW
• Three point mounting
• 1 Planetary Stage
• 2 Helical Stages
• Induction generator
GRC Drivetrain on Dynamometer (DYNO)
Test Object 1: Dyno

18. DYN
O
• Main shaft & HSS torque-bending
• Speed at LSS and HSS
• Strain gauges in HSS meshes
• Strain gauges in HSS TRBs
• Currents-Voltages at generator
Instrumentation
Test Object 1: Dyno

19. • GE 1.5MW
• Three point mounting
• 1 planetary stage
• 2 helical gear stages
• Doubly-fed induction generator
Field Turbine (FIELD)
Test Object 2: Field

20. • Main shaft torque-bending
• Speed at LSS and HSS
• Acceleros spread over gearbox housing
• Currents-Voltages at generator
Torque-RPM Signature
Test Object 2: Field

21. Torque-RPM Signature
Test Object 2: Field

22. Torque-RPM Signature
Test Object 2: Field

23. • Emergency stop pitch rate determines negative torque behavior
Influence Of Pitch
Test Object 2: Field

24. Torque reversals govern dynamic part of the event
Speed increase and decrease due to accelerations and decelerations linked to torque
reversals
Similar frequency content in response
Torque-RPM Signature
Comparison

25. • Gear impacts during torque reversals
HSS Gear Mesh
Test Object 1: Dyno

26. • Similarly constant torque periods à traveling through backlash à impacts present
HSS Gear Mesh
Test Object 2: Field

27. DYN
O
• Significant bending during negative torque periods
• Decreasing effect at higher power levels
HSS Bending
Test Object 1: Dyno

28. DYN
O
• Bearing load distribution during event
• For different power levels
Positive torque
HSS Bearing Forces
Reproduced from GRC instrumentation design
Test Object 1: Dyno

29. DYN
O
• Bearing load distribution during event
• For different power levels
Negative torque
HSS Bearing Slip
Reproduced from GRC instrumentation design
Clear unloading
Test Object 1: Dyno

30. DYN
O
• Frequency domain analysis combined with ordertracking for different power levels: negative torque periods
HSS Bearing Slip
Test Object 1: Dyno

31. • Impacts measured by acc at HSS
• Potentially linked to changing loading
• Conditions in HSS stage during reversal of torque
HSS Axial Impacts
Test Object 2: Field

32. Turbine Reliability: Condition Monitoring
www.owi-lab.be

33. Big Data Analytics
Platform
SCADA
1sec
Status
Codes
Fully Automated CMS: Dataflow

34. 05-12-17 | 34
Streaming data

35. • Spectrogram calculated in
streaming way
• Continuous moving window
• With overlap
• 5120Hz data from drivetrain
Streaming data

36. Streaming data

37. 37
Public domain case
NREL GRC Condition Monitoring Round Robin

38. Machine Learning
Methods
Vibration Analysis
Methods
Bearing Temp
1 year

39. Complex Time-Intensive Calculation
à Run Parallelized in Cloud
BPFO of the planet carrier clearly visible

40. Turbine Reliability: Status Log Analysis
www.owi-lab.be
In collaboration with U Antwerpen ADREM

41. • What do we want to learn?
• What sequence of events lead to failure?
• Comes down to: trigger followed by turbine response actions eventually resulting in failure
• Detect this high level action sequence
External Trigger Turbine Action 1 … Turbine Action n Failure
Find those patterns interesting for failure detection

42. Each of these high-level actions comes down to a sequence of subactions
External Trigger Turbine Action 1 …
Example: Start-up
RPM
Increases
Power
Increases
Blade Pitch
Angle Constant Or
Slightly Changing
Sufficient
Wind Speed
Blade Pitch
Angle Changes
Significantly

43. Transaction
database
Sequence of
items
Sliding Windows
Transform
Frequent Itemset Mining (Eclat)
with high support
…
07:40
+ 60m
08:07
+ 60m
08:10
+ 60m
Frequent Itemsets
…

44. Transaction
database
Select windows
near alarm X
Frequent Itemset Mining (Eclat)
Frequent Itemsets
near alarm X
Projected
Transaction
database

45. • Use Pattern mining techniques to extract these episodes from the data
• Use External Trigger to define search window

46. Turbine Event 1 Turbine Event 2
Turbine Event 1 Turbine Event 2

47. • Association rule best matching domain expert:
• [Extremely High Wind, Speed Alert, ERROR Occurred, Paused State, Attempt to restart,
rpm very low, pitch median, power very low]
• Confidence of approx. 70%
• Sequence of items not considered
• Named “Stop after extremely high wind speed alarm” and added to level 1

48. Conclusions
www.owi-lab.be

49. Conclusions
• Physics-based approaches using big data of added value to design and monitoring
• Condition monitoring on long term data-sets for trend tracking
• Vibration data augmented with temperature analysis
• Status log pattern mining for detecting episodes in turbine event sequences

50. Thank you for your attention
jan.helsen@owi-lab.be
+32 479 85 58 79