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Prof Jan Helsen
Vrije Universiteit Brussel/OWI-lab
Visiting Scholar MIT
jan.helsen@owi-lab.be
Leveraging physics with big-...
§ Set-up in 2010 as a new application lab coordinated by Sirris to support wind energy RD&I activities
§ Partnership with ...
Introduction OWI-Lab: Belgian RD&I center for wind energy
Climatic test lab
= Environmental testing
of wind turbine compon...
Why	big	data?
Image:	NCTA
• Exponential	rise	in	the	number	of	devices	connected	to	internet
• Wide	coverage	of	high	speed	...
Status	
Log
Data
Sensor
Data
Stream
Status	
Log
Data
Sensor
Data
Stream
Farm	1
Status	
Log
Data
Sensor
Data
Stream
Status	...
Big	Data	Sources
SCADA
1sec
Status
Codes
Dedicated
Measure-
ments
CMS
SCADA
10min
Not	just	the	numbers	matter,	the	conclusion	
counts
High	Quality	
Actionable	Data
Data	Stream
Data	Stream
Data	Stream
Data...
Data	Driven	Design	
Validation
All	Comes	Down	To	
Probability/Risk	Of	Failure
Need	for	deeper	understanding	of	
critical	phenomena	
&
Risk	Minimization
Field-data	enabled	monitoring	&	design
Re-design	based	on	what	can	be	learned	
from	the	information	acquired	during	
long-...
Turbine Reliability: Understand the failure
Mode
Grid Loss Event
www.owi-lab.be
Unveil	The	Fingerprint	Of	Failure/Flaws	In	Your	Design
Introducti
on
• Failure	modes	extracted	from	failure	data:
• Example:	NREL	Failure	database
• HSS	Bearings
• WEC	failures
...
Introducti
on
• Bearing	slip:
• Roller	slip
• Cage	slip
• Widely	believed	to	be	playing	important	role	in	bearing	failure
...
Introducti
on
• Drivetrain	torsional	resonance:
• Rotational	motion	of	rotor	and	generator	inertia
• About	drivetrain	stif...
• 750kW	
• Three	point	mounting
• 1	Planetary	Stage
• 2	Helical	Stages
• Induction	generator
GRC	Drivetrain	on	Dynamometer...
DYN
O
• Main	shaft	&	HSS	torque-bending
• Speed	at	LSS	and	HSS
• Strain	gauges	in	HSS	meshes
• Strain	gauges	in	HSS	TRBs
•...
• GE	1.5MW
• Three	point	mounting
• 1	planetary	stage
• 2	helical	gear	stages
• Doubly-fed	induction	generator
Field	Turbi...
• Main	shaft	torque-bending
• Speed	at	LSS	and	HSS
• Acceleros spread	over	gearbox	housing
• Currents-Voltages	at	generato...
Torque-RPM	Signature
Test	Object	2:	Field
Torque-RPM	Signature
Test	Object	2:	Field
• Emergency	stop	pitch	rate	determines	negative	torque	behavior	
Influence	Of	Pitch
Test	Object	2:	Field
Torque	reversals	govern	dynamic	part	of	the	event
Speed	increase	and	decrease	due	to	accelerations	and	decelerations	linke...
• Gear	impacts	during	torque	reversals
HSS	Gear	Mesh
Test	Object	1:	Dyno
• Similarly	constant	torque	periods	à traveling	through	backlash	à impacts	present	
HSS	Gear	Mesh
Test	Object	2:	Field
DYN
O
• Significant	bending	during	negative	torque	periods	
• Decreasing	effect	at	higher	power	levels
HSS	Bending
Test	Ob...
DYN
O	
• Bearing	load	distribution	during	event	
• For	different	power	levels
Positive	torque
HSS	Bearing	Forces
Reproduce...
DYN
O	
• Bearing	load	distribution	during	event	
• For	different	power	levels
Negative	torque
HSS	Bearing	Slip
Reproduced ...
DYN
O				
• Frequency	domain	analysis	combined	with	ordertracking for	different	power	levels:	negative	torque	periods
HSS	...
• Impacts	measured	by	acc at	HSS
• Potentially	linked	to	changing	loading	
• Conditions	in	HSS	stage	during	reversal	of	to...
Turbine Reliability: Condition Monitoring
www.owi-lab.be
Big	Data	Analytics	
Platform
SCADA
1sec
Status
Codes
Fully	Automated	CMS:	Dataflow
05-12-17 |	34
Streaming	data
• Spectrogram	calculated	in	
streaming	way	
• Continuous	moving	window
• With	overlap
• 5120Hz	data	from	drivetrain
Stream...
Streaming	data
37
Public	domain	case
NREL	GRC	Condition	Monitoring	Round	Robin
Machine	Learning
Methods
Vibration	Analysis	
Methods
Bearing	Temp
1	year
Complex Time-Intensive Calculation
à Run Parallelized in Cloud
BPFO of the planet carrier clearly visible
Turbine Reliability: Status Log Analysis
www.owi-lab.be
In	collaboration	with	U	Antwerpen ADREM
• What	do	we	want	to	learn?	
• What	sequence	of	events	lead	to	failure?
• Comes	down	to:	trigger	followed	by	turbine	respo...
Each	of	these	high-level	actions	comes	down	to	a	sequence	of	subactions
External	Trigger Turbine	Action	1								…
Example...
Transaction	
database
Sequence	of	
items
Sliding	Windows	
Transform
Frequent	Itemset Mining	(Eclat)	
with	high	support
…
0...
Transaction	
database
Select	windows	
near	alarm	X
Frequent	Itemset Mining	(Eclat)	
Frequent	Itemsets
near	alarm	X
Project...
• Use	Pattern	mining	techniques	to	extract	these	episodes	from	the	data
• Use	External	Trigger	to	define	search	window
Turbine	Event	1 Turbine	Event	2
Turbine	Event	1 Turbine	Event	2
• Association	rule	best	matching	domain	expert:
• [Extremely	High	Wind,	Speed	Alert,	ERROR	Occurred,	Paused	State,	Attempt...
Conclusions
www.owi-lab.be
Conclusions
• Physics-based approaches using big data of added value to design and monitoring
• Condition monitoring on lo...
Thank you for your attention
jan.helsen@owi-lab.be
+32	479	85	58	79
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BDE SC3.3 Workshop - Big Data in Wind Turbine Condition Monitoring

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

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BDE SC3.3 Workshop - Big Data in Wind Turbine Condition Monitoring

  1. 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. 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. 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. 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. 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. 6. Big Data Sources SCADA 1sec Status Codes Dedicated Measure- ments CMS SCADA 10min
  7. 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. 8. Data Driven Design Validation
  9. 9. All Comes Down To Probability/Risk Of Failure
  10. 10. Need for deeper understanding of critical phenomena & Risk Minimization
  11. 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. 12. Turbine Reliability: Understand the failure Mode Grid Loss Event www.owi-lab.be
  13. 13. Unveil The Fingerprint Of Failure/Flaws In Your Design
  14. 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. 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. 16. Introducti on • Drivetrain torsional resonance: • Rotational motion of rotor and generator inertia • About drivetrain stiffness Context
  17. 17. • 750kW • Three point mounting • 1 Planetary Stage • 2 Helical Stages • Induction generator GRC Drivetrain on Dynamometer (DYNO) Test Object 1: Dyno
  18. 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. 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. 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. 21. Torque-RPM Signature Test Object 2: Field
  22. 22. Torque-RPM Signature Test Object 2: Field
  23. 23. • Emergency stop pitch rate determines negative torque behavior Influence Of Pitch Test Object 2: Field
  24. 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. 25. • Gear impacts during torque reversals HSS Gear Mesh Test Object 1: Dyno
  26. 26. • Similarly constant torque periods à traveling through backlash à impacts present HSS Gear Mesh Test Object 2: Field
  27. 27. DYN O • Significant bending during negative torque periods • Decreasing effect at higher power levels HSS Bending Test Object 1: Dyno
  28. 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. 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. 30. DYN O • Frequency domain analysis combined with ordertracking for different power levels: negative torque periods HSS Bearing Slip Test Object 1: Dyno
  31. 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. 32. Turbine Reliability: Condition Monitoring www.owi-lab.be
  33. 33. Big Data Analytics Platform SCADA 1sec Status Codes Fully Automated CMS: Dataflow
  34. 34. 05-12-17 | 34 Streaming data
  35. 35. • Spectrogram calculated in streaming way • Continuous moving window • With overlap • 5120Hz data from drivetrain Streaming data
  36. 36. Streaming data
  37. 37. 37 Public domain case NREL GRC Condition Monitoring Round Robin
  38. 38. Machine Learning Methods Vibration Analysis Methods Bearing Temp 1 year
  39. 39. Complex Time-Intensive Calculation à Run Parallelized in Cloud BPFO of the planet carrier clearly visible
  40. 40. Turbine Reliability: Status Log Analysis www.owi-lab.be In collaboration with U Antwerpen ADREM
  41. 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. 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. 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. 44. Transaction database Select windows near alarm X Frequent Itemset Mining (Eclat) Frequent Itemsets near alarm X Projected Transaction database
  45. 45. • Use Pattern mining techniques to extract these episodes from the data • Use External Trigger to define search window
  46. 46. Turbine Event 1 Turbine Event 2 Turbine Event 1 Turbine Event 2
  47. 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. 48. Conclusions www.owi-lab.be
  49. 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. 50. Thank you for your attention jan.helsen@owi-lab.be +32 479 85 58 79

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