Vibration-based Fault Diagnostics of Planetary Gearbox Using Time ...
Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis
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Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis Donatella Zappalà, Peter J. Tavner, Christopher J. Crabtree Durham University, UK Shuangwen Sheng NREL - National Wind Technology Center, Golden, Colorado EWEA 2013 7 th February 2013
description
Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis. Donatella Zappalà, Peter J. Tavner, Christopher J. Crabtree Durham University, UK Shuangwen Sheng NREL - National Wind Technology Center, Golden, Colorado EWEA 2013 7 th February 2013. - PowerPoint PPT Presentation
Transcript of Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis
Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis
Donatella Zappalà, Peter J. Tavner, Christopher J. CrabtreeDurham University, UK
Shuangwen ShengNREL - National Wind Technology Center, Golden, Colorado
EWEA 2013 7th February 2013
Wind Turbine Gearbox Gearboxes fail to meet 20‐year design life
Premature failure increases O&M costs Cost of Energy (CoE)•Turbine downtime•Unplanned maintenance
ONSHORE: gearbox has one of the highest downtimes per failure
OFFSHORE: increased downtime•Complex logistics•Technical repairs•Weather windows Si
emen
s pre
ss p
ictu
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• Timely detection and diagnosis of gear defects essential - Minimise unplanned downtime
• Reliable and cost-effective condition monitoring systems (CMS) - Plan maintenance activities more effectively
- Reduce O&M costs Reduce CoE
• Current vibration-based CMSs mainly use FFT analysis - Large amounts of data - Costly and time-consuming manual analysis - Frequent false alarms
Automation of Condition Monitoring
AUTOMATE data interpretation IMPROVE diagnostic accuracy and reliability
Wind Turbine Condition Monitoring Test Rig
Tests: Gearbox Gear Tooth Damage
Healthy Tooth Missing ToothEarly Stages of Tooth Wear
Investigate the progression of a High Speed Shaft Pinion tooth defect on the gearbox vibration signature at variable-speed and generator load
30kW Gearbox Vibration Signature
110 111 112 113 114 115 116 117 118 119 120 121 1220
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Frequency [Order]
Am
plitu
de [g
P]
Healthy Tooth
Accelerometer
2nd Harmonic of HSS Mesh Frequency (2Xfmesh,HS)
1560 rev/min HSS speed51% maximum generator output
110 111 112 113 114 115 116 117 118 119 120 121 1220
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Frequency [Order]
Am
plitu
de [g
P]
Healthy ToothDamaged Tooth
30kW Gearbox Vibration SignatureAccelerometer
2nd Harmonic of HSS Mesh Frequency (2Xfmesh,HS)
1560 rev/min HSS speed51% maximum generator output
110 111 112 113 114 115 116 117 118 119 120 121 1220
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Frequency [Order]
Am
plitu
de [g
P]
Healthy ToothDamaged ToothMissing Tooth
30kW Gearbox Vibration SignatureAccelerometer
2nd Harmonic of HSS Mesh Frequency (2Xfmesh,HS)
1560 rev/min HSS speed51% maximum generator output
110 111 112 113 114 115 116 117 118 119 120 121 1220
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Frequency [Order]
Am
plitu
de [g
P]
Healthy ToothDamaged ToothMissing Tooth
30kW Gearbox Vibration SignatureAccelerometer
Indication of severe damage on the HS Pinion
Modulation by HSS Speed
Modulation by HSS Speed
2nd Harmonic of HSS Mesh Frequency (2Xfmesh,HS)
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09PS
A [g
P2 ]
SB-5 SB-4 SB-3 SB-2 SB-1 2Xfmesh,HS SB+1 SB+2 SB+3 SB+4 SB+5
Sideband Power Factor algorithm Track the overall power of the spectra 2Xfmesh,HS sideband narrowband
𝑆𝐵𝑃𝐹=𝑃𝑆𝐴 ( 2𝑋 𝑓 h𝑚𝑒𝑠 ,𝐻𝑆 )+ ∑𝑖=−5
+5
𝑃𝑆𝐴 (𝑆𝐵𝑖 )
SBPF vs. Fault Level - 30kW Gearbox
SBPF = 0.0029e0.0433*P
R² = 0.7502
SBPF vs. Fault Level - 30kW Gearbox
SBPF = 0.0029e0.0433*P
R² = 0.7502
SBPF = 0.0057e0.0437*P
R² = 0.8974
SBPF vs. Fault Level - 30kW Gearbox
SBPF = 0.0029e0.0433*P
R² = 0.7502
SBPF = 0.0057e0.0437*P
R² = 0.8974
SBPF = 0.013e0.042*P
R² = 0.8808
Detection Sensitivity - 30kW Gearbox
%𝑆𝐵𝑃𝐹=𝑆𝐵𝑃𝐹 𝑓 −𝑆𝐵𝑃𝐹 h
𝑆𝐵𝑃𝐹 h∗100
Mean %SBPF = 100%
Mean %SBPF = 320%
NREL 750kW Gearbox Data source: Wind Turbine Gearbox Condition Monitoring Round Robin project
Damaged Gearbox:1. Completed dynamometer run-in test2. Field test: experienced two oil losses3. Stopped field test4. Retested in the dynamometer under
controlled conditions
Photo by Lee Jay Fingersh / N
RE
L 16913
Photo by GE
AR
TE
CH
, NR
EL / 19743
HSS Pinion
750kW Gearbox
1140 1170 1200 1230 1260 1290 1320 1350 1380 1410 1440 1470 150015000
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
Frequency [Hz]
Am
plitu
de [g
P]
Healthy Gearbox
• Available dataset: 1800 rev/min HSS speed and 50% rated power• Healthy Gearbox: one FFT spectrum (baseline)• Faulty Gearbox: 10 minutes raw vibration data
750kW Gearbox Vibration Signature
2nd Harmonic of HSS Mesh Frequency (2Xfmesh,HS)
1140 1170 1200 1230 1260 1290 1320 1350 1380 1410 1440 1470 15000
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
Frequency [Hz]
Am
plitu
de [g
P]
Healthy GearboxFaulty Gearbox
• Available dataset: 1800 rev/min HSS speed and 50% rated power• Healthy Gearbox: one FFT spectrum (baseline)• Faulty Gearbox: 10 minutes raw vibration data
750kW Gearbox Vibration Signature
2nd Harmonic of HSS Mesh Frequency
(2Xfmesh,HS)
Photo by GEARTECH, NREL / 19743
1140 1170 1200 1230 1260 1290 1320 1350 1380 1410 1440 1470 15000
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
Frequency [Hz]
Am
plitu
de [g
P]
Healthy GearboxFaulty Gearbox
• Available dataset: 1800 rev/min HSS speed and 50% rated power• Healthy Gearbox: one FFT spectrum (baseline)• Faulty Gearbox: 10 minutes raw vibration data
750kW Gearbox Vibration Signature
Modulation by HSS Speed Modulation by HSS Speed Indication of damage on
the HS Pinion
2nd Harmonic of HSS Mesh Frequency
(2Xfmesh,HS)
Photo by GEARTECH, NREL / 19743
SBPF - 750kW Wind Turbine Gearbox
SBPF - 750kW Wind Turbine Gearbox
Mean SBPF = 0.025 (gP2)
Photo by GEARTECH, NREL / 19743
Detection Sensitivity - 750kW Gearbox
%𝑆𝐵𝑃𝐹=𝑆𝐵𝑃𝐹 𝑓 −𝑆𝐵𝑃𝐹 h
𝑆𝐵𝑃𝐹 h∗100
Mean %SBPF = 1251%
Conclusions • SBPF algorithm proved successful for automatic gear damage detection and
diagnosis within the Durham 30kW test rig gearbox - 100% detection sensitivity for early stages of tooth wear - 320% detection sensitivity for missing tooth
• SBPF successfully tested on NREL 750kW gearbox dataset - 1251% detection sensitivity
• Simple to implement into commercial WT CMSs - low risk of false alarms
• Easily adaptable to all the WT gearbox parallel stages - further investigation needed for planetary stages
• SBPF trends and magnitude thresholds may indicate when a maintenance action needs to be performed
Thank you for your attention
Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis
Donatella Zappalà[email protected]
This work is funded as part of the UK EPSRC Supergen Wind Energy Technologies programme, EP/H018662/1.
