Wind turbine blades condition asssment bad on vibration measurements and the level of an empirically decompod feature
Abdelnasr Abouhnik,Alhusin Albarbar ⇑
Advanced Industrial Diagnostics Centre,Faculty of Science &Engineering,Manchester Metropolitan University,Manchester M15GD,United Kingdom
a r t i c l e i n f o Article history:
Available online 24August 2012
Keywords:
Empirically decompod feature intensity level (EDFIL)
Wind turbine vibration Turbine blade crack
Finite element method (FEM)
a b s t r a c t
Vibration bad monitoring techniques are well understood and widely adopted for monitoring the con-dition of rotating machinery.However,in the ca of wind turbines the measured vibration is complex due to the high number of vibration sources and modulation phenomenon.Therefore,extracting condi-tion related information of a specific blade condition is very difficult.
In the work prented in this paper wind turbine vibration sources are outlined and then a three bladed wind turbine vibration was simulated by building its model in the ANSYS finite element progra物理学类
m.Dynamic analysis was performed and the fundamental vibration characteristics were extracted under two healthy blades and one blade with one of four cracks introduced.The cracks were of length (10mm,20mm,30mm and 40mm),all had a consistent 3mm width and 2mm depth.The tests were carried out for three rotation speeds;150,250and 360r/min.
邓婵玉扮演者The effects of the eded faults were revealed by using a novel approach called empirically decompod feature intensity level (EDFIL).The developed EDFIL algorithm is bad on decomposing the measured vibration into its fundamental components and then determines the shaft rotational speed amplitude.A real model of the simulated wind turbine was constructed and the simulation outcomes were com-pared with real-time vibration measurements.The cracks were eded quentially in one of the blades and their prence and verity were determined by decomposing the measured vibration signal into its main components and evaluating the intensity level at the main shaft rotating speed.
The application of the developed monitoring approach on empirical vibration data gave reasonable results and was in good agreement with the simulation predicted levels.
Ó2012Elvier Ltd.All rights rerved.
1.Introduction
Recognition of the need to increa the u of renewable energy sources has recently become widespread due to growing aware-ness of climate change.Among such sources wind energy is gaining importance and it is expected that 10%of world electricity could be supplied by wind power by 2020[1].Hence,availability and reli-ability of wind turbines is esntial.Although damage can occur to any component or part of the wind turbine,the most common type is rotor or blade damage and tower damage.Thus,special attention should be given to the structural health of blades becau they are the elements of a wind power generation system most likely to be damaged and the cost of the blades can account for 15–20%of the total turbine [2].Blade failure can be caud by excessive stress,fatigue,material properties,load and environ-mental factors.
Wind turbines are a rotating machine designed to perform cer-tain functions,so this monitoring technique aims to ensure that it performs the required functions such as generating the power out-put as planned.When there is little knowledge about the types of faults and the time of fault occurrence,no maintenance actions can be planned in advance.With incread knowledge and experience of the nature of the faults,some actions may be organized rou-tinely or prepared well before the occurrence of the fault.
Park et al.[3]have studied a rotating wind turbine blade to ob-tain its vibratory characteristics,they derived a computational algorithm bad on blade stiffness variation due to centrifugal iner-tia forces.Jündert [4]ud two different acoustic techniques for wind turbine blade inspection;local resonance spectroscopy and audible sound.Both methods were found to give information about the internal structure of the area being inspected.Ultrasound has also been ud to check the bonding areas beneath thick glass fiber reinforced plastics (GFRPs)laminates.An ultrasonic air-coupled technique (non-contact and frequency 290kHz)was applied to de-tect two different sizes of internal defects (19mm and 49mm)in the wind turbine blade by Raisutis et al.[5].Acoustic emission has successfully been ud for monitoring damage development and fault location during a full scale test of a 25m long wind turbine blade [6].Sajauskas et al.[7]ud condary longitudinal surface
0196-8904/$-e front matter Ó2012Elvier Ltd.All rights rerved.dx.doi/10.an.2012.06.008
美国常青藤
Corresponding author.
E-mail address:a.albarbar@mmu.ac.uk (A.Albarbar).
acoustic waves(LSAWs II)to detect surface defects on the inacces-sible inner surface of sheet produ
cts and showed that this method was particularly efficient in the investigation of regular shape de-fects(cracks)with predictable orientation.Ghoshal et al.[8]studied four different algorithms for detecting damage on a wind turbine blade bad on the vibration respon of the blade:transmittance function,resonant comparison,operational deflection shape and wave propagation.
优柔寡断的意思Yang et al.[9]stated that they developed a new method to deal with non-stationary and non-linear signals.Empirical mode decomposition(EMD)was ud to analysis the feature intensity le-vel power signals which is measured from the terminals of3-pha wind turbine induction generator.The result showed that the intrinsic mode functions(IMFs)are always able to give an obvious indication of change of machine running condition.In another study Yang et al.[10]ud bivariate empirical mode decomposi-tion(BEMD)to detect both incipient mechanical and electrical faults.The result showed the propod BEMD-bad technique is convenient for processing shaft vibration signals.Ghalib and Albar-bar[11]propod a computational algorithm bad on EMD to de-tect the gear related fault prence within a5.5kW transmission gearbox.The modal characteristics were determined and the vibra-tory characteristics of healthy and faulty gears were investigated through equations of motion,this work shows that the propod method is uful to predict the vibratory behavior of a two stage helical gearbox.Furthermore,the developed EMD bad algorithm
was found more reliable than time–frequency analysis methods.In another recent rearch paper,Abouhnik et al.[12]investigated the possibility of extracting wind turbine blades health related information bad on EMD for a small scale,six bladed wind tur-bines.Abouhnik et al.[13]prented a new and nsitive approach, to detect faults in rotating machines;bad on principal compo-nent techniques and residual matrix analysis(PCRMA)of the vibra-tion measured signals.PCRMA method has been applied to vibration data ts collected from veral kinds of rotating machin-ery using accelerometers.The propod approach successfully dif-ferentiated the signals for healthy and faulty conditions.Abouhnik et al.[14]ud principal components analysis technique(PCA)to reduce noi from original vibration signals and then to study the effect of damage in wind turbine blade by extracting wind tur-bine blades related information for,three bladed wind turbines.
This paper prents a novel method bad on applying EMD to the measured signal and then calculating the intensity level con-tained within certain frequency bands;particularly the rotor fre-quency and its sideband zones.The developed method is supported by simulation study and validated using real-time vibra-tion measurements taken from an operational wind turbine.
2.Wind turbine theoretical simulation
2.1.Wind turbine vibration sources
Understanding the construction and working principles of wind turbines is esntial for designing and implementing effective con-dition monitoring(CM)systems and maintenance strategies.Wind turbines can be affected by the different types of vibration gener-ated by such components as blades,generator,gears,bearings and tower e Fig.1.
Within the wind turbine the drive train will generate significant and sometimes substantial vibration levels.Esntially the drive train consists of the drive,gears,bearings and shafts and the major excitation will take place at the gear mesh frequency,f gm:
f gm¼ZÁN gearð1Þwhere Z is the number of teeth on the gear and N gear is its rotational speed(in Hz).
So-called bearing frequencies,each of which originates from a particular type of bearing vibration,are widely ud for CM of bearings[15].
The generator may be a source of excitation for the drive train at higher frequencies due to:
––the notch passing frequency of the stator in the generator which is normally equal to the number of poles multiplied by the rotational speed of the rotor,and
––controlling the frequency of a generator indirectly coupled to the grid,with possible higher harmonics in the electric voltage.
Vibration due to the rotating blades can be of large magnitude and has been a major cau of failure in wind turbines.Thus there is much rearch effort directed at determining reliable methods for measuring and asssing this vibration[16].The wind turbine blade pass frequency,the frequency at which the blades pass afixed position(BPF)is obviously the number of blades multiplied by the rotational speed:
BPF¼nÁx=60ð2Þwhere n is the speed of rotation of the turbine blades in r/min,and x is the number of blades.
The vibration signal at any measurement point on a wind tur-bine will be a mix of many components at different frequencies, different amplitudes and different phas,thus it needs special techniques to analysis the obtained signal.Becau the vibration signal will vary with time it should be treated as non-stationary. To better understand the sources and nature of the signals a3Dfi-nite element model was created to predict blade vibration signatures.
2.2.FEM modeling and simulation
A model to simulate the wind turbine,described in Section3, was created in the ANSYS program,to simulate the real wind tur-bine ud in the Laboratory.The model simulated healthy blades and a blade suffering from cracks.The cracks were of length (10mm,20mm,30mm and40mm),all had a consistent3mm width and2mm depth.The tests were carried out for three rota-
1 2 4
3 6
年赚百万
of wind turbine:1.blade,2.Bearing,3.
Tower.
A.Abouhnik,A.Albarbar/Energy Conversion and Management64(2012)606–613607
tion speeds;150,250and 360r/min.Vibration signals were ex-tracted and the time waveform of the simulation signal for healthy ca is shown in Fig.2.Each signal for both the healthy and faulty conditions was analyzed using EMD;as explained in Section 6.3.Experimental test
A three bladed wind turbine with 32cm long airfoil was de-signed and manufactured in the Manchest
er Metropolitan Univer-sity,Advanced Industrial Diagnostics Laboratory.The wind turbine compris one stage spur gearbox and 12V DC permanent magnet generator.The experimental work was carried out in the Labora-tory and the wind turbine was placed about 1m in front of the wind tunnel normal to (in line with)the airflow.The accelerome-ters were B&K type 4371with nsitivity of 10mV/g and were mounted on the nacelle of the wind turbine.A B&K type 2635charge amplifier was ud to convert the output of the accelerom-eter to mV.A National Instruments data acquisition card (NI US
B 9233)was connected between a P
C and the charge amplifier to col-lect data,e Fig.3.Fig.4reprents the time waveform of the experimental vibration signal for the healthy ca.Measured vibra-tion signals for both healthy and faulty conditions were analyzed using EMD,as shown in Section 6.
Wind turbine
Wind turbine nacelle
PC for data analysis
turbine monitoring system.
608 A.Abouhnik,A.Albarbar /Energy Conversion and Management 64(2012)606–613
4.Vibration analysis techniques
Vibration analysis is probably the most widely ud CM tech-nology for rotating equipment such as gearboxes,bearings and wind turbines.Features extracted from signals which can无私奉献的
A.Abouhnik,A.Albarbar /Energy Conversion and Management 64(2012)606–613609
nsitively reflect the characteristics of machinery conditions are very much sought after.Statistical methods including Kurtosis, Root Mean Square and Crest Factor are commonly ud to asss the verity of any damage.The parameters were extracted from vibration signals for different conditions and at three different shaft speeds,150r/min,250r/min and360r/min.As can be en from Fig.5,which shows three ts of results for comparison,there arefluctuations both within and between healthy and faulty sig-nals so that the measures cannot be ud to accurately detect changes in wind turbine blade structure and cannot be ud to diagno wind turbine blade defects.Thus the statistical param-eters at least are not suitable for u with non-stationary signals.In order,the faults f1,f2,f3and f4were cracks of lengths10mm, 20mm,30mm and40mm.
The fast Fourier transform(FFT)is a commonly ud technique to transform signals from the time to the frequency domain and is often applied to vibration signals where the signal may be consid-ered stationary,but the FFT is not a uful tool for non-stationary signals.Fig.6shows the results of the transform over the frequency range from DC to20Hz,when the shaft speed was360r/min.How-ever,there were no clear trends in the signals probably becau the frequency content of the signal changes with time and due to the substantial amount of noi prent.
Wind turbines require more advancing monitoring techniques to deal with noi-contaminated and non-stationary signals.In this paper EMD is ud to remove noi and decompo data to extract shaft frequency from the original signals.
欢乐反义词5.Empirical mode decomposition
The EMD is defined by a process called sifting.It decompos a given signal X(t)into afinite t of signals called IMFs,to give K modes d k(t)and a residual term r(t)[17]:
XðtÞ¼
X K
k¼1
d kðtÞþrðtÞ;ð3Þk¼1;2;...;K
The EMD algorithm is summarized by the following steps:
1.Start with the signal d1(t),k=1.Sifting process h j(t)=d k(t),j=0.
2.Identify all local extrema of h j(t).
3.Compute the upper and the lower envelopes(EnvMax and Env-
Min respectively)by cubic spline line interpolation of the max-ima and the minima.
4.Calculate the mean of the lower and upper envelopes:
mðtÞ¼1=2½En v MinðtÞþEn v MaxðtÞ ð4Þ
5.Extract the detail h j+1=h j(t)Àm(t)
6.If h j+1(t)is an IMF,go to step7,el,iterate steps2–5upon the
signal h j+1(t),j=j+1
7.Extract the mode d k(t)=h j+1(t).
8.Calculate the residual.
r kðtÞ¼XðtÞÀd kðtÞð5Þ9.If r k(t)has less than2minima or2extrema,the extraction isfin-
ished r(t)=r k(t),el iterate the algorithm from step1upon the residual r k(t),k=k+1.Fig.7shows theflowchart of the EMD algorithm.
6.The propod method
The propod method starts by measuring the wind turbine na-celle vibration,decompos the measured signal into its fundamen-tal frequency components and calculates the shaft speed signal intensity level.It then compares it with the threshold amplitude of the baline data.It us curvefitting for the FFT spectrum in the re-gion of the shaft frequency and its sideband zones,e Fig.8.
If thefitted curve for the signal is p(f)the feature intensity level (FIL)of the signal in the frequency band from f1to f2,can be ex-presd as:
FIL¼
感谢家长的话Z f2
f1
pðfÞdfð6Þ
where f1and f2are upper and lower frequencies of the frequency band.
610 A.Abouhnik,A.Albarbar/Energy Conversion and Management64(2012)606–613
Digitally using the discrete FFT,the feature intensity level(FIL) of the signal in the frequency band from f1to f2,can be expresd as:
FIL¼
X f1
f21=2½pðf iÞþpðfðiþ1ÞÞ dfð7Þ
where df=f(i+1)Àf i,p(f i)=amplitude of signal at f i and p(f(i+1))=
amplitude of signal at f(i+1).
The integration may be done for the whole range of frequencies
obtained using the FFT.In this study the area the area contained
between adjacent points on the spectral envelope was calculated
using the trapezoidal rule via MATLAB.
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