Encyclopedia of Structural Health Monitoring || Wind Turbines

Chapter 147Wind Turbines

Goutham R. Kirikera1, Mannur Sundaresan2,Francis Nkrumah2, Gangadhararao Grandhi2, Bashir Ali2,Sai L. Mullapudi3, Vesselin Shanov4 and Mark Schulz3

1 Center for Quality Engineering and Failure Prevention, Northwestern University, Evanston, IL, USA2 Department of Mechanical and Chemical Engineering, North Carolina A&T State University, Greensboro,NC, USA3 Department of Mechanical Engineering, University of Cincinnati, Cincinnati, OH, USA4 Department of Chemical and Materials Engineering, University of Cincinnati, Cincinnati, OH, USA

1 Introduction 12 Methods for Damage Assessment and

Prognosis on Wind Turbines 43 Monitoring a Wind Turbine Blade

During Proof Testing 124 Buckling Health Monitoring Techniques 145 Multistate Continuous Sensors 166 Wireless MEMS Accelerometers for

SHM in Rotating Systems 207 Summary and Conclusions 21

Acknowledgments 21References 21

Encyclopedia of Structural Health Monitoring. Edited byChristian Boller, Fu-Kuo Chang and Yozo Fujino 2009John Wiley & Sons, Ltd. ISBN: 978-0-470-05822-0.

1 INTRODUCTION

With increasing costs associated with fossil-basedenergy, there is large interest in deploying small andlarge wind turbines (WTs) for generating distributedpower for residential and rural areas [1]. It is esti-mated that a small but significant part of the energyneeds of the United States could be obtained throughlarge-scale deployment of WTs [2]. WTs are deployedin rural areas where access to these structures is some-times not feasible because of the elevated positions.Damage to the WT could cause large financial lossesincluding loss of energy tapped during this outage;see Migliore et al. [1]. Structural health monitoring(SHM) of WTs is needed to ensure safety and to avoidoverdesign of components. Overdesign will not allowmaximum power from the wind to be captured. Thisarticle is a review of the current literature describingtechniques for health monitoring of WTs. The mainfocus of the article is on monitoring the turbineblades, which may be the most critical component of

2 Other Applications

the turbine. Continuous health monitoring of the drivetrain (gearbox and bearings) and the wind turbineblade (WTB) will ensure proper performance of theWT. Bently Nevada Inc. has developed a commer-cially available module for continuous monitoring ofthe drive train [3] of WTs. Monitoring bearings andother components may be done using accelerome-ters. Since the frequencies of vibration and operationare low, the data-acquisition problem is simplifiedas a consequence of which allows a larger numberof accelerometers or other sensors to be used. Simi-larly, continuous health monitoring of the WTB isneeded. The design of the WTB is a critical factorin the performance (power production) and relia-bility of WT systems. The trend in blade design istoward improving the aerodynamic efficiency of theblade thus necessitating higher strength-to-weight-ratio materials such as carbon composites owing totheir thinner airfoil profiles. A low-cost continuoushealth monitoring system could provide critical infor-mation about the location and propagation of damagein WTBs, and provide predictive maintenance infor-mation prior to a blade becoming unsafe. If a bladefails, the rotor can become unbalanced and cause theblade to impact the turbine tower, which can severelydamage the turbine drive train. It is common forblades made of composites to have sudden audibleacoustic emissions (AEs) during damage growth. AEis defined as the class of phenomena whereby tran-sient elastic waves are generated by a rapid release ofenergy from a localized source or sources of damage;see Wells et al. [4]. Acoustic emission techniques(AETs) are often used to locate damage and to detectthe growth of cracks during qualification testing ofWTBs. Use of AE and other techniques for SHM ofblades is discussed in this article.

Several researchers have attempted in the past tomonitor WTBs for damage in a laboratory settingusing various techniques. The simplest technique isto use strain gauges to detect high strains that couldindicate damage. In general, too many strain gaugeswould be needed to have a high probability to detectsmall damage before failure could occur. Anotherapproach is to use AE sensors. These are typicallyheavy barrel-type sensors that would be difficult touse in practice owing to the size and requirementthat a large number of sensors and channels of high-rate analog-to-digital (A/D) data conversion would beneeded to monitor the blade. Typically triangulation

is used to locate damage similar to how the epicenterof an earthquake is located. Strain gauges and AEmethods are passive methods wherein no artificialexcitation of the structure is used, and the struc-ture must be operating for damage to be detected.Active methods are another approach for damagedetection. In active methods, a diagnostic wave-form or some other form of artificial excitation isused to probe the structure for damage. Sundaresanet al. [5] used an actuator to pulse the WTB inter-mittently during quasi-static loading and store thereceived waveforms. The current data was comparedwith baseline data to identify damage. Migliore et al.[1], Dutton et al. [6], Joosse et al. [7], and otherresearchers performed testing on WTBs using AETs.AE “threshold-based” arrival systems can be used toapproximately locate damage. An advantage of AEmethods is that knowledge of the wave speed is notrequired to detect damage. Wave speed is a functionof material properties and the geometry of the struc-ture. WTBs consist of multiple anisotropic materialsand have complex geometric features. An advantageof AE methods is that they can detect damage oncomplex anisotropic structures like the WTB shownin Figure 1. On the other hand, the following disad-vantages are inherent in existing conventional AE-based systems: (i) each sensor requires an electricalcircuit containing a preamplifier that increases theoverall mass of the sensor; (ii) each sensor’s outputmust be converted into a digital format using a highsampling rate A/D converter; (iii) a large amountof data is obtained, which requires rapid real-timedata collection, storage, and processing; and (iv) thecost of the overall health monitoring system increasesbecause of the above factors.

With the improvement in the field of microelec-tromechanical systems (MEMSs), the mass of thesensor and associated electronics can be mitigated.Recently, a structural neural system (SNS) has beendeveloped to overcome some of the limitations ofcurrent AETs. The SNS is a signal-processing systemthat emulates how the human body processes signalsfrom large numbers of highly distributed neuronsand receptors (sensors). The SNS uses piezoelectricsensors bonded onto the blade. The surface-mountedsensors are easy to install, and can be repaired,retrofitted, or updated, and the sensor cannot degradethe integrity of the blade. These sensors are highlysensitive and can detect AE stress waves caused by

Wind Turbines 3

Assembly bond #4

Assembly bond #2

Assembly bond #1

Assembly bond #3High pressure skin

Shear web

Unidirectional spar cap

Low pressure skin

Balsa core

Figure 1. Wind turbines in action and cross section of a typical blade. [Reproduced from Ref. 8. Derek Berry, 2004.].

small damage propagating in the blade, as shown byJoosse et al. [7]. The SNS is based on receiving AEsignals and is a passive health monitoring methodsince no artificial means of exciting the structure isused. The frequency of the AE signal that is sensedis on the order of hundreds of kilohertz dependingon the blade material and geometry. The SNS archi-tecture was installed on a 9-m-long WTB that wastested to failure during quasi-static loading. Results ofdetecting and locating growing damage are summa-rized in this article. Information on testing of bladesusing conventional AE methods can be found at theUS National Renewable Energy Laboratory (NREL)web site [2]. Information on testing different sensortypes on WTBs for health monitoring and controlis at present available through Sandia National Labs[8–11]. Nondestructive evaluation (NDE) and SHMof WTs is discussed in [9–36]. WT failures thathave occurred over the past 30 years and safetyconcerns are briefly discussed next to help designersdecide how practical SHM techniques should bedeveloped.

1.1 Wind turbine failures and safety

To make WTs widely accepted, the public mustbe assured that WTs are reliable, safe, and benign[37–40]. This section provides an overview of WTfailures to give designers an idea of how SHMsystems might improve reliability and safety of WTs.WT failures are not well documented and data isnot available for new designs. An unofficial and not

comprehensive summary of WT accidents from 1975to November 30, 2007 is given in [39]. The totalnumber of accidents was 403. These resulted in 49fatalities, of which about 35 involved wind industryworkers. Fourteen accidents were public fatalities.The most common cause of fatalities was falls fromturbines. Human injury occurred in a further 18 acci-dents. Blade failure is the most common failuremode on turbines. A total of 118 separate incidencesof blade failure were reported in the study. Bladefailure may result in whole blades or pieces of bladebeing thrown from the turbine. Fire is the secondmost common accident cause. Fire can arise from anumber of sources including electrical malfunctionand lightning strikes. Turbine fires cannot usually bestopped because of the turbine height, unless there isan onboard fire suppression system. Burning debrisfrom a turbine may be scattered causing a wider-area fire risk. Structural failure is the third mostcommon accident cause as per the data in [12]. Struc-tural failure is assumed to be a major componentfailure usually owing to high wind gust exposure.Ice falling or throwing is another mode of danger.Transport accidents involve turbine sections fallingfrom transporters including at sea. Driver distractionby turbines, thrown ice, and blade pieces landing onthe road have also caused accidents. Some cases ofenvironmental damage including bird and bat deathshave been reported. Other types of damage and acci-dents are owing to component malfunction, hail, andlightning strikes. Some of the possible indirect prob-lems caused by WTs include interference with TV or


microwave reception, depreciating property values,WT noise, increased traffic, road damage, rotatingshadows from the blades, aesthetics, concerns aboutelectrical danger, and increased lightening strikes.

Failures of WT components [36] on a percent basisare electrical control 13%; gearbox 12%; yaw system8%; entire turbine 7%; generator 5%; hydraulic5%; grid 5%; blades 5%; brakes 3%; entire nacelle1%; mechanical control 2%; air brake 2%; axle/bearing 1%; other components 30%; and the tower,foundation, hub and coupling <1%. Large WTsare equipped with a number of safety devices toensure safe operation during their lifetime. Existingsafety devices/approaches include vibration analysis,oil analysis, component temperature measurement,thermographics, shaft alignment, strain measurement,acoustic analysis, photo/thermo elastic analysis,electrical effects, overspeed protection, aerodynamicbraking systems including tip brakes and mechanicalbraking systems, and visual and aural inspection.Difficulties that are to be overcome in order to installdetailed SHM systems on WTs include the following:the requirement that they should last 30 years withoutcausing false positives for failure, the cost to monitordata from the turbine, and the difficulty in servicingthe SHM system. Research in SHM of WTs is beingconducted in national labs [2, 11, 17, 33, 35–38],industry [3, 8, 36, 39], and universities [4, 7, 13,21–28, 31, 34]. In particular, the Danish WT industryis a leader in commercialization. Twenty per cent ofDanish domestic electricity production comes fromwind. Based on the above data, SHM systems couldbe applied to the major areas where failures occur,such as blades and system components, to increasethe safety and reliability of turbines. The SHM systemwould identify degraded parts in time for replacementto prevent failure, and this could prevent injuries thatoccur owing to failure of components or repair offailed turbines. Approaches for SHM of WTBs arediscussed next.

2 METHODS FOR DAMAGEASSESSMENT AND PROGNOSISON WIND TURBINES

This section discusses possible new methods of SHMof WTs [12, 13, 40]. Several different methods areneeded to monitor the entire turbine because of the

variety of mechanical components that are present inthe turbine.

2.1 General methods for SHM of windturbines

There are many methods of SHM being developedfor a large number of applications. General char-acteristics of these methods are presented in orderto help developers consider candidate techniques forspecific WT applications. Monitoring the blades in arotating system presents the problem of data transferfrom the rotating frame to the fixed frame for all themethods discussed. A summary of the main methodsis given in Table 1. It is anticipated that a medleyof sensors and methods will be the best approach tomonitor WTs. A fully integrated SHM system thatcan use different damage detection techniques andtypes of sensors would be the ultimate goal. Recentpapers in the area of SHM (2002-up to the present)are published in Structural Health Monitoring: AnInternational Journal [14]. This journal has a fairlycomprehensive list of techniques for SHM. Otherreferences on SHM are given in [9–11, 15–35] andcan be found in [41–54].

Most of the SHM techniques and sensor typeslisted in Table 1 are discussed in other articles of thisencyclopedia. However, there is not much informa-tion on application of these techniques to SHM ofWTs. Several methods that have been applied to WTSHM include a scanning laser vibrometer [32], thefiber-optic method [33], vibration and other methods[34], and the impedance method [35]. The SNS, inparticular, was developed for SHM of WTs and isnot discussed elsewhere in the encyclopedia. Recentresults are also available using the SNS to monitordamage on a WTB in a proof test. Thus, an overviewof the SNS method and its testing are given in thisarticle.

2.2 Introduction to the acoustic emissiontechnique as an SHM tool

The AET has been used as a nondestructive testing(NDT) technique for several decades and is fairlywell developed with commercially available instru-mentation and standardized testing procedures [15].

Wind Turbines 5

Table 1. General methods for SHM

Method Sensor/actuator type Description of the method

Vibration Accelerometer,piezo, or MEMS

The natural frequencies of the blade can be monitored forchanges indicating damage. Small damage is difficult todetect

Strain Foil strain gauge orfiber-optic cable

Strain can be monitored at critical points in the blade and othercomponents. It is difficult to measure strain inside thecomposite and to have enough gauges to detect small damage.Fiber-optic Bragg gratings can provide a large number oflow-bandwidth strain measurements. Passive method [33]

Ultrasonic wavepropagation

Piezoelectric wafer Good for monitoring uniform sections or hot spots. Needpredamage reference data that may vary owing toenvironmental changes or sensor aging. Can detect damagewhen the blade is operating or not operating. Commercialsystems are well along in development for severalapplications. Active method [12, 34]

Smart paint Piezoelectric orfluorescentparticles

Paint changes color when damaged. Visual technique that is lowcost but not automated for remote applications. Passivemethod

Acousticemissionconventional

AE wideband barrelsensor

Can detect damage in complex structures. Fretting can causefalse indications of damage and the turbine must be operating.Sensors are large and many are required. Fast multiplexing issimplifying the hardware requirements. MEMS AE sensorsare reducing size and weight of the system. Passive method

Structural neuralsystem (SNS)

Piezoelectric wafersor other sensortypes

Overcomes problems of conventional AE by using piezoelectricwafer sensors and biomimetic highly distributed massivelyparallel signal processing. Multistate sensors (nanotubethread, pressure, temperature, etc.) can also be used. Passivemethod. Active SNS has also been tested and uses a simplemethod of neuron firing to detect damage in the passive andactive systems [13]

Impedance Piezoelectric wafer High-frequency method that can detect local damage. Activemethod that uses an impedance analyzer and diagnosticsignal; Inman [34]

Laservibrometry

Scanning laserDopplervibrometer

Difficult to measure the rotating blade which is also changingorientation, and the cost is high. Good for characterizing theblade in the lab. Active or passive

Impedancetomography

Carbon nanotube orother conductivefiller

This method uses carbon nanotubes or other conductiveparticles to make the blade material electrically conductive.The impedance between arrays of electrodes is monitored todetect damage. The size of damage the method can detectdepends on the size of the electrode patterns, and the methodis passive and simple and does not require the structure to beoperating.

Thermography Infrared camera This method uses an infrared camera to map the temperature ofthe structure. Damage can produce a local temperature riseowing to crack and delamination breathing. The method isexpensive and difficult to use on a rotating system and it isdifficult to detect interior damage

Laser ultrasound Laser A laser beam pulse excites the structure and also measures theresponse of the structure. Like the NDE ultrasound techniquebut noncontact. Difficult and expensive to use in the field andminor damage to the surface of a composite may occur bythe laser excitation. Good for automated NDE of metals.

(continued overleaf )


Table 1. (continued )

Method Sensor/actuator type Description of the method

Nanosensors Electronic particle This future approach uses small particles embedded in the bladeduring the fabrication process. The sensors are transceiversthat reflect an RF signal which changes owing to very localdamage [13]. See Nanoengineering of Sensory Materialsfor further information

Nonlinear dyn.,conditionmonitoring,etc.

Accelerometers,strain gauges,others

A wide variety of specialized methods for SHM are describedin [14] by Todd, Adams, Zimmerman, Chang, Farrar, Pai,Peairs and Inman, and many others

Buckling healthmonitoring(BHM)

PZT patches BHM has received little attention in the literature but isimportant for wind turbine blades and other structures such asradomes, civil infrastructure, and tower structures. Somework has been done by Frank Pai using the nonlinear finiteelement code Geometrically Exact Structural Analysis(GESA) and by Sundaresan using wave propagation

The AET relies on the dynamic release of elasticstrain energy as damage grows within materials understress. The released elastic energy propagates throughthe structure in the form of guided waves. AE sensorssuitably located on the structure can detect thesesignals, as shown in Figure 2. The requirement ofloading the structure and the need for the growthof damage for the AET to evaluate the structureseparates this technique from other NDE or SHMtechniques, which frequently do not need the damageto be propagating under load. This very same require-ment also provides the AET the potential to directlyquantify the damage severity possibly in terms ofcrack growth rate or the remaining fatigue life, and

makes it suitable for health monitoring of structuresin the field. However, one of the major shortcom-ings of the traditional AET is its susceptibility tofalse positives. False positives can be triggered by alarge number of extraneous signals including mechan-ically induced noise signals such as friction of matingsurfaces (fretting) and hydraulic noise, as well asradio frequency electrical noise. With the availabilityof new types of sensors including MEMS devices,miniaturized electronics, advanced signal-processingtechniques, and pattern recognition, it is likely thatfalse positives will be reduced significantly.

Health monitoring of WTBs is currently practicedonly during laboratory tests and certification tests

A = 110 mV

Single node continuous sensor–crack signalRT = 4

µs

D = 150µs

Th = 10 mV

0.1

0.05

−0.05

−0.1

0

0 1 2 3 4 5

Figure 2. Conventional acoustic emission parameters extracted from a waveform.

Wind Turbines 7

[7, 16–18]. Such tests have been monitored usingmultichannel AE monitoring systems during repeatedstatic loading to meet the certification requirements aswell as during fatigue loading. Weak blades have beensuccessfully identified by the AET during repeatedramp loading to the normal operational load ofthe blade, or a slightly higher level, followed byconstant load hold periods and subsequent unloading.Composite materials that make up the wind turbineblades are known to be profuse sources of AE eventsand the difficulty of evaluating the blade lies inseparating AE signals that are related to the criticaldamage events from the noncritical damage events.During these blade tests, it is assumed that goodquality blades would emit AE signals during the loadramp-up and remain quiet during the hold period.When AE signals continue during the hold period,it is assumed to be a sign of active damage growthwhen none is expected and hence is an indication ofa weak blade.

In addition to the AET, other techniques thathave been considered for the SHM of WTBs includevibration monitoring using accelerometers and fiber-optic sensors [18]. Since the critical damage sizesin WTBs are relatively large and may introducemeasurable changes in the frequency and modeshapes, accelerometers are considered a viable option.Microbend-type fiber-optic sensors are suggested formonitoring bond lines in WTBs. Remote moni-toring of rotor blades in large offshore WTBs

[18, 33] is considered to be both economical andtechnically feasible, and it is estimated that thecost of the additional expense related to the SHMinstrumentation will be recouped within 3–8 yearsof the operation of the turbine. The large WTs(with capacities approaching 5 MW for a singleturbine) that are being designed now are among thelargest composite structures ever built. The initialinvestment and the reliability requirement of theseblades are sufficiently high to justify the develop-ment and incorporation of SHM capability in theseblades.

The transition of the techniques that have beenused to monitor structural tests in the laboratoryto actual SHM on operating turbines will requireconsiderable effort. Embeddable sensors, instrumen-tation, and information transfer are needed. Thedesign of the SHM system will have to consider theunique requirements of the current and next gener-ation WTs including fabrication techniques (oftenon-site), material failure behavior [18, 34], the loadspectrum, environmental conditions, and economics.Figure 3 shows a schematic of a WTB embeddedwith sensors and electronics that can potentiallymonitor AE signals, vibration characteristics, andblade deformation. Moreover, there is recent interestin using sensors on blades for control purposes.Thus, sensor systems on turbines may serve dualuses, for SHM and also for feedback control of theturbine.

Fiber-opticcables for strainmeasurement

Digitaldata busTBIM

(embedded signalconditioning anddata acquisition/analysis module)

Sensor/transmittersembedded on the skin

of upper and lowersurfaces

Sensor/transmitterson the shear web

Signal, power,and data cable

Figure 3. Schematic illustration of a blade with sensors for SHM under field conditions.


2.3 Damage assessment and prognosisusing the AE method

Newer WTBs are made from several different mate-rials and are tailored to harvest the maximumenergy from the wind through bending-induced twistcoupling and other means. Simultaneous achievementof good dynamic properties, light weight, and struc-tural integrity is a challenge. Both gradual as well assudden changes in the material properties are antic-ipated during the service life. Gradual reduction ofproperties could be owing to normal fatigue, ultra-violet (UV) radiation, hygrothermal effects, erosion,etc., while the sudden changes could be owing tolightning strike or gust loads. In addition, bladefailure could result from local buckling, which is oneof the most common modes of failure. An SHM tech-nique should be able to recognize and address theinteraction between these different causes of damageevolution. In addition, the sensors integrated into theblade could be utilized to assess the initial conditionof the blade after the fabrication and establish thebaseline responses useful for tracking the evolutionof damage in the blade. A combination of sensorscapable of addressing different aspects of damageevolution is likely to be successful. Embedded wire-less resistance strain gauges and accelerometers willbe inexpensive approaches to track the gradual degra-dation of elastic moduli and the blade’s naturalfrequencies. Power harvesting from vibration of theblade is an approach to power the wireless sensors.

The AET offers a real-time SHM capability, whichis particularly useful to identify sudden increasesin the damage growth and hence is likely to beparticularly useful in the prevention of catastrophicfailure. AE-based SHM has the advantage of directly

assessing the damage growth rate unlike othertechniques that attempt to measure the damagesize. Recently, bondable “continuous sensors” withindividual nodes having wideband characteristicsand sensitivities comparable to commercial resonantfrequency AE sensors have been developed. Thiscontinuous sensor and the array sensor with multiplesensor nodes interconnected and with one signaloutput offer a means of simple and inexpensivemonitoring of large WTBs with minimal hardwareand signal processing.

The continuous sensors were shown to performwell in the fatigue life prediction of individualcomposite specimens and for their life extension.This approach of predicting the fatigue life of indi-vidual components has been tested on four differentgroups of specimens with different combinations ofgeometry, material properties, and loading conditions.Figure 4 shows a schematic representation of theservice life of a structural component experiencinga spectrum loading, gradual aging, and encoun-tering adverse events such as impact or lightningstrike. While it is not possible to prevent damageto the blade owing to such unexpected events, it ispossible to assess the combined effect of these factorsand prevent further steep degradation in structuralintegrity by limiting the operational envelope of theturbine.

Figure 5 shows a portion of the life of theturbine simulated using laboratory specimens. Agroup of 30 woven fabric composite tensile spec-imens were subjected to varying levels of impactdamage followed by simulated spectrum loading withload spikes. For these specimens, AE signals werecollected only during the load increment. The speci-mens were subjected to fatigue load with peak stress

Loadspikes

Adverseevents

Materialproperty

Loadspectrum

Time

100

50

0

Nor

mal

ized

leve

l (%

)

Figure 4. Schematic representation of load, adverse events, and aging of wind turbine blades.

Wind Turbines 9

Correlation of AE energyand fatigue life

Life predicted at 50% static loadLine of best fit for initial correlation plot

Actual fatigue life at 40% static load

Line of best fit for actual fatigue lifeat 40% static load

108

106

104

102

100

102 103 104 105 106

No of fatigue cycles to failure

Cum

ulat

ive

AE

ene

rgy

(atto

joul

es)

Figure 5. Correlation between AE prediction and actual fatigue life of woven fabric composite specimens subjected toimpact load. The left dashed line represent the 50% static load case, the right dashed line represents the 40% static loadcase.

at 50% of the average static strength of these speci-mens. As indicated by the circles near the left dashedline in Figure 5, the cumulative AE energy duringthe incremental load correlated well with the subse-quent fatigue durability of these specimens. A subsetof this group was selected to verify if this prognosiscould be used to identify and separate specimens thatare bound to fail prematurely so that they could beused in less demanding services. The predicted livesof these specimens, at the load amplitude employedfor the rest of the group, are shown as black trianglesin Figure 5. To simulate a less demanding servicelife, the fatigue load was reduced from 50 to 40%of the average static strength. The distribution offatigue lives at this reduced load amplitude is shownas circles near the right dashed line in Figure 5. Thereasonable correlation between AE data and extendedfatigue life for these specimens appears to supportboth the validity of AE-based prognosis and theopportunity for optimal utilization of available WTBs.

2.4 The structural neural system

Composite structures deteriorate gradually owingto operational effects and aging, and owing tounpredictable events such as impact and lightning

strikes, and contamination from the environment. Itis important to monitor the condition of large systemsto prevent catastrophic failure owing to the combina-tion of all the deterioration effects. Because multiplephysical states exist in systems, the use of differenttypes of sensors simultaneously may be a more accu-rate way to identify degradation, contamination, anddamage before it reaches a critical size or level.From a practical standpoint, conventional methods ofsensing and signal processing are often too expen-sive, heavy, and complex for comprehensive in situmonitoring of large systems, such as WTs, wheredegradation or chemical contamination (e.g., owing torain erosion, acid rain, water ingestion, UV degrada-tion, and volcanic ash in the air) can occur anywherein the system.

Approaches for structural damage detection thatuse a large number of individually wired sensors, orstoring large sets of predamage data, or using ampli-fiers to generate diagnostic waves, or performingcomplex signal processing may not be feasiblefor WTs. Future sensor architectures ideally wouldproduce information based on ambient conditions,rather than using complex analytical models andpredamage data to diagnose degradation and damage.To develop an efficient health monitoring system,we can gain inspiration from the human neural


system [19, 20]. The human body is composedof complex anisotropic heterogeneous materials. Tomonitor its health, millions of receptors and excitatoryand inhibitory neurons are distributed throughout thebody, which simultaneously process signals in an effi-cient hierarchical way. This section describes how thefunctional capability of the human neural system canbe mimicked using electronic components. Contin-uous sensors attached to a signal processor form aneuron, which is analogous to the biological cell bodyor soma. The processor performs thresholding, firing,and inhibition to measure waves or strains that arecaused by growing or breathing damage in a struc-ture, or the change in impedance of a chemical sensor,or by measuring other physical states of the system.The receptor neurons can be designed to sense manytypes of physical response thus opening the doorfor many important applications on WTs and otherstructures.

2.4.1 Description of the SNS

The SNS is a biomimetic signal-processing system,which uses a multiple state continuous sensor networkfor health monitoring of large composite and metallicstructures and other large systems. Passive sensingbased on AE monitoring is used to detect damage

such as fiber breaking and delamination in compos-ites. Parallel signal processing inspired by the biolog-ical neural system is used to combine continuoussensors in a grid pattern into four channels of dataacquisition to locate damage and capture the sensorsignal. The SNS is a generic biomimetic signal-processing architecture designed to simplify healthmonitoring of large structures. In Figure 6(a), eachvertical line indicates a column neuron and the hori-zontal line represents a row neuron. The neurons arenot connected to each other. Figure 6(b) shows thegeneral architecture of the SNS. The neurons are acontinuous sensor connected to an analog processorat the end [21–28]. A continuous sensor also calleda neuron is a series connection of many individualsensors with only one output signal per neuron. Theoutput of the neurons is represented by V1 throughV20 in Figure 6. V1–V10 are column neurons andV11–V20 are row neurons. Thus, a large sensorgrid is formed wherein a reduction in the requirednumber of channels of data acquisition is achievedby processing the outputs of only the neurons thatproduce anomalous signals, and keeping a track ofwhich neurons are firing using the structural neuralsystem analog processor (SNSAP). The output of thefirst 10 channels (column neurons V1–V10) is theinput to the SNSAP 1, which reduces the required

− −

−

−

−

−

+ +

−+

−

+

−

+

+

+

+

+

− −

−

−

+

−+

+

+

−+

−+

−++

−+

V8 V9 V10

V11

V12

V13

Row continuous sensor V13

Column continuous sensor V9 SNSAP1

SN

SA

P2

T1

T2V1 V10

V11

V20

C1

C2Damage

PanelTwo sensors

(a) (b)

Figure 6. The SNS for WTBs: (a) detail of sensor connections and (b) sensor array.

Wind Turbines 11

number of data-acquisition channels to only two usingfiring of the neurons. One of the channels (C1)predicts the location of damage based on firing of theneurons. An algorithm is used to decode the firingsignals and tell which neurons are firing. The otherchannel (T1) is the actual time response of the neuronand is used to qualitatively predict the severity ofthe damage. The same procedure follows for the rowneurons (V11–V20) using the SNSAP 2 processorand the outputs C2 and T2. Thus, with an arbitrarynumber of sensor nodes, only four channels of dataacquisition are required to predict the location andestimate the severity of the damage within a grid ofsensors. Since each neuron can have 10 (or more)piezoelectric sensor nodes, and there are 20 neuronsin this example, signals from 200 sensors are moni-tored using four channels of data acquisition.

The configuration of the electronic processor usedto mimic the biological neural system is describedin [21–26]. A brief explanation of how the neuronsfire is as follows. When a neuron receives an AEsignal, the signal is evaluated by the analog processor.If the signal meets set frequency and amplitudecharacteristics related to damage, the neuron “fires”a unique location signal and also passes the acousticsignal with those of any other neurons that are firing.A combined AE response analog signal and a secondanalog signal containing information on the locationsof damages are sent to a central PC that performs A/Dconversion and locates the damage and approximatesthe magnitude of the damage. Because of the highcoverage of continuous sensors (neurons) on thestructure, a neuron can be close to any damage site.This will allow the damage signal to be detectedbefore it is attenuated and distorted and this makessignal processing and filtering easier when comparedto using conventional AE sensor systems.

An important application of SHM is detectingcrack initiation in areas of high feature density suchas at joints. It is difficult to place strain gauges or topropagate waves to detect small damage in joints. TheSNS is a simple onboard real-time NDE approach thatlistens for AE signals from damage in joints. There isno other sensor system that enables the simultaneousmonitoring of tens or more long continuous sensorsusing as few as four channels of data acquisition. Incomparison to other techniques, the SNS does notneed an amplifier, diagnostic waves, or individualwires for large numbers of discrete sensors/actuators,

and there is no need for storage of predamagedata. The SNS provides in situ simultaneous sensingand intelligence at the sensor level that reduceshundreds of signals into easily interpretable damageinformation. The SNS is applicable for onboard real-time monitoring of any type of large sensor system inwhich anomalous events must be detected. The SNSis accurate, simple, miniaturized, lightweight, interioror exterior surface mounted, repairable, redundant,passive, requires low power, and is safe. This meetsthe objectives of reliable, accurate, and cost-effectiveoperation. The SNS characterizes the severity of thedamage right from damage initiation and tells if thedamage is serious while the structure is operating.

2.4.2 Damage location algorithm

The neurons that are firing are used to locate damage.A combinatorial algorithm in MATLAB is used todecode which neurons are firing at any time. Becauseof the limited voltage range of the electronics andthe limited number of unique combinations of volt-ages that can be decoded, this approach of identi-fying which neurons are firing is limited to about10 neurons. Thus, a digital approach to locate thefiring neurons is suggested for future work to allowup to hundreds of neurons to operate on a digital databus. Four digital data buses and one power supplywire from a computer are expected to be able totransmit signals from hundreds of neurons in the SNS.This approach can make NDE using the AET prac-tical and can allow monitoring of tens of row andcolumn neurons and hundreds of miniature sensorson a large structure.

2.4.3 Developing the SNS

Initially a two-neuron first prototype of the SNS wasdesigned and built. Results of testing the passiveSNS two-neuron prototype are given in [24]. Subse-quently, an SNSAP was designed with much fasterelectronic components and also with better electronicshielding. Also, to prove that the system is prac-tical on a larger scale, the two-neuron prototypewas extended to a four-neuron prototype. Results oftesting the four-neuron prototype on a thin compositeplate in a laboratory-controlled environment werereported by Kirikera et al. [25]. Kirikera et al. [26]implemented the SNS on a WTB for identifying


the location of propagating cracks during the quasi-static testing. Also, to understand the propagation ofLamb waves on a thin plate, a wave simulation algo-rithm was developed [25, 27]. The wave simulationalgorithm was derived using the modal superposi-tion method and is based on classical thin platetheory. The wave simulation algorithm was extendedto excite a composite structure using an actuator ata specific frequency. Long continuous piezoelectricsensors were recently modeled in the wave simulationalgorithm to study the ability of continuous sensors,which detect acoustic wave propagation [28]. Testingof the SNS is described in the next section.

3 MONITORING A WINDTURBINE BLADE DURINGPROOF TESTING

The SNS was used to identify damage initiation andpropagation on a 9-m-long WTB during a quasi-static proof test to failure at the NREL test facilityin Golden, CO. The spar caps of the blade wereconstructed using a constant thickness variable widthquasi-unidirectional unitary 3WEAVE carbon/Glass

hybrid material [55, 56] developed by 3TEX Inc.The shear web is constructed from fiberglass andbalsa wood. Balsa wood is also used in the leadingand trailing edge and serves as a panel stiffener. Abrief summary of identifying damage on the WTB isdiscussed in this section. For a detailed explanationthe reader is referred to the article by Kirikeraet al. [22, 26].

WTBs are composite structures with complexgeometry and sections that are built of different mate-rials. The 3D structure, large size, anisotropic materialproperties, and the potential for damage to occuranywhere on the blade make damage identificationa significant challenge. During this test, 12 piezo-electric sensors were bonded onto the surface ofthe WTB and were connected to form four contin-uous sensors, which were used in the SNS to iden-tify damage. Although 12 sensors monitored theWTB, the SNS produced only two output signals;the first signal identified and located damage andthe second comprised combined AE waveforms.Figure 7(a) shows the top view of the outline ofthe blade with the locations of three load saddles.The load saddles are used for quasi-static testing.Figure 7(b) is an enlarged view of the sensor area

L1 L2 L3

1

2

3

4 6

5 79

8 10

11

12

V1 V2 V3 V4

(a)

(b) (c)

Figure 7. Experimental setup for the blade and SNS sensors: (a) geometry of the blade; (b) sensor locations on theblade; and (c) test setup with three load saddle. (Figure 7c is courtesy of 3TEX Inc. and the National Renewable EnergyLaboratory).

Wind Turbines 13

on the blade, which is represented by a circle inFigure 7(a). The circled portion has surface-bondedcontinuous sensors (V1–V4). Twelve piezoelectricwafer sensors are connected in series to form fourcontinuous sensors. Signals from these four contin-uous sensors are sent as analog inputs to the SNSAP.These signals are further reduced to two channels (T1and C1). A load profile was applied to the blade usingthe saddles as shown in Figure 7(c). It is noted thatthis blade failed at a load above the design load.

Figure 8(a) shows the load profile applied tothe WTB and also the response of SNSAP (theT1 and C1 channels in Figure 6). Each contin-uous sensor was assigned a unique voltage by theresponse of the C1 channel of SNSAP. A softwarewas used to convert the unique voltages into corre-sponding neuron numbers, as shown on the ordinateof Figure 8(b). Figure 8(b) indicates the times atwhich each neuron received the AE signal.

Based on the neuron that receives the AE, multipledamage zones were manually mapped out as seen inFigure 9(a). At the end of the test, multiple damagezones were identified by the SNS (Figure 9a). Afterthe predictions of the damage zones were madebased on the SNS results, the blade was cut intosections by the NREL engineers to determine theactual locations of damage. The predicted damagezones were compared with three observed damagelocations (Figure 9b) based on cutting the blade intosections. Zones 1A and 1B correspond to damage 1,zones 5, 1A, 1B, and 2 correspond to damage 3,and zones 3A and 3B correspond to damage 2.Zone 4 as predicted by the SNS was not identified

in the visual analysis. On subsequent discussionswith NREL engineers, it was concluded that damageindeed existed at zone 4 and was missed during visualpostfailure observations.

Figure 10 shows the number of AE hits receivedfrom each zone. The number of zones is shown inFigure 9(a). On the basis of the number of AE hits,it is concluded that zone 1A has a large portion ofthe damage compared to zone 1B. Both zones 1Aand 1B correspond to the same damage (damage 1in Figure 9b). Similarly zone 3B has a larger damagethan zone 3A as concluded from the visual postfailureanalysis of Figure 9(b).

Damages 1 and 2 were visible on the surface of theWTB, damage 3 was not visible on the surface of theWTB. The blade was cut open to understand the causeof failure. The primary mode of failure was panelbuckling [29]. The buckling could have started byeither the leading edge of the WTB buckling inwardor the trailing edge buckling outward leading to arotation of the spar cap about its axis effectivelypeeling the spar cap off from the shear web sectionof the WTB. The rotation of the spar cap was likelya secondary mode of failure [29]. The out-of-planemovement of the panel led to catastrophic damage onthe surface of the WTB, Figure 11. The separation ofthe spar cap from the shear web caused a bond linefailure running from 700 to 1650 mm on the WTB,and is shown in Figure 12. Also, a dye penetrantwas used to detect brittle gel coat cracks present onthe surface of the WTB. The dye pattern did notcorrelate with the damage locations. The spar capremained intact indicating that it had performed its

−10−10

−5−5

00

55

1010

Vol

tage

(V

)V

olta

ge (

V)

Load

(lb

s)Lo

ad (

lbs)

0 100 200 300 400 500 60000

20002000

40004000

60006000

80008000Loadprofile

AE waveform

Time (s)

Stableregion

Unstableregion

0

1

2

3

4

100 200 300 400 500 600

Time (s)

Neu

ron

(a) (b)

Figure 8. Test data from the SNSAP: (a) T1 channel of the SNSAP superimposed on the load profile and (b) responseof the C1 channel of SNSAP used to locate the damages.


4 6

54

5

1 3

2 810

97

11

12

V4V3V2V13B

3A

(a) (b)

1 35 7

911

1210

Damage 1

Damage 3

Damage 2

V1 V2 V3 V4

2 4 6 8

1A 1B 2

Figure 9. Comparison of the predicted and actual damage locations on the wind turbine blade: (a) damage locationspredicted by the SNS before sectioning of the blade and (b) damage locations observed by the NREL engineers after thepostfailure sectioning of the blade.

function of being the primary load carrying member,without sustaining catastrophic damage. Also, straingauges were used on the WTB. The strain data didnot indicate the damage progression, but detected thedamage just at the onset of buckling failure. Straingauge locations were not sufficiently close to thefailure region to adequately capture the damage.

The SNS indicated the general area where thedamage started and how the damage progressed,which is valuable information for verifying andimproving the blade design and for verifying themanufacturing procedure. In the future, a grid patternof sensors (neurons) could be attached inside theblade along the shear web to detect damage inside

25

0

20

15

10

5

AE

hits

1A 1B 2 3A 3B 4 5

Zones

Figure 10. The number of AE hits produced from eachzone.

the blade with greater sensitivity. A major outcomeof this testing was to provide confidence that SHMof large anisotropic composite structures that havecomplex geometry and multiple materials is practicalusing a simple, low-cost SNS. This test also showedthat the SNS can detect cracking that precedes buck-ling and may be useful for buckling health monitoring(BHM) of large structures.

4 BUCKLING HEALTHMONITORING TECHNIQUES

WTBs are hollow tubes designed to have bendingtwisting coupling and are prone to local buckling.Uncontrolled local buckling can lead to catastrophicfailure of individual blades and subsequently thewhole turbine. Since buckling initiates as an elasticdeformation, the subsequent damage to the structurecould be entirely prevented if this tendency is identi-fied in real time and countermeasures are taken. Twodifferent approaches have been examined for identi-fying the local buckling in WTBs.

The first approach is to propagate low-frequencyLamb waves across the region that has the tendencyto undergo local buckling, regions A and B inFigure 13(a), as was used during static testing ofa WTB [5]. During this test, a 5-kHz Lamb wavesignal was able to indicate the initiation of bucklingdeformation at 40% length of the blade from the root

Wind Turbines 15

Damage 2

Damage 1

(a) (b)

Figure 11. Postfailure sectioning showing visible damage: (a) “damage 1” on the trailing edge of the WTB and(b) “damage 2” on the leading edge of the WTB. [Pictures courtesy of NREL. Reproduced with permission.]

Balsawoodcore

Fiberglass

Graphite

Crack

Trailingedge

Spar cap

Shear web

Leadingedge

(a) (b)

Figure 12. Cut sections of the WTB (a) indicating bond line failure between the spar cap and shear web and (b) showingthe interior geometry of the blade. [Pictures courtesy of NREL. Reproduced with permission.]

end. The blade failure at about 4500 lbs (2040.82 kg)load, Figure 13(b), was premature and was caused bythis buckling. The initiation of buckling is indicatedby the reduction in amplitude of the received Lambwave signals around 4000 lbs (1814.34 kg) as shownin Figure 13(c).

The second approach is the monitoring of thenatural frequency of the region that has a poten-tial for buckling. This condition was simulated ina 4-in.-wide, 49-in.-long, and 0.125-in.-thick glassfiber epoxy composite strip. Finite element analysisof the vibration characteristics of this bar at variousstages of buckling indicated that easily recognizablechanges in frequency and mode shapes result even in

the early stages of buckling. Experimental measure-ment of the frequency and mode shapes confirmedthe feasibility of this approach. In this experiment,the bar was excited at the third natural frequencycorresponding to the unbuckled configuration usinga five-cycle windowed sine wave. The oscillation inthe unbuckled bar lasted longer than 1 s. The dura-tion of oscillation dropped significantly even in theearly stages of buckling when the deformation wasless than 0.25 in., providing a clear indication ofthe instability. Figure 14(a) shows the experimentalconfiguration and Figure 14(b) shows the differenttime responses of the beam for different stages ofprebuckling.


0.9 m 0.9 m

9 m0.5m

0.5m

72%of length

(Region B)

37.5%of length

(Region A)(a)

(b)

0.8

0.4

0

−0.4

−0.8

Am

plitu

de, V

−0.0005 0.0005 0.00150.0010

Signal R1: 5 kHz 7 cycles

Triggerlocationon R1

Time (s)

(c)

0 lbs.2000 lbs.2000 lbs.Failed

Figure 13. Early detection of local buckling in a WTB using low-frequency Lamb wave signals.

5.875′′ 20.5′′

Fixedclamp

49′′Transmitting

PZTReceiving

PZTMovableclamp

Supporting frame

Bar in the straight position

Bar in the buckled position

0.125′′

2

1

0

−2

−1

0 0.4 0.8 1.2

Vol

tage

(a) (b)

0.00 cm before buckling0.50 cm1.00 cm1.75 cm2.50 cm0.00 cm after buckling

Time (s)

Figure 14. Detection of buckling through changes in natural frequency.

5 MULTISTATE CONTINUOUSSENSORS

This section describes recent advances in contin-uous sensor technology that allows different types of

distributed sensors to be utilized inexpensively, reli-ably, and with low weight for monitoring complexstructures such as WTs. The continuous sensor archi-tecture is for use in an SNS described above. SNS isan electronic sensor network developed using highly

Wind Turbines 17

distributed interconnected continuous sensors coupledto a parallel signal-processing system to enable insitu real-time monitoring of WTBs. The generic SNScan use almost any type of passive sensor. Exam-ples include piezoelectric ceramic wafers to monitorAEs due to crack propagation, and carbon nanotube(CNT) neurons (threads or film) to monitor strains,detect large cracks, and to detect electrolytes relatedto early stage corrosion and hygrothermal degrada-tion. In structural applications, an SNS can detectdamage such as composite fiber breaking and tellwhen impact damage is propagating. SNS is lowcost and simple because no predamage data, actua-tion, or multiplexing is needed. Also, it is relativelyinsensitive to drift in properties of the sensors owingto environmental effects because reference data isnot used. From an operational viewpoint, SNS ismore than a monitoring system—it is an artificialcentral nervous system that monitors the condition ofthe structure without interruption and identifies whenanomalous events and early degradation occur. It alsodefines the operational performance envelope andprovides confidence in terms of safety and reliabilityof the system. In structures, degradation often occursat complex built-up or joined sections where the loadand acoustical paths are complex. SNS is excellentfor use in these areas of high feature density whereother monitoring techniques are impractical to use.Multistate continuous sensors extend the usefulnessof SNS.

This section describes ongoing development ofa suite of multistate continuous sensors, includingMEMSs continuous accelerometers, CNT crack andcorrosion sensors, pressure, temperature, and othertypes of sensors for use in an SNS. It reduces signalsfrom many sensors to a small amount of essential

information that can be equated to degradation ofthe system. Different types of sensors can be usedwith an SNS, but the design of the continuous sensormight differ for these different types of sensors.Moreover, the SNSAP parameters change dependingon the type of continuous sensor. Details of filteringand thresholding also will, in general, be different forevery sensor type. We are presently developing a suiteof multistate continuous sensors for use in an SNS.The goal is to achieve integration of multiple sensortypes into a single signal-processing architecture.

Dual use application of the SHM techniques forWT monitoring and other applications should beconsidered to reduce development cost and to takemaximum advantage of technology. Besides mechan-ical applications such as monitoring WTBs, sensornetworks are becoming increasingly important tosafeguard the environment and homeland, and formedical applications. SNS has potential applicationin many types of sensor systems to simplify thehardware by reducing the number of wires and A/Dconvert boards. SNS is designed to sense anoma-lous values of a particular state variable over a largearea using a continuous sensor. Figure 15 showssome of the individual sensors and sensor materialsthat can be used to form continuous sensors. Thedesign of different multistate sensors for an SNS isdiscussed in the following text. It is also important tomodel the transfer function of the continuous sensorand SNS for new types of sensors. In particular,individual sensor nodes must be integrated properly toform continuous sensors—they cannot be arbitrarilyconnected. The rows overlap the column neurons andare electrically insulated. The size of the cells in thegrid can be small to detect small damage. A descrip-tion of the different sensor types is as follows.

(a) (b) (c) (d) (e) (f)

Figure 15. Sensor elements that can be used to form continuous sensors: (a) nanotube impedance film sensors to detectcorrosion, cracking, and delamination for an SNS; (b) MEMS accelerometer on a circuit board; (c) thermistor that canfrom a continuous temperature sensor; (d) PZT commercial wafers with brass backing used to detect acoustic emissions;(e) strain gauge sensor; and (f) pressure mapping sensor.


1. Carbon nanotube continuous sensorThis sensor is to detect cracks, delamination, andcorrosion damage based on electrical impedance.A prototype CNT-based sensor has been built andcracking and electrolyte representing corrosion havebeen detected based on the impedance of the sensor.CNT film sensors, highly distributed on the surfaceor embedded within a composite, are used to forma continuous sensor for crack and corrosion moni-toring. An example of nanotube neurons on a simplepanel is explained. A highly distributed network ofnanotube continuous sensors, which sense along theirentire length and have a biomimetic architecture,allows coverage of large structures. An example ofthe change in resistance as a crack passes through asingle neuron is shown in Figure 16(a). There is asmall change in resistance as the crack begins prop-agating through the neuron. The change becomeslarger and approaches infinity as the crack passesthrough the neuron. An example of the change incapacitance as electrolyte is put on a single neuronis shown in Figure 16(b). Up to a factor of 50increase in capacitance occurs because of the doublelayer supercapacitance property of nanotubes. Thechange in resistance owing to the electrolyte is only6% as shown in Figure 16(b). Therefore, crack andcorrosion sensing are mostly decoupled and can bemeasured using the same neuron. Signal processingusing the nanotube neurons is greatly simplifiedbecause the electrical properties of the neurons,rather than structural waves, are used to characterizedamage.

A prognostic method for damage modeling canbe developed based on the changes in electricalparameters of the CNT neurons. The modeling ofthe neuron is based on the Randal Warburg circuit.The CNT is a strain sensor (piezoresistive effect)and a highly sensitive corrosion sensor (electro-chemical impedance spectroscopy (EIS) effect). Amodel should be developed to predict the cracklength based on the EIS spectra of the nanotubecomposite. An electrode configuration can also beused on a nanocomposite plate where the nanotubesare dispersed in the polymer matrix. Low-cost carbonnanofibers can replace high-cost CNT for large appli-cations of an SNS.

An important application of the CNT neuronin WTB is health management of polymer matrixcomposites (PMCs). The CNT neuron can be devel-oped for service life monitoring of PMCs usedWTB structural applications. Two of the primarylife-limiting mechanisms in polymer composites arehygrothermal degradation and oxidative degradation.Hygrothermal and oxidative degradation can leadto chemical changes in the resin system causingcracking and embrittlement in the surface layers ofthe composites. Within the oxidized layer of thecomposite, the tensile strength, strain to failure, flex-ural strength, density, and toughness decrease whilethe modulus increases. Surface cracks provide path-ways for the transport of moisture and oxidants tothe fiber/matrix interfaces that act as high-diffusionpaths thereby increasing the degradation rate. TheCNT neuron is a passive continuous sensor that

1.01

1.00

1.02

1.03

1.04

1.05

1.06

Res

ista

nce

char

ge r

atio

Cap

acita

nce

char

ge r

atio

1

20

40

80

0 20 40 60 80 100

Normalized crack progress (%)

Resistance

Capacitance

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

Res

ista

nce

char

ge r

atio

Cap

acita

nce

char

ge r

atio

0.5

0.6

0.7

0.8

0.9

1.0

1.1

0 20 40 60 80 100

Normalized crack progress (%)

ResistanceCapacitance

(a) (b)

Figure 16. Change in capacitance and resistance of a carbon nanofiber neuron owing to simulated damage: (a) crackpropagation damage, the resistance changed from 9.3 k� to infinity, the capacitance changed from 44 pF to zero;(b) electrolyte corrosion damage, the resistance changed from 117 to 123 k�, which is a 5% increase, the capacitancechanged from 19.1 to 1109 pF, a 60 times increase.

Wind Turbines 19

can monitor the electrical impedance of compositematerials to indicate degradation of PMCs usedin WT structures. Future work should investigatethe scientific, technical, and commercial feasibilityof the CNT neuron for PMC health managementincluding monitoring oxidative degradation for aneat resin aged in low temperature and oxidizingenvironments.

2. MEMS capacitive accelerometerAccelerometer evaluation boards from Analog Devi-ces Inc. were used to build a four-element continuoussensor. The accelerometers are two axis each andmeasure in-plane acceleration. The four accelerom-eters were used to form a continuous accelerometerwith four inputs and one output in the x axis anda second continuous accelerometer with four inputsand one output in the y axis. These accelerome-ters work based on the capacitance of a MEMScantilever. The accelerometers required a modifiedcircuit to form the continuous sensor to sum andamplify signals and to prevent low-frequency signalfeed-through. Initial testing was successful, openingup the use of MEMS continuous accelerometersfor vibration measurement, impact detection, andcondition monitoring of bearings, unbalance, anddamage.

3. Thermistor continuous temperature sensorThis sensor is to monitor operating temperaturesbased on resistance of the sensor. Certain types ofdamage may cause an increase in temperature. Thecontinuous temperature sensor may indicate hot spotsin a structure.

4. Piezoelectric Lead Zirconium Titanate (PZT)wafersThese are being tested with a brass backing to senseAEs owing to cracks and delamination in metallicand composite structures. Prototypes were tested inthe lab with good results. This sensor is more ruggedthan PZT wafers without a backing. Studies using thetransfer function voltage output of the sensor dividedby strain input (V0/S) show that a large number ofpiezoelectric sensors will not reduce the quality ofthe data obtained for AE detection. Transfer func-tions must be determined for other sensor types inthe project.

5. Continuous strain gaugeThis sensor can be formed using miniature straingauges in series or parallel. Equivalent electriccircuits can be used to develop a transfer function.The strain gauge continuous sensor will be tested inthe project to monitor large strain simultaneously atmultiple gauges.

6. Continuous pressure sensorThis sensor can be built using a piezoresistive orcapacitive film sensor typically with applications inbiomechanics. The piezoresistive and capacitive pres-sure sensors are easy to incorporate as a continuoussensor. This sensor has many types of commercialapplications.

Other sensor types include an inclinometer, humid-ity sensors, proximity sensor, magnetic sensor, flowsensor, and liquid level sensors. Another class ofsensors is nanowire sensors. A nickel nanowireneuron continuous sensor is expected to be usefulto detect cracks, delamination, and corrosion damageusing electrical impedance or eddy current. TheNi nanowires are being produced at University ofCincinnati. Use of multiple sensor types and fusingdata is possible using SNS and is an area of futurework.

5.1 Manufacturing an SNS

Features of an SNS include the following: (i) itis a passive system; (ii) detects anomalous events;(iii) continuous sensors combine 10 or more sensornodes into a one wire sensor; (iv) highly distributedcontinuous sensors called neurons can detect smalldamage on large structures with complex geom-etry and high feature density; (v) bioinspired parallelsignal processing reduces the number of channelsof data acquisition from tens or hundreds to four;(vi) sensor modules can be developed for differenttypes of sensors (piezoelectric, piezoresistive, ther-mistor, strain, magnetic, etc.); (vii) multistate sensingis possible in one system architecture; (viii) sensingAEs to detect cracking is possible using high-bandwidth data-acquisition boards (up to 1 MHz);(ix) sensing vibration using accelerometers is possibleusing moderate-bandwidth data-acquisition boards(up to 50 kHz); and (x) sensing strain, pressure,temperature is possible using low-bandwidth data-acquisition boards (up to 1 kHz). Overall, SNS is


simple, low cost, redundant, and reliable. Other typesof SHM systems may also have many of the attributesmentioned above. It is suggested that some type ofSHM test bed be developed for WTBs to practicallyevaluate the different techniques for SHM.

A plan for manufacturing SNS should be developedsuch that the SNS is available for prototype testingon operating WTs. To a first-order approximation, thecost of an SNS can be quite reasonable (∼$20 000,including the analog electronics, computer, A/Dboards, and sensors), and the system can be smalland lightweight. Dual use commercialization of SNSshould be possible. In military, the technology canbe applied to aircraft for structural and engine appli-cations, and for exhaust wash structures to monitorhigh-temperature PMCs. For commercial applica-tions, the technology can be applied to civilian andmilitary aircraft and nonaerospace applications suchas bridges, buildings, rocket casings, and any struc-ture with complex geometry that must be monitoredfor degradation in real time.

6 WIRELESS MEMSACCELEROMETERS FOR SHMIN ROTATING SYSTEMS

Existing accelerometers and the data-acquisition sys-tems used for structural analysis and monitoringstudies are often cumbersome to use. Therefore, awireless system using small MEMS accelerometersis being developed. The first stage of this develop-ment has been performed successfully at the Univer-sity of Cincinnati. The accelerometer system was

built by integrating commercial MEMS accelerom-eters (ADXL 278, Analog Devices Inc.) with wire-less sensor technology from MicroStrain Inc. Thewireless measurement system can be installed ona rotating WTB without being tethered to anyexternal devices or computers. The current prototypesystem has eight MEMS accelerometers wired to anA/D converter and wireless transmitter (MicroStrainInc.). The cost is about $3000. This battery-poweredsystem (Figure 17) is in initial testing and continu-ously streams acceleration data wirelessly to a laptopcomputer with a bay station. This wireless systemcannot store much data locally, but seems suitablefor WT use. Battery life is a limitation and powerharvesting from blade vibration may be used to chargethe batteries. SNS could be adapted to the wire-less system.

6.1 Damage detection and fatigue testingof complex joints

Detecting and locating cracks in WT structuralcomponents that have high feature densities is avery challenging problem and is not discussed muchin the literature. In general, few SHM techniqueshave been applied to the monitoring of joints andcomplex structural geometries. However, reliablelow-cost assessment of joints is crucial to main-tain operational availability and productivity, reducemaintenance cost, and prevent catastrophic failureof WTs. SNS and other SHM systems should bedeployed on components or sections of compositestructures and tested together to understand the capa-bilities of the different approaches for SHM. SNS

0 0.5 1 1.5 2−10

−5

0

5

10

Gra

vity

(G

)

(a) (b)

Figure 17. Wireless data-acquisition system with eight MEMS accelerometers: (a) MEMS accelerometer/board, battery-powered wireless module, laptop computer with bay station and (b) one channel response due to a shock input. This systemwas assembled at the University of Cincinnati using commercial components. Continuous accelerometers can be used toallow 32 or more individual MEMS accelerometers to be used with this system.

Wind Turbines 21

could become a standard signal-processing architec-ture for general SHM systems, where reducing thenumber of data-acquisition channels and hardware isof critical importance.

6.2 Resources for SHM

An increasing number of publishers, companies, andresearch organizations are developing and providinginformation, sensors, and measurement systems thatmay be used for SHM including for WTs. A partiallist of resources for SHM components and systems isgiven in [41–54].

7 SUMMARY AND CONCLUSIONS

Different techniques for monitoring wind turbines andparticularly WTBs are discussed. Overall, we can saythat monitoring degradation in WT structures is acomplex problem that might be solved using multipletypes of sensors and different damage detection tech-niques. New signal-processing architectures are alsoneeded that simplify the hardware and reduce thecost of the overall health monitoring system. An SNSwas developed as a simple approach for health moni-toring of WTs. The neural system listens for AEs todetect damage in a WTB. The SNS was tested duringproof testing of WTB and multiple damage zoneswere located. Subsequent postfailure sectioning ofthe failed blade showed close correlation between thepredicted and actual damage locations. An SNS withmultistate continuous sensors is also being developedto measure other variables such as pressure, acceler-ation, and electrical impedance to identify differenttypes of damage. Power harvesting and wireless datatransmission from the rotating blades to the fixedframe are other areas where research is needed. It issuggested that a test bed for WTBs be developed topractically evaluate the different techniques for SHM.

ACKNOWLEDGMENTS

The work related to the SNS was supported by theNREL under subcontract number XCX-2-31214-01.Mr Alan Laxson was the technical monitor of thisproject. Much of the help in making the prototype ofthe SNSAP was provided by engineers from Texas

Instruments and Analog Devices. Also Mr HenryWestheider, Instrumentation specialist, and Mr DougHurd, Machinist, in the Department of MechanicalEngineering at the University of Cincinnati providedvaluable help in building the SNS prototype. Thestatic test of the 3-TEX-100 blade was performed atNREL test facility in Golden, CO. The LABVIEWexpertise provided by Dr Vahan Gevorgian of NRELduring the on-site testing of the SNSAP on theWTB is gratefully appreciated. Mr Derek Berry andDr Mansour Mohamed are the principal technicalleads from TPI Composites and 3 TEX, Inc., respec-tively. Mr Scott Hughes and Mr Jeroen van Dam arethe NREL engineers who assisted during the WTBtesting.

REFERENCES[1] Migliore P, van Dam J, Huskey A. Acoustic Tests of

Small Wind Turbines, AIAA-2004-1185, 1983.

[2] NREL Publications, National Renewable EnergyLaboratory, Wind Technology Center, Golden, CO,http://www.nrel.gov/wind/ (accessed Apr 2008).

[3] Bently Nevada, Product Overview of Wind TurbineCondition Monitoring, http://www.proximityprobes.com/applications/wind.htm (accessed Jun 2006).

[4] Wells R, Hamstad MA, Mukherjee AK. On theorigin of the first peak of acoustic emission in 7075aluminum alloy. Journal of Materials Science 198318:1015–1020.

[5] Sundaresan MJ, Schulz MJ, Ghoshal A. StructuralHealth Monitoring Static Test of a Wind TurbineBlade, NREL/SR-500-28719, Subcontractor report,March 2002.

[6] Dutton AG, et al. Acoustic emission monitoringfrom wind turbine blades undergoing static andfatigue testing. Proceedings of the 15th World Confer-ence on Non-Destructive Testing. Roma, 15–21October 2000, http://www.ndt.net/article/wcndt00/papers/idn553/idn553.htm.

[7] Joosse PA, Blanch MJ, Dutton AG, Kouroussis DA,Philippidis TP, Vionis PS. Acoustic emission moni-toring of small wind turbine blades. Journal of SolarEnergy Engineering, Transactions of the ASME 2002124:446–454.

[8] Berry D. Wind Turbine Blades ManufacturingImprovements and Issues, Feb 2004, Available onlineat http://www.sandia.gov/wind/2004BladeWorkshopPDFs/DerekBerry.pdf.


[9] Sutherland HJ. On the Fatigue Analysis of WindTurbines, Report SAND99-0089. Sandia NationalLaboratory, 1999.

[10] Sutherland HJ, Mandell JF. Effect of mean stress onthe damage of wind turbine blades. Journal of SolarEnergy Engineering 2004 126(4):1041–1049.

[11] Sandia National Laboratory, Wind Energy Group,Annual Workshop Proceedings, http://www.sandia.gov/wind/topical.htm (accessed Apr 2008).

[12] Chang FK (ed). Structural health monitoring. Procee-dings of the Workshop on Structural Health Moni-toring, Stanford University, Stanford, CA. DEStechPublications, 1997, 1999, 2001, 2003, 2005.

[13] Smart Materials Nanotechnology Lab, http://www.min.uc.edu/∼mschulz/smartlab/smartlab.html(accessed Apr 2008).

[14] Structural Health Monitoring: An InternationalJournal 2002.

[15] Miller RK, Hill EVK (eds). Acoustic emissiontesting. Nondestructive Testing Handbook, ThirdEdition. American Society for NondestructiveTesting, 2005; Vol. 6.

[16] Larwood S, Musial W. Comprehensive testing ofNedwind 12-Meter wind turbine blades at NREL.19th American Society of Mechanical Engineers(ASME) Wind Energy Symposium, 2000.

[17] Sorensen BF, et al. Fundamentals for RemoteStructural Health Monitoring of Wind TurbineBlades—A Preproject, Report Riso-R-1336(EN).Riso National Laboratory: Roskilde, May 2002.

[18] Musial W. Energy in the Wind Presentation. NREL,Kidwind Teachers’ Workshop, May 2005.

[19] Harvey RL. Neural Network Principles. PrenticeHall, 1994.

[20] Zigmond MJ, Bloom FE (eds). Fundamental Neuro-science. Academic Press: San Diego, CA, 1999.

[21] Kirikera GR. An Artificial Neural System forStructural Health Monitoring, MS thesis, Universityof Cincinnati, August 2003.

[22] Kirikera GR. A Structural Neural System for HealthMonitoring of Structures, Ph.D., University ofCincinnati, August 2006.

[23] Kirikera GR, et al. Mimicking the biological neuralsystem using active fiber continuous sensors andelectronic logic circuits. SPIE 11th InternationalSymposium, Smart Sensor Technology and Measure-ment Systems. San Diego, CA, 14–18 March 2004;pp. 148–157.

[24] Kirikera GR, Shinde V, Schulz MJ, Ghoshal A,Allemang R, Sundaresan MJ. Damage localization incomposite and metallic structures using a structuralneural system and simulated acoustic emissions.Mechanical Systems and Signal Processing 200721(1):280–297.

[25] Kirikera GR, Shinde V, Schulz MJ, Ghoshal A,Sundaresan MJ, Allemang RJ, Lee JW. A structuralneural system for real time health monitoring ofcomposite materials. Structural Health Monitoring—An International Journal 2008 7(1):65–83.

[26] Kirikera GR, Shinde V, Schulz MJ, Sundaresan MJ,Hughes S, van Dam J, Nkrumah F, Grandhi G,Ghoshal A. Monitoring multi-site damage growthduring quasi-static testing of a wind turbineblade using a structural neural system. StructuralHealth Monitoring—An International Journal 2007(Accepted, In Press).

[27] Ghoshal A, Martin WN, Schulz MJ, Chattopad-hyay A, Prosser WH. Simulation of asymmetricLamb waves for smart sensing and actuationsystems in plates. Shock and Vibration Journal 200512(4):243–271.

[28] Lee JW, Kirikera GR, Kang I, Schulz MJ, Shanov V.Structural health monitoring using continuoussensors and neural network analysis. Smart Materialsand Structures 2006 15:1266–1274.

[29] van Dam J, Hughes S. NWTC Structural TestingTest Report—Static Test of the 3-TEX 9meter Blade,NWTC-ST-3TEX-STA-01-1205, Internal NRELreport, April 2006.

[30] Frank Pai P. SHM and Use of the Nonlinear FiniteElement Code GESA, http://web.missouri.edu/∼umcengrmaeweb/faculty/pai/pai.html (accessed Apr2008).

[31] Ghoshal A, Sundaresan MJ, Schulz MJ, Frank Pai P.Structural health monitoring techniques for windturbine blades. Journal of Wind Engineering andIndustrial Aerodynamics 2000 85(3, 24):309–324.

[32] Insensys, Structural Health Monitoring Using Fiberoptics, http://www.insensys.com/wind downloads.asp (accessed Apr 2008).

[33] Lading L, McGugan M, Sendrup P, Rheinlander J,Rusborg J. Fundamentals for Remote StructuralHealth Monitoring of Wind Turbine Blades—APreproject, Annex B–Sensors and Non-DestructiveTesting Methods for Damage Detection in WindTurbine Blades, Riso-R-1341(EN), Riso NationalLaboratory, Roskilde, May 2002.

[34] Pitchford CW, Grisso BL, Inman DJ. Impedance-based structural health monitoring of wind turbine

Wind Turbines 23

blades. In Proceedings of the SPIE. SPIE, April 2007;Vol. 6532, pp. 65321I.

[35] Drewry MA, Georgiou GA. A review of NDTtechniques for wind turbines. Insight 2007 49(3):137–141.

[36] A Review of NDT Techniques for Wind Turbines,http://en.wikipedia.org/wiki/Wind turbine (accessedMar 2007).

[37] McMillan D, Ault G. Towards Quantification ofCondition Monitoring Benefit for Wind TurbineGenerators. European Wind Energy Conferenceand Exhibition, EWEC 2007, http://ewec2007proceedings.info/index2.php?page=searchresult&tr=3 (accessed Apr 2008).

[38] Risoe National Laboratory, http://www.risoe.dk/(accessed Apr 2008).

[39] The American Wind Energy Association (AWEA)2007. http://www.awea.org/.

[40] Caithness Windfarm Information Forum. http://www.caithnesswindfarms.co.uk/ 2007.

[41] Smart Materials and Structures Journal, http://www.iop.org/EJ/journal/SMS (accessed Apr 2008).

[42] Journal of Intelligent Material Systems and Struc-tures, http://www.sagepub.com/journalsProdDesc.nav?prodId=Journal201582 (accessed Apr 2008).

[43] Acellent Technologies. http://www.acellent.com/2007.

[44] Physical Acoustics Corporation, http://www.pacndt.com/index.aspx?go=research&focus=/capabilities/structural health monitoring.htm (accessed Apr2008).

[45] Advitam. http://www.advitam-group.com/ 2007.

[46] Sawyer T, Aragon G. Structural Health Monitoringis Sensitive Subject, August 2007, http://enr.construction.com/features/transportation/archives/070808c.asp.

[47] ASMOTE. http://www.asmote.com/ 2007.

[48] Motion and Control Group, University of Sheffield,http://www.dynamics.group.shef.ac.uk/health/health.html (accessed Apr 2008).

[49] Ultra Electronics Limited, http://www.ultracontrols.aero/Products/pdf/15 crackdetection.pdf (accessedApr 2008).

[50] Smart Fibers, http://www.smartfibres.com/SHM.htm(accessed Apr 2008).

[51] CrackFirst, http://www.twi.co.uk/j32k/unprotected/band 1/c1352.html (accessed Apr 2008).

[52] Los Alamos National Laboratory, Structural HealthMonitoring, http://www.lanl.gov/damage id/ (acces-sed Apr 2008).

[53] Ben Franklin Center of Excellence in StructuralHealth Monitoring, http://www.esm.psu.edu/wiki/research:cjl9:structural health monitoring (accessedApr 2008).

[54] Center for Intelligent Material Systems and Struc-tures, http://www.cimss.vt.edu/ (accessed Apr 2008).

[55] Mohamed MH, Wetzel KK. 3D woven carbon/glasshybrid spar cap for wind turbine rotor blade,Transactions of the ASME, Journal of Solar EnergyEngineering 2006 128:562–573.

[56] Mohamed MH. Wind Blade Spar Cap and Method ofMaking, USP No. 7,377,752, May 27, 2008.

Documents

Encyclopedia of Structural Health Monitoring || Wind Turbines