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Flight validation of an embedded structural health monitoring system for an unmanned aerial

vehicle

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2015 Smart Mater. Struct. 24 075022

(http://iopscience.iop.org/0964-1726/24/7/075022)

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Flight validation of an embedded structuralhealth monitoring system for an unmannedaerial vehicle

I Kressel1, B Dorfman1, Y Botsev2, A Handelman2, J Balter1, A C R Pillai3,M H Prasad3, N Gupta4, A M Joseph4, R Sundaram4 and M Tur2

1 IAI Engineering Division, Ben-Gurion International Airport, Israel2 School of Electrical Engineering, Tel-Aviv University, Tel-Aviv, Israel3 Aeronautical Development Establishment, Bangalore, India4National Aerospace Laboratories, Bangalore, India

E-mail: ikressel@iai.co.il

Received 1 December 2014, revised 30 March 2015Accepted for publication 21 April 2015Published 11 June 2015

AbstractThis paper presents the design and flight validation of an embedded fiber Bragg gratings (FBG)based structural health monitoring (SHM) system for the Indian unmanned aerial vehicle (UAV),Nishant. The embedding of the sensors was integrated with the manufacturing process, takinginto account the trimming of parts and assembly considerations. Reliable flight data wererecorded on board the vehicle and analyzed so that deviations from normal structural behaviorscould be identified, evaluated and tracked. Based on the data obtained, it was possible to trackboth the loads and vibration signatures by direct sensors’ cross correlation using principalcomponent analysis (PCA) and artificial neural networks (ANNs). Sensor placement combinedwith proper ground calibration, enabled the distinction between strain and temperature readings.The start of a minor local structural temporary instability was identified during landing, provingthe value of such continuous structural airworthy assessment for UAV structures.

Keywords: fiber Bragg grating, smart structure, structural health monitoring

(Some figures may appear in colour only in the online journal)

1. Introduction

The increasing value of modern unmanned aerial vehicles(UAVs), together with their high usage at extreme environ-mental conditions of temperature and humidity, demand thecontinuous monitoring of their structural airworthiness overtheir life span. Reliability, safety and the economic implica-tions of maintaining an aging UAV fleet, subject to cyclicloading, corrosion, wear and material degradation are theprime concerns of both the operator and the UAV manu-facturer due to their logistics implications [1].

In recent years, in order to ensure safe operation ofUAVs, especially over populated areas, the industry hasmoved towards establishing airworthiness requirements likethe STANAG 4671 [2], based on commercial manned aircraftairworthiness regulations.

An attractive option towards maintaining structuralintegrity is the utilization of structural health monitoring(SHM). SHM aims towards the autonomous real-time struc-tural airworthy assessment of the individual vehicle [1],alerting for maintenance actions only when needed. It isexpected that when fully developed, SHM will qualify as oneof the ‘repeatable and reliable non-destructive inspectiontechniques’, stipulated in the aforementioned regulations [2].

SHM has a unique benefit for UAVs since conventionalperiodic inspection methods, requiring direct access to all ofthe UAVs critical structural components, are hindered bylimited accessibility and the need for highly trained, i.e. costlytechnicians, not always available at remote sites. It shouldalso be noted that UAVs do not have a human pilot, whoprofessionally acts as a full time highly sophisticated sensor,capable of tracking/reporting unusual behaviors caused by

Smart Materials and Structures

Smart Mater. Struct. 24 (2015) 075022 (9pp) doi:10.1088/0964-1726/24/7/075022

0964-1726/15/075022+09$33.00 © 2015 IOP Publishing Ltd Printed in the UK1

wear, fatigue, bird or hail impact, lightning strike etc [1]. Inprinciple, SHM can fill this gap, as well.

The concept of SHM has been discussed for many years[3–7]. The summary of early health monitoring literature [4],as well as recent applications [8] indicate that a multi-disciplinary effort should be directed to solve issues in sensorreliability, and data processing for damage diagnostics andprognostics.

Among the available SHM technologies, the use of fiber-optic sensing techniques, and in particular the use of multi-plexed fiber Bragg gratings (FBGs) [8] appear quite attractive.Due to their small diameters optical fibers can be easilyembedded within composite materials, currently having aubiquitous presence in modern UAVs. Alternatively, opticalfibers can be externally attached to either metal or compositestructures. They are flexible, passive, quite tolerant to envir-onmental conditions, and insensitive to electromagnetic dis-turbances. Quite a few sensors can be multiplexed on a singlefiber strand, and the technology, including the interrogators, isalready matured enough, commercially available and well-known to be highly reliable [9–11].

The ability to track both composite and metal structuresby optical sensors has been already successfully demonstrated[e.g. 12–20]. For in-flight applications this was done bysurface bonded sensors [16]. The application of FBG sensorsfor UAV leading edges for bird strike damage assessment wasalso tested on the ground [14]. In-flight measurements usingsurface bonded FBG sensors were also successfully applied toUAV wing shape sensing [15]. Health and fire monitoringusing an FBG sensor net was also tested on a compositepersonal aircraft [16]. Damage detection using distributedstrain sensing was also tested using an embedded opticalfiber [17].

This work presents a complete, airworthy, SHM systemthat was designed to be permanently embedded and integratedin the Indian Nishant UAV for its whole life span, taking intoaccount manufacturing and assembly processes, combinedwith handling requirements. This airworthy system is basedon an array of FBG sensors, embedded in the two compositetail booms of the Nishant during manufacturing, and an FBGmulti-channel interrogator, which was qualified to meet theextreme environmental conditions associated with the catapultlaunch and parachute landing of this UAV. The FBG sensornet was tailored to monitor critical locations along the tailbooms, based on a detailed finite element analysis. Thematurity of FBG technology in terms of both sensor manu-facturing and interrogation instrumentation enables the lightweight design of such a system, which is a critical issue inUAV systems. Previous conference papers [18, 19] havedescribed ground testing of the system under static anddynamic loadings, as well as the accompanying strain-to-loadcalibration procedures. Structural characteristics like straindistribution under static loading, impact response, and normalmodes were all successfully monitored by the system. Thehigh signal to noise ratio of the optical sensors also enabledtracking of the boom’s vibration signature during groundengine runs. As a final and definitive proof of the concept, thesystem, including the FBG multi-channel interrogator and

data storage device, fully integrated in the UAV, was suc-cessfully flown, producing valuable data on the structuralbehavior of the booms.

The main tasks that were carried out in terms of flightdata analyses were: (i) temperature compensation: since FBGsare sensitive not only to strain but also to temperature it is ofgreat interest to extract the mechanical strain out for structuraldiagnostics and prognostics; (ii) application of artificial neuralnetwork (ANN) analysis [20–24] to estimate the loads actingon the boom based on data acquired during flight; and finally:(iii) application of damage detection algorithms using prin-cipal component analysis (PCA) [25–27].

Section 2 describes the implementation of the SHMconcept, while the sensitivities of the embedded FBGs tomechanical strain and temperature are established insection 3. Flight test data, which are reported in section 4, arethen used in section 5 for load monitoring during all phases ofthe flight. section 6 describes a useful viewing tool for themassive volume of collected data. Finally, the diagnosis andprognosis of the structure’s health are extracted using PCA, insection 7, followed by some conclusions, in section 8.

2. The implemented SHM concept

The Nishant UAV (figure 1), designed and manufactured inIndia by The Indian Aeronautical Development Establishment(ADE) was selected as the test-bed for the evaluation of thisfiber-optic based SHM concept. Each of the two Nishant tailbooms is a composite structure made of two thin wall ‘C’section channels, trimmed and assembled to form a closedrectangular beam. The back of the booms holds the empen-nage, comprising a horizontal tail (with elevator) and twovertical tails. The thickness of the composite boom wallsvaries along their length, which are optimally designed forboth strength and stiffness.

The boom is basically a cantilever beam with a relativelylarge mass at the back end. The main boom loading condi-tions are vertical and horizontal bending. In order to track theexpected loading conditions, two fibers were embedded at thecenter of the boom (‘center fibers’, CH2, CH3 in figure 2 andanother pair of fibers were embedded near the corners (‘sidefibers’, CH1, CH4 in figure 2). Four FBG sensors wereimprinted on each fiber at specific critical locations (where thewall thickness changes). Thus, each critical section of theboom is monitored by four FBGs: two on the top and two onthe bottom. For such a sensing net arrangement, the twocenter fibers are only sensitive to vertical bending, while theside fibers are sensitive to both vertical and horizontal

Figure 1. The Nishant UAV. Optical fiber sensors were embedded ineach tail boom.

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Smart Mater. Struct. 24 (2015) 075022 I Kressel et al

bending. Since the cross-section of the boom is symmetrical,vertical bending will theoretically introduce identical butopposite strains in the corresponding top and bottom FBGs.Note that the two side fibers are on the same side of the boomwith respect to the center line, so that horizontal bending willinduce similar strains on both, in addition to the verticalbending contribution.

The optical fibers were polyimide-coated to ensure goodbonding to the composite structure during embedment and forcompatibility with the curing process. To successfully solvethe ingress/egress issue, a special 40 by 40 mm pre-curedcomposite protective patch, made of the same fabric as that ofthe boom, was used for each pair of fibers at the egress point,enabling the final trimming of the boom after curing. Whiletwo well protected fibers (900 micron coating, strengthenedby heat shrinks) entered the patch, their bare, polyimide-coated continuations (140 micron in diameter) emerged asfree fibers, ready for embedment. The patch was located at thewing-side end of the boom, filling in for a corresponding cutwhich was made in the layer under the sensing fibers. Otherthan the precise placement of the sensing fibers (using atemplate) between the composite layers of choice as well asthe placement of the protective patch, none of the normalsteps in the manufacturing, trimming, inspection and assem-bly of the boom has been modified. These two structurallyminor modifications were analytically found to have noimplications on the structural integrity of the Nishant booms.The emerging shrink-protected 900 micron fibers were ter-minated in an FC/APC connector, further strengthened byKevlar and 3 mm acrylic sleeves. Finally, these four protectedand connectorized fibers were internally routed inside theUAV from each boom to the interrogation unit in the pay-load bay.

A commercial solid-state, high sampling rate (2500samples/sec) FBG interrogation unit (Wx-04M, made bySmartFibre) was used, that is capable of the simultaneoustracking of four fibers with multiple FBGs on each. Thisinterrogation unit was accompanied by a data-logger and thetwo were placed in the UAV payload bay. Both the inter-rogator and data-logger have been successfully subjected to

environmental testing, and are fully compatible with the fly-ing envelope of the Nishant UAV. While a four channelinterrogator can track only four separate fibers, proper allo-cations of the center wavelengths of the different FBGsamong the eight embedded fibers (subject to maximumanticipated strains) could, in principle, allow for the simul-taneous tracking of all of them, simply by addressing pairs ina parallel manner. For the test flight discussed below, datawere collected from the two booms’ center top and centerbottom fibers. This enables simultaneous tracking of thevertical bending moment, which is the dominant tail load, onboth booms. While in the future, processing will be performedon-board with results (alarms, etc) transmitted to ground inreal-time, this phase of the project concentrated on the SHMsensing and data collection so that all of the data analysisreported here was done offline.

3. Adressing the strain and temperature cross-sensitivity of FBGs

FBG interrogation amounts to tracking the wavelengthdeviations, ,Δλ of their peak reflection from a referencewavelength, λ0, normally taken to be the zero-load value.These wavelength are related to variations in the local strain(ε) and temperature (ΔT) according to [9]:

C C T. (1)z T0

Δλλ

ε Δ= +ε

Here, εz is the longitudinal strain and Cε and CT are constantsto be determined through calibration. Actual tail loading,performed on the fully equipped UAV during ground testinghas established the value of Cε to be 0.79 [17, 18].

The flight envelope of modern UAVs encompasses quitea wide range of temperature changes: from an ambient tem-perature in excess of +40 °C at take-off and landing down to−55 °C at high altitudes. Temperature affects the FBG readingin two ways: (i) both the grating period and the FBGrefractive index are temperature-dependent; and (ii) anembedded FBG will follow the temperature-induced expan-sion/contraction of the embedded substrate, whose coefficientof thermal expansion may be quite different from that of thefree FBG. In order to evaluate the sensitivity of the boom-embedded sensors (figure 3) to temperature (under no externalloads), a heating test was conducted. The fiber-equippedboom was placed in an oven and was heated according to thefollowing controlled thermal profile: (i) the temperature was

Figure 2. The boom’s general layout, and routing of the embeddedoptical fibers inside the boom. 16 FBGs were located at points ofinterest (the right hand side boom is shown).

Figure 3. Embedded FBGs temperature calibration test.

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Smart Mater. Struct. 24 (2015) 075022 I Kressel et al

first raised from room temperature to 60 °C, where it wassustained for 1 hr; (ii) the temperature was raised again from60 °C to 70 °C and was sustained at 70 °C for an hour. Thetemperature effect on the FBG readings was calculated foreach FBG using the data taken at 60 °C and 70 °C, comparedto the room temperature readings as a baseline. In this way,CT was determined to be (6 ± 0.5) 10−6 Kelvin−1 so the overallnow calibrated sensitivity of the sensors to both strain andtemperature is given by:

T0.79 (6 0.5)10 , (2)z0

6Δλλ

ε Δ= + ± −

where zε is the longitudinal strain induced only by mechanicalloads other than those related to the temperature, and

T T T0Δ = − (in °C) is the temperature change with respectto a reference value, T ,0 e.g. ground temperature.

Equation (2) requires an independent determination ofthe temperature in order to isolate the load-induced strainfrom the optically measured / .0Δλ λ Rather than the optionalembedding of additional temperature sensitive, strain-inde-pendent but somewhat more cumbersome FBGs, we opted touse a different approach at this stage of the project. As dis-cussed in section 2, vertical bending should theoreticallyintroduce identical but opposite strains in the correspondingtop and bottom center fiber FBGs. Temperature, on the otherhand, will introduce similar strains on both the top and bottomopposite sensors. Therefore, the average of the readings fromthe top and bottom sensors is expected to be highly correlatedwith the local temperature at the sensors.

Mathematically, it follows from (2) that providedΔλtop =Δλbottom, the temperature change TΔ can be extractedfrom:

T 0.5 / / (6 0.5) 10 .

(3)

0 top 0 bottom6⎡

⎣⎢⎤⎦⎥ ⎡⎣ ⎤⎦Δ Δλ λ Δλ λ= + ± ⋅ −

Note that the global UAV temperature could also be estimatedfrom altitude data as recorded by an onboard sensor.

By the same reasoning, temperature-independentmechanical strain data can be obtained from the difference ofthe top and bottom readings:

0.5 / / 0.79.

(4)

z ztop bottom

0 top 0 bottom

⎡⎣⎢

⎤⎦⎥ε ε Δλ λ Δλ λ= − = −

4. System evaluation during flight test

The Nishant UAV, equipped with the airworthy SHM system,was flown at Kolar airfield near Bangalore, India. The care-fully designed flight-worthy interrogation system, integratedinto the Nishant UAV, withstood all flight conditions,including a catapulted ∼9 g launch, flight maneuvers and aparachute/air-bag landing. Temporally-condensed measuredreadings, in terms of / 0Δλ λ for one of the sensors (top centerFBG4 of figure 2), is shown in figure 4 together with altitudeinformation for the duration of the entire flight. The takeoff

and landing are clearly seen as the major events during thisflight. Upon climbing, / 0Δλ λ monotonically decreases due tothe reduction of the ambient temperature. As the UAV des-cends back for landing, the temperature increases, togetherwith the sensor readings.

Temporally-resolved responses of the top center sensorsFBG1 and FBG4 (figure 2) are shown in figure 5 (takeoff).While located at different distances from the wing, these twosensors show comparable strains due to the variable thicknessof the structure.

Strain readings from a pair of opposite FBG1 gratings onthe boom top and bottom appear in figure 6 (landing). Asexpected, the top and bottom readings are 180 ° out of phase,while having similar magnitude.

Using equation (3) and the height/temperature conver-sion factor of [1 °C]/[500 Ft] the boom altitude can be esti-mated from the temperature indirect measurements. Altitudedata (received from an onboard sensor) from figure 4, togetherwith the FBG-derived altitude, appear in figure 7 and showvery good agreement.

Figure 4. Optical strain ( / 10 )06Δλ λ ⋅ readings for the top center

FBG4 sensor of figure 2 for the whole duration of the flight (blue).Also shown (red) is the UAV altitude during the flight.

Figure 5. FBG mechanical strain readings during takeoff.

Figure 6. FBG mechanical strain readings during landing.

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Smart Mater. Struct. 24 (2015) 075022 I Kressel et al

5. Load monitoring during flight using artificialneural networks (ANN)

Information gathered from sensors (either of same type ordifferent types) needs to be combined with linguistic andknowledge-based data. The load estimation, based on thestrain measured from embedded sensors, forms the primaryobjective within the scope of the work presented here.Although on an enhanced scale of the SHM system damagediagnosis and prognosis will form the other branches of thereliable SHM algorithms, these have not been attempted asof now.

There are different types of techniques which can be usedfor the development of SHM algorithms (load estimation,damage diagnosis and prognosis) such as signal processingtechniques, fuzzy logic, genetic algorithms, artificial neuralnetwork (ANN) etc The first technique is more predominantwherein there is a direct mathematical relationship betweendamage and its effect. The other methods are usually requiredwhen a specific equation or algorithm is not applicable, butwhen adequate knowledge or data exists (either fromexperiments or analytically or both) to derive a knowledgebased solution [21–24]. This study focuses on the use of anANN using MATLAB as a possible tool for the SHMmethodology (for boom vertical load estimation).

Implementation of the ANN is a two-step process. In thefirst step, the network is trained using known input and outputdata. Once trained, then the network can be used for predic-tion for a new input, which was not used for training. Theconnections between elements largely determine the networkfunction. Neural networks can be trained to perform a parti-cular function by adjusting the values of the connections(weights) between elements. Typically, neural networks areadjusted or trained, so that a particular input leads to a specifictarget output. The network weights are adjusted and tunedbased on the comparison of network output and targets; thisprocess continues until the difference between the networkoutput and the targets falls within an acceptable tolerance.Typically, many such input/target pairs are needed to train anetwork. For the current case, a feed-forward back propaga-tion ANN-based load estimator was developed. The feedforward back propagation does not have feedback connec-tions, but errors are back propagated during training. The leastmean squared error is used as the error function while trainingthe network. Errors in the output determine measures of thehidden layer output errors, which are used as a basis for

adjustment of connection weights between the input andhidden layers. Adjusting the weights between the layers is aniterative process that is carried on until the error falls below atolerance level.

In this study for the purpose of training the ANN, thestrains used as the input and load values were used as therespective target. As practically it is not feasible to generatethe whole gamut of the load versus strain data set for trainingpurposes, hence a finite element (FE) model, validated withlimited experiments, was used to generate the training data setfor different loads on boom. The philosophy of the ANNtraining and performance testing is shown in figure 8.

The data captured from the boom calibration test dis-cussed previously was used as the validation for the FEmodel. The validated FE model was then further used togenerate the different training data sets for the ANNalgorithm.

The ANN consists of one input, one hidden and oneoutput layer. The input layer (which is non-functional) con-sists of an input vector with eight elements (one for each FBGsensor in the selected two boom channels). The hidden layerconsists of 16 neurons and each neuron has ‘tansig’ transferfunction. The output layer has one neuron (since the outputvector has only one element, i.e. load) and the transferfunction for this neuron is ‘purelin’ (for more information ontransfer function [21, 22]). The training of the network wascarried out from the data sets generated during the experi-mental validation of the FE model. The trained network interms of weights, biases and neuron transfer function wasstored so that it can be used further to estimate the loads.

The accuracy of the ANN load estimator was validatedduring ground experiments, where boom strains induced bycalibrated tail loads, were measured and found to be in closeagreement with the predictions of the ANN estimator. Later,strains acquired from the flight test were input to the networkand the estimated tail load is shown in figure 9.

6. Flight data playback software quickview

A LabVIEW based QuickVIEW software was developed tovisualize the sensor data immediately after flight recovery.The software provided a flight playback of the sensor data at auser defined read rate, coupled with other flight parameterssuch as altitude, engine RPM, yaw, roll and pitch angle,separately recorded by UAV avionics, as shown in figure 10((a) flightVIEW tab, (b) strainVIEW tab).

7. Structure diagnostics and prognostics

The proposed concept for tracking the structural integrity ofthe booms is based on cross-correlation between normalizedsensors [3, 4]. Since the boom itself is light with respect to theweight of the horizontal and vertical tails, the first vibrationalbending mode is dominant. This is especially true for the caseof ground touch-down impact during UAV landing.

Figure 7. Comparison between the altitude as calculated using FBGreadings and the telemetry recorded altitude.

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Smart Mater. Struct. 24 (2015) 075022 I Kressel et al

Unlike metal structures, where cracks can grow underrelatively moderate loading while high loads may retard theirgrowth, composite materials are especially vulnerable to highloading. The most critical loading condition during theNishant UAV flight is its landing, especially the groundtouch-down, as can be seen in figures 6 and 9. The main focusof the current work was to track and identify events duringlanding that are associated with high loading, and determine ifdamage occurred during such incidents.

In view of the similarity among the different sensors, aload and damage tracking algorithm was constructed, basedon the detection of deviations from the expected modalbehavior. The need to handle large amounts of data, obtainedby many sensors also calls for th emeans to reduce the orderof the problem. In the current case, where only the deviationsfrom the first mode of vertical bending are of interest, prin-cipal component analysis (PCA) [25–27] seems to be a goodchoice. Using this approach, the main dominant boom read-ing, at a specific time interval, is identified, and each sensorreading is evaluated with respect to this first principal com-ponent. PCA was performed on data from two time intervals:launching and landing. In PCA the data set of n sensors (eightfor each boom) with m samples in the time domain per sensor,is defined as an mxn yY { }ij= matrix, where the m-long

column Yk represents temporal data from sensor k. The scalar

product and its corresponding norm are defined by:

Y Y y y Y Y Y Y

Y Y Y

, , , , ˆ

, . (5)

k j

i

m

ik il k k k k

k k k

1

∑= ⋅ =

==

PCA transforms the original data set of the n sensors into a‘rotated’ uncorrelated set of n principal components,comprising the n columns of matrix V, given byV W YT T T= ⋅ (T stands for transpose). Here W is an nxnmatrix, whose columns are the eigenvectors of the nxncovariance matrix C ,Y {C Y Ycov( , ) }.kl

Yk l= The eigenvectors

within matrix W are ordered so that their correspondingeigenvalues, k n{ , 1,..., ; .. },k n1 2λ λ λ λ= ⩾ ⩾ ⩾ decreasein value from the leftmost columnW1 to the rightmost one,W .n

This ensures that the covariance matrix of the principalcomponents V k n{ , 1,..., },k = given by C V V{ cov( , )kl

Vk l= =

}k klλ δ is diagonal ( klδ is the Kronecker delta function), so thatV k n{ , 1,..., }k = are orthogonal. Thus, the normalizedprincipal components V V k n{ ˆ , 1,..., }k k kλ= = can nowserve as an orthonormal vector basis for the original data:Y p V k n{ ˆ ˆ , 1,..., }k l

nkl l1= ∑ == with p Y Vˆ , ˆ

kl k l= .For well correlated data there is only one dominant

principal component. In the rather theoretical case where theboom’s vertical motion exactly follows that of its funda-mental first mechanical mode, all of the time stampsy t k{ ( ), 1,...,8}k = of the eight central sensors (of channels 2and 3 in figure 2) are just multiples of a single normalizedfunction y t¯ ( ), namely: y t y t k{ ( ) ¯ ( ), 1,...,8}.k kα= =Clearly, these eight y t{ ( )}k functions are highly correlated.The PCA procedure will process these correlated functions tofind eight new principal components, v t k{ ( ), 1,...,8}.k = Itwill find y t¯ ( ) (up to a normalization factor) and will identify itwith the first principal component v t( ).1 All remaining sevenprincipal components will be set to zero. Any deviation ofy t k{ ( ), 1,...,8}k = from being simple multiples of y t¯ ( ),either due the excitation of higher-order modes or simplyfrom the inevitable presence of noise, will result inhaving non-zero high-order principal components v t{ ( ),k

k 2,...,8}.= It is expected that damage in a structure will

Figure 8. ANN-based load estimator training and testing scheme.

Figure 9. The ANN estimated temporal profile of the tail load.

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Smart Mater. Struct. 24 (2015) 075022 I Kressel et al

affect the distribution of its principal components undersimilar static/dynamic loading [27].

The normalized sensor data and the calculated principalcomponents for the launching event are shown in figure 11 andfigure 12 respectively. Figure 12, shows that the first principalcomponent is much larger than all other principal components,indicating well correlated data. As a final step, all sensors areplotted with respect to the 1st principal component (figure 13):

all sensors, after proper normalization, follow the 1st principalcomponent during the UAV launching time interval.

A similar PCA procedure was applied to the landingevent. The corresponding normalized sensors data and prin-cipal components appear in figure 14 and figure 15. An arrowin figure 14 points at a high-load landing event, where onesensor has deviated from its expected behavior, indicating anonlinear phenomenon, to be discussed below. This nonlineardeviation wakes up a second principal component from itsotherwise dormant status, figure 15. In practice, when we areconfronted with data from many sensors, collected over along flight, it may be quite difficult to scan the whole record,e.g. Figure 14, looking for deviations from normal behavior.The decomposition of the readings into principal components,figure 15, makes the task easier, although one still has to dealwith a long temporal sequence. A still easier and morecompact approach is to use figure 16, where the normalizedsensor data are plotted versus the first principal componentand where the above-mentioned deviation is easily identified.

Figure 10. ANN based load estimator integrated with the QuickVIEW software.

Figure 11. Normalized sensor data during launch.

Figure 12. Principal components.

Figure 13. Principal component correlation.

Figure 14. Normalized sensor data during landing.

Figure 15. Landing: principal components.

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Smart Mater. Struct. 24 (2015) 075022 I Kressel et al

Since the second principal component contribution wassignificant only during a short time interval, figure 15, it isconcluded that the structure remained intact and this phe-nomenon was probably associated with a local buckling.Indeed the structure was inspected after landing with noevidence of permanent failure.

8. Summary and conclusions

A fully-embedded fiber-optic-based SHM concept for track-ing the structural integrity of UAV composite booms wasdemonstrated, including on-board data collection. The sensorembedding was integrated with the manufacturing process,taking into account the trimming of parts and assemblyconsiderations. The ability to have many sensors on eachboom, at virtually no weight penalty, made it possible to tracknatural frequencies and normal modes. In addition, loadestimation has been obtained using artificial neural networks.By studying the cross-correlations among the normalizedreadings from multiple sensors, both boom overload andboom damage can be classified without the need for anyadditional flight parameters information. Tracking vibrationmodes by analyzing normalized sensors data using PCA alsoreduces the order of the problem. Using this approach, localbuckling was identified at the high touch-down impact. It wasalso shown that this local buckling did not cause permanentstructural damage since the sensor cross correlation, evaluatedby PCA, did not change after this event.

The result of the PCA processing, where a single prin-cipal component stands out as the dominant one, is an indi-cation of a healthy structure. Anomalies in the sensor data areeasily tracked. It is expected that many types of damage willcause irregularities in the readings of sensors located inproximity to the damaged areas which are easily seen whenthe sensor data is compared to the first principal component.

In conclusion, high count fiber-optic sensing arraystogether with proper data processing techniques promise toreduce the periodic grounding of airborne platforms and pavethe way to condition-based maintenance (CBM). Lessonslearnt from this Nishant collaborative experiment have beenrecently applied to a class of operational UAVs [28] con-stantly collecting the load and vibration signatures duringflight.

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Figure 16. Sensor data versus P.C. 1 during landing.

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