Contact Versus Non-Contact Measurement of a Helicopter
Main Rotor Composite Blade
Marcin Luczak1,a, Kajetan Dziedziech 1,b, Marianna Vivolo 2,c Wim Desmet 2,d, Bart
Peeters 1,e, Herman Van der Auweraer 1,f,
1
LMS International, Interleuvenlaan 68, B-3001 Leuven, Belgium
2
Katholieke Universiteit Leuven, PMA, Belgium
Abstract. The dynamic characterization of lightweight structures is particularly complex as the impact of the weight of
sensors and instrumentation (cables, mounting of exciters…) can distort the results. Varying mass loading or constraint
effects between partial measurements may determine several errors on the final conclusions. Frequency shifts can lead to
erroneous interpretations of the dynamics parameters. Typically these errors remain limited to a few percent. Inconsistent
data sets however can result in major processing errors, with all related consequences towards applications based on the
consistency assumption, such as global modal parameter identification, model-based damage detection and FRF-based
matrix inversion in substructuring, load identification and transfer path analysis [1]. This paper addresses the subject of
accuracy in the context of the measurement of the dynamic properties of a particular lightweight structure. It presents a
comprehensive comparative study between the use of accelerometer, laser vibrometer (scanning LDV) and PU-probe
(acoustic particle velocity and pressure) measurements to measure the structural responses, with as final aim the
comparison of modal model quality assessment. The object of the investigation is a composite material blade from the
main rotor of a helicopter. The presented results are part of an extensive test campaign performed with application of
SIMO, MIMO, random and harmonic excitation, and the use of the mentioned contact and non-contact measurement
techniques. The advantages and disadvantages of the applied instrumentation are discussed. Presented are real-life
measurement problems related to the different set up conditions. Finally an analysis of estimated models is made in view
of assessing the applicability of the various measurement approaches for successful fault detection based on modal
parameters observation as well as in uncertain non-deterministic numerical model updating.
Keywords: laser Doppler velocimetry, accelerometers, microflown probe, composite structures, modal analysis, rotor
blades, helicopter
PACS: 43.40.+s
Structural acoustics and vibration
INTRODUCTION
This research presented in this paper has been performed in the context of the PROND research project [2] that
focuses on composite structure test data variability and uncertain parameter numerical model updating of composite
structures. The basic methodology which is used is Experimental Modal Analysis (EMA). The EMA technique is an
established tool for the identification of dynamic properties of structures [3-5]. Modal models can be applied in
many ways. The EMA models allow adding a physical interpretation to classical black-box system identification.
Tracking the evolution of models as a function of operational parameters is vital in applications such as flight flutter
testing [6, 7]. Many EMA applications are related to the confrontation of experimental data with numerical models.
One of these is the Finite Element Method (FEM) model updating procedure [8-10]. The test data is used against the
numerical simulation results to correct the parameters of the FE model to better represent the results from the
measurement. Another important area of application of EMA is Structural Health Monitoring (SHM) based on the
observation of the evolution of the values of selected modal parameters [11-15]. For these two applications reliable
modal test data are of vital importance. However test data are subject to variability. Data Variability is hence the
subject of extensive studies in many research centers [16-19]. Variability of test data may come from number of
sources [20]. Internal source is non-repetitive production process causing that two nominally identical units have
geometric and material properties within production tolerances. Example of the external source of test data
variability is an environmental parameter change like temperature [21-23] . External sources of test data variability
could be also linked to the test setup [24, 25]. Within test setups there are three main components of measurement
variability for the same specimen. These are boundary conditions, excitation method and measurement technique.
For the EMA both contact and non-contact measurement techniques are applicable. Within the presented research
the response signal acquisition is performed by means of piezoelectric accelerometers, Laser Doppler Scanning
Vibrometer (LDV) and microflown probes for the identical boundary and excitation parameters. The application of
LDV technique for modal analysis is adequately reported in many scientific papers [26, 27]. Mass loading effect
from adding piezoelectric accelerometers is also well described [24, 28, 29]. Experimental research has been
reported using microflown probes and LDV for the identification of acoustic properties [30]. A large amount of
papers have been published in the literature regarding abovementioned aspects of modal test data. Certainly many
more works have been published than those papers listed here. But little is known about the comparison of the
microflown probes and LDV measurement [31-33] for the purpose of EMA. Reported cases mainly focus on the
comparison of the signals acquired by a microflown, laser and piezoelectric sensors [34, 35]. The present paper fills
this gap by offering comparison of lightweight composite structure experimental modal models estimated from laser,
microflown and piezoceramic sensors. It has a following outline. First part presents the investigated specimen, test
rig and setups. The methods of excitation and measurement are presented. In the second part a comparison of modal
models estimated from three different measurement techniques, applied on a blade from the main rotor of a
helicopter, is discussed. Different comparison criteria are presented, test data variability is assessed.
EXPERIMENT
Goal and scope
The main goal of the presented investigation is an assessment of the influence of the contact and non-contact
measurement technique on the modal data and models variability. Reaching the primary objective requires to put
into effect several auxiliary tasks of a test campaign. Due to a high number of measurement points and a limited
number of piezoelectric sensors applied to the structure (in order to reduce the mass loading phenomena), a large
number of tests was carried out. Random signals were applied. For all the configurations the linearity and reciprocity
checks were done to verify whether the structure meets the modal analysis assumptions. FRF and coherence
functions were estimated and stored. Global modal models were estimated as a final result. One modal model is
based on the use of piezoelectric acceleration sensors. A second one is a modal model from a microflown
measurement. A third model is estimated from a LDV velocity acquisition.
Test object
The objects of the investigation are three blades from the main rotor of a PZL Swidnik W-3 helicopter presented
in FIGURE 1. Blades are made of Glass Fiber Reinforced Plastics. Dimension of the investigated blades are: Length
≈ 7300 [mm], Width ≈ 520 [mm], Approximate weight of the structure is 70 [kg].
FIGURE 1 Test Setup of Blade from Main Rotor from Helicopter PZL Swidnik W-3.
Test equipment
In test campaign the following measurement and analysis tools were used:
1.
Blade supported by 2 rubber cords for providing free-free boundary conditions,
2.
1 electromagnetic shakers, amplifiers, with impedance heads incorporating acceleration and force
sensor in the same housing to measure driving point FRF’s (in the accelerometer case, also a dual
exciation with two shakers was applied as comparison, this case is not further discussed),
3.
10 uniaxial modal piezoelectric accelerometers PCB 333B32,
4.
Scanning Laser Vibrometer OFV3001S Controller, OFV055/OFV303.8 Optics, OFV042 Interface
5.
Microflown probes: PU-mini NT0712-44 and USP-mini UT0608-01,
6.
16 channels in frontend LMS SCADAS Mobile with computer with a Test.Lab acquisition and analysis
suite,
7.
Bandwidth: 200 [Hz], Resolution: 0.05 Hz
Geometry definition
A dense grid of measurement points is defined all over the blade surface, in order to successfully identify the
dynamic properties of this rather big structure. Measurement points are set with a distance of 0.25 [m] one from each
other in the spanwise (Z) direction and 0.1 [m] in the edgewise direction (Y). Geometry definition for blade is
presented on FIGURE 2. It consists of 78 points, 77 of which are acquisition locations and the remaining 1 is the
driving point.
FIGURE 2 Cartesian Coordinate System for Piezoelectric, Microflown and Laser Sensors.
MODAL MODELS COMPARISONS
Experimental modal models
Based on the experimental data collection, modal models were estimated. This is a very important step in the
assessment of the test data variability because it preserves the gross errors to be included into an analysis. Control of
the collected data quality and evaluation of the estimated models by means of coherence functions synthesis and
AutoMAC criterion is done. The estimated modal models are compared for the contact and non-contact
measurement.
For the piezoelectric sensors the measurement was done in “sets” which means not all the points were measured
at the same time. As a consequence a number of partial modal models were estimated for each of the set. Next the
partial models were merged into a global model by means of multi-run modal analysis. Modal models have to be
validated to provide confident information about the structural dynamics of an object.
Measurement errors discussion
All measurements are prone to systematic errors. A systematic error is any biasing effect in the methods of
observation or instruments used which introduces error into an experiment and is such that it always affects the
results of an experiment in the same direction. When accelerometers are used to record the system’s response, 8 sets
of sensor locations are measured, in order to cover the whole grid of measurement points, using a maximum number
of 10 sensors/set. The mass of a single sensor is 0.005 [kg]. Weighted mass of all sensors and wiring system is 0.06
[kg]. This distributed mass is moved along the 8 blade regions, which are characterized by a different flexibility.
Additional mass always causes local structural modification which results in natural frequencies shift. Frequency
shift due to mass loading for the driving point FRFs is presented on Fig. 3, for the second natural mode (second
flapwise mode, around 10.2Hz).
Amplitude
1.00
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
-70.00
0.00
F
FRF Drvp:1:+X/Drvp:1:-X
8.05
Hz
13.56
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
FIGURE 3 FRF plot for the impedance head for all the measured sets for a selected Fpoint FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
Measuring in sets poses another engineering practice problem. Moving sensors from one
location
to another
F
FRF Drvp:1:+X/Drvp:1:-X
requires decoupling the shakers from the structure, since it is not possible to provide a requiredF qualityFRF
ofDrvp:1:+X/Drvp:1:-X
the sensorFRF Drvp:1:+X/Drvp:1:-X
structure wax connection, keeping the shakers coupled. Re-coupling the shakers also alters theFconnection’s
dynamic
F
characteristic which consequently contributes to the measured signal. In other words the physical,
realFRF
lifeDrvp:1:+X/Drvp:1:-X
conditions
F
FRF Drvp:1:+X/Drvp:1:-X
of the test realization make each set of measurements to be in fact a measurement of a slightlyF different
of
FRFassembly
Drvp:1:+X/Drvp:1:-X
the structure, sensors, wiring system and shaker fastening. Therefore contact measurements Fsuffer from
more than
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
just mass loading effect.
F
FRF Drvp:1:+X/Drvp:1:-X
Laser and microflown measurements are free of these systematic errors since no
mass
and no deF
FRF Drvp:1:+X/Drvp:1:-X
coupling/coupling of the shakers is performed. Laser measurement is affected by another type
of FRF
error.
Velocity
F
Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
measurement is realised along the optical axis of the beam. In situations in which a velocity vector
(which
is normal
FRFthe
Drvp:1:+X/Drvp:1:-X
to the surface of the blade) is not in line with the optical axis of the LVD, the correction factor,F equal to
cosine of
F
FRF Drvp:1:+X/Drvp:1:-X
the difference angle, has to be applied. LMS (CADA-X) software, used for the LVD tests, hasFa built-in
algorithm
of
FRF Drvp:1:+X/Drvp:1:-X
correction of such errors for flat surfaces. Since the surface of the blades is curved it was Fdivided FRF
intoDrvp:1:+X/Drvp:1:-X
3 regions
F minimize
FRF Drvp:1:+X/Drvp:1:-X
(assumed to be flat with a satisfactory approximation) along the spanwise direction, in order to
this effect.
FRF Drvp:1:+X/Drvp:1:-X
Therefore for each of these sets a slightly difference velocity field was obtained (FIGURE 4).FF
FRF Drvp:1:+X/Drvp:1:-X
The Microflown probe measurements were also not completely error free. Direct result ofF the sensing
principle
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
of the microflown probes is a non-correct measurement of the low frequency mode.
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
F
FRF Drvp:1:+X/Drvp:1:-X
g/N
dB
-10.00
FIGURE 4 Error of the Curvature Correction in Laser Measurement
Measurement results
Vibration measure of the blades done was different for all three techniques. Therefore it is difficult to present the
direct comparison of the acquired response signal for the particular point. Signal from a microflown probes is a
subject of post processing. For this sensor there is no single frequency independent sensitivity value. The calibration
of the signal data is being computed afterwards and is multiplied by frequency dependent curve. Measurement
results shown on the Figure 5 clearly confirm the mass loading effect of the piezoceramic sensors.
0.00
1.00
Amplitude
FRF Drvp:1:+X/Drvp:1:-X
FRF Drvp:1:+X/Drvp:1:-X
FRF Drvp:1:-X/Drvp:1:+X
(
)
dB
g/N
F
F
F
-80.00
0.00
0.00
Hz
81.36
FIGURE 5 Mass influence within investigated frequency range for driving point. Piezoceramic accelerometers measurement
(red) and Microflown probes (green) measurements are the upper curves, the Laser vibrometer measurement (blue) is the lower
curve.
Driving points FRFs for both contactless measurement techniques have higher resonant frequency values than
piezoceramic sensors FRFs towards increasing frequency range which is mass influence area.
Modal models comparison
The data presented in previous section was used for the estimation of the modal models.
TABLE 1 presents a part of the comparison of the natural frequency and damping ratio values for the identified
modes. Data for these models were acquired by means of contact and non-contact techniques.
TABLE 1 Experimental natural frequencies and damping.
1
2
3
4
5
6
7
8
9
10
Flapwise 1st
Flapwise 2nd
Flapwise 3rd
Torsion 1st
Flapwise 4th
Flapwise 5th
Torsion 2nd
Flapwise 6th
Flapwise 7th
Torsion 3rd
Accelerometers Microflown
Frequency [Hz]
3,62
10,20
10,29
20,35
20,32
30,29
30,33
33,91
33,92
49,76
49,78
61,32
61,55
67,43
67,42
86,13
86,15
88,09
88,40
Laser
3,61
10,20
20,27
30,27
33,91
49,75
61,41
67,41
86,16
88,19
Accelerometers Microflown
Damping [%]
0,16
0,40
0,69
0,40
0,44
0,96
0,89
0,33
0,35
0,35
0,37
0,91
0,87
0,39
0,39
0,48
0,49
0,89
0,99
Laser
0,12
0,53
0,37
0,76
0,35
0,34
0,74
0,37
0,30
0,76
Within the bandwidth of interest there are all modes (except the first one using the microflown sensor)
successfully identified for all modal models. Visual inspection of the mode shapes is presented in
TABLE 2
TABLE 2 Mode shapes.
Accelerometers
Microflown
Laser
Flapwise 2nd
Torsion 1st
Discussed systematic errors do not influence the correctness of the model estimation which can be observed in
MAC matrix plots in Table 3.
TABLE 3 MAC Criterion for modal vectors comparison.
Acceleration (A) vs LASER (B)
Acceleration (A) vs Microflown (B)
Laser (A) vs Microflown (B)
MAC matrix comparison also confirms that both contactless measurement models coincide better than contact,
piezoceramic accelerometer model.
Another relevant comparison method for these models is a correlation plot on shown in FIGURE 6. For contact
and non-contact models the correlation plot of damping ratio and frequency is being drawn. Damping ratios for the
torsional modes (see TABLE 1) are clearly demonstrating the significantly higher value with respect to the
translational modes. This concerns all the considered models. A new observation comes from the comparison
between the microflown and laser measurement models. Both are non-contact techniques, therefore the same values
of estimated damping ratio could be expected for both models. This is not a case and what is more, the higher is the
frequency of the mode the larger the difference becomes (see the trend line of the difference on the bottom part of
FIGURE 6).
Contact (accelerometers) vs non-contact (microflown and laser)
1,20
1,00
88,40
30,29
61,32
61,55
30,33
88,09
0,80
Damping [%]
30,27
88,19
61,41
10,29
0,60
10,20
86,15
86,13
0,40
10,20
20,32
20,35
20,27
67,43
67,42
67,41
49,78
49,76
49,75
33,92
33,91
33,91
86,16
(microflown-laser); 0,22
(microflown-laser); 0,19
0,20
3,62
3,61
(microflown-laser); 0,16
(microflown-laser); 0,13
(microflown-laser); 0,13
(microflown-laser); 0,07
0,00
0,00
10,00
20,00
30,00
(microflown-laser); 0,02 (microflown-laser); 0,02
(microflown-laser); 0,00
40,00
50,00
60,00
70,00
80,00
90,00
100,00
Frequency [Hz]
Accelerometers
Microflown
Laser
(microflown-laser)
Linear ((microflown-laser))
FIGURE 6 Assessment of the laser vibrometer, microflown probes and piezoceramic measurement on modal parameters. It is
also assessment of acceleration and velocity measurement.
The explanation for this could be looked into the plots of measured (FIGURE 3, TABLE 3) and reported [34,
35] data. Both reported laser and microflown measured velocity signals comparisons are done for the constant
distance of a probe from a surface. Due to the curvature of a large blade surface it is difficult to maintain exactly the
same distance between the probe and the surface all over the measurements. Therefore this variability also
contributes to a final modal model quality and especially the damping ratio is affected. Both abovementioned
reasons result in increasing differences of estimated mode shapes (see TABLE 3) and damping ratios (FIGURE 6).
CONCLUSIONS
This paper presents some aspects of the multidisciplinary and interdisciplinary research oriented for the test data
variability. It presents an extensive test campaign lead on the composite material main rotor helicopter blade. Test
setups included different measurement techniques of contact and non-contact type. Experimental test data examples
were are shown and used for modal models estimation. Measurement systematic errors were are identified and
examined. Estimated modal models are compared by means of natural frequency, damping ratio and mode shape.
Common observation from displayed comparisons is that the accuracy of the results is frequency dependent. The
discrepancy between models grows in frequency. However all three measurement techniques lead to a correct
experimental modal models. One has to be aware of proper choice of the measurement technique and its impact on
test data, modal model estimated afterwards and the needed time-effort.
ACKNOWLEDGMENTS
This research was supported by the FP7 Marie Curie European Reintegration Grant No. 239191 PROND and
FP6 Marie Curie EST project SIMVIA2. The authors gratefully acknowledge the support provided by the European
Commission.
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