Report 96-8
ISSN 1103-1581
ISRN HKR-RES--96/8--SE
Frequency Analysis of Helicopter
Sound in the AS332 Super Puma
by
Thomas L. Lagö
University of Karlskrona/Ronneby
Department of Signal Processing
S-372 25 Ronneby, Sweden
Frequency Analysis of Helicopter
Sound in the AS332 Super Puma
by
Thomas L. Lagö
University of Karlskrona/Ronneby
Department of Signal Processing
Report 96-8
ISSN 1103-1581
ISRN HKR-RES--96/8--SE
© Copyright 1996, HK/R, ISb
Abstract
This technical report describes a series of measurements performed on an AS332 “Super Puma”,
MKII (HKP10) helicopter. The measurements are part of a research project, “A New Generation
Active Headsets and its Psychological Effects,” financed by the KKS board (Board of Knowledge
and Competence). The project participants are: Lindholmen Development, Hellberg Safety,
Active Control and the University of Karlskrona/Ronneby. CelsiusTech has recently joined the
project as an industrial partner. The Air Force base at F17 in Kallinge and the AMI group in
Ronneby are involved as evaluation groups.
There are substantial noise levels in helicopters, especially at low frequency. These noise levels
are normally not harmful to the ear. However, the low frequency content masks the speech. For
this reason, pilots tend to set the intercom system at maximum sound level, producing potentially
damaging sound levels for the human ear. Dr. PA Hellström at Lindholmen Development has
measured almost 100 dBA inside the ear canal when the intercom system is in use. This high
sound level exposes the ear to fatigue and hearing loss.
The background noise is the key reason for the problem, although it is not the key source of
sound damage to the ear. The frequency content in the masking background sound is of great
importance. It was thus important to investigate if the sound consisted of pure tones or if it was
more broadband in nature. The dominant sound sources needed to be identified and the number
of harmonics for each source established. It was also important to investigate if there was a strong
connection between the structure borne and the air borne sound.
Table of Contents
Abstract 0
Notation Page 2
1. Background Page 3
2. Measurement Setup Page 4
3. Spectrum Scaling Page 6
4. Frequency Analysis Page 14
5. Results of the Analysis Page 15
6. Comments regarding an ANR system Page 19
Acknowledgment Page 20
Measurement Figures Page 21
MKII Super Puma HKP10 frequency table (in Hz) Page 35
References Page 36
Notation
xc(t) Analog time signal
x[n] Discrete time signal
?t Sampling increment
T Time record length
N Number of samples
XW(fk) Windowed frequency spectra
BW FFT analysis bin bandwidth
Kr Random signal scaling
Ks Tonal signal scaling
Kw,PSD Window scaling for broadband signals
Kw,PS Window scaling for sinusoidal signals
PPS Power Spectrum
PPSD Power Spectral Density
PESD Energy Spectral Density
Adb ANR Attenuation in dB
?2 Coherence
Gxy(f) Cross Power Spectrum
Gxx(f) Auto Power Spectrum
Gyy(f) AutoPower Spectrum
Rxy(f) Cross correlation function
Nb Number of blades of the rotor
BPF Blade passage frequency
R Main rotor rpm
T Tail rotor rpm
rpm Rotation per minute
LMS Least mean square
1. Background
The air force uses a helicopter, the AS332 “Super Puma,” MKII (HKP10). This helicopter has
replaced the Vertol from Boeing. There are substantial noise levels in the helicopters, especially
at low frequency. These levels are not normally harmful to the ear. However, the low frequency
content masks the speech. The intercom sound levels thus sometimes reach severe levels. Dr.
PA Hellström at Lindholmen Development, Hearing Research Lab, has measured up to almost
100 dB levels inside the ear canal when the intercom system is in use. This high sound level
exposes the ear to fatigue and hearing loss. When this was discovered in the fall 1995, a project
group was formed to develop a new generation of active headsets, and to investigate the
psychological effects these would create. The University of Karlskrona/Ronneby was selected
as Project Manager. The partners in the group are: The University of Karlskrona/Ronneby,
Lindholmen Development, Active Control AB and Hellberg Safety AB. Financial support was
provided by the Foundation for Knowledge and Competence Development. The F17 air force at
Kallinge and AMI in Ronneby were selected as evaluation groups. More information about the
project can be found at http://www.hk-r.se/research_sv/headset_sv.html
It is important to investigate the frequency content of the sound in order to know how many low
and high frequency components are present. A series of measurements was thus performed at
the F17 air force field in Kallinge.
Helicopter sound is rather complex. It was thus important to investigate if the sound consisted
of pure tones or if it was more broadband in nature. The dominant sound sources were found and
analyzed, and the number of harmonics present for each of them was also established. It was also
important to investigate if there was a strong connection between the structure borne and the air
borne sound. This is important when selecting a reference sensor for the active noise control
system in the headset.
2. Measurement Setup
Helicopters are known to have high noise levels, especially at low frequency. The helicopter crew
use an intercom system for communication between themselves and for communication with the
tower. These headsets have a built-in passive ear protection. Damage to the hearing may be
caused nonetheless. This is due to the masking effects from the low frequency background noise
present in the helicopter. The helicopter analyzed in this research project was a Eurocopter, AS
332 Super Puma MKII (HKP10): see Figure 3 below, showing the helicopter in a hovering
position over the air field at F17 in Kallinge.
Key frequency components in the helicopter, measured in Hz.
R represents the rotation speed of the main rotor, and T represents the tail rotor. BPF represents the blade passage
frequency for each rotor. BPF is 4xR for the main rotor since there are four blades.
1R, 265 rpm @ 100% load 4.42
2R 8.88
3R 13.25
4R 17.67 BPF for rotor
1T 21.29
8R 35.33 2*BPF for rotor
12R 53.00 3*BPF for rotor
16R 70.67 4*BPF for rotor
5T 106.67 BPF for tail
2*Bendix Shafts 761.34
The measurement setup consists of a Hewlett-Packard HP35670A, 4-channel Dynamic Signal
Analyzer. Two Larson & Davis ½" microphones model 2541 were used, along with the Larson
& Davis 900B 2432 microphone preamplifier and Larson & Davis 2200C power supply. An
accelerometer, PCB 353B31, from PCB Piezotronics was connected directly to the HP35670A
using the built-in ICP supply. The complete measurement setup is illustrated in Figure 4 below.
Several measurements were performed during the flight. In the first phase, an analysis of different
frequency ranges and with different estimation methods was performed in order to establish the
frequency components present. Both FFT-based narrowband analysis and third octave analysis
were used in this investigation phase. On completion of this phase, several analysis bands were
selected in order to gain a good understanding of the frequency content.
The transducers were positioned as in Figure 5. One microphone was placed close to the pilot.
This transducer is called “the pilot.” The other microphone was placed between the two
passenger seats in the front of the helicopter, hanging from the ceiling. This microphone is called
“the seat.” The accelerometer was mounted on the wall on the left door opening just in front of
the left passenger seat. The right rear door was open during flight, as the flight was a training
session for a new group of surface divers. This open door gave an increased low frequency
content in the seat microphone.
3. Spectrum Scaling
Measurement of spectral components in an unknown environment such as a helicopter is not an
easy task. The frequency to time conversion very often assumes an a-priori knowledge of the
class of signal estimated: sinusoidal or white noise. The errors that can be introduced due to this
lack of a-priori knowledge are described in this chapter.
Assume we have a continuous time series xc(t) that we wish to sample with equidistant samples,
?t. We will then receive a discrete time series
where N is the number of samples in the series. The corresponding frequency information may
be achieved using an FFT, Fast Fourier Transform, which samples the Discrete Fourier
Transform, DFT. The frequency information is thus given by
where N is the length of the block of data in the transform, and is of the length 2N. The DFT
transform will produce frequency information at discrete frequencies given by
where ?t is the sampling increment. If the signal is completely periodic with the length of the
time record T, the DFT transform will produce the correct frequency information at the
corresponding fk. If this is not the case, frequency information may leak from one frequency line
to another. This leakage effect can be reduced by introducing a time window. A time window
w(n) will be multiplied on the time signal x[n] as
and the frequency information will thus be convolved with this window, since a multiplication
in time leads to a convolution in frequency,
where Xw(fk) denotes the windowed frequency information and Kw, PS is the amplitude scaling
necessary for a sinusoid, due to the decrease in power caused by the window. The scaling Kw, PS
is given by
where w[n] is the window used. The scaling for a power spectrum density is given by
The window will reduce leakage, but also make the analysis bandwidth larger and decrease the
energy content in the time signal. There is normally a trade off between time signal energy,
analysis bandwidth, picket fence effect (amplitude ripple), side lobes and spectral leakage. There
are several windows available, but the most commonly used in industrial measurements are: No
Window (Rectangular), Hanning, Flat top and Exponential. It is important to note that there are
several Flat top windows. The “best” flat top window is the P401 by Hewlett Packard. This
window has lower side lobes than the P301, which is the “most common” Flat top window used
in general measurement equipment. The coefficients for the P301 Flat top window are available,
but the P401 Flat top window is Hewlett-Packard proprietary information and is not generally
available. It is the P401 that is implemented in the HP35670A analyzer. For the measurements
acquired during the test flight, a Hanning window was used, since spectral resolution was the
most important parameter in this measurement. Different windows are presented with their key
parameters in Table 1 below. It is very clear that the spectral resolution is good with a Hanning
window, and amplitude accuracy is best for the Flat top window. It was however important to be
able to separate different tones in this measurement. For this reason, the Hanning window was
chosen.
Table 1: Description of key window parameters given a frequency range from DC-3.2 kHz, 2048 samples.
Window
Amplitude error
Bandwidth, BW
First Sidelobe
Hanning
1.43 dB
6 Hz
-31.5 dB
Hamming
1.75 dB
5.5 Hz
-43.2 dB
Flat top, P301
0.01 dB
13.7 Hz
-70.4 dB
Flat top, P401
0.01 dB
15.3 Hz
-82.1 dB
Rect, no window
3.94 dB
4 Hz
-13.2 dB
In order to decrease the variance in the averaging process, given a limited measurement time,
overlap data processing can be used. For a Hanning window, a common overlap processing
choice is 50% and retreives 90% of the data [1]. With a 63% overlap 100% of the data is used.
When using a window to decrease leakage some energy in the signal is lost, as described above.
This energy must be compensated for according to equation (5). In Figure 6 the effects of three
common windows are presented.
In order to use the total energy in the time signal, overlap data processing should be used where
the analysis system allows this. When using a Flat top window a great deal of energy is lost as
can be seen in Figure 6. The amplitude accuracy for the Flat top window is however high, though
at the price of increased variance. This overlap processing is especially important for slowly
varying time-data such as the data in this helicopter measurement.
The frequency domain signal consists of a real and an imaginary part, and has negative
frequencies. In most cases, a Power Spectrum with only positive frequencies is required. This is
created by
where PPS stands for the Power Spectrum. This amplitude scaling is correct assuming a periodic
narrowband signal as the input signal. For white noise, a Power Spectral Density is needed, since
this is a broadband signal. The PPSD is given by
For transient signals it is also important to scale for the time dependency. This is achieved by
In the above equations, the voltage dimension is included. There are three different scaling
methods that can be used. This is a difficult situation, since it requires a-priori information about
the signal before choosing the right type of scaling. Without this information it is impossible to
be sure that the amplitude is correct, [7]. In [8] it is stated that PPS and PPSD are the same. That
is a correct statement given the assumption in the book, a 1 Hz analysis bandwidth. In most
practical cases the analysis bandwidth is not 1 Hz. An amplitude scaling error will thus be the
consequence, often several 1000%. The relationship between PPS and PPSD is given by
Example:
Let us assume that x[n] is a sinusoid with an amplitude of 1V peak and a frequency of f0 that is
periodic with the time record, and is measured with Power Spectrum Density. In this case,
If we introduce the sampling frequency fs and the sinusoid frequency f0 we will achieve
The Power Spectrum Density is given by (9) as
For the sinusoid above it then follows
and consequently for a Power Spectra scaling method
Since all the power is located in one bin (ideally), f0, this bin must be equal to the result. In this
case, the PPS is giving the correct amplitude while PPSD is estimating with a bias error given by
The N?t term is the dominating error and we are thus introducing a bias that is directly related
to the analysis bandwidth, or the number of bins, that is given by (for a rectangular window)
Assume that we now change to a white noise signal that is given by
Given the previous discussion it follows that the Power Spectrum Density is given by
and consequently for a Power Spectra scaling
Since the random signal is spread out over the frequency band, each frequency bin must give the
correct amplitude. In this case, each bin should give A2 not NA2 as before, irrespective of the FFT
length, since there are N bins. However, the frequency width ?f is directly coupled to the FFT
length N as given by equation ?. We thus need to scale the data with this factor ?f.
In this case, there is an amplitude difference of mainly ?f. If ?f is equal to 1 Hz, there is no
problem, the scaling is still one. However, in most practical cases?f is not equal to 1 Hz. If the
correct amplitude scaling method is not used, severe amplitude scaling errors will result. The
correct frequency transforms in this case are given by equation (9) above. The above discussion
has assumed that a rectangular window has been used for the FFT calculation. In most cases
another window is used, such as a Hanning or Flattop window to decrease spectral leakage. This
window must also be compensated since it decreases the energy in time domain as well as
changing the?f term to a larger value. Table 1 illustrates the typical Bw for the most commonly
used windows.
The above equations show that it is important to have a-priori information about the signal in
order to evaluate any absolute amplitude levels. The method used to avoid problems when
analyzing unknown signals is to use a set of measurements and observe amplitude changes for
the frequency components of interest. It is not possible, in general, to find absolute amplitude
levels without some information about the signal. This information can be achieved by using a
set of measurements and using the information given by them as the base for futher action.
The following approach is a recommendation on how to conduct measurements when absolute
amplitude signal levels are of importance.
Measurement Procedure:
Start the analyzer with a Power Spectrum scaling method. Then,
1. Measure with one frequency range. Read all amplitude levels.
2. Measure the same signal again, but with a factor of two decrease in frequency span. This
gives a doubled measurement time and consequently half the Bw. If some amplitude levels
(peaks) change levels when compared to the previous measurement, the signal cannot be
scaled correctly using PPS.
3. Continue to change the frequency range until the amplitude levels are stable and do not
change when the measurement settings are changed. When this happens, the amplitude
values can be read, and they have the correct amplitude scaling.
If the signal always changes for each change of frequency range, try using PPSD scaling instead.
If the levels do not change when using a PPSD scaling, the amplitude levels are correctly scaled.
Observe that there are signals where it is not possible to reach a solution for either PPS or PPSD.
In such cases, it is difficult to rely on the amplitude values. PESD is not treated in more detail
since this analysis is not part of this measurement series.
A rule of thumb when determining the amplitude scaling method is:
If the analysis bandwidth << the signal bandwidth: Use PPSD scaling (=broadband signals).
If the analysis bandwidth >> the signal bandwidth: Use PPS scaling (=tonal components).
If the input signal is transient: Use PESD scaling (Energy Density) (=transients).
The left figure in Figure 7 illustrates how the scaling is correct using PPS, since there is no
compensation for the width of the filter. If there should be more than one signal component in
the left marked filter, the amplitude is wrong. The inverse is applicable for the right figure. In this
case, it is necessary to compensate for the width of the filter. If no compensation is made, the
amplitude will be scaled incorrectly, that is the signal would be overestimated if the analysis
bandwidth BW is larger than 1 Hz, otherwise underestimated. In Table 2 below some typical
measurement settings are presented and the errors that will result from a wrong assumption about
the signal.
Table 2: Complex FFT, 2048 samples (=800 effective lines), Hanning window.
Signal
Scaling
Span
BW assuming a
Hanning window
Measurement
error %
Sinusoid
PPS
0-12.8 kHz
48 Hz
4800
Sinusoid
PPS
0-50 Hz
187 mHz
500
White noise
PPSD
0-12.8 kHz
48 Hz
4800
White noise
PPSD
0-50 Hz
187 mHz
500
Cross Spectrum and Coherence
A cross spectrum measurement has been performed during the helicopter measurements. This
is an important measure, since the cross spectrum Gxy indicates the possible active reduction,
where the reference signal is the reference for the Filtered X-LMS algorithm. There has to be a
correlation or coherence between the two measurement points for the cross spectrum to produce
“peaks” when averaging several spectra together. The averaged cross spectrum Gxy(fk) is given
by
where Gxy(fk,m) are the individual estimates from reference x to response y and is given by,
where X(fk) and Y(fk) is given by equation (5) respectively. It is also possible to calculate the
cross spectrum from the cross correlation function Rxy(n) by
The cross correlation function Rxy(n) is given by the convolution sum
and also verifies the strong connection between the cross spectrum and the cross correlation. The
cross spectrum is the Fourier transform of the cross correlation function.
The coherence function is given by
which is a measure of how much of the output y is caused by the input x in a linear and stationary
sense. This measure is also a good indicator of how much attenuation an active feedforward LMS
system is capable of suppressing. Equation (29) expresses this. It is important to note that the
coherence function is always within the bounds
and is always one for one measurement. This makes the coherence function “invalid” for one
measurement. In the analysis described in this report the number of averages used is from 10 to
50.
4. Frequency Analysis
The measurement situation in the helicopter, is rather complicated. We do not know enough
about the signal beforehand to decide on the probable signal type. When analyzing the frequency
content of a signal using, for instance, a FFT, it is most important to know if the signal is
narrowband or broadband, as described in the previous section. If the signal consists of a
sinusoid, the amplitude scaling should be PPS (Power Spectrum). If the signal is broadband noise,
the amplitude scaling should be PPSD (Power Spectral Density). If this is not the case, severe
amplitude scaling errors will result, [1] [2].
If the data is unknown, as in our case, it is important to measure using a number of frequency
spans, and compare results. If the amplitude peaks keep their levels, we know that we have a
sinusoid. If this is not the case, the signal is more broadband than narrowband and we have to be
careful. The analysis of the helicopter sound has thus been split into several frequency ranges,
analyzed and compared one by one. By these comparisons of the amplitudes when changing the
frequency span, it is possible to determine if the component is narrowband or broadband.
The sound field varies slowly during flight. It is thus very important to establish the average
sound field. A Hanning window was used to reduce leakage, and maintain the frequency
resolution, RMS averaging with 50% overlap was also used. This is a common choice for the
Hanning window as we have shown before. 10 to 50 averages were normally used.
The Super Puma helicopter has one main rotor and one tail rotor, as well as several gear boxes.
There are four blades on the main rotor and five on the tail rotor. This gives an rpm for each rotor
in accordance with the following equation:
where Nb is the number of blades and the BPF is given in Hz. The main rotor has four blades and
the tail rotor, five. This gives Nb=4 and Nb=5 respectively. According to the frequency analysis,
the BPF (Blade Passage Frequency) is 17.6 Hz for the main rotor, which gives an rpm of
17.6x60/4=264. There is a 107 Hz component that is quite strong. This is the BPF of the tail
rotor. The sixth order of the main rotor BPF is at almost the same frequency as the 6th order of
the main rotor BPF (106.2 for the 6xBPF for the main rotor versus 106.67 for the 1xBPF for the
tail rotor, calculated @265 rpm, see table on page 35). Since the BPF for the tail rotor is 107 Hz,
this corresponds to an rpm of 1284, using equation (28). The main rotor is 16.2 meters in
diameter and the tail rotor 3.15 meters.
There are substantial noise levels at 8 kHz and 12 kHz. These components are created by the
turbine engines, Turbomeca MAKILA IA2, with 2109 hp each and by the gear boxes. The 768
Hz comes from the 2xBendix Shaft, see the table on page 35 and the analysis later in the “Results
of Analysis” section. There is a large component at 1.6 kHz which dominates the accelerometer
signal, see measurement figure 13. This component comes from the 4xFreewheel. It is interesting
to note that this component is low in the microphones, but high in the accelerometer signal.
5. Results of the Analysis
Measurement figure 1:
Measurement type: Power Spectrum
Measurement position: mic in seat
Number of averages: 10
Hanning Window: 50% overlap, BW=187.5 mHz
Frequency range: DC-50 Hz
The fundamental frequency of the main rotor is clearly visible. This analysis with many
frequency lines is most important in order to find out if the component is narrowband or
otherwise. It is clear from this analysis that the BPF is narrowband. The second peak is the first
harmonic of the BPF, 35 Hz. The BPF of the main rotor creates large infra-sound levels inside
the cabin. This component is not audible but affects the body. The headset need not treat this
component.
Measurement figure 2:
Measurement type: Power Spectrum
Measurement position: mic in seat
Number of averages: 10
Hanning Window: 50% overlap, BW=375 mHz
Frequency range: DC-100 Hz
The fundamental frequency of the main rotor is clearly visible in the frequency plot, and we know
from previous analysis that the signal is narrowband. By using the built-in harmonic marker it
is possible to mark the components that are harmonics of the main rotor BPF. One peak at 82 Hz
is not an order of the main rotor BPF, as can be seen in measurement figure 2. This is the Tail
Drive shaft fundamental. This component is however coupled to the BPF, but without an integer
number.
Measurement figure 3:
Measurement type: Power Spectrum
Measurement position: mic in seat
Number of averages: 10
Hanning Window: 50% overlap, BW=750 mHz
Frequency range: DC-200 Hz
The fundamental frequency of the main rotor is still clearly visible. The built-in harmonic marker
marks the harmonics of the BPF. One peak at 82 Hz is not an order of the main rotor BPF. This
is the Tail Drive shaft fundamental. This should be the BPF of the tail rotor. The main
components in the 200 Hz range are, however, due to the main rotor and its first orders, and the
BPF of the tail rotor. It is important to note that the low frequency components at 10 Hz and
below have increased in level. This is due to the PS scaling, and is an analysis error. We have
already verified a more correct analysis in measurement figure 1 by using more frequency
resolution. This is why it is so dangerous to analyze with only one frequency range.
Measurement figure 4:
Measurement type: Power Spectrum
Measurement position: mic at pilot
Number of averages: 10
Hanning Window: 50% overlap, BW=750 mHz
Frequency range: DC-200 Hz
The sound level at the pilot’s seat is almost identical to the seat microphone. A decrease in the
low frequency content is, however, visible. The level is not accurate in this figure, as already
discussed, but is valid as a relative measurement compared to figure 3.
Measurement figure 5:
Measurement type: Power Spectrum
Measurement position: mic in seat
Number of averages: 10
Hanning Window: 50% overlap, BW=1.5 Hz
Frequency range: DC-400 Hz
There are no major new frequency components in this frequency range. It is interesting to note
how it seems as if the low frequency content increases with each doubling of the frequency range.
This is correct given a PS scaling, but wrong given the reality. If the signal is broadband, in
comparison to the analysis bandwidth there should be a 6 dB amplitude increase for each
doubling, when using a PS scaling method.
Measurement figure 6:
Measurement type: Power Spectrum
Measurement position: mic in seat
Number of averages: 27
Hanning Window: 50% overlap, BW=12 Hz
Frequency range: DC-3.2 kHz
A new component is visible. The frequency is 1.8 kHz, and the level is quite high. This is the 2nd
order Meshing Tail component, according to the frequency table at page 35. This component is
also clearly visible in the accelerometer signal.
Measurement figure 7:
Measurement type: Power Spectrum
Measurement position: mic in seat
Number of averages: 10
Hanning Window: 50% overlap, BW=48 Hz
Frequency range: DC-12.8 kHz
Two new frequency groups are visible, one at 8 kHz and one 10 kHz. The levels are quite high.
This sound probably comes from the turbines. Note that it looks as if the low frequency
components are higher in level. This is not the case. Compare with the earlier analysis, which is
more correct. It is important to warn about this common measurement error. For this kind of
analysis, it is very important to measure in several frequency spans before judging the signal
levels.
Measurement figure 8:
Measurement type: Cross Spectrum
Measurement position: acc/mic in seat
Number of averages: 10
Hanning Window: 50% overlap, BW=6 Hz
Frequency range: DC-1.6 kHz
This measurement consists of a cross spectrum between the accelerometer and the microphone.
If there is a correlation between the structural borne and the air borne sound there will be peaks
in the spectra. It is very clear that the BPF from the main rotor is coupling through the helicopter
structure. The cross spectrum verifies that the airborne sound field inside the cabin is coherent
with the structural vibration levels measured by the accelerometer.
Measurement figure 9:
Measurement type: Cross Spectrum
Measurement position: acc/mic in seat
Number of averages: 10
Hanning Window: 50% overlap, BW =750 mHz
Frequency range: DC-200 Hz
There is a strong correlation between the structure borne and the air borne sound in the
helicopter. It is very clear that the BPF is coupling through the structure. This data indicates that
it could be possible to use an accelerometer as a primary field sensor.
Measurement figure 10:
Measurement type: Power Spectrum
Measurement position: accelerometer
Number of averages: 10
Hanning Window: 50% overlap, BW=750 mHz
Frequency range: DC-200 Hz
This measurement is made on the accelerometer. The BPF from the main rotor is very strong in
the structure. It is interesting to note that there is not as much low frequency contribution in this
signal as compared with the microphone signal. The fact that the main rotor BPF is as clear in
the accelerometer is a good sign for our work. However, there is an 82 Hz component visible,
that is the Tail Drive shaft fundamental. This tone is not a problem despite the fact that it is not
an order of the BPF, since this component is coupled to the BPF with a rational number and not
an integer.
Measurement figure 11:
Measurement type: Power Spectrum
Measurement position: accelerometer
Number of averages: 10
Hanning Window: 50% overlap, BW=6 Hz
Frequency range: DC-1.6 kHz
A large frequency component is visible at 776 Hz. This is this the 4xFreewheel component. The
passive part of the headset should be able to handle this component. However, it is expected that
the ANR system will reduce this tone as well, given a combined attenuation.
Measurement figure 12:
Measurement type: Power Spectrum
Measurement position: accelerometer
Number of averages: 50
Hanning Window: 50% overlap, BW=24 Hz
Frequency range: DC-6.4 kHz
The 787 Hz component is the strongest of them all. The analysis in the last measurement was
776 Hz. The rpm is changing slowly. This frequency component from the 4xFreewheel seems
to be the key contribution in the accelerometer.
Measurement figure 13:
Measurement type: Power Spectrum
Measurement position: accelerometer
Number of averages: 10
Hanning Window: 50% overlap, BW=48 Hz
Frequency range: DC-12.8 kHz
The 768 Hz component is still the strongest of them all. This component is the key contribution
in the accelerometer, but is very low in the microphones. This component can thus be used as a
good reference signal for the ANR system.
6. Comments regarding an ANR system
The sound field in the helicopter analyzed consists of few, though large tones in the 0-400 Hz
range with a good correlation. This is a good sign for an ANR system, since an X-LMS feed-
forward algorithm is capable of giving a large attenuation given these circumstances. It is
necessary to use a reference signal that is coupled to the rpm. The effective active attenuation is
given, as a rule of thumb, by the coherence between the reference signal and the error
microphone according to
where Adb is the attenuation and ? is the coherence between the reference and the error
microphone. The coherence is excellent since the active system is close to the ear and the
disturbance is narrow-band.
There are some tones that are very sharp and have high levels, it should be possible to use them
as a prediction of an rpm reference. This would have the advantage that a separate tacho signal
is not needed. This technique has been successfully used in a car application for Volvo, [9]. By
using this prediction algorithm the X-LMS algorithm is able to create a reference signal directly
from the primary sound field.
The 17.5 Hz tone creates very high infrasound levels. The wave length at 17.5 Hz is
approximately 18 meters. In a relatively small cavity like the inside of the Super Puma, this gives
a good chance to suppress this component by an ANR system using loudspeakers. The silent zone
around the error microphones will be approximately 2 meters in radius, which makes it possible
to receive a global reduction with few microphones, [11]. This is a good situation for an ANR
system. It is thus recommended to study the suppression of this component by a “volumetric
cancellation system” using loudspeakers and microphones, [10]. This part is not included in the
research project “A New Generation Active Headset and its Psychological Effects.”
The high levels at 1.8 kHz and around 8-10 kHz should be possible to handle by a good classical
passive attenuation in the headset.
The microphone signal used for the communication system is severely contaminated with the low
frequency background noise as well as the high frequency components. By using a combination
of filtering techniques it is likely that a large reduction in the noise level in the microphone can
be achieved. Initial tests have shown a suppression of the order of 25 dB, which is very
promising. It is very important to reduce these components in the microphone since the speech
intelligibility is otherwise rapidly decreased due to the noise level increase due to the
microphones.
Acknowledgment
We would like to thank Captain Arne Sjölund and his crew at the F17 air force base in Kallinge
for all their support in making these measurements. There are many cables and much equipment
that must be securely fastened. Everything went smoothly and the personnel were most
supportive. I would also like to thank Mr. A. Asplund for supplying all the frequency/RPM
information, which has been most helpful. Mr. Sven Johnsson kindly supplied a set of photos for
use in the report, which was greatly appreciated. The photo of the Super Puma helicopter was
taken by Mr. Gösta Bolander.
Measurement Figures
The next 13 pages contain plots of the data, with some key comments.
MKII Super Puma HKP10 frequency table (in Hz)
1R, 265 rpm @ 100% 4.42 1
2R 8.84 2
1*2nd stage Epicyclic Sun 12.85 2.91
3R 13.25 3.90
4R 17.67 BPF for rotor 4
1*2nd stage Planet Wheels 18.51 4.19
1T 21.29 4.82
8R 35.33 2*BPF for rotor 8
High Spot 2nd Stage Planet 37.06 9.89
High Spot 2nd Stage Annulus 39.75 9
1*1st Stage Epicyclic Sun 39.79 9.01
1*Main Bevel Rear 39.79 9.01
1*1st Stage Planet Wheels 49.16 11.13
12R 53 3*BPF for rotor 12
Port & Stbd Acc. Drive Idlers 61.74 13.98
1*Tail Rylon Drive Shaft 62.50 14.13
Prim/Sec Oil Pump 67.97 15.39
16R 70.67 4*BPF for rotor 16
High Spot 2nd Stage Sun 76.01 17.21
Port & Stbd Hydraulic Pipe 76.19 17.25
MRG Input Bevel Gear 81.44 18.44
1*Tail Drive Shaft 81.44 18.44
30R 88.33 20
High Spot 1st Stage Planet 88.36 22.27
High Spot 1st Stage Annulus 102.88 23.29
5T 106.67 BPF for tail 24.13
Port/Stbd Alt Annulus Gears 132.63 30.03
Free Wheel Drive Shafts 132.63 30.03
Oil Cooler Fan 140.27 31.78
2*Tail Drive Shaft 162.93 36.89
Port & Stbd Alternators 193.45 48.80
10T 213.10 48.25
High Spots 1st Stage Sun 215.71 48.84
2*Oil Cooler Fan 280.55 68.52
15T 319.77 72.40
1*Bendix Shafts 380.67 86.19
3*Oil Cooler Fan 420.82 95.23
2*Bendix Shafts 761.34 173.38
Meshing Tail Rotor Gearbox 937.92 213.36
Meshing Intermediate Gearbox 2688.87 603.69
M*Oil Pump 1st Stage Reduction 2788.32 631.32
M*Port & Stbd Hydraulic Pumps 3582.17 811.06
M*Port & Stbd Acc. Drive Idlers 3582.44 811.12
M*Port & Stbd Alt Annulus Gear 4643.18 1051.29
M*2nd Stage Engine Reduction 4643.32 1051.32
M*Oil Cooler Fan Gear 6173.16 1347.70
M*1st Stage Engine Reduction 11801.13 2671.96
References
[1] Julius S. Bendat & A. Piersol, Random Data, New York, 1984.
[2] Alan V. Oppenheim and Ronald W. Schafer, Discrete-Time Signal Processing, Prentice
Hall, 1989.
[3] Papoulis, Probability, Random Variables, and Stochastic Processes, McGraw-Hill, New
York, 1991.
[4] Julius S. Bendat & A. Piersol, Engineering Applications of Correlation and Spectral
Analysis, Second edition. New York, 1993.
[5] Jens Blauert, Spatial Hearing, The MIT Press, 1983.
[6] J. G. Proakis and D. G. Manolakis, Introduction to Digital Signal Processing, MacMillan,
New York, 1988.
[7] Thomas L. Lagö and Ingvar Claesson, “Hur analyserar jag en okänd signal?”, SVIB AU-1-
konferens, Ronneby, April 1995.
[8] R. W. Potter, “Compilation of Time Windows and Line Shapes for Fourier Analysis,”
Hewlett-Packard.
[9] H. Håkansson, T. L. Lagö and S. Olsson, “A Non-tachometer Based Order Analysis
Method for Interior Noise Measurements in Cars”, Proceedings of International Modal
Analysis Conference IMAC-XII, Vol II, pp 1491-1495, Hawaii, February 1994.
[10] P. Sjösten, S. Johansson, T. L. Lagö and I. Claesson, “Active Noise Control in a Twin-
Engine Patrol Boat”, Invited paper, Inter-Noise 96, Liverpool, August 1996.
[11] P. Sjösten, “Computer Simulations in Active Noise Control”, Proceedings of Acoustics,
Vol 8, Part 1, pp 187-190, 1986.
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