1. Experimental measurements and
assessment of the noise sources in train
sets using an acoustic array
Ing. Lenka Vavráková∗
Technical University of Košice (TUKE). Department of environment. May 2013
Abstract
In this paper the measuring outcomes of an experimental research about
the noise sources in train sets will be presented. The aim of this research is
to compare different trains in terms of Sound Pressure Levels (SPL) and to
obtain the location of the main noise sources using a phased acoustic array
(also called acoustic camera), which consists of 48 microphones. Once the
acoustic data is collected by the microphones and data acqusition system, a
frequency domain beamforming software, called NoiseImage [1], is applied
to it in order to obtain the noise source maps. The series of 13 measurements
took place in the railway next to Družstevná pri Hornáde, Slovakia the
15th of April of 2013. The whole research highlights the large noise levels
in the area due to the passing trains and analyzes the main parameters of
the trains that influence the noise generation. The noise levels produced
also depend on the train speed, train type and braking system considered.
Afterwards, some noise reduction recommendations are made in order to
reduce the SPL generated, such as reducing the train speed when possible
and using newer disc brake systems.
∗
e-mail of the author: lenka.vavrakova@gmail.com
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2. 1 INTRODUCTION
1 Introduction
With the growing size of the urban areas and the increase of the amount of dis-
placements between them, the train traffic figures are rising with the consequent
important increase of the noise levels next to the areas next to the railways. In
order to reduce these noise levels, it is essential to know the noise sources and
their location to get a better understanding of the physical processes involved and
to obtain improving solutions for the future.
A popular approach to achieve this objective is the use of phased acoustic
arrays (see figure 3) to determine the different sound sources, their locations and
their relative intensity. Hence, the actual noise levels for each case are obtained,
instead of using the conventional, less accurate Noise-Power-Distance (NPD) ta-
bles, which are based in simplifying assumptions, which lead to considerable errors.
In this research, a commercial “Star 48 AC Pro” acoustic camera, designed for
outdoor applications and consisting of 48 microphones placed in three different
segments forming a triangle(see figure 3), was used.
A special emphasis was made when analyzing the influence of the train speed
and number of train sets with the SPL recorded and an study of the braking sys-
tems was also performed, in order to assess their performance. Two main braking
systems are used in the measurements specimens, the older metallic braking ver-
sion, known as “jaw brake”, and the newer model made mainly of composite
materials, known as “disc brake”, see figure 1.
With the acoustic data from the microphone array, a beamforming algorithm
can be used to localize the sound sources from the train, see figure 8. In this
approach, the conventional frequency domain beamforming, also known as delay-
and-sum beamforming was used due to its robustness and fast computational
time. Before the beamforming can be applied, several considerations have to be
taken into account, such as the Doppler effect, the effect of a moving sound source,
sound spreading, atmospheric attenuation, ground reflection and wind direction.
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3. (a) (b)
Figure 1: The two main braking systems employed in this research. a) The older
jaw braking system. b) The newer model disc braking system.
2 Experimental set-up
2.1 Atmospheric and geographic data
The experiment took place in the railway next to Družstevná pri Hornáde, Slovakia
(see figure 2) the 15th of April of 2013 from 11:00 to 14:30. In table 1 the
main atmospheric parameters values are gathered for that date, obtained from
the Slovak Hydrometeorology Institute (SHMU)[2]. Due to the short period of
measurements, the atmospheric conditions were considered constant.
Figure 2: Exact location where the measurement took place, indicated by a red
marker.
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4. 2 EXPERIMENTAL SET-UP
Parameter Value
Coordinates of the array 48.816719 North; 21.233857 East
Date of the experiment 15th of April 2013 (from 11:00 to 14:30)
Atmospheric pressure 989 hPa
Temperature 15 ºC
Wind speed 2 m/s
Humidity 60-70 %
Table 1: Average technical values of the experimental measurements.
2.2 Hardware specifications
As stated before, a “Star 48 AC Pro” acoustic camera was used for the passing
train measurements. Figure 3 shows the microphone layout of the camera and the
experimental set-up, while table 2 presents the most relevant parameters of the
acoustic camera used. The perpendicular distance from the array to the railway
was fixed in 16.5 m.
Parameter Value
Number of microphones 48
Array diameter 3.2 m
Frequency response of microphones 20 Hz-20 kHz (± 3dB)
Dynamic range of microphones 28-130 dB (A-weighted)
Recommended mapping frequencies 100-13000 Hz
Recommended measurement distance 4-500 m
Table 2: Technical parameters of the acoustic camera Star 48 AC Pro.
An advantage of this hardware is that a video camera is attached to the centre
of the acoustic array, therefore, the source maps can be overlaid to the optical
frames immediately, as depicted in picture 8, making the interpretation of the
results easier. A software plugin, developed by the provider [1], called “Pass-by”
was used because it is specifically recommended for moving sound sources.
In order to calibrate the acoustic camera and to measure the Overall A-
weighted Sound Pressure Level (OASPL), a sound level meter NORSONIC-NOR
140 was employed in each measurement. The technical specifications of this in-
strument can be found in reference [3].
In addition, the train speeds were measured with a portable radar equipment for
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5. performing a further analysis of the noise levels variability.
3 Experimental results
As previously mentioned, 13 different measurements were performed. Table 3
contains the most relevant data for each measurement, from left to right: train
type, time of the measurements, number of trainsets, braking system, train speed
and OASPL. All the trains considered have one locomotive set, except for mea-
surements 3 and 4 which have 2 locomotive sets. Table 4 shows the average and
standard deviation values of the OASPL for both braking systems and the global
values for all the measurements.
Meas. nº Train type Time Trainsets Brake Speed [km/h] LA,max [dB]
1 Passenger 11:35 3 Disc 96 82.6
2 Freight 11:45 15 Jaw 80 86.1
3 Passenger 12:08 2 Disc 100 84.9
4 Express 12:15 11 Disc 95 81.5
5 Passenger 12:26 2 Jaw 100 92.4
6 Freight 12:34 38 Jaw 82 87.9
7 Freight 12:40 25 Jaw 83 86.9
8 Freight 13:07 43 Jaw 83 89.9
9 Freight 13:24 25 Jaw 85 90.8
10 IC 504 13:28 7 Disc 109 88.4
11 Express 13:33 4 Disc 85 79.9
12 Freight 13:58 34 Jaw 83 87.1
13 Freight 14:04 26 Jaw 83 90.3
Table 3: Experimental results obtained.
OASPL Jaw Brake Disc Brake Global
Average value (dBA) 88.9 83.5 86.8
Standard Deviation (dBA) 2.2 3.3 3.8
Table 4: Average and standard deviation values (dBA) for the jaw brake, disc
brake and all the measurements.
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6. 3 EXPERIMENTAL RESULTS
3.1 Influence of the train velocity
Figures 4 depicts the influence of the train passing speed over the OASPL mea-
sured. In both figures a) and b) it can be observed a clear dependency between
the OASPL and the train velocity for the disc braking system and for the jaw
braking system. The correlation coefficient in each case was found to be 0.87 and
0.69 respectively. The p-values were found to be 0.0002 and 0.0011 respectively,
confirming a clear dependency between both variables. Especially in the case of
the disc braking system, the measurements are more correlated than in the jaw
braking system. In addition, the boxplot for both braking systems is shown in
figure5, where it can be observed that the disc brake produces approximately 5
dBA lower OASPL.
3.2 Influence of the number of train sets
However, the influence of the train set number on the OASPL is not so clear and
only a slight increasing trend is found, as seen in figure 6, probably due to more
scattered results.
3.3 Frequency componets analysis
For analyzing the frequency components of the acoustic data, three different
spectrograms representing one different train class (conventional passenger train,
freight train and InterCity) are provided in figure 7. It can be appreciated that the
freight train has louder SPLs in a wider frequency range, including more annoying
frequencies, during more time than the other two cases. This can be explained
with the fact that freight trains are usually much older and noisy than new pas-
senger trains, especially InterCity trains. As it can be appreciated, all the cases
show broadband noise instead of having any clear tonal peaks.
3.4 Beamforming results
As explained before, the software available allows the representation of the con-
ventional frequency domain beamforming source maps overlaid with the optical
frames of the videocamera. An example of these source maps is presented in figure
8, where it can be seen that the main noise sources are located next to the wheels,
as it could be expected. However, the InterCity train has much lower SPLs than
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7. the other two cases and presents a secondary source of comparable strength in a
higher position, possibly due to aerodynamic noise. This fact can be due to the
newer design of this type of train, as mentioned before, especially considering the
disc braking system.
4 Conclusions and recommendations
In the previous sections, the influence of the train speed, number of train sets and
braking system has been estimated. A strong dependence between the OASPL
measured and the train speed is found. In addition, newer braking systems,
such as the disc brake, perform much better than older ones, such as the jaw
brake, showing a reduction of the OASPL measured of approximately 5 dBA.
However, other parameters analyzed, like the number of train sets did not show
any remarkable influence on the OASPL generated.
4.1 Recommendations
After the analysis performed in the previous sections, some recommendations can
also be made about the design and use of trains that would pass next to residential
areas:
• Although changing all the trains currently circulating is practically impos-
sible and would generate huge costs, it might be possible to start gradually
changing the braking systems of old noisy trains to new braking systems,
such as the disc brake, which has proved to perform in a more satisfactory
and quieter way.
• The train speed is probably the easiest parameter to control and providing
maps of the areas close to residential areas can allow the train driver to
reduce the train velocity in order to generate lower sound levels without
critically deteriorating the train schedules and times.
• In the future, further analysis of this topic should also consider the railways
as an analysis element and also the lifecycle of the braking systems and
railways for getting more accurate data.
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8. REFERENCES
References
[1] Acoustic Camera website.
URL (website): http://www.acoustic-camera.com
URL (NoiseImage software): http://www.acoustic-camera.com/en/products/
software-noiseimage/pass-by.html
[2] Slovak Hydrometeorology Institute (SHMU) website.
URL: http://www.shmu.sk
[3] Norsonic - Sound Analyser Nor140 Manual.
URL: http://www.norsonic.com/filestore/PDF-filer/Brochures/
Nor140SoundLevelMeterbrochure.pdf
[4] L. Štulíková. Influence of input parameters in different prediction methods for
railway traffic noise. CTU in Prague, Faculty of Civil Engineering, Department
of Railway Structures
[5] U.Moehler, M. Liepert, U.J. Kurze, H.Onnich. The new German prediction
model for railway noise “Schall 03 2006” - Potentials of the new calculation
method for noise mitigation of planned rail traffic. Munich, Germany.
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9. REFERENCES
(a)
(b)
Figure 3: Acoustic camera Star 48 AC Pro: (a) commercial product, (b) facing
the railways with the data acquisition system (left) an the sound level meter
NORSONIC-NOR 140 used for calibration and OASPL measurements (right).
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10. REFERENCES
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Train velocity (Km/h)
OverallA−WeightedSoundPressureLevel(dBA)
OASPL (dBA) vs Train Speed for Disc Braking System
(a)
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Train velocity (Km/h)
OverallA−WeightedSoundPressureLevel(dBA)
OASPL (dBA) vs Train Speed for Jaw Braking System
(b)
Figure 4: Overall A-Weighted Sound Pressure Level (dBA) vs. the train velocity
(km/h) for: (a) the disc braking system and (b) the jaw braking system. In
addition, least squares lines are also presented in order to see the trends.
Disc Brake Jaw Brake
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OASPL(dBA)
Boxplot for the OASPL for the two braking systems
Figure 5: Boxplot of the OASPL (dBA) for both braking systems: Disc and Jaw.
The red line in each box indicates de median value, each horizontal blue line in
the box represents the 25th and 75th percentiles and the whiskers extend to the
most extreme datapoints. The average OASPL for all the measurements is also
represented as a black dashed line.
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11. REFERENCES
0 5 10 15 20 25 30 35 40 45 50
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Train set number
OverallA−WeightedSoundPressureLevel(dBA)
OASPL (dBA) vs Train set number
Figure 6: Overall A-Weighted Sound Pressure Level (dBA) vs. the train set
number. In addition, least squares lines are also presented in order to see the
trends.
Figure 7: Spectrograms (Frequency vs time and SPL in the colorbar) for three
different measurements, representing from top to bottom: Conventional passenger
train, Freight train and InterCity train.
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12. REFERENCES
Figure 8: Beamforming source maps for three different measurements, represent-
ing from top to bottom: Conventional passenger train, Freight train and InterCity
train.
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