Article
citation information:
Warczek, J. Identification of
rattle noise sources in the vehicle cabin using an acoustic camera. Scientific Journal of Silesian University of
Technology. Series Transport. 2025, 126,
267-289. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2025.126.17.
Jan WARCZEK[1]
IDENTIFICATION
OF RATTLE NOISE SOURCES IN THE VEHICLE CABIN USING AN ACOUSTIC CAMERA
Summary. Driving comfort in the
car cabin depends on the prevailing acoustic climate. The occurrence of various
types of intermittent noises is a common phenomenon observed by users of motor
vehicles. In the cabins of motor vehicles, a local source of non-stationary
noise due to the propagation of sound waves in the air and in the structural
structure makes it practically impossible to determine its location organoleptically. The article presents the use of an
acoustic camera to locate noise sources. Research was presented in the field of
recognizing the location of sources intentionally introduced into the cabin and
identifying spontaneous sources caused by operational wear. The obtained
results confirmed the usefulness of using an acoustic camera in identifying
noise sources, however, the presence of apparent sources may in some cases
result in incorrect diagnoses.
Keywords: acoustics of car cabins, rattle noise sources,
acoustic camera
1. INTRODUCTION
Being in the cabin of a vehicle that performs a
transport task is always associated with receiving auditory sensations caused
by operational factors and related to unintended physical phenomena related,
for example, to residual energy conversion processes. Sounds that occur as a
result of the operation of the drive system or aerodynamic effects are usually
perceived by users as an indispensable element of using a given means of
transport. However, all types of sounds that are not related to the functioning
of the transport device are perceived negatively as unwanted noises. A typical
example of bothersome sounds that negatively affect people staying in vehicle
cabins are various types of impacts coming from mechanical sources (e.g.
vibrations, impacts, friction) and electrical sources (e.g. magnetic, magnetostrictive) [28].
The occurrence of various types of
discontinuous noises is a common phenomenon observed by car users [19].
Particular intensification of this type of phenomena is observed with
increasing operational mileage (including repairs carried out in the meantime).
Vehicle users often report faults in the form of various types of squeaks,
creaking and grinding noises. Apart from the direct cause of such noises, the
key issue is to determine the location of their direct source.
In the cabins of motor vehicles, a local source
of noise with discontinuous characteristics due to the propagation of sound
waves in the air and in the structural structure makes it practically
impossible to determine its location organoleptically.
An additional difficulty is the limited space of the cabin and some of the
materials creating partitions that strongly reflect sound waves (e.g. car
windows).
To a first approximation, the best solution to
the problem of locating noise sources seems to be the use of methods based on
determining the directionality of sound propagation. Currently, devices using
beamforming (acoustic cameras) are becoming more and more common, in which the
recorded sound pressure can be superimposed on the image of an object in order
to illustrate the sound pressure distribution. Thanks to the acoustic camera,
you can "see" the location of the noise source.
In the conducted research, a number of
experiments were carried out to assess the possibility of using an acoustic
camera to locate noise sources in the vehicle cabin. For this purpose, two sound
sources were designed and manufactured. The main assumptions regarding the
designed sound sources concerned their dimensions and the emitted acoustic
power. The size of the sound source allows it to be placed in selected
locations in the passenger car cabin (including inside the cabin ventilation
openings). The acoustic power value was regulated using a built-in amplifier.
Generally, the main assumption regarding emissions was to make the sound
audible to people staying in the vehicle cabin. Before carrying out the basic
tests in laboratory conditions, the sources were tested with an acoustic camera
equipped with a microphone array the same as the one used later in the
experiments inside the cabin. The obtained results of preliminary tests were
then verified on a real facility. Research was carried out to identify noise
sources in the vehicle cabin during normal operation.
2. ANALYSIS OF SOUND SOURCE DETECTION METHODS
The basic scope of application of
acoustic cameras is the location of sound sources. Detecting the source may
also be the purpose of detecting changes in the technical condition of the
object. An acoustic camera is a device containing elements that allow the
recording of images and sound pressure waveforms. The acoustic camera matrix consists
of several dozen microphones appropriately placed relative to each other and a
centrally located video recorder. Measurements are carried out by pointing the
matrix at the test object, the video recorder allows you to display the
measurement area online, which makes it easier to set the matrix in the
appropriate position. Starting the measurement triggers the recording of the sound
pressure by each microphone and the recording of an image or video from the
tested object.
Entering parameters such as the
distance of the camera from the sound source, temperature and selecting an
appropriate computational algorithm, most often beamforming, allows the
recorded sound pressure to be superimposed on the image in order to generate an
acoustic map showing the sound pressure distribution (Figures 1 and 2). The
principle of beamforming is beam shaping by delay and summing [8].
Fig. 1. A microphone array, a
far-field focus direction,
and a plane wave incident from the focus direction
A measurement matrix composed of M
microphones located in locations rm (m = 1,
2,..., M) in the x-y plane of the coordinate system. When such a surface is used for
Delay-and-Sum Beamforming, the measured pressure signals pm are individually
delayed and then summed:
where: wm - the set of weighting factors applied to individual
signals from individual microphones.
The individual time delays Δm are chosen with the aim of
achieving selective directional sensitivity in a specific direction,
characterized by the κ vector (fig. 1). This is achieved
by adjusting the delay times so that the plane wave signals originate in the κ direction and are aligned in time before they are summed.
where: c - speed of sound.
Fig. 2. In near-field focusing,
spherical waves emitted by
a monopole source at the focus point r are assumed
When the near field occurs (Fig.
2.), then for a point source the time delays Δm depend on the distance r:
where: rm(r)=|r-rm| is the distance
from microphone m to the focus point.
In the article [1] there
is an example of an acoustic camera for detecting the failure state of an
electric motor. The work focuses on detecting eccentricity, as it is one of the
most common failure states of an induction motor. The presented measurements made
with an acoustic camera were compared with vibration analysis as a reference
method.
In the article [26],
research was carried out at Amsterdam Schiphol Airport using a set of 32
microphones, during which 115 flights of landing aircraft were recorded. The
aim was to determine the differences in acoustic signals for different types of
aircraft and investigate the causes. It was assumed that the main cause of this
variability are differences in the noise emitted by aircraft, as previous
experience has confirmed that the impact of the variable atmosphere (for the
distances considered) is negligible. A strong correlation was found between the
noise level and the rotational speed of the turbine engine. The use of a
microphone array allowed for acoustic imaging, thus distinguishing the noise of
aircraft components from noise from other sound sources. It has been shown that
turbofan engines are the main source of noise for many types of aircraft.
The problems of
identifying noise sources in the far field are completely different than those
in the near field. The problem of correctly identifying sources from a long
distance was presented in detail in [18]. The presented simulation studies
indicate an important aspect of the variable sound speed in an air stream with
variable temperature.
In article [31], a
method was proposed to locate the sound source in the frequency band from 100
Hz to 4 kHz in two dimensions using a microphone array by calculating the
direction of arrival of acoustic signals. Assessing the direction of arrival of
acoustic signals using a set of spatially separated microphones uses the phase
information present in the signals. For this purpose, time delays are estimated
for each pair of microphones in the array. Knowing the geometry of the system
and the direction of arrival of the sound wave, it is possible to determine the
location of the source. In the cited work, information about the phase of the
signals and the direction of arrival of the signals in the microphone arrays
were used to estimate the source location.
In work [39], the
concept of an "acoustic camera" was used to study noise sources in
railway wagons moving on the track. Instead of a visual image, sound markers
placed in the wagon were used. A new adaptive "beamforming" signal
processing algorithm has been developed to locate the loudest noise sources on
board a rail car passing a stationary array of trackside microphones. The
proposed microphone beamforming system tracks the spatial movement of the wagon
using two inaudible acoustic signals placed on the board of the wagon. The
proposed scheme then locates the noise sources with respect to the wagon
coordinates. No supporting infrastructure (e.g. radar or video camera) is
required beyond on-board navigation beacons. Monte Carlo simulations and
anechoic chamber experiments confirmed the effectiveness of the proposed
scheme.
Acoustic cameras are
becoming more and more widely used in environmental acoustics. Reference [12]
presents the results of locating the main noise sources in an industrial plant.
The main noise sources were identified using an acoustic camera using the
Beamforming Method. In parallel with the acoustic camera measurements, sound
level measurements were made at the main noise sources. Based on the
calculations, a forecast was made for noise emissions in residential buildings
located near the power plant. Acoustic noise maps were made using LEQ Professional software, which takes into account the 3D
geometry of buildings inside the plant. In this work, the location of the main
external noise sources in the production plant was carried out using an
acoustic camera. Based on the results, actions to reduce noise were proposed.
In paper [41] a new
approach to monitoring vehicle traffic on a large scale was presented. To meet
the growing demand for more accurate traffic monitoring, the use of road sounds
has become a popular approach because they provide insight into the types of
traffic occurring. This paper presents an approach to vehicle classification
based on acoustic signals, using Mel frequency cepstral coefficients (MFCC) and long short-term memory (LSTM) networks. This
study showed a classification accuracy of 82–86.2% in four vehicle categories:
motorcycle, passenger car, truck and non-engined
vehicle. The results showed that large-scale and low-cost acoustic processing
can be effectively used for vehicle monitoring.
Identification of noise
sources is one of the basic elements of the process of reducing hazards in the
work environment. Acoustic imaging, or sound visualization, is a graphic form
of presenting acoustic phenomena, in which the parameters of the emitted noise
are in the form of a color map superimposed on the image of its source. In work
[27] one of the main acoustic imaging techniques, beamforming and acoustic
monitoring devices using this technique, i.e. acoustic cameras, are discussed.
Examples of laboratory research results and practical applications are
provided. The possibilities of using this technique and its application are
also presented.
The work [33] presents
preliminary research results on the use of an acoustic camera mounted on an
aircraft. The design and implementation of an acoustic camera for direct
mounting on the UAV hull was presented. The camera consists of 64 microphones,
a central processing unit and data acquisition and processing software
specifically developed to detect low-level acoustic signals in the far field.
The built camera has an aperture of 2 m and was designed for observations from
a height of up to 300 m, with a spatial resolution of 12 m. This allows for
acoustic mapping of a large area, performed in a similar way to orthophotometry.
Most acoustic cameras
used today are based on similar technical solutions. Despite this, research
work is still being carried out to develop new concepts of technical solutions
for acoustic cameras. Development works concern, for example, the
miniaturization of acoustic cameras and new areas of their applications. The
book [3] presents theoretical assumptions and extended analyzes regarding the
basics of acoustic cameras and recognizing the location of sound sources.
Work [2] presents a new
design of acoustic cameras with small dimensions. Artificial intelligence
methods were used to design the optimal shape of the microphone array. The work
presents various prototype versions of small acoustic cameras.
The works [6, 7]
presents research results on the processing of information from the sensor
matrix. In particular, the so-called blind beamforming method was used. Blind
beamforming is an operation similar to conventional beamforming, except that it
does not require knowledge of the sensor responses and locations. In other
words, blind beamforming enhances the signal by processing only the data from
the chip, without much information about the chip.
In work [14], a binaural
recorder based on digital MEMS microphones was developed as part of the
research. Novel approaches have been used to circumvent the shortcomings of
MEMS. The proposed system offers advanced features such as real-time filtering,
low-power consumption and small size. This is an example of examining the
acoustic climate in the cabin of a motor vehicle.
The paper [42] describes
a low-complexity field-programmable gate array (FPGA)-based prototype that
computes and visualizes acoustic intensity images in real time. The system
consists of 32 microphones and performs all signal processing tasks on a
low-cost Xilinx Spartan 3E FPGA. The prototype
calculates the intensity of images at a resolution of 320×240 pixels at 10
frames per second.
In work [35], an
acoustic camera was designed and built based on hardware and software available
on the market. As a result of this work, a programming environment for an
acoustic camera system was proposed based on the use of a digital microphone
and Raspberry Pi. The built measuring equipment makes it possible to locate the
noise source to a large extent while maintaining the low cost of such a
solution.
Another concept of
building acoustic cameras was presented in [25]. The purpose of this work was
to use a spherical microphone array and a spherical video camera. Several
different static and adaptive beamforming techniques were implemented in the
system, and every effort was made to make the proposed system accessible to a
wide range of acoustics practitioners. The proposed solution allows you to
capture and analyze the sound scene using a microphone system, and then
estimate the parameter to determine the activity of the sound source in
specific directions. Additionally, the developed VST
software plug-in significantly increased the real-time capabilities of the
developed solution.
In article [16], a
methodology for miniaturization of acoustic camera systems was investigated to
improve their mobility. Generally, the problem concerns the size of the microphone
array. The work proposes minimizing the physical aperture through careful
selection of the position and number of microphones with adapted spatial filter
synthesis. This irregular layout geometry focuses sensitivity towards the
target while avoiding aliasing artifacts. The increased portability of compact
acoustic cameras could expand applications in car monitoring, urban noise
mapping and other industrial areas currently limited by large systems.
In parallel with the
development of acoustic camera designs, methods for processing signals obtained
from acoustic pressure sensors are still being developed. The article [4]
presents the results of research and analyzes using acoustic cameras used to
recognize various sound sources. Based on the assessment, two methods based on
recognizing the energy of acoustic sources were identified as the best, i.e.
the multi-resolution search method and the effective expectation maximization
method.
The article [20]
presents a closed, one-stage the least squares algorithm for source
localization and shows that it is mathematically equivalent to the so-called
spherical interpolation method, but is characterized by lower computational
complexity. This approach allowed for reducing the necessary computing power in
real-time acoustic cameras.
Acoustic localization
allows measuring the range of an emission source along its angular coordinates.
Spatially differentiated receiving units enable measurement of received signals
(triangulation), i.e. the difference in arrival time. However, if there is more
than one emission source and several sources are located simultaneously,
undesirable line intersections may generate spectral spatial responses, i.e.
location uncertainty. The work [22] focused on creating acoustic images using
an acoustic camera converted to work in the biostatic mode. The proposed
focalization of a bistatic acoustic camera (on nodes
of three-dimensional spatial coordinates) uses both temporal and spatial
information for imaging.
The work [29] presents
the results of acoustic recognition using a real-time audio camera, which uses
the output signal of a spherical beamforming system of microphones controlled
in all directions to create a central projection. A panoramic, mosaic image of
space is created with a superimposed sound intensity distribution. Since both
the visual and audio images from the camera constitute a central projection,
the resulting audio and video images can be recorded using standard computer
vision techniques. The presented method was used to study the relationship
between acoustic features and architectural details of a concert hall.
The development of
methods increasing the resolution of acoustic imaging is presented in [40]. By
using efficient algorithms running on dedicated computing equipment, it is
possible to obtain high-resolution acoustic images while maintaining high time
efficiency.
Wireless acoustic sensor
networks (WASNs) consist of a distributed group of
acoustic sensing devices equipped with sound playback and recording functions.
Current mobile computing platforms offer enormous opportunities to design
audio-related applications involving acoustic sensing nodes. In this context,
the localization of acoustic sources is one of the application fields that has
developed greatly in recent decades. In general, the localization of acoustic
sources can be achieved by examining the energy and temporal and/or directional
characteristics of the incoming sound at different microphones and using an
appropriate model that relates these characteristics to the spatial location of
the sound source(s). The paper [9] reviews common approaches to source
localization in WASNs that focus on different types
of acoustic features, namely the energy of the incoming signals, their time of
arrival (TOA) or time difference of arrival (TDOA),
direction of arrival (DOA), and controlled response power (SRP)
resulting from combining multiple microphone signals.
The spatial localization
of sound sources is increasingly used for biological research. The recent
development of new, deployable acoustic sensor platforms provides opportunities
to develop automated tools for bioacoustic field
research. In [30] was implemented an AML-based source localization algorithm
and used it to locate marmot alarm calls. The results show that the AML source
localization algorithm can be used to locate real animals in their natural
habitat using a platform that is practical to implement.
Paper [11] presents
newly developed mapping of moving sources, including video overlay and
necessary measurement techniques. Issues related to the synchronization of
optical and acoustic film were discussed. The second topic was
three-dimensional mapping of acoustic sources onto a 3D model of the
measurement object. Simple mapping of a virtual plane at a fixed distance has
now been replaced by varying measurement distances to individual points on the
surface of the 3D model. Complete 3D mapping of indoor spaces depends on
omnidirectional, non-planar layouts. Such mappings may be useful in car
interiors, where CAD models of the driver's cabin are often available.
The work [36] presents
novel probabilistic three-dimensional (3D) mapping effects that use acoustic
images recorded in the underwater environment. The acoustic camera is a
future-proof imaging sonar that has recently been widely used in underwater
inspections. In this article, a volumetric 3D model is used to reconstruct the
underwater environment, and Bayesian inference is used to update the
probability of occurrence of each voxel that makes up the 3D model. To make the
occupancy mapping theory more suitable for the acoustic camera, a novel inverse
sensor model is designed. Using occupancy mapping theory and a novel inverse
sensor model, it is possible to robustly and efficiently reconstruct a dense 3D
underwater environment from arbitrary acoustic images.
Acoustic cameras are
also used in sound beam mapping studies. The work [38] examined the
metrological capabilities of the acoustic beamforming technique, and in
particular analyzed its accuracy in estimating the acoustic power of sources
and locating their spatial position. The uncertainty of the system was
determined by combining the statistical effects of the uncertainty of the input
parameters under the basic hypothesis that the sound source can be represented
as a distribution of independent monopoles. This analysis was performed using a
Type B approach based on the analytical model according to the ISO Guide for
the expression of measurement uncertainty, followed by a numerical model based
on Monte Carlo simulation. Systematic errors resulting from deviations in input
parameters (e.g. sound speed or source-system distance) were analyzed,
affecting the output level and spatial accuracy. Once these inaccuracies have
been quantified, suggestions are provided for minimizing them. Finally, a
criterion was developed to optimize the focusing of the beamforming technique
based on maximizing the contrast of the acoustic image.
Examples of automation
of measurements with acoustic cameras also appear, based on sound signal
processing algorithms.
Paper [10] presents the
results of research on the automation of fish detection in acoustic research
conditions. A neural network was used to recognize acoustic images used in
sonar research. An alternative approach to identifying traffic flows is
presented in [17]. Instead of using a visual image analysis system and a
computer vision algorithm, acoustic recognition was used to recognize vehicles.
Acoustic traffic monitoring is cost-effective and can provide greater accuracy,
especially in low-light conditions and where cameras cannot be installed. In
this article, the task of classifying vehicle subtypes based on acoustic
signatures allowed for the categorization of vehicles into car, truck, bicycle
and no vehicle.
An interesting technical
problem that can be solved using algorithms similar to those in acoustic
cameras is the analysis of the climate in the cabin of a means of transport.
The article [34]
presents methods of influencing the acoustic climate in vehicle cabins using
active sound control. The aim is to show that automotive sound control systems
are an exciting field of research that can significantly improve passenger
comfort in acoustically difficult environments.
When assessing the
auditory impressions occurring in the cabin of a car, a very good solution is
to use loudspeaker systems with full channel separation. In the work [32],
attempts were made to build a loudspeaker system that would well reproduce
binaural recordings, but the proposed solutions had a number of drawbacks,
which consisted in the fact that the listener's head must be in a designated
place, head movements cause problems in locating sound sources, the loudspeaker
system must be precisely spaced, moving them by a small angle may disturb the
correct sound localization.
The work [5] presents
research results related to the acoustic environment in the car cabin, which
has a significant impact on the perceived quality of the vehicle. The acoustic
environment in a car cabin consists of two elements: noise produced by
automotive processes and the sound produced by the car audio system. In both
cases, active methods can be used to improve the acoustic environment, and the
paper presents research on both active car noise control and active sound
reproduction systems in cars. The aim of the research was to broadly understand
the improvement of acoustic comfort in the cabin.
The study [23] examined
the comparison of the volume of sounds inside a car from various sound sources
in situations similar to everyday life. In the described experiment, the sound
sensations while driving were assessed continuously using the continuous rating
method by category. This method makes it possible to evaluate sensations evoked
by different sounds without paying particular attention to any specific sound
in the same context, and to examine the impact of each noise individually. The
results suggest that some sounds tend to be overestimated compared to
background noise, while others are not. The results of this experiment can be helpful
in determining the influence of various factors in the sound environment and
taking effective countermeasures.
A separate, important
issue related to the detection of noise sources is the use of acoustic cameras
in limited spaces. In [24], a compressive non-stationary near-field acoustic
holography based on the time-domain plane wave superposition method was
proposed to precisely reconstruct the instantaneous sound field using a smaller
number of measurement points. In the proposed method, a time-domain convolution
superposition pattern is determined using the instantaneous propagation kernel
to relate the time-varying pressure of the hologram plane to the pressure and
wavenumber spectra of the virtual source plane.
A separate issue
directly related to the study of auditory sensations in car cabins is the
reproduction of the spatial distribution of sounds. Auralization
is a well-known method used to create virtual acoustic scenarios. The work [13]
discusses techniques for extracting binaural impulse responses inside a
passenger car cabin. The article analyzes the results of noise measurements
inside a battery-powered electric vehicle. Detailed methods for determining the
torsional vibrations of the drive system as reference values are also
presented. Moreover, a method of measuring and interpreting the path of
transmission of acoustic phenomena from the drive system of a battery electric
vehicle to the passenger cabin is presented.
Detecting stud sources
using acoustic cameras is not a problem in the case of continuous emission
sources. In this work, the research was focused on examining discontinuous
sounds (unsteady noise) that often occur in car cabins.
3. DESCRIPTION OF RESEARCH AND ANALYSIS OF
RESULTS
As part of the
research conducted in the passenger car cabin, various recorded signals from
transient noise measurements were used. Fragments of structural elements used
in car cabins were used in the preparation of test signals. The next stage of
the research was the recording of sounds coming from the collision of elements
using a standard sound recording set. The signals obtained were non-stationary.
Examples of time courses obtained during collisions of vehicle interior
equipment elements are presented in Figures 3, 4 and 5. It should be emphasized
that the collisions of elements were not periodic.
Fig. 3. Acoustic signal obtained
during collisions of elements made of plastic
Fig. 4. An enlarged fragment of the
effect of the collision of
two metal elements of the interior of the cabin
The most common
method of describing random signals is to provide their amplitude-time
characteristics in selected intervals. By determining the values of the central
moments and the autocorrelation function, it is possible to determine whether
the signal is stationary. Methods for analyzing stationary signals are widely
described in many works. The choice of method for analyzing a non-stationary
process is primarily determined by the dynamics of changes in its parameters
over time. If these changes are small enough, the process can be assumed to be
stationary. In a given time interval, commonly used methods are used to analyze
such a signal, e.g. frequency analysis, point measures, etc. In the case
of high dynamics of changes in the parameters of a random signal, it is common
practice to examine it using two-dimensional methods, e.g. using the short-time
Fourier transform, Wavelet transform, Wigner-Ville transform.
Fig. 5. An enlarged fragment of the
effect of a collision between two elements of
the interior of the cabin made of plastic
In the case of
signals obtained from collisions of vehicle interior equipment elements, the
analysis was performed using the Wavelet transform. Wavelet Analysis enables
the use of windows that automatically narrow for high-frequency analysis and
expand for low-frequency analysis. Both wavelets and their spectra can be
rapidly decaying functions, and this makes wavelets very convenient windows for
integral transformations. The Wavelet Transform is defined as follows:
where:
x(t) – analyzed signal,
Ψ(t) – wavelet family.
Most often, the
family of wavelets constituting the basis is generated using the formula
proposed by Grossman and Morlet through the operations of shifting and scaling
the basis function:
where:
a – scaling
parameter that implements the "change" of frequency:
b – shift parameter
An inherent element of function
approximation using wavelets are scaling functions. Each basic wavelet ψ(t) has a specific scaling function φ(t), the average values of which, unlike
the wavelet, are always different from zero. Scaling functions are created,
like wavelets, by scaling and shifting operations. The functions φ(t) i ψ(t) form pairs, based on which the
families of scaling functions Φa,b(t) and the families of wavelets Ψa,b(t) . are built. Wavelets
with resolution (a-1) are a linear
combination of scaling functions from level a. There are gk coefficients that meet the conditions:
The same applies to scaling
functions. The lower level φ functions are linear combinations
of the higher level scaling functions and the corresponding hk coefficients:
Using the above relationships, both
wavelets and scaling functions from level a can be decomposed into a series
created from a scaling function with a higher frequency resolution. Proceeding
in this way, any signal x(t) can be decomposed into factors with
graded frequency resolutions using the wavelet transformation and obtain a WT
spectrum, which is the time-frequency characteristic of the tested signal.
The function
Signal expansions based on wavelet
bases cannot be well adapted to the representation of signals with narrow
frequency spectra located in the high-frequency range. In this case, the
wavelet expansion coefficients do not clearly reflect the nature of the signal
because the information about the signal is blurred throughout the database.
Most of the energy of the fundamental wavelet
The B0 band of the
fundamental wavelet is the same as the window width σω in the direction of the frequency
axis.
where the center frequency of the
fundamental wavelet:
Hence, for a wavelet with scale
value a, the band Ba and the center frequency ωa
are:
The Wavelet
transform on smaller scales extracts the high-frequency components of the
analyzed signal. Increasing the scale causes the wavelet represented by the
band-pass filter to shift towards lower frequencies. At the same time, for an
increasing scale, we have a reduction in bandwidth, i.e. an increase in
resolution in the frequency domain. The center frequency ω0 and the
bandwidth B0 depend on the selected analysis wavelet.
The result of the
wavelet transform is the time distribution of wavelet coefficients for a scale
that is closely related to frequency. Continuous wavelet transform converts the
time signal into a scale-time distribution over a selected range. After taking
into account the frequency properties of the wavelet, the obtained result can
be presented in a time-frequency system. This form of presentation of the
wavelet transform results facilitates the interpretation of the obtained
results, in particular their frequency properties. An example result of the
Wavelet analysis of the signal obtained when two metal parts collide, using the
Morlet wavelet, is shown in Figure 6. The time-frequency distribution of
the collision signal of plastic parts is shown in Figure 7.
Fig. 6. Time-frequency distribution
of the signal resulting from the collision of metal parts
Fig. 7. Time-frequency
distribution of the signal from the collision of plastic parts
The wavelet
decomposition shown in Figure 6 reveals that the signal after the collision
shows a dominant initial impulse, which later transforms into free vibrations
of the structure. The example signal when rubbing plastic elements looks
similar, but there is a reduction in the frequency range. The duration of the
entire signal is approximately 150 ms. Random signals recorded in this way were
used to drive micro-speakers placed in the vehicle cabin. The sound sources
were placed in the car cabin. The location of the sound source was identified
using an acoustic camera. The research was conducted using the SoundCam Bionic
acoustic camera. A view of the location of the acoustic camera in the tested
vehicle is shown in Figure 8. Due to limited space, it was decided to use a
small size of the microphone array (XS matrix).
Fig. 8. View
of the camera used and a diagram of the tested vehicle with the location of the acoustic
camera marked in green
In the
studies, the source was hidden under the interior trim elements of the vehicle,
which corresponded to the most common cases of noise caused, for example, by
increased clearances in the connections of elements [28]. Noisy sounds
occurring in the vehicle cabin are also influenced by sources located outside,
whose operation is intermittent [15, 19, 21, 37]. In the current research, the
aim was to detect the source of discontinuous noise located in the car cabin.
In the first stage, the tests were carried out with the car stationary and the engine
turned off. The obtained results of correct identification of noise sources are
presented in Figures 9 and 10.
Fig. 9. Identification of the noise source
located in the right air outlet
– The sound emitted is the collision of two metal elements
Fig. 10. Identification of the noise
source located in the central air outlet
– The sound emitted is the collision of two plastic elements
During the
research, there were also results of source identification, which indicate the
occurrence of erroneous diagnoses. An example of the appearance of two
locations of noise sources (real and apparent) is shown in figure 11. Figure 12
shows the identification of an apparent source, the occurrence of which is
determined by the reflection of acoustic waves from the hard surface of the
car's windshield.
Fig. 11. Recognition of two noise sources (real
and apparent)
– The sound emitted is the collision of two plastic elements
Fig. 12. Incorrect identification of the noise
source
– Only the apparent source is visible
The
next stage of the research was to verify the possibility of using an acoustic
camera to locate noise sources coming from incorrectly fitted vehicle interior
equipment elements. For testing purposes, the screws securing the glove
compartment on the passenger side were loosened. The body rocking caused by the
force generated by two passengers was used as an excitation. Examples of
detecting a noise source in the passenger compartment area are shown in Figures
13 and 14.
Fig. 13. Detection of the noise
source during tests on the test stand
Fig. 14. Detection of the noise
source during tests at the test stand
As can be
seen, by selecting the frequency band range and determining the distance from
the source, the precision of detecting the place of sound emission can be
improved (Fig. 14). The described procedure was validated in road test
conditions, which are the most common area of occurrence of bothersome sounds
in the vehicle cabin. The tests were conducted during normal operation (during
road tests). Due to the assumption that such a method could be used to detect
noise sources in vehicle workshop conditions, the camera was not mounted in a
holder. Measurements were made using the camera manually. An example of
detecting the location of the noise source in the car cabin during road tests
is presented in Figure 15.
Fig. 15. The location of the noise source at
the junction of the rear trunk shelf with
the left rear body pillar – Result obtained during the road test
The problem of incorrect
identification of source locations was also observed in road tests. An example
of incorrect detection of the place of sound production is shown in Figure 16.
An acoustic camera seems to be a
useful tool for improving the acoustic climate in car cabins. Its use allows
you to locate the place where bothersome noises occur. Thus, by using an
acoustic camera after detecting a place, you can focus on removing the direct
cause of the sound. Sometimes it is enough to properly fit the vehicle's
interior equipment. The obvious issue is the size of the microphone array. Due
to the lack of space, small matrix sizes should be used, which unfortunately
translates into the spatial resolution of the source location. Optimization of
microphone signal processing algorithms should be considered in terms of
interpolating the spatial resolution, taking into account the short duration of
the acoustic signal. The author intends to address this issue in further research.
Fig. 16. Incorrect identification of
the location of the noise source on the rear side window – Result obtained
during the road test
4. SUMMARY
The
acoustic climate in car cabins is often dependent on the occurrence of not very
loud but annoying noises. The reason for the occurrence of such intermittent
sounds is the general technical condition of the vehicle's interior equipment.
This technical condition is influenced, on the one hand, by normal operation,
but also by various activities related to the disassembly and reassembly of
cabin interior equipment elements. The problem of various "strange"
noises is often reported by vehicle users.
The
use of an acoustic camera allowed for the detection of the location of the
noise source, but the results obtained in some cases of the analyzed frequency
band settings revealed the effect of an apparent sound source occurring in the
vehicle cabin.
The
obtained results confirmed the usefulness of using an acoustic camera in
identifying noise sources. However, the presence of apparent noise sources may
in some cases result in incorrect diagnoses. Source location verification
should be evaluated using expert knowledge about the structural structure of
specific solutions used in the vehicle under test.
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Received 16.09.2024; accepted in revised form 08.01.2025
Scientific
Journal of Silesian University of Technology. Series Transport is licensed under
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[1] Silesian University of
Technology, Faculty of Transport and Aviation Engineering, Krasińskiego
8, 40-019 Katowice, Poland. Email: jan.warczek@polsl.pl. ORCID:
0000-0002-4767-5588