PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

The Effects of Weather Conditions and Wheel Wear on Curve Squeal
Thomas MALY 1; Florian BIEBL 2; Michael OSTERMANN 3
1
2

Vienna University of Technology, Austria

psiacoustic Umweltforschung und Engineering GmbH
3

Vienna University of Technology, Austria

ABSTRACTThe mechanisms of curve squeal generation (lateral stick-slip effect on the top of the rail and/or friction of thewheel flange) are well known. Even if various influencing parameters were already investigated mostly on atheoretical level, in real train operation the parameters are in general not measurable in their entirety and theireffects due to the complex interdependencies between them are often not predictable. That is why curvesqueal generation often seems to have a random character.
As a continuation of previous studies the aim of the work presented was to investigate the influence of
weather condition changes over a whole year and wheel profile parameters changes due to wear on theoccurrence frequency of curve squeal. For this, measurements of the pass-by noise emissions and the weather
conditions in a narrow curve (radius 230 m) of a Vienna suburban railway line were carried out over a periodof 11 months. Due to a previously developed algorithm for automatic detection of curve squealing more than
20000 data sets were available for subsequent statistical analysis. In the paper the dependencies of squeal
frequency on rain, air humidity, dew point, and rail temperature, as well as wheel parameters are presented indetail.
Keywords: Curve Squeal, Weather Conditions, Wheel Wear

1. INTRODUCTION
In narrow curves, in addition to the ordinary rolling noise, which basically arises from rail andwheel roughness, also noise components with high amplitudes at high frequencies may occur. These
components are often a major source of annoyance for people who live near of curved railway tracks.
The high importance with regard to noise protection is emphasized by the consideration of theincreased emissions in computational models for strategic noise mapping (e.g. European directive
2015/996 for common noise assessment methods (1)).
Components with a dominant tonal characteristic are usually called curve squealing. As the maincause of such emissions lateral creepage at the contact between the wheel tread and the top of the rail
head was already identified by Rudd in 1976 (2). Thereby alternating sticking and slipping phases
excite the wheel to vibrate corresponding to one of its axial wheel modes. Zenzerovic (3) states, thatfor the initiating and persisting stick-slip vibrations the falling friction law (decrease of friction
coefficient with increasing slip velocity) and/or a geometric coupling of wheel modes will be needed.
The observed squeal frequencies range from several hundred Hertz to few Kilohertz (the statedfrequency ranges vary widely: for instance Meehan et al. (4) report a frequency range from 600 Hz to
10 kHz, whereas Thompson (5) quantify the range from 250 Hz to 5 kHz).
Apart from to stick-slip squealing, in narrow curves also the contact between wheel flange and rail
gauge face may be source of considerable annoying noise components. However, due to wheel flange
rubbing a different sound characteristic will be generated: Thompson (5) describes flange squeal
compared to stick-slip squeal as more broadband and more intermittent noise with higher fundamental
frequencies, which may have lower levels.
Even if the basic mechanisms which are responsible for squeal generation are well known, there are1

thomas.maly@tuwien.ac.atbiebl@psia.at
3michael.ostermann@tuwien.ac.at21559

many different influence parameters which may have significant effects. For instance, Dittrich et al.
(6) pointed out 18 different parameters related to vehicle, track, and/or environment. This high numberof parameters emphasizes the complexity of phenomenon of curve squeal generation. Furthermore, in
real-life observations of curve squealing of trains in operation, it will almost be impossible to record
all potential influence parameters so that squeal noise generation often seems to be more random rather
than deterministic.
In this context, in Austria two granted research projects (BEGEL 2013-2015 and ESB 2015-2018)
were carried out to investigate selected influence parameters based on comprehensive soundmeasurements of pass-bys of trains in operation. Present paper covers a part of these research activities
and will focus on the effects of varying weather conditions and of wheel profile changes due to wear.

2. MEASUREMENT DATA
As basis for further evaluations the pass-by noise in a narrow curve with a radius of 230 m of aVienna suburban railway line were recorded over 11 months (20265 trains). The following sections
describe the setup of acoustic measurements, the properties of the pass-bys of selected train types,
collected wheel profile parameters, and the way of identifying stick-slip squeal and flanging squeal.
2.1 Pass-by measurements
Sound emissions of train pass-bys were measured in 7.5 m from the center line of the track and1.2 m above the top of the rail head, accordingly to the position for sound emission measurements oftrain pass-bys described in DIN EN ISO 3095 (7). The used acoustic railway monitoring system(acramos ®) utilize two adjacent microphones to verify their accurate functioning. The recordings were
sampled with 50 kHz to be able to analyze the entire audible range up to 20 kHz. Acramos ® also
determine the time of axle pass-bys in the cross section by inductive axle counters. The recorded axle
patterns enable the system to distinguish automatically between different categories of trains and to
determine train directions and axle speeds. In addition, meteorology parameters like air and rail
temperature, air pressure, air humidity, wind speed and wind direction, dew point and rainfall were
measured.
In the cross section the superstructure consisted of rails of type S49 mounted on wooden sleepers.
Because of a cant of approximately 90 mm to 95 mm the balanced speed without any lateral
acceleration can be estimated at 43 km/h.
For improved interpretation of evaluation results rail roughness and track decay rates were
measured in the beginning and at the end of the pass-by noise measurements. Both roughness
measurements show that the roughness of the inner rail almost met the limiting curve for type testingaccording to DIN EN ISO 3095 (7). On the contrary, the roughness of the inner rail exceed this limiting
curve in the wavelength range from 6.3 cm to 16 cm significantly (in parts more than 15 dB). The track
decay rates mostly fulfill the limiting curve of the DIN EN ISO 3095 (7). Only the lateral decay rate ofboth rails falls at 1600 Hz under the limiting curve at the initial measurements and the vertical rates of
the inner rail show at both measurements (slightly) too low values in the frequency range of 500 Hz to800 Hz.
The traffic in the measurement section consists of 82.9 % (17097 trains) of the same suburban train
type (train type A). Further 15.2 % (3128 trains) of the traffic were of another suburban train type(train type B). Thus, for further evaluations train type A will primarily be considered and type B will
only be added if meaningful. The distribution of mean train speeds in the left diagram of figure 1
shows that there is an accumulation point between 55 km/h and 60 km/h and that most of the trainspasses the cross section with speeds substantially above the balanced speed where lateral acceleration

Figure 1 – relative frequencies of mean train speeds (left) and empirical cumulative distribution function(cdf) of speed differences between last and first axles (right) of suburban trains of type A and B

1560

Figure 2 – basic wheel profile parameters used for further evaluation
disappears. An electrically isolated cut point of the catenary in front of the measuring section leads tothe absence of any traction in the measurement section (cf. low variation of speed differences between
last and first axle in right diagram of figure 1) even if the power units and the auxiliary systems werealready powered up in the measurement section.
2.2 Wheel profiles
At each stop for maintenance or inspection of mentioned suburban train types in workshops ofAustrian Federal Railways the profiles of all wheels were measured. Thus the profile parameters werecollected for all eleven units of suburban train type A which passed the measurement site and of all
workshop stops during the whole period of acoustic measurements as well as of the stops right before
and after this period. This enables linear interpolation of wheel parameters over time and allows
assigning them to the pass-by noise data sets with the aid of unit schedules.
For detailed analysis only those profile parameter were selected, where a considerable impact waspresumed (for instance, rim thickness, wheel width, or tread rollover were discarded). Furthermore,hollow tread was also excluded because of its very rare appearance. The remaining profile parameters
were illustrated in figure 2. As an additional parameter the maximal diameter differences of wheelsets
were derived based on wheel diameters.
2.3 Detection of curve squealing
To analyze the large quantity of recorded pass-bys, an algorithm for automatic detection of thecharacteristic sound components, which are generated by flange contact with the rail head (broadband
flanging noise) or by lateral stick-slip effect (tonal squeal noise), was applied. The empiricallydeveloped identification bases on interpretation of third octave spectra over time. Examples ofdifferent noise characteristics of pass-bys of type A suburban trains were given in figure 3: left
diagram shows a pass-by without any squeal or other disturbing noise, middle diagram shows clearly
perceptible time-varying flanging noise with increased sound pressure levels (SPL) in the upper thirdoctave bands, and right diagram shows an isolated tonal noise component with increased SPLs of theneighboring 3.15 kHz and 4 kHz bands as well as slightly increased SPL of the 8 kHz band due to thefirst harmonic. According to these noise characteristics the already well-tested algorithm assesses themean value of the upper third octave bands for the detection of flange squeal and peaks of one or twoneighboring bands in the medium frequency range for each time sample of the spectra. If the time
behavior of such anomalies also met requirements concerning their length of time and in case of tonal

Figure 3 – examples of 3rd octave spectra over time for suburban trains of type A without any squealing (left),with flange squealing (middle) and with stick-slip squealing (right)

1561

components their continuity over time, trains will be classified as broadband conspicuous(representative for flange squeal) or tonal (representative for stick-slip squeal) conspicuous.
An initial evaluation of the overall squealing rates in absence of rainfall reveals a tendency to lessstick-slip squeal (~7-8 %) compared to other suburban or regional train types considered in previous
investigations, especially in the frequency range lower than 5 kHz. And identified tonal conspicuities
often (~90 %) occur together with broadband noise, which might be an indication, that thesecomponents arise from flange rubbing or have at least a causal relationship. Flanging noise rate is on
similar level (~29 %) compared to the experiences of a former study. Compared to type A, train type B
show for both noise types somewhat higher rates (tonal ~13 % and broadband ~45 %).

3. WEATHER CONDITIONSIn order to analyze the influence of various weather conditions, the measured data was separated inincrements of 10 as shown in figure 4. The different ranges are always shown with their highest
possible value (10 means data from 0.1 to 10). In figure 4 the number of measured data for eachweather condition range for the velocity 60 km/h is shown. The colors of the fields should give an easy
way to evaluate the reliability of a certain weather condition, depending on the number of measured
trains. It ranges from dark green (>50), light green (50-20), orange (20-10) to red (<10). For theevaluation only areas of at least 10 pass-by trains were evaluated.
For the analyses, the data was evaluated for the occurrence of tonal stick-slip squeal and broadband
flange squeal. In addition to that, the sound power level was calculated for each single train. This
calculation was done using a previous developed calculation method (see (8) in German language)
which is based on the Austrian Standard ÖNORM S5026 (9) but only uses one single measurement
point in 7.5 m from the rail and in 1.2 m above rail surface.
In the following the influence of the different weather parameters are discussed in detail for thevelocity range of 55-65 km/h (highest amount of trains measured, see figure 1).

Figure 4 – Measured trains (suburban train type A and B) for the velocity range 55-65 km/h with different
weather conditions
3.1 Temperature
The measured data shows that for the suburban train type A the frequency of occurrence of all typesof curve effects declines with higher temperature (see figure 5). Especially for the broadband effects
however, a local maxima at the temperature range of 10-20°C is visible. This maximum is highest forlower velocities and for the suburban train type B it is also visible when looking at the occurrence of
tonal effects. Generally the data of the train type B shows a slightly rising tendency with higher

1562

Figure 5 – frequency of occurrence and sound power level – rail temperatures suburban train type A (velocity
range 55-65 km/h)
temperature. Only the values for the tonal effects in the frequency range 8 kHz and higher shows
declining frequency occurrences.
When looking at the sound power levels, they show falling levels with rising temperature for both
vehicle types and for all velocities. When comparing the rail and the air temperature the average values
differ slightly, however the general effects are comparable to one another.
3.2 Dew point
In order to evaluate the influence of a wet rail respectively lowered friction coefficients, not only
the occurrence of rain had to be monitored, but also the effect of a humid rail due to a rail temperature
below the dew point was investigated. The data was separated by the information weather the dew
point calculated with the air pressure was higher or lower than the measured rail temperature. The
comparison of the frequency of occurrence of tonal and broadband curve noise in relation to the dew
point, showed for both train types significant lower probabilities for a pass-by with a rail temperature
lower than the dew point. This effect is relatively unchanged by different weather conditions.
3.3 Air humidity
When separating the data according to the air humidity the frequency of occurrence shows a peakfor the percentage of 60 % to 70 % with a lower frequency for higher and lower humidity (see figure 6).

Figure 6 – frequency of occurrence and sound power level – air humidity suburban train type A (velocity
range 55-65 km/h)

1563

When only looking at the data in the temperature range below 10°C, the frequency of occurrence isrising for lower humidity, leading to an overall effect of declining frequency of occurrence with risinghumidity.
The sound power levels show a slightly rising slope for the train type A. Only the data in thetemperature range of 10°C and lower shows the opposite effect. When looking at the train type B thesound power levels give relatively constant levels for all weather conditions.
3.4 Rain
When looking at the pass-bys with rainy conditions, the results show the lowest probabilities for thefrequency occurrence of all three types of curve noise. The effect of rain is highest in the temperature
range of >0°C to 20°C. The effect of rain is mostly similar to the effect of a rail temperature below thedew point and results in a similar effect. When comparing the sound power levels the data with rainy
conditions slightly lower levels, especially in the temperature range between 0°C and 20°C.

4. WHEEL WEAR
Due to the higher number of pass-bys and the lower number of different units, only suburban trainsof type A were considered in subsequence statistical analysis of effects of wheel wear. Initially, foreach of the eleven train units time plots of squeal rates and of wheel profile parameters, both including
times of the workshop visits, were made in order to get a rough overview of time behavior andvariations between different units over time.
4.1 Regression analysis
As a consequence of overlaid sound emissions of nearby axles at the measuring point in a distanceof 7.5 m, a reliable assignment of acoustic properties of the measured signal to axles is not possible.
The time plots of wheel profile parameters (except parameters based on wheel diameter) usually show
similar behavior with moderate spread of all wheels of a train unit: reprofiling sets these parameters toinitial values and afterwards the profile (and its parameters) wear in a reasonably comparable way.
Thus, within the statistical analysis, primarily mean values but also minimum and maximum values ofwheel profile parameters were used as independent variables and the identified squeal (flange or
stick-slip) were used as dependent variables. Due to the binarity of the dependent variable logistic
regression was applied to evaluate dependencies. In face of the absence of the coefficient ofdetermination ܴ ଶ in logistic regression analysis, which represents the proportion of variance, threecommonly used pseudo-ܴ ଶ (McFadden, Cox and Snell and Nagelkerke) were calculated to quantify
differences in the goodness of fit between regression results.
As already discussed in chapter 3 weather conditions can influence squeal occurrence. The
variation of these dependencies is exemplarily illustrated in figure 7 for the difference οܶrail-dew point

Figure 7 – dependences of squeal on difference οܶrail-dew point between rail temperature and dew point (leftdiagrams) and on humidity ߮ (right diagrams) while cold (Nov. 10th, 2016 – Jan. 10th, 2017, 2673 pass-bys
of suburban train type A, upper diagrams) and warm (March 10th – Sept. 9th, 2016 with 6762 pass-bys of
suburban train type A, lower diagrams) periods without rainfall estimated by logistic regression and related
histograms of pass-bys with (upper histograms) and without (lower histograms) detection of squeal noise

1564

between rail temperature and dew point and for air humidity ߮: while there seems to be a reasonabledependency during the cold (wet) period, the influence is clearly reduced in the warmer period. Even
if it can be assumed, that weather and wheel conditions are uncorrelated, for improving the sensitivityof regressions results to the examined effect, subsequent analysis of the influence of wheel wear was
generally restricted to the period from March 10 th to Sept. 9th, 2016 and to difference οܶrail-dew point  K.
In addition periods with detected rainfall were ignored, too.
4.2 Results
The pseudo- ܴ ଶ indicators of the regressions with wheel profile parameters were generally
relatively low, which might be explained by the (necessarily) applied train based approach withaggregated wheel parameters over whole trains. The results of the regressions with all train units oftype A are shown in figure 8. Thereby, squeal noise show a low to moderate dependency on the four
profile parameters related to the wheel flange and on the diameter difference within the wheelset. The
former relation can be interpreted as a decrease of the squeal probability with ongoing wheel wear
where the flange contact points (cf. figure 2) move to wheel exteriors and to lower diameters. This
trend of highest squeal frequencies at initial parameter values after reprofiling was also observed in thetime plots of squeal occurrences. On the contrary, the later dependency with highest predicted squeal
probability when diameter differences within the wheelsets tend towards zero was unexpected andseems not to be justifiable by physical relations of the wheel-rail contact. Thus this dependency may be
the consequence of a correlation between flange parameters and the diameter difference (all theseparameters increase with ongoing wear).
The described dependencies appear also if maximum or minimum instead of average parameters
were considered (minimum parameters in parts with reduced pseudo-ܴ ଶ indicators). Moreover, theregressions calculated separately for each train unit provide similar trends for ten of the eleven units,
whereas the eleventh unit evinces rather independency. And also the regressions after a division of thepass-bys in classes of constant speed or constant humidity emphasize observed trends. On the other
hand, the observed dependencies completely disappear between the last months of measurements(September to January) which indicates that the effect of changing weather conditions clearly

Figure 8 – dependences of squeal on different mean wheel parameters of measurements from March
10th to Sept. 9th, 2016, with differences οܶrail-dew point  K, and without rainfall (4281 suburban trains of typeA) estimated by logistic regression and related histograms of pass-bys with (upper histograms) and without(lower histograms) detection of squeal noise

1565

outweighs the effect of wheel wear under cold and/or moist conditions even under the mentioned
restrictions (οܶrail-dew point  K and no rainfall).
Despite of all these indications for a (weak) dependency in the absence of weather influences it has
to be noted, that neither the concrete physical cause for a diminished squeal probability of worn wheelson suburban trains of type A nor the question accounting the generalization of these findings can beclarified sufficiently without further investigations (e.g. axle based and/or including rail profile wear).
But on the other hand these findings indicate that at least under certain circumstances wheel profile
wear have an impact on curve squeal.

5. CONCLUSIONS
The work presented analyses sound emissions in a narrow curve of a high number of trains inoperation with the aim to investigate dependencies of curve squeal on weather conditions and on
ongoing wheel wear.
The occurrence of tonal and broadband conspicuous is notably lower at rainy conditions and withrail temperatures below the dew point. Humidity in general shows the highest values at the range of
60 % to 80 %. With rising rail temperatures the occurrences for both types are declining with nearly no
occurrences above rail temperatures of 30°C. The sound power level is slightly lower for higher
rail-temperatures while showing a slight rise with higher humidity. For temperatures below 10°C, thesound power level shows significant higher sound power levels in the low air humidity range.
Regarding the wear of wheels, a weak dependency was observed. Accordingly, new or reprofiled
wheels with initial geometric shape seem to excite more squeal as worn wheels do. But the underlying
mechanism and thus the question of a generalization for other train types and/or other conditions could
not be studied without further measurements with extended setup (e.g. microphones near the rails),
which enables a more in-depth analysis like axle based evaluations and/or assessment of the contact
geometry based on rail and wheel profiles.

ACKNOWLEDGEMENTS
This work was supported by the Austrian Research Promotion Agency (FFG, project 850558), theAustrian Ministry for Transport, Innovation and Technology, and the Austrian Federal Railways(ÖBB-Infrastruktur AG).

REFERENCES
1. Commission directive (EU) 2015/996 establishing common noise assessment methods according to
Directive 2002/49/EG of the European Parliament and of the Council. May 19th, 2015. Official Journalof the European Union L168/1.
2. Rudd M.J. Wheel/Rail Noise – Part II: Wheel Squeal. Journal of Sound and Vibration; 1976; 46(3). p.
381-394.
3. Zenzerovic I. Engineering model for curve squeal formulated in the time domain. Thesis for the degreeof licentiate of engineering. Division of Applied Acoustics, Department of Civil and Environmental
Engineering, Chalmers University of Technology; Gothenburg, Sweden, 2014. ISSN 1652-9146.
4. Meehan P A, Bellette P A, Jones P A, and Anderson D. Probabilistic Prediction of Wheel Squeal under
Field Humidity Variation. Proceedings of 20th International Congress on Acoustics, ICA 2010, Sydney,
Australia, Aug. 2010
5. Thompson D. Railway Noise and Vibration: Mechanisms, Modelling and Means of Control. 1st Edition.
Oxford, United Kingdom; Elsevier Ltd., 2009. ISBN 978-0-08-045147-3.
6. Dittrich M, Jansen E, López B M abd Gómez J C. ACOUTRAIN, Deliverable D1.10: Braking noise andCurving noise: corresponding indicators and measurement procedures. Final, update of D1.3, 2014
7. DIN EN ISO 3095 – Acoustics – Railway applications – Measurement of noise emitted by railboundvehicles. 2013
8. Maly T, Ostermann N, Biebl F, Hoisbauer H, Gunacker M, and Djurdev V. Bewertung des akustischen
Einflusses von Gleisbögen für die Erstellung von Lärmkarten - BEGEL", report of research project
BEGEL (project 840495), supported by Austrian Research Promotion Agency, the Austrian Ministry forTransport, Innovation and Technology, and the Austrian Railways, Dec. 2015.
9. ÖNORM S 5026 - Measurement of noise emitted by railbound vehicles - Sound power level per unit
length. March 1996. withdrawn May 20191566