identification of railway track … rd international congress on sound & vibration!"# 23...

23I SVC23rd International Congress on Sound & Vibration

10-14 July 2016Athens, Greece

IDENTIFICATION OF RAILWAY TRACK COMPONENTS ANDDEFECTS BY ANALYSIS OF WHEEL-RAIL INTERACTIONNOISEAthanasios Synodinos52 Westwood Road, SO17 1DP Southampton, UKemail: [email protected]

The ability of newer trains to run faster and carry heavier loads, coupled with the continuouslygrowing traffic volumes in modern rail networks has not only significantly increased the wear rateof rails but also shrunk the time-gaps between circulating high-speed trains. This has highlightedthe need to introduce fast and automated maintenance processes that involve fewer on-track work-ers. This paper investigates the feasibility of extracting realistic rail condition information fromrecorded rail-wheel interface noise of in-service high-speed trains. This could be integrated intoa complete automated maintenance solution for railways, consisting of several non-destructivetesting monitoring approaches, such as ultrasonics. The study’s purpose was achieved by per-forming a recording session of rail-wheel noise in the Athens Suburban Railway, using on boardmounted microphones and a GPS system. The resulting noise dataset was subjected to correlationand adaptive filtering noise reduction techniques, followed by advanced time-frequency analy-sis. Most of the known track components within the test area have been detected and identified,including corrugation, which was an aim of previous acoustic monitoring works.

1. Introduction

Recent safety statistics from the Federal Railroad Administration (FRA) [1] and the EuropeanRailway Agency Database of Interoperability and Safety (ERADIS) [2] reveal that about 30% of as-signed factors for train accidents that occurred in the last decade were track-related. These factorsincluded both internal rail damage, as well as surface defects and irregularities. Irregularities in par-ticular are also responsible for considerable residential disturbance, since they significantly increaserolling noise and vibration from railways. Consequently, effective predictive and preventive mainte-nance is a crucial factor in ensuring a reliable, undisturbed railway performance.

Nowadays, rail traffic is increasing, not only because railway is often the fastest transport modefor short to medium journeys, but also one of the most environmentally friendly ones [3]. Hence,time-gaps between circulating trains become shorter, leaving small space for in-situ maintenance andvisual inspection. Moreover, the ability of newer trains to run faster and carry heavier loads hasincreased rolling contact fatigue (RCF), which is the main cause for the initiation and growth of raildefects at operating conditions [4]. These facts have highlighted the need to introduce automateddefect identification and monitoring systems for railways, and involve fewer on-track workers.

This paper investigates the feasibility of Acoustic Track Monitoring (ATM); that is, extracting re-alistic rail condition information from rail-wheel interface noise of in-service high-speed trains. ATMcould allow continuous track analysis, detect abrupt or long-term track alterations and thus contributetowards satisfying the aforestated need for fast and automated track maintenance. A rail-wheel in-terface noise database was created from recordings in the Athens Suburban Railway. The aim then

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The 23rd International Congress of Sound and Vibration

was to perform data analysis and assign acoustic signatures to as many track components and defectsas allowed by the dataset content and quality. The possibility to use standard and adaptive noisereduction techniques to remove redundant noise and upgrade the dataset quality, was also examined.

2. Towards a modern railway track maintenance

Defect detectors mechanisms, which used a magnetic flux leakage technique [5], have been con-structed since as early as 1877, assisting to the slow visual inspection, which was restricted to sur-face defects and often depended on the inspector’s experience and judgment. Since then, numer-ous non-destructive testing (NDT) rail inspection methods have been introduced. These range frompedestrian-operated test equipment to inspection systems involving ultrasonics, video cameras andthermography. Yet, the limited speed of such methods due to the employment of dedicated low-speedvehicles or manually operated censors became a significant drawback in modern, busy rail networks.

An efficient method to eliminate train schedule disruption while enabling both early fault detec-tion and continuous monitoring over the whole railroad network, is to conceive on board solutions.The feasibility of such approaches has been demonstrated in reference [6], which uses accelerom-eters mounted on in-service high speed trains in combination with speed and position measuringdevices (like a GPS) to detect rail irregularities. It must be noted though that such approaches mayoccasionally have limitations or discrepancies on the measurable irregularities wavelength due to thedependence on the wagon’s suspensions [6].

Analogous works exploit acoustic emission rather than vibration signals. For instance, ‘RailBAM’[7] identifies bearing and wheel faults from emitted noise. Likewise, Rail Safety and Standards Board(RSSB) [8] introduced ATM, by analysing rolling noise and detecting some gross known track ele-ments, such as crossings. The present study further investigates the relationship between rolling noiseand track characteristics in an attempt to expose the potential capabilities of ATM.

3. Experimental setup

The recording session was executed over the 33 km long track part of the Athens suburban railwaybetween stations ‘Liosia‘ and ‘Airport’. The sunny weather and light wind offered ideal recordingconditions. Measurement equipment consisted of a mobile phone GPS, which tracked speed andlocation data and two recording systems that store uncompressed audio (.wav) files; a MicW i436class 2 microphone, coupled to an IOS device recording at 44.1k and 16 bits and a Zoom H1 recorder,which used a 96k sampling rate at 24 bits. Since the SPL of passenger trains at 160 km/h is around80 dB (at the reference distance of 7.5 m) [9], input gain was set to an upper limit of 100 dB. WhileMicW packaging included its own windscreen, a custom one was constructed for the Zoom recorder,using 2 cm thick foam. Since powered wheels are typically rougher and noisier than unpowered ones,microphones were fastened to the unpowered last bogie of a Siemens Desiro EMU-5 electric train,facing against the rolling direction to minimise influence from wind and auxiliary noises. The selectedlocation (displayed in Fig. 1), also protected the equipment from any damage by debris or water.

4. Recordings analysis

The recording session was followed by visual inspection of defect prone track segments, such asnarrow curves and switches. Figure 2 shows some defects encountered; corrugation, point wear andgauge corner checking. Locations were noted to allow investigation of the matching recorded signals.

The recordings quality was enhanced by reducing interfering or constant noise, further isolatingtransient events that were probably associated with track components or defects. This was achievedby first exploiting correlation between signals obtained by each microphone. And secondly, by em-ploying adaptive filter capabilities of Adobe Audition software to the signals. Recordings were then

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Figure 1: Microphones setup (left) and IOS recording device (right).

Figure 2: Some defects encountered; corrugation (left), point wear (mid), gauge corner checking.

processed by a number of methods in both time and frequency domains. Statistical methods (such aspercentile levels) were also employed, but are not discussed here, due to space constraints.

In general, gross artifacts, such as uneven joints were associated with visible transients in the timehistory. Yet, a more comprehensive examination was required to identify them and to spot finer trackdefects, such as corrugation. Analysis purely in the frequency domain, like the Fourier transform(FT) in the left plot of Fig. 4 is meaningless, since any localisation information (i.e. location of trackcomponents or defects) is ‘buried’. Thus, a Short-time Fourier Transform (STFT) was used instead,which allowed a visual representation in both time and frequency domains. Conforming with BS EN15610:2009 [10], this study adopted 50 % overlapping Hanning windows. Still, STFT was incapableof spotting finer track events because it analyses each signal using a fixed window size and hencesuffers in resolution flexibility. This difficulty is bypassed through wavelet analysis that employswindows with adaptive length. Wavelet method used in this study was wavelet packets (WP). Afterexperimentation with several wavelet types that all produced similar results, the Daubechies 10 WPwas ultimately employed, which in fact conforms to the standards of similar studies, like [11].

5. Results and discussion

This Section presents this study’s most significant results. Figure 3 shows time profile of rollingnoise recorded between two adjacent stations. Also shown are track elements (switches and unevenwelds) found to be related to some waveform spikes. Signal playback implied that weaker spikes atthe waveform’s middle represent wind noise. Also, amplitude widens while the train accelerates and

ICSV23, Athens (Greece), 10-14 July 2016 3


narrows as it decelerates. The spectrogram in Fig. 4 represents the transformed signal of Fig. 3.Vertical and horizontal lines are associated with transient and long-lasting events respectively, whilehorizontal curves relate to the train speed; relationship between frequency and train speed is clear.

Figure 3: Track elements associated with spikes in the time history of Kifissias - Neratziotissa section.Map source: Google maps, 2013.

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Figure 4: Fourier spectrum and spectrogram of noise recorded between stations Kifissias - Neratz.

Figure 5 shows spectrograms of two squeal noise sequences, occurring at switches near Kifissiasstation. Both spectrograms are associated with relatively low train speed; the left one represents aslow approach to the station (at 15 km/h) while the right spectrogram corresponds to the departurefrom it, where the train accelerates from 10 to 50 km/h. The evenly spaced horizontal parallel linesrepresent the squeal harmonics. Some are stronger than others and lower frequency content tend to



weaken as the train accelerates. It is apparent that amplitude varies in a difficult to predict way. Thisis because squeal noise is essentially generated due to creepage and rubbing in the wheel-rail contactregions that in turn depend on many parameters, such as the track geometry, train speed and frictionconditions. Squeals were also noticed during train breaking (spectrograms are not presented heredue to space constraints); these had fewer and clearer harmonics, which tend to have discontinuities,possibly related to wheel-track conditions, wheel speed and brake force applied by the driver.

Figure 5: Spectrogram for a sequence of squeals at fixed train speed of 15 km/h (left) and at acceler-ation from 10 to 50 km/h.

Time history and spectrograms of Fig. 6 describe the noise of train running through switches.Switches are typically associated with discontinuities and flange rubbing that respectively generatebroad frequency bursts and low frequency crunching and grinding noise [12]. This behaviour con-forms with the discrete large amplitude impulses and the overall noise level increase shown in theleft-side plots of Fig. 6. While this behaviour is predictable, it would be beneficial to spot switchdefects through rolling noise. Right-side plots of Fig. 6 refer to a longer event, which is a sequence ofswitches with major wear and possibly, misalignment. Since the train crosses them at very low speed,external noise is significantly decreased so comparison with healthy switch may not be totally fair.However, the numerous transients occurring while travelling on these switches and the two relativelystrong squeals at around 10 and 14 seconds may be indications of aforementioned faults. It was alsonoticed that there is a dependency between emitted noise and relative position of train’s vehicle; whenrolled over a section with severe gauge corner checking, middle positioned vehicles generated muchlouder squeals than the last one, where microphones were mounted.

The next track component examined are curves; due to the flange rubbing in curves, they areexpected to generate squeals that would be stronger in tighter curves. Still, additional forms of noisewere noticed, as depicted in spectrograms of Fig. 7, where curves are clearly represented by broadfrequency rectangular patterns. Curve noise is probably affected not only by the train’s dynamicbehaviour alteration during the curve, but also from the fact that microphones are possibly moreexposed to airflow on curves. It can be assumed that louder high frequency content of the left-sidespectrogram is associated with the slightest smaller curve radius and the fact that train enters the curveat a higher speed and starts braking before leaving it. Lastly, it must be noted that curves examined inthis section had no signs of corrugation, which often appears in curves.

Corrugation is a wave shaped rail surface wear whose wavelength and amplitude are relevant torolling noise. Excluding extreme cases, roughness and resulting rolling noise are linearly related sothat corrugation with wavelength λ results in excitation at frequency f = V/λ, where V is the trainvelocity (m/s) [12]. Figure 8 shows photograph of a corrugated moderate curve next to the airportplatforms, along with its location and train velocity (about 28 km/h) data. Corrugation wavelength wasmeasured at 19 cm; smaller waves were noticed within the main corrugation, with wavelength closeto 8 cm. According to the aforementioned linear formula for rail roughness, frequencies associatedto corrugation should approximately be f1 = 41 Hz and f2 = 98 Hz. To investigate activity at



Figure 6: Rolling noise on switch (left) and on sequence of faulty switches (right).

Figure 7: Curving noise spectrograms; left curve is slightly sharper. Map source: Google maps, 2013.

these frequencies the recorded signal was subjected to level 9 WP, which decomposed the signalinto 256 sub-bands with bandwidth 43 Hz. Figure 9 plots WP sub-bands of the corrugation-relatedfrequencies along with the corresponding waveform and spectrogram. Level 9 packets (9,0) and (9,6)that are linked to 0-43 Hz and 86-129 Hz respectively, have clear activity in the corrugated part.



Figure 8: Corrugated track section near Airport station. Map source: Google maps, 2013.

Figure 9: Waveform, spectrogram and signal approximation at WP nodes at frequencies related to acorrugated curve at the airport area. Train speed is approximately 28 km/h.



6. Conclusions and suggestions for further work

This paper investigated the feasibility of extracting track condition information from rail-wheelinteraction noise. A relatively rich rail-wheel interaction noise dataset has been created, whose con-tent and quality permitted extensive analysis, able to provide potential for ATM. Speed and locationinformation, that are indispensable for the method’s reliability, were tracked through a GPS system.

Regarding the recordings analysis, short STFT windows were suitable for detecting track featuresassociated with fast changes in the records, such as welds, whereas long lasting events, such as curves,required finer frequency resolution. WP was the most CPU demanding process among the ones usedbut showed promising capability in detecting track corrugation. Further outcomes were the influenceof train’s vehicle relative position and the linear relationship between train speed and noise level.

Although no information was available on wheel condition, which contributes to the total rough-ness and emitted noise, wheel noise can be assumed a constant factor that does not affect the abilityto extract information from recordings. The track sub-structure was almost uniform along the sectionassessed, consisting of ballast supported concrete sleepers; exceptions were switches and sectionsequipped with guard rails, respectively supported by wooden and larger concrete sleepers.

Overall, considering the wide range and unique characteristics of differing rail defects, ATM couldnot exclusively handle track condition surveillance. Rather, it could be part of a complete automatedmaintenance solution for railways, consisting of several NDT monitoring approaches.

Further work includes assigning acoustic signatures to more defects through WP and creatingadditional datasets, using dedicated high-quality microphones mounted at strategic bogie positions.Furthermore, machine-learning techniques can introduce independency from external factors.

Acknowledgement

I would like to express my gratitude toward Prof. David Waddington (University of Salford, UK) forhis support and invaluable guidance and to G. Apostolou for his expert help during the measurements.

REFERENCES

1. Federal Railroad Administration, (2016). Safety Statistics Data - 3 - Train Accidents. [Online.] available:http://safetydata.fra.dot.gov/OfficeofSafety/Default.aspx.

2. European Railway Agency Database of Interoperability and Safety ERADIS, (2016). Railway accident and incidentinvestigations. [Online.] available: https://erail.era.europa.eu/investigations.aspx.

3. de Vos, P. Railway Noise in Europe - State of the Art Report, International Union of Railways (UIC), (2016).

4. Cannon, D. F., Edel, K. O., Grassies, S. L. and Sawley, K. Rail defects: an overview, Fatigue & Fracture of Engi-neering Materials & Structures, 26, 865-886, (2003).

5. Armitage, P. R. The use of low-frequency Rayleigh waves to detect gauge corner cracking in railway lines, Insight44 (6), 369-372, (2002).

6. Lee, S. J., Choi, S., Kim, S., Park, C. and Kim, Y. G, A mixed filtering approach for track condition monitoring usingaccelerometers on the axle box and bogie, IEEE Trans. on Instrumentation and Measurement, 61, 749-758, (2012).

7. RailBAM, (2016). Bearing Acoustic Monitor. [Online.] available:http://www.trackiq.com.au/railbam.html.

8. Cawser, S., Hardy, A. and Wright, C. Technical Report AEA TR-PCE-2002-RR-005 Issue1, Feasibility of detectingrail flaws using acoustic equipment fitted to vehicles, (2004).

9. Clausen, U. et al. Reducing railway noise pollution, European Parliament Committee on Transport & Tourism, (2012).

10. British Standards Institute, BS EN 15610:2009, Railway applications. Noise emission. Rail roughness measurementrelated to rolling noise generation, (2009).

11. Bocciolone, et al. A measurement system for quick rail inspection and effective track maintenance strategy, Mechan-ical Systems and Signal Processing, 21 (3), 1242-1254, (2007).

12. Thompson, D. J., Railway Noise and Vibration: Mechanisms, Modelling and Means of Control, Elsevier, (2009).


http://safetydata.fra.dot.gov/OfficeofSafety/Default.aspx

https://erail.era.europa.eu/investigations.aspx

http://www.trackiq.com.au/railbam.html

identification of railway track … rd international congress on sound & vibration!"# 23...

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