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WO2018044245A1 - Procédé de détection d'un défaut d'aplatissement de roue sur un train mobile - Google Patents

Procédé de détection d'un défaut d'aplatissement de roue sur un train mobile Download PDF

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Publication number
WO2018044245A1
WO2018044245A1 PCT/TR2016/050320 TR2016050320W WO2018044245A1 WO 2018044245 A1 WO2018044245 A1 WO 2018044245A1 TR 2016050320 W TR2016050320 W TR 2016050320W WO 2018044245 A1 WO2018044245 A1 WO 2018044245A1
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WO
WIPO (PCT)
Prior art keywords
defect
wheel
signal
peaks
detection
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/TR2016/050320
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English (en)
Inventor
Metin AKTAS
Pinar YILMAZER
Ethem Hakan GUNEL
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tcdd Datem Isletme Mudurlugu
Aselsan Elektronik Sanayi ve Ticaret AS
Original Assignee
Tcdd Datem Isletme Mudurlugu
Aselsan Elektronik Sanayi ve Ticaret AS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tcdd Datem Isletme Mudurlugu, Aselsan Elektronik Sanayi ve Ticaret AS filed Critical Tcdd Datem Isletme Mudurlugu
Priority to PCT/TR2016/050320 priority Critical patent/WO2018044245A1/fr
Publication of WO2018044245A1 publication Critical patent/WO2018044245A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
    • B61L27/50—Trackside diagnosis or maintenance, e.g. software upgrades
    • B61L27/57—Trackside diagnosis or maintenance, e.g. software upgrades for vehicles or trains, e.g. trackside supervision of train conditions

Definitions

  • the present invention relates to detection methods of wheel flatten defects on a moving train, especially relates to methods using acoustic emission sensors.
  • the railway industry is an interdisciplinary industry composed of a complex engineering system with numerous critical components of the railway infrastructure together with the rolling stock. Deterioration of the structural integrity of these critical railway components can result in emergency maintenance being required or even potential accidents, causing severe disruption on normal services, delays and unnecessary costs (Papaelias, Amini, Huang, Vallely, Dias, & Kerkyras, 2014). The aim of this project is for the rail industry to significantly reduce delays, to increase network capacity, to improve efficiency and to minimize accidents.
  • the patent document no. US6951132 discloses a system and method for determining at least one parameter related to a train traversing on a railway track.
  • the system comprises a sensor coupled to a detection location and configured for sensing acoustic signals at the detection location on the railway track and a processor coupled to the sensor and configured for analyzing a temporal progression of a frequency spectrum corresponding to the acoustic signals.
  • US4129276 discloses a method and apparatus for detecting the presence of flat wheels on railroad cars, comprising an electro- acoustic transducer located on the track wayside so as to pick up the vibrations generated by a passing train. If a flat wheel is present it will generate a periodic clanging sound at a frequency proportional to train speed and wheel diameter.
  • the invention capitalizes particularly on the measurement of train speed to control the response of an adaptive filter so as to enhance the periodic clanging frequency with respect to the background noise, thereby to improve the signal-to-noise ratio; the enhanced signal is further auto-correlated for ten wheel revolutions and if a periodic signal is present in the narrow frequency band of interest, a large periodic autocorrelation output will result and, as a consequence, any wheel flat will be readily detected and will act to trigger an alarm to alert the train crew of the condition.
  • the object of the invention is to realize a method for detection of wheel flatten defect on a moving train which is operable by using only one acoustic emission sensor and also operable by using plurality of sensors if desired.
  • Figure 1 is the flowchart of the method.
  • Figure 2 is the measured signal by acoustic sensors.
  • Acquiring data during a train passes on rail, by at least one acoustic emission sensor mounted on at least one predetermined location on the rail,
  • the Acoustic Emission sensor measures a noisy periodic impulsive signal x(t)), where the signal period depends on the train speed ⁇ 9) and the wheel diameter (D). In that case, it is desired to detect the event, where an unknown impulsive signal periodically repeats with a specified period of ⁇ /9: Since the unknown signal p(t) is impulsive, the location and signal power of the impulsive signals are determined with computationally efficient Root-Mean-Square (RMS) signal power calculation method and identify the peaks. Then the time delays and power differences between the consecutive peaks is defined to generate a "defect score curve" that represents the joint time delay and power deviation of the detected peaks. Then, a decision is made about the wheel flatten defect by comparing the measured defect score curve with the threshold curve, which is obtained in the experimental data. The details of each step are explained in the following sections.
  • RMS Root-Mean-Square
  • the parameters a and T are independent of the type and the size of the flattening surface.
  • the attenuation parameter a determines how the signal power degrades with the distance between the measurement point and the point, where the flattening surface hits the rail and depends on the rail characteristics.
  • the parameter 3 ⁇ 4 determines the k'th hit time of the flattening surface and when the train travels it directly relates with the train speed 9 and wheel diameter D as
  • the measured signal x(t)) becomes a noisy periodic impulsive signal (102).
  • the goal is to decide whether there is a wheel flatten defect or not for a given signal x(t)) measured when the train travels. It is handled as a binary classification problem, where observing the periodic impulsive signal is considered as "Defect" and observing noise-only signal is considered as "Normal".
  • One of the steps in the proposed defect detection method is to identify the peaks in the measured signal x(t) that corresponds to the hit events, i.e., the event, where the flattening surface of the wheel hits the rail.
  • the peak identification There are many methods for the peak identification that can be used for this application. Since the acoustic emission sensor(s) measures all the signal harmonics, derivative search based peak detection methods may result too much peaks, which cannot be handled effectively. Moreover, these methods are computationally inefficient for long sequence signals. Therefore, a simpler and computationally more efficient peak identification method is proposed that is based on searching the maximum signal power within predefined window.
  • the block size ⁇ is selected by considering the wheel diameter D and the train speed 9- as
  • the impulsive signal p(t) is observed at each block. Then, the occurrence time of the peaks in the signal and the signal power at that time can be measured by calculating the Root-Mean- Square (RMS) power at each block with a sliding window as
  • W and F are the window size and the offset between the successive windows in samples, respectively (104).
  • RMS values are calculated for M point, which is related with the window size W, offset size F and the block size B as
  • the point in r 3 ⁇ 4 [m] with the maximum RMS power corresponds to the b'th peak in the measured signal x(t).
  • the occurrence time 3 ⁇ 4 and the power ⁇ 3 ⁇ 4 of the b'th peak can be defined as where
  • m * corresponds to a set of indexes that are used for RMS calculation. Therefore, the occurrence time of the peaks can be estimated with an ambiguity, i.e., the exact location of the peak time domain can be any point within the window size W. More clearly, W- ⁇ > where is the actual occurrence time of the b'th peak. Without loss of generality, all the occurrence time of the peaks are selected as the first point of the window in
  • the window size W should be decreased.
  • small W may result selecting the impulsive noise with high instantaneous power and small duration as a peak instead of longer duration impulsive signal, which is the one desired to be located and decreases the estimation accuracy of . Therefore, the window size W should be selected by considering the characteristics of the impulsive signal p(t) and the noise signal measured on the rail.
  • the deviations of the occurrence time and the signal power are calculated to determine the peaks, which are periodically repeated, i.e., the peaks generated when the flattening surface of the wheel hits the rail (105).
  • the defect score curve 0 ⁇ ⁇ ( ⁇ , ⁇ ) ⁇ 1 measures the ratio of the number of case, where the time and power differences between two consecutive peaks are within the limits defined by parameters a and ⁇ over the total number of peaks.
  • a flattening surface on the wheel it generates periodic peaks due to the observed impulsive signals and the time and power differences between consecutive peaks are small, which results large ⁇ .
  • the flattening surface is not exist on the wheel, the detected peaks are most probably due to the noise or any arbitrary events that is not periodic and in this case ⁇ is small as compared with the previous case for the same parameters a and ⁇ . Therefore, a likelihood test condition to identify the case is proposed (107), where there exist a flattening surface on the wheel, i.e., by using the defect score curve ⁇ ( ⁇ , ⁇ ) for various a and ⁇ values as
  • the likelihood ratio L measures the ratio of the total number of points that are greater than the threshold ⁇ ( ⁇ , ⁇ ), to the total number of points that are less than the threshold over the defined set of parameters A.
  • 0 ⁇ ⁇ ⁇ 1 is a user defined parameter that controls the desired detection and false alarm rates
  • test condition can be defined for Wheel Flatten Defect detection as se
  • the critical point in the proposed decision mechanism is to select the threshold
  • ⁇ ( ⁇ , ⁇ ) and the search region defined by ? ⁇ and P properly a training method for estimating the threshold and identifying the search region for a given labeled data, i.e., Defect and Normal by considering the desired detection rate and false alarm rate is defined.
  • a search based supervised training method is proposed that is controlled by the desired detection and false alarm rates.
  • the method estimates the optimum threshold values or optimum threshold curve in terms of required minimum detection rate and maximum false alarm rate at each selected time and power deviation parameters a and ⁇ independently and identifies the valid regions for these parameters.
  • each measured data is labeled as either "Defect" or "Normal”.
  • a defect score curve ⁇ ( ⁇ , ⁇ ) is calculated for each data for a given parameter sets a and ⁇ . Let ) denotes the defect score for the z'th signal in the data set 3 ⁇ 4%that contains Wheel Flatten
  • the condition in that the optimum threshold value is defined above guarantees that the estimated threshold value ⁇ ( ⁇ , ⁇ ) satisfies the user defined detection and false rate limits at each parameter set ( ⁇ , ⁇ ) and the minimization operation with respect to ⁇ that select the threshold value as the maximum distant point to the boundaries of both sets, i.e., Defect and Normal.
  • the threshold values are set in these parameter sets ( ⁇ , ⁇ ) as invalid and are not used for the wheel flatten defect detection.
  • the valid parameter sets i3 ⁇ 4 P) can be defined that are used for the Wheel Flatten Defect detection in (13) as
  • the size of the wheel defect can be calculated according to power of the peak determined in the step "Determining periodically repeated peaks by calculating deviations of the occurrence times and the signal powers (105)".
  • a trigger system is used for activating the acoustic emission sensors. As the transmission speed of sound on the rails is very high, acoustic emission sensors may start to generate signals even if the train is very far away from the location that the data acquiring desired to be started.
  • a trigger system that comprises; - a trigger unit for detecting the passage of the train wheel, located on a predetermined location on the rail,
  • an activation unit that activates the at least one acoustic emission sensor after receiving the data of the wheel passage from trigger unit via a communication unit is used.
  • the trigger unit may be an axle counter, an acoustic probe, a guard sensor, a laser sensor or an infrared sensor.

Landscapes

  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Train Traffic Observation, Control, And Security (AREA)

Abstract

La présente invention concerne des procédés de détection de défauts d'aplatissement de roue sur un train mobile, concerne en particulier des procédés utilisant des capteurs d'émission acoustique. L'objet de l'invention est de réaliser un procédé de détection d'un défaut d'aplatissement de roue sur un train mobile qui peut fonctionner à l'aide d'un seul capteur d'émission acoustique 10 et peut également fonctionner à l'aide d'une pluralité de capteurs d'émission acoustique si on le souhaite.
PCT/TR2016/050320 2016-08-31 2016-08-31 Procédé de détection d'un défaut d'aplatissement de roue sur un train mobile Ceased WO2018044245A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/TR2016/050320 WO2018044245A1 (fr) 2016-08-31 2016-08-31 Procédé de détection d'un défaut d'aplatissement de roue sur un train mobile

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/TR2016/050320 WO2018044245A1 (fr) 2016-08-31 2016-08-31 Procédé de détection d'un défaut d'aplatissement de roue sur un train mobile

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112179299A (zh) * 2020-10-10 2021-01-05 孙树光 一种基于声发射的接触网平顺性检测装置及方法

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4129276A (en) 1978-01-30 1978-12-12 General Signal Corporation Technique for the detection of flat wheels on railroad cars by acoustical measuring means
US20040261533A1 (en) * 2003-06-27 2004-12-30 General Electric Company Rail and train monitoring system and method
DE102010052667A1 (de) * 2010-11-26 2012-05-31 Knorr-Bremse Systeme für Schienenfahrzeuge GmbH Vorrichtung und Verfahren zur Erfassung von Störungen einer Rollbewegung eines Wagonrades eines Zuges
US20160207552A1 (en) * 2015-01-16 2016-07-21 International Electronic Machines Corporation Abnormal Vehicle Dynamics Detection

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4129276A (en) 1978-01-30 1978-12-12 General Signal Corporation Technique for the detection of flat wheels on railroad cars by acoustical measuring means
US20040261533A1 (en) * 2003-06-27 2004-12-30 General Electric Company Rail and train monitoring system and method
US6951132B2 (en) 2003-06-27 2005-10-04 General Electric Company Rail and train monitoring system and method
DE102010052667A1 (de) * 2010-11-26 2012-05-31 Knorr-Bremse Systeme für Schienenfahrzeuge GmbH Vorrichtung und Verfahren zur Erfassung von Störungen einer Rollbewegung eines Wagonrades eines Zuges
US20160207552A1 (en) * 2015-01-16 2016-07-21 International Electronic Machines Corporation Abnormal Vehicle Dynamics Detection

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
PAPAELIAS; ROBERTS; DAVIS, A REVIEW ON NON-DESTRUCTIVE EVALUATION OF RAILS: STATE-OF-THE-ART AND FUTURE DEVELOPMENT, 2008

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112179299A (zh) * 2020-10-10 2021-01-05 孙树光 一种基于声发射的接触网平顺性检测装置及方法
CN112179299B (zh) * 2020-10-10 2022-10-21 孙树光 一种基于声发射的接触网平顺性检测装置及方法

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