CN110956164A - Train wheel damage identification method based on noise signals - Google Patents
Train wheel damage identification method based on noise signals Download PDFInfo
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- CN110956164A CN110956164A CN201911422141.1A CN201911422141A CN110956164A CN 110956164 A CN110956164 A CN 110956164A CN 201911422141 A CN201911422141 A CN 201911422141A CN 110956164 A CN110956164 A CN 110956164A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
- G06F2218/10—Feature extraction by analysing the shape of a waveform, e.g. extracting parameters relating to peaks
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- B61K—AUXILIARY EQUIPMENT SPECIALLY ADAPTED FOR RAILWAYS, NOT OTHERWISE PROVIDED FOR
- B61K9/00—Railway vehicle profile gauges; Detecting or indicating overheating of components; Apparatus on locomotives or cars to indicate bad track sections; General design of track recording vehicles
- B61K9/12—Measuring or surveying wheel-rims
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
Abstract
The invention relates to a train wheel damage identification method based on noise signals.A noise acquisition device is arranged at two sides of a straight line section of a train running at a uniform speed and is used for acquiring instantaneous sound pressure of noise around a wheel-rail contact surface in real time in the process that the train passes through the noise acquisition device; firstly, acquiring a noise signal through a noise acquisition device, carrying out blind source separation on the noise signal, and reserving a wheel-track noise signal; determining the characteristics of the wheel rail noise signals, and performing short-time Fourier transform on the wheel rail noise to obtain the time-frequency distribution characteristics of the wheel rail noise; and finally, identifying wheel damage, comparing the instantaneous sound pressure time frequency spectrum of the wheel-rail noise when the train passes through the device with the characteristic time frequency spectrum of various damages of the train wheels, judging the vehicle and the position to which the damaged wheel belongs, and obtaining the type of the wheel damage. The intelligent control system has the advantages of convenience in installation, good economy, high intelligence degree, real-time online performance and the like.
Description
Technical Field
The invention relates to the technical field of rail transit structure monitoring, in particular to a train wheel damage identification method based on noise signals.
Background
Urban rail transit line conditions are poor, small-radius curves and large slopes generally exist, and damage to wheel treads is serious. Seriously damaged wheels have become an important potential hazard for influencing the running safety of urban rail vehicles. In addition, wheel damage inevitably causes changes in the wheel-rail contact relationship, which in turn causes changes in wheel-rail noise. The urban rail transit has low running speed, the wheel-rail noise occupies a dominant position in the total noise of the system when the vehicle runs, and the damage of wheels has obvious influence on the system noise when the vehicle runs. Therefore, the wheel damage state is predicted through the characteristic analysis of the noise signal, and the method has important significance for guiding the turning, replacement and safe train operation.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a train wheel damage identification method based on noise signals, which can identify wheel damage through analysis of noise signal characteristics and can judge the damage type and the damage position of a train wheel. The intelligent control system has the advantages of convenience in installation, good economical efficiency, high intelligent degree and real-time online.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a train wheel damage identification method based on noise signals is characterized in that: the noise acquisition device is arranged on two sides of a straight line section where a train runs at a uniform speed, and is used for acquiring instantaneous sound pressure of noise around a wheel-rail contact surface of the train in the process of passing through the noise acquisition device in real time, analyzing and processing acquired noise signal data, and identifying and judging wheel damage of a rail transit train, and the method specifically comprises the following steps:
step 2, determining the characteristics of the wheel rail noise signals, and performing short-time Fourier transform on the wheel rail noise to obtain the time-frequency distribution characteristics of the wheel rail noise;
and 3, identifying wheel damage, comparing the instantaneous sound pressure time frequency spectrum of the wheel track noise when the train passes through the device with the characteristic time frequency spectrum of various types of damage of the train wheels, judging the vehicle and the position to which the damaged wheel belongs, and obtaining the type of the wheel damage.
The noise acquisition device is arranged on the wall of a straight tunnel between two stations, and the train runs at the same speed.
The noise signal acquired by the noise acquisition device in the step 1 includes but is not limited to: rail noise of a train, mechanical equipment noise of a rolling stock, aerodynamic noise, electromechanical system noise, and noise occurring from other equipment not related to operation.
The method for blind source separation in step 1 specifically comprises the following steps:
S(t)＝W(t)X(t)
wherein t is monitoring time; x (t) ═ x1(t)，…，xi(t))TIs the measured mixed signal; s (t) ═ s1(t)，…，si(t))TW (t) is the demixing matrix for the separation result;
the noise signals comprise wheel track noise of a train, mechanical equipment noise of a rolling stock, aerodynamic noise, electromechanical system noise and noise generated by other equipment irrelevant to operation, and the collected observation signals are marked as x1(t)，x2(t)，x3(t)，x4(t)，x5(t) the original signal is denoted as s (t), s2(t)，s3(t)，s4(t)，s5(t)，xi(t) is si(t) and a weighting factor dependent on the distance between the source and the noise acquisition device, the following equation being obtained:
x1(t)＝a11S1(t)+α12s2(t)+a13s3(t)+a14s4(t)+a15s5(t)
x2(t)＝a21s1(t)+a22s2(t)+a23s3(t)+a24s4(t)+a25s5(t)
x3(t)＝a31s1(t)+a32s2(t)+a33s3(t)+α34s4(t)+a35s5(t)
x4(t)＝a41s1(t)+a42s2(t)+a43s3(t)+a44s4(t)+a45s5(t)
x5(t)＝a51s1(t)+a52s2(t)+a53s3(t)+a54s4(t)+a55s5(t)
in the formula, aijIs a constant coefficient representing the weight of the mix;
suppose that a is composed ofijThe constituent matrices are invertible, in which case there is one element wijCan separate the source signals:
s1(t)＝w11x1(t)+w12x2(t)+w13x3(t)+w14x4(t)+w15x5(t)
s2(t)＝w21x1(t)+w22x2(t)+w23x3(t)+w24x4(t)+w25x5(t)
s3(t)＝w31x1(t)+w32x2(t)+w33x3(t)+w34x4(t)+w35x5(t)
s4(t)＝w41x1(t)+w42x2(t)+w43x3(t)+w44x4(t)+w45x5(t)
s5(t)＝w51x1(t)+w52x2(t)+w53x3(t)+w54x(t)+w55x5(t)
and after obtaining a demixing matrix W (t), calculating to obtain separated wheel-track noise data P (t).
And in the step 2, short-time Fourier transform is performed on the wheel-rail noise P (t) to obtain a frequency spectrum H (omega, t) of the wheel-rail noise.
And 3, judging the damage mode of the train wheels as follows: contrast wheel-rail noise monitoring frequencySpectrum H (omega, t) and characteristic spectrum H of various damages of train wheels0(ω, t), identifying the wheel damage type.
The method for judging the damage position of the train wheel in the step 3 comprises the following steps: when the damaged wheel occurs, the damaged wheel is gradually close to the noise acquisition device and then gradually far away from the noise acquisition device in the process that the train passes through the noise acquisition device, so that the noise peak value generated when the damaged wheel passes through the noise acquisition device can be monitored, the time of the noise peak value is recorded, and the position of the damaged wheel can be calculated according to the time point of the noise peak value and the train speed.
The driving interval needs to be subjected to segmentation analysis, the driving interval is segmented according to the structure of the wheel and the rail and the contact mode, and the noise time domain distribution characteristic and the time-frequency characteristic evolution rule of each driving interval in a normal driving state are recorded.
The train wheel damage identification method based on the noise signals has the following beneficial effects:
firstly, the noise collecting device is arranged on the wall of a straight tunnel of a train running at a uniform speed between two stations, so that the noise change generated in the speed change or over-bending condition can be eliminated.
And secondly, the economy is good, manpower and material resources are saved, monitoring can be performed in the daily running process of the train, and special scheduling and maintenance for monitoring are avoided.
And thirdly, the intelligent degree is high, and the damage of the wheel is automatically identified through the analysis of the noise signal characteristics.
Fourthly, the wheel damage condition of each passing train can be monitored online in real time.
Drawings
Fig. 1 is a working flow chart of a train wheel damage identification method based on noise signals according to the present invention.
Fig. 2 is a schematic installation position diagram of a train wheel damage identification method based on noise signals according to the present invention.
Fig. 3 is a schematic diagram of a blind source separation observation signal in the train wheel damage identification method based on the noise signal.
Fig. 4 is a schematic diagram of blind source separation noise signals in the train wheel damage identification method based on the noise signals.
Fig. 5 is a schematic diagram of a separation result of blind source separation in the train wheel damage identification method based on the noise signal.
Description of the drawings: 1. noise collection device 2, tunnel wall, 3, train carriage, 4, wheel, 5, rail.
Detailed Description
The invention is further described below with reference to the drawings and specific preferred embodiments.
A train wheel damage identification method based on noise signals is characterized in that: the noise acquisition device is arranged on two sides of a straight line section where a train runs at a uniform speed, and is used for acquiring instantaneous sound pressure of noise around a wheel-rail contact surface of the train in the process of passing through the noise acquisition device in real time, analyzing and processing acquired noise signal data, and identifying and judging wheel damage of a rail transit train, and the method specifically comprises the following steps:
step 2, determining the characteristics of the wheel rail noise signals, and performing short-time Fourier transform on the wheel rail noise to obtain the time-frequency distribution characteristics of the wheel rail noise;
and 3, identifying wheel damage, comparing the instantaneous sound pressure time frequency spectrum of the wheel track noise when the train passes through the device with the characteristic time frequency spectrum of various types of damage of the train wheels, judging the vehicle and the position to which the damaged wheel belongs, and obtaining the type of the wheel damage.
In order to identify the damage condition of the train wheels, when a noise signal at a certain moment is abnormal in the process of uniformly passing a certain fixed steel rail 5 of a train carriage 4, the wheel 4 passing at the corresponding moment is judged to be damaged, and the noise acquisition device 1 can be specifically arranged on a straight-line tunnel wall 2 between two stations where a train runs at the same speed, so that the noise change condition generated in the case of speed change or over-bending is reduced as much as possible.
The noise signal acquired by the noise acquisition device in the step 1 includes but is not limited to: rail noise of a train, mechanical equipment noise of a rolling stock, aerodynamic noise, electromechanical system noise, and noise occurring from other equipment not related to operation.
The method for blind source separation in step 1 specifically comprises the following steps:
S(t)＝W(t)X(t)
wherein t is monitoring time; x (t) ═ x1(t)，…，xi(t))TIs the measured mixed signal; s (t) ═ s1(t)，…，si(t))TW (t) is the demixing matrix for the separation result;
the noise signals comprise wheel track noise of a train, mechanical equipment noise of a rolling stock, aerodynamic noise, electromechanical system noise and noise generated by other equipment irrelevant to operation, and the collected observation signals are marked as x1(t)，x2(t)，x3(t)，x4(t)，x5(t) the original signal is denoted as s (t), s2(t)，s3(t)，s4(t)，s5(t)，xi(t) is si(t) and a weighting factor dependent on the distance between the source and the noise acquisition device, the following equation being obtained:
x1(t)＝a11s1(t)+a12s2(t)+a13s3(t)+a14s4(t)+a15s5(t)
x2(t)＝a21s1(t)+a22s2(t)+a23s3(t)+a24s4(t)+a25s5(t)
x3(t)＝a31s1(t)+a32s2(t)+a33s3(t)+a34s4(t)+a35s5(t)
x4(t)＝a41s1(t)+a42s2(t)+a43s3(t)+a44s4(t)+a45s5(t)
x5(t)＝a51s1(t)+a52s2(t)+a53s3(t)+a54s4(t)+a55s5(t)
in the formula, aijIs a constant coefficient representing the weight of the mix; further, aijIs unknown, as is the source signal. Blind source separation can use the amount of mixing to find the original signal.
Suppose that the code is αijThe constituent matrices are invertible, in which case there is one element wijCan separate the source signals:
s1(t)＝w11x1(t)+w12x2(t)+w13x3(t)+w14x4(t)+w15x5(t)
s2(t)＝w21x1(t)+w22x2(t)+w23x3(t)+w24x4(t)+w25x5(t)
s3(t)＝w31x1(t)+w32x2(t)+w33x3(t)+w34x4(t)+w35x5(t)
s4(t)＝w41x1(t)+w42x2(t)+w43x3(t)+w44x4(t)+w45x5(t)
s5(t)＝w51x1(t)+w52x2(t)+w53x3(t)+w54x(t)+w55x5(t)
and after obtaining a demixing matrix W (t), calculating to obtain separated wheel-track noise data P (t).
And in the step 2, short-time Fourier transform is carried out on the wheel rail noise P (t) to obtain a frequency spectrum H (omega, t) of the wheel rail noise.
And 3, judging the damage mode of the train wheels as follows: contrast wheel track noise monitoringFrequency spectrum H (omega, t) and characteristic frequency spectrum H of various types of damage of train wheels0(ω, t), identifying the wheel damage type.
The method for judging the damage position of the train wheel in the step 3 comprises the following steps: when the damaged wheel occurs, the damaged wheel is gradually close to the noise acquisition device and then gradually far away from the noise acquisition device in the process that the train passes through the noise acquisition device, so that the noise peak value generated when the damaged wheel passes through the noise acquisition device can be monitored, the time of the noise peak value is recorded, and the position of the damaged wheel can be calculated according to the time point of the noise peak value and the train speed.
The driving interval needs to be segmented and analyzed, the driving interval is segmented according to the structure of the wheel and the rail and the contact mode, and the noise time domain distribution characteristic and the time-frequency characteristic evolution rule of each driving interval in the normal driving state are recorded.
The driving interval needs to be segmented and analyzed, the driving interval is segmented according to the structure of the wheel and the rail and the contact mode, and the noise time domain distribution characteristic and the time-frequency characteristic evolution rule of each driving interval in the normal driving state are recorded. Three types of noise may be generated by the wheel and rail interface: roll noise, impact noise and whistling noise. Rolling noise is generally noise generated when an unworn wheel rolls on a continuously welded linear steel rail, and due to the fact that tiny unevenness exists on the contact surface of the wheel and the steel rail, the wheel and the rail can generate structural vibration due to poor contact, and noise with higher decibel is generated; the impact noise is the noise generated when the wheel passes through a welding seam, a turnout or when the wheel is scratched and rolls on a steel rail, and when the conditions are met, the vertical speed of the wheel is changed, so that a large acting force is generated on the contact surface of the wheel and the rail, and the vibration of the wheel and the steel rail is excited to generate the radiation sound of the wheel and the rail; the squeal noise is a strong noise whose tone is related to the small radius curve of the vehicle through the track, the main source being the rim, the size and material characteristics resulting in the rim being a good high frequency sound source. Because different vehicle speeds, different types of track plates, turnouts and the like have great influence on the noise of the wheel tracks, the accuracy of judgment can be effectively improved by carrying out sectional statistical analysis during noise analysis.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (8)
1. A train wheel damage identification method based on noise signals is characterized in that: the noise acquisition device is arranged on two sides of a straight line section where a train runs at a uniform speed, and is used for acquiring instantaneous sound pressure of noise around a wheel-rail contact surface of the train in the process of passing through the noise acquisition device in real time, analyzing and processing acquired noise signal data, and identifying and judging wheel damage of a rail transit train, and the method specifically comprises the following steps:
step 1, acquiring a noise signal through a noise acquisition device, performing blind source separation on the noise signal, and reserving a wheel-track noise signal;
step 2, determining the characteristics of the wheel rail noise signals, and performing short-time Fourier transform on the wheel rail noise to obtain the time-frequency distribution characteristics of the wheel rail noise;
and 3, identifying wheel damage, comparing the instantaneous sound pressure time frequency spectrum of the wheel track noise when the train passes through the device with the characteristic time frequency spectrum of various types of damage of the train wheels, judging the vehicle and the position to which the damaged wheel belongs, and obtaining the type of the wheel damage.
2. The train wheel damage identification method based on the noise signal as claimed in claim 1, wherein: the noise acquisition device is arranged on the wall of a straight tunnel between two stations, and the train runs at the same speed.
3. The train wheel damage identification method based on the noise signal as claimed in claim 1, wherein: the noise signal acquired by the noise acquisition device in the step 1 includes but is not limited to: rail noise of a train, mechanical equipment noise of a rolling stock, aerodynamic noise, electromechanical system noise, and noise occurring from other equipment not related to operation.
4. The train wheel damage identification method based on the noise signal as claimed in claim 1, wherein: the method for blind source separation in step 1 specifically comprises the following steps:
S(t)＝W(t)X(t)
wherein t is monitoring time; x (t) ═ x1(t),…,xi(t))TIs the measured mixed signal; s (t) ═ s1(t),…,si(t))TW (t) is the demixing matrix for the separation result;
the noise signals comprise wheel track noise of a train, mechanical equipment noise of a rolling stock, aerodynamic noise, electromechanical system noise and noise generated by other equipment irrelevant to operation, and the collected observation signals are marked as x1(t)，x2(t)，x3(t)，x4(x)，x5(t) the original signal is denoted as s (t), s2(t)，s3(t)，s4(t)，s5(t)，xi(t) is si(t) and a weighting factor dependent on the distance between the source and the noise acquisition device, the following equation being obtained:
x1(t)＝a11s1(t)+a12s2(t)+a13s3(t)+a14s4(t)+a15s5(t)
x2(t)＝a21s1(t)+a22s2(t)+a23s3(t)+a24s4(t)+a25s5(t)
x3(t)＝a31s1(t)+a32s2(t)+a33s3(t)a34s4(t)+a35s5(t)
x4(t)＝a41s1(t)+a42s2(t)+a43s3(t)+a44s4(t)+a45s5(t)
x5(t)＝a51s1(t)+a52s2(t)+a53s3(t)+a54s4(t)+a55s5(t)
in the formula, aijIs a constant coefficient representing the weight of the mix;
suppose that a is composed ofijThe constituent matrices are invertible, in which case there is one element wijCan separate the source signals:
s1(t)＝w11x1(t)+w12x2(t)+w13x3(t)+w14x4(t)+w15x5(t)
s2(t)＝w21x1(t)+w22x2(t)+w23x3(t)+w24x4(t)+w25x5(t)
s3(t)＝w31x1(t)+w32x2(t)+w33x3(t)+w34x4(t)+w35x5(t)
s4(t)＝w41x1(t)+w42x2(t)+w43x3(t)+w44x4(t)+w45x5(t)
s5(t)＝w51x1(t)+w52x2(t)+w53x3(t)+w54x(t)+w55x5(t)
and after obtaining a demixing matrix W (t), calculating to obtain separated wheel-track noise data P (t).
5. The train wheel damage identification method based on the noise signal as claimed in claim 4, wherein: and in the step 2, short-time Fourier transform is performed on the wheel-rail noise P (t) to obtain a frequency spectrum H (omega, t) of the wheel-rail noise.
6. The train wheel damage identification method based on the noise signal as claimed in claim 5, wherein: and 3, judging the damage mode of the train wheels as follows: comparing the wheel-rail noise monitoring frequency spectrum H (omega, t) with the characteristic frequency spectrum H of various damages of the train wheels0(ω, t), identifying the wheel damage type.
7. The train wheel damage identification method based on the noise signal as claimed in claim 5, wherein: the method for judging the damage position of the train wheel in the step 3 comprises the following steps: when the damaged wheel occurs, the damaged wheel is gradually close to the noise acquisition device and then gradually far away from the noise acquisition device in the process that the train passes through the noise acquisition device, so that the noise peak value generated when the damaged wheel passes through the noise acquisition device can be monitored, the time of the noise peak value is recorded, and the position of the damaged wheel can be calculated according to the time point of the noise peak value and the train speed.
8. The train wheel damage identification method based on the noise signal as claimed in claim 1, wherein: the driving interval needs to be subjected to segmentation analysis, the driving interval is segmented according to the structure of the wheel and the rail and the contact mode, and the noise time domain distribution characteristic and the time-frequency characteristic evolution rule of each driving interval in a normal driving state are recorded.
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