CN115384580A - Steel rail online detection method and system - Google Patents
Steel rail online detection method and system Download PDFInfo
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- CN115384580A CN115384580A CN202110563600.9A CN202110563600A CN115384580A CN 115384580 A CN115384580 A CN 115384580A CN 202110563600 A CN202110563600 A CN 202110563600A CN 115384580 A CN115384580 A CN 115384580A
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- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 140
- 239000010959 steel Substances 0.000 title claims abstract description 140
- 238000001514 detection method Methods 0.000 title claims abstract description 52
- 238000001228 spectrum Methods 0.000 claims abstract description 47
- 230000036541 health Effects 0.000 claims abstract description 17
- 238000001914 filtration Methods 0.000 claims abstract description 8
- 238000000034 method Methods 0.000 claims abstract description 8
- 238000004364 calculation method Methods 0.000 claims description 20
- 230000009466 transformation Effects 0.000 claims description 15
- 239000004973 liquid crystal related substance Substances 0.000 claims description 6
- 230000005540 biological transmission Effects 0.000 description 11
- 238000012544 monitoring process Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 8
- 230000003862 health status Effects 0.000 description 7
- 238000005070 sampling Methods 0.000 description 7
- 238000004891 communication Methods 0.000 description 3
- 230000005236 sound signal Effects 0.000 description 3
- 230000008878 coupling Effects 0.000 description 2
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- 230000007423 decrease Effects 0.000 description 2
- 230000002452 interceptive effect Effects 0.000 description 2
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- 241001391944 Commicarpus scandens Species 0.000 description 1
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- 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 vehicle trains
- B61L23/04—Control, warning, or like safety means along the route or between vehicles or vehicle trains for monitoring the mechanical state of the route
- B61L23/042—Track changes detection
- B61L23/044—Broken rails
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/04—Analysing solids
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/4472—Mathematical theories or simulation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/02—Indexing codes associated with the analysed material
- G01N2291/028—Material parameters
- G01N2291/0289—Internal structure, e.g. defects, grain size, texture
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/26—Scanned objects
- G01N2291/262—Linear objects
- G01N2291/2623—Rails; Railroads
Abstract
The invention relates to a steel rail online detection method and a steel rail online detection system, wherein the method comprises the following steps: collecting an original signal and filtering; carrying out Fourier transform on the filtered signal to obtain a modulus; calculating the effective value of the frequency spectrum of the filtered signal after Fourier transform according to the modulus value; determining a peak factor according to the effective value; and detecting the health state of the steel rail according to the magnitude of the peak factor. The system comprises: a sensor, a filter, and a receiver. The steel rail online detection method and the system provided by the invention can monitor the damage of the steel rail in different degrees (rail head fracture, rail web fracture, intact steel rail and total fracture of the steel rail) in real time, so that the steel rail damage degree is reported before the steel rail is completely fractured, and the driving safety is improved.
Description
Technical Field
The invention belongs to the technical field of railway track state detection, and particularly relates to a steel rail online detection method and a steel rail online detection system.
Background
The health state of the steel rail directly concerns the running safety of the train, and the steel rail in the turnout area is easy to break because of repeated moving to provide steering for the train, thereby seriously threatening the running safety of the train and the life and property safety of people. At present, the detection of the steel rail in the turnout area is mainly carried out in a manual mode of an ultrasonic flaw detector or a hand-push type steel rail flaw detection vehicle and the like at a skylight point of railway operation. This mode need consume great manpower resources, and is inefficient, need occupy the train operation line to can't detect during train operation. Along with the popularization of high-speed trains and the improvement of line occupancy rate, the impact, extrusion force and frequency of the trains on the steel rails are increased, so that the service life of the steel rails is shortened, the occurrence probability of rail breakage accidents is increased continuously, and the running safety of the trains is seriously threatened. And the steel rail in the turnout zone needs to be repeatedly moved in order to change the running direction of the train, so that the rail is easily broken. Therefore, it is necessary to research a real-time on-line monitoring technology for the health status of the steel rail in the turnout area.
At present, the monitoring of the health state of the steel rail mainly comprises two types of on-line monitoring and off-line detection. The on-line monitoring mainly depends on a track circuit, and the mode can realize the monitoring of the complete breaking state of the steel rail. However, in the turnout zone, because the slide plate forms electrical connection between the turnout switch rail and the stock rail, when the steel rail is broken, the electric signal can still pass through the slide plate to bypass to the receiving end of the track circuit, and the steel rail breakage monitoring can not be realized. The off-line detection is mainly carried out by a large steel rail flaw detection vehicle and a small flaw detector, the off-line detection can only be carried out at a skylight point where a train stops running, line resources are occupied, and whether the steel rail is broken or not can not be monitored in real time. In addition, the method is inefficient and requires a large amount of human resources.
Some turnout area steel rail health state on-line monitoring systems mainly rely on receiving end signal amplitude to judge, a big shortcoming of this mode is that receiving end signal amplitude fluctuation range is great, sometimes with the rail fracture amplitude equivalent, can't effectively monitor the rail state. The problem is mainly that the transducer mounted on the steel rail is greatly influenced by temperature, and when the temperature changes, the density of the steel rail changes, so that the acoustic impedance between the transducer and the steel rail is not matched; in addition, the coupling state between the transducer and the steel rail is greatly changed along with the temperature, and finally, the energy of the acoustic signal coupled to the steel rail fluctuates and the frequency shifts.
Therefore, it is urgently needed to provide a steel rail online detection method and a steel rail online detection system, which can solve the problems of frequency deviation and large energy fluctuation of an acoustic signal transmitted on a steel rail when the temperature changes, and can realize all-weather real-time online monitoring of the health condition of the steel rail in a turnout area on the premise of not interfering the safety environment of the existing rail.
Disclosure of Invention
Aiming at the problems, the invention provides an online detection method for steel rails, which is used for online detection of cracks of the steel rails in different degrees based on peak factors of single-frequency point signal frequency spectrums.
An online steel rail detection method comprises the following steps:
collecting an original signal and filtering;
carrying out Fourier transform on the filtered signal to obtain a modulus;
calculating the effective value of the frequency spectrum of the filtered signal after Fourier transform according to the modulus value;
determining a peak factor according to the effective value;
and detecting the health state of the steel rail according to the magnitude of the peak factor.
Further, when the peak factor is larger than a first threshold value and not larger than a second threshold value, the steel rail is judged to be completely broken; alternatively, the first and second liquid crystal display panels may be,
when the peak factor is larger than a second threshold and not larger than a third threshold, judging that the steel rail is broken to a rail waist; alternatively, the first and second electrodes may be,
when the peak factor is larger than a third threshold and not larger than a fourth threshold, judging that the steel rail is broken to a rail head; alternatively, the first and second liquid crystal display panels may be,
and when the peak factor is larger than a fourth threshold value, judging that the steel rail is intact.
Further, the specific calculation method for obtaining the modulus value by performing fourier transform on the filtered signal is as follows:
for the filtered signal x filtered (n) performing Fourier transform to obtain X (k);
wherein k =0,1.., N-1; n =0,1, …, N-1,N is a predetermined value, X (k) is the kth value of the discrete spectrum;
calculating the module value X of the filtered signal after Fourier transformation abs (k)：
Wherein a (k) is the real part of X (k), and b (k) is the imaginary part of X (k).
Further, the specific calculation method for calculating the effective value of the frequency spectrum of the filtered signal after fourier transform according to the modulus value is as follows:
wherein, X rms K is the total length of the spectrum, K =1,2.
Further, the specific calculation method for determining the peak factor according to the effective value is as follows:
wherein C is the crest factor, X max Is the maximum value, X, of the frequency spectrum of the filtered signal after Fourier transformation min Is the minimum value of the frequency spectrum of the filtered signal after Fourier transform.
The invention also provides a steel rail online detection system, which comprises:
the system includes a sensor, a filter, and a receiver;
the sensor is used for acquiring a raw signal;
the filter is used for filtering the original signal;
the receiver is used for carrying out Fourier transform on the filtered signal to obtain a module value;
the receiver is further used for calculating the effective value of the frequency spectrum of the filtered signal after Fourier transform according to the modulus value;
the receiver is further configured to determine a peaking factor based on the effective value;
the receiver is also used for detecting the health state of the steel rail according to the size of the peak value factor.
Further, the receiver is used for judging that the steel rail is completely broken when the peak factor is greater than a first threshold and not greater than a second threshold; alternatively, the first and second liquid crystal display panels may be,
when the peak factor is larger than a second threshold and not larger than a third threshold, judging that the steel rail is broken to a rail waist; alternatively, the first and second electrodes may be,
when the peak factor is larger than a third threshold and not larger than a fourth threshold, judging that the steel rail is broken to a rail head; alternatively, the first and second liquid crystal display panels may be,
and when the peak factor is larger than a fourth threshold value, judging that the steel rail is intact.
Further, the specific calculation of the module value obtained by performing fourier transform on the filtered signal by the receiver includes:
for the filtered signal x filtered (n) performing Fourier transform to obtain X (k);
wherein k =0,1, …, N-1; n =0,1, …, N-1,N is a predetermined value, X (k) is the kth value of the discrete spectrum;
calculating the module value X of the filtered signal after Fourier transformation abs (k)：
Wherein a (k) is the real part of X (k), and b (k) is the imaginary part of X (k).
Further, the specific calculation that the receiver is configured to calculate the effective value of the frequency spectrum of the filtered signal after fourier transform according to the modulus value is:
wherein, X rms K is the total length of the spectrum, K =1,2.
Further, the specific calculation used by the receiver to determine the crest factor from the significant value is:
wherein C is the crest factor, X max Is the maximum value, X, of the frequency spectrum of the filtered signal after Fourier transformation min Is the minimum value of the frequency spectrum of the filtered signal after Fourier transform.
The invention has the following beneficial effects:
(1) When the steel rail in the turnout area is broken, an alarm can be given accurately and timely, the broken steel rail can be conveniently and rapidly checked and replaced by a worker, the real-time performance and the accuracy of rail inspection are improved, and the labor cost can be reduced;
(2) The damage of the steel rail in different degrees (rail head fracture, rail web fracture, steel rail intact and steel rail complete fracture) can be monitored in real time, so that the damage degree of the steel rail is reported before the steel rail is completely fractured, and the driving safety is improved;
(3) The problem of signal fluctuation of the steel rail receiving end caused by changes of external temperature and other environmental factors is effectively avoided, and the problem of unstable energy of the receiving end calculated by only Fourier transform is solved;
(4) The method can realize all-weather real-time on-line monitoring of the health condition of the steel rail in the turnout area on the premise of not interfering the safety environment of the existing track.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic structural diagram of a steel rail online detection system according to an embodiment of the invention;
FIG. 2 is a flow chart of a steel rail online detection method according to an embodiment of the invention;
FIG. 3 is a schematic diagram illustrating an on-line detection of a raw acoustic signal of a steel rail according to an embodiment of the present invention;
FIG. 4 shows a high pass filter amplitude frequency characteristic graph according to an embodiment of the invention;
FIG. 5 shows a schematic diagram of a high pass filter filtered acoustic signal according to an embodiment of the invention;
FIG. 6 shows a frequency spectrum diagram of a steel rail after Fourier transform on-line detection according to an embodiment of the invention;
figure 7 shows a graph of the peak factor for 4 different levels of damage to a rail according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a steel rail on-line detection method and a steel rail on-line detection system, which are used for detecting the health state of a steel rail by calculating the magnitude of a peak factor of a frequency spectrum of an acquired signal after Fourier transform. The embodiment of the invention is exemplified by detecting the health status of the turnout steel rail, but the invention is not limited to detecting the health status of the turnout steel rail, and the health status of any section steel rail can be detected by the invention.
In the health state detection of the steel rail in the turnout zone, a plurality of devices are included, such as a transmitter, a transducer, a sensor, a filter, a receiver, a data transmission module, a power supply module, a display terminal and the like. In order to realize the health state detection of the steel rail in the turnout area, different devices have interaction relation.
The health status detection of the turnout zone steel rail in the embodiment of the invention is exemplarily illustrated by using several common devices, according to the connection relationship between the relevant devices for detecting the health status of the turnout zone steel rail, the connection relationship between the interfaces of the devices is shown in fig. 1, fig. 1 shows a structural schematic diagram of the steel rail online detection system in the embodiment of the invention, and as can be seen from fig. 1, a transmitter and a transducer which are positioned in an outdoor trackside cabinet are connected with each other, the transducer and a sensor are connected with each other, and the sensor and a receiver are connected with each other. The electrical signal is emitted by a transmitter and converted into an acoustic signal by a transducer, and the acoustic signal is transmitted to a receiver by a sensor. The sound signal is converted into a peak factor at the receiver, the receiver is connected with the data transmission module, the receiver transmits the peak factor value to the data transmission module, and the data transmission module transmits the received peak factor value to the display terminal located outdoors. The power supply module is connected with the receiver, the transmitter and the data transmission module and used for supplying power to the receiver, the transmitter and the data transmission module. It should be noted that the embodiment of the present invention is only exemplified by the above-mentioned apparatuses, but is not limited to the above-mentioned apparatuses.
Illustratively, the steel rail online detection method provided by the invention is shown in fig. 2, and fig. 2 shows a flow chart of the steel rail online detection method according to the embodiment of the invention, and the detection method includes: collecting an original signal and filtering; carrying out Fourier transform on the filtered signal to obtain a modulus; calculating an effective value of a frequency spectrum of the filtered signal after Fourier transform according to the modulus value; determining a peak factor according to the effective value; and detecting the health state of the steel rail according to the peak factor.
Specifically, the original signal (in this embodiment, the original signal is an electrical signal) is generated by sending an electrical signal with a frequency of 10kHz by a transmitter, the electrical signal is converted into an acoustic signal by a transducer installed at a rail web at the root end of the steel rail in the turnout area and is coupled to the steel rail, the acoustic signal of the rail web at the tip of the steel rail in the turnout area is collected by a sensor, and the sensor is installed on the rail web at the tip of the steel rail in the turnout area. It should be noted that the acoustic signal in the embodiment of the present invention is a single-frequency point sinusoidal signal.
In sampling determination, the sampling rate must be more than 2 times of the highest frequency, theoretically, the higher the sampling rate is, the better the sampling rate is, and when the sampling rate is too high, the hardware cost of the device is large, so the system sampling frequency is set to 100kHz, the signal length is N =4096 points, and an original acoustic signal x (N) is obtained, wherein N =0,1, …, and N-1,x (N) represents the nth value of time-domain sampling. The original acoustic signal is shown in fig. 3, and fig. 3 shows a schematic diagram of the original acoustic signal for on-line detection of the steel rail, wherein the horizontal axis represents time and the vertical axis represents amplitude.
The acquired rail waist sound signals at the tips of the steel rails in the turnout area are filtered by a high-pass filter, the high-pass filter can be a Chebyshev high-pass filter, as shown in FIG. 4, FIG. 4 shows a graph of amplitude-frequency characteristics of the high-pass filter according to the embodiment of the invention, the horizontal axis represents frequency, and the vertical axis represents amplitude.
In order to filter low-frequency noise and interference generated by passing vehicles, the passband frequency of the filter is set to be 2kHz-100kHz, as shown in FIG. 5, and FIG. 5 shows a schematic diagram of an acoustic signal filtered by a high-pass filter according to an embodiment of the invention, wherein the horizontal axis represents time and the vertical axis represents amplitude.
Further, fourier transform is performed on the filtered signal to obtain a modulus, and the specific calculation method is as follows: for the filtered signal x filtered (n) performing Fourier transform to obtain X (k);
wherein k =0,1, …, N-1; n =0,1, …, N-1,N is a predetermined value, X (k) is the kth value of the discrete spectrum;
calculating the module value X of the filtered signal after Fourier transformation abs (k)：
Wherein a (k) is the real part of X (k), and b (k) is the imaginary part of X (k).
The result of fourier transform of the filtered signal is shown in fig. 6, and fig. 6 shows a frequency spectrum graph of the steel rail online detection after fourier transform according to the embodiment of the invention, wherein the horizontal axis represents frequency, and the vertical axis represents amplitude;
further, a specific calculation method for calculating an effective value of a frequency spectrum of the filtered signal after fourier transform according to the modulus value is as follows:
wherein, X rms K is the total length of the spectrum, K =1,2.
Further, the specific calculation method for determining the peak factor according to the effective value is as follows:
wherein C is the crest factor, X max Is the maximum value, X, of the frequency spectrum of the filtered signal after Fourier transformation min Is the minimum value of the frequency spectrum of the filtered signal after Fourier transform.
The crest factor in the embodiment of the invention refers to the ratio of the peak value of the signal spectrum to the effective value (RMS) of the spectrum, and represents the extreme degree of the peak value in the waveform. The conventional calculation of the peak factor of a signal is directed to a time domain signal to detect whether a pulse exists in the signal. The invention takes the frequency spectrum of single frequency point sinusoidal signal after Fourier transform as the calculation object of peak value factor. For a single-frequency point sinusoidal signal, when a steel rail is intact, the frequency spectrum of the signal received by a receiving end should be a spectral line with concentrated energy. When the steel rail has cracks, the sound waves are reflected at the cracks, and the signal energy value received by the steel rail tip sensor is reduced; and as the crack depth increases, the reflected wave energy increases and the direct wave energy decreases, resulting in a decrease in the maximum value of the spectral energy, thereby decreasing the crest factor of the signal spectrum.
Further, the detection of the health status of the steel rail according to the magnitude of the peak factor specifically comprises:
when the peak value factor is larger than a first threshold value and not larger than a second threshold value, judging that the steel rail is completely broken off; or when the peak factor is greater than a second threshold and not greater than a third threshold, judging that the steel rail breaks to the rail waist; or when the peak factor is greater than a third threshold and not greater than a fourth threshold, judging that the steel rail is broken to the rail head; or when the peak value factor is larger than a fourth threshold value, judging that the steel rail is intact.
The embodiment of the invention collects test data for a plurality of times under 4 different damage degrees of the steel rail, and comprises four states of the steel rail being intact, the steel rail being broken to a rail head, the steel rail being broken to a rail waist and the steel rail being broken completely. According to the algorithm steps of the invention, the peak factor of the collected sound signals is calculated, and the result is shown in fig. 6, and fig. 6 shows a peak factor curve chart according to 4 different damage degrees of the steel rail, wherein the abscissa is the sample number and the ordinate is the peak factor. When the steel rail is intact, the peak factor value fluctuates around 25; when the rail is broken to the rail head, the peak factor value fluctuates around 18; when the steel rail is broken to the rail waist, the peak factor value fluctuates around 9; when the rail is completely broken, its peak factor value fluctuates around 5. Therefore, the deeper the rail damage degree is, the smaller the peak factor value is. Therefore, the magnitude of the peak factor can be used to describe the degree of rail damage. In addition, according to the experimental results, the embodiment of the present invention sets the first threshold to 0, the second threshold to 8, the third threshold to 14, and the fourth threshold to 22, and specifically detects the different damage degrees of the rail as follows:
when the peak factor is more than 0 and less than or equal to 8, judging that the steel rail is completely broken; or when the peak factor is more than 8 and less than or equal to 14, judging that the steel rail is broken to the rail waist; or when the peak factor is greater than 14 and less than or equal to 22, judging that the steel rail is broken to the rail head; or when the peak factor is larger than 22, judging that the steel rail is intact.
The steel rail online detection method has obvious crack distinguishing effect on different damage degrees, and the accuracy of the steel rail online detection method is greatly improved compared with that of the original algorithm detection. The results of the measurement accuracy calculation are shown in table 1 below.
TABLE 1 crack detection accuracy for different damage levels
As can be seen from the above Table 1, the steel rail damage degree on-line detection method provided by the invention has high accuracy. Specifically, the detection accuracy rate of the intact steel rail and the detection accuracy rate of the steel rail from the fracture to the rail head are both up to 100%, and the detection accuracy rate of the steel rail from the fracture to the rail web and the complete fracture of the steel rail is up to 99.83%.
The invention also provides a steel rail online detection system, as shown in fig. 1, fig. 1 shows a schematic structural diagram of the steel rail online detection system, and the system comprises:
a sensor, a filter (not shown in the figure, the filter being in the receiver) and a receiver;
the sensor is used for collecting original signals; the filter is used for filtering the original signal;
the receiver is used for carrying out Fourier transform on the filtered signal to obtain a module value; calculating an effective value of a frequency spectrum of the filtered signal after Fourier transform according to the modulus value; determining a peak factor according to the effective value; and detecting the health state of the steel rail according to the peak factor.
Further, the receiver is used for judging that the steel rail is completely broken when the peak factor is larger than a first threshold value and not larger than a second threshold value; or when the peak factor is greater than a second threshold and not greater than a third threshold, judging that the steel rail is broken to the rail waist; or when the peak factor is greater than a third threshold and not greater than a fourth threshold, judging that the steel rail is broken to the rail head; or when the peak factor is larger than a fourth threshold value, judging that the steel rail is intact.
Specifically, the specific calculation, which is used by the receiver for performing fourier transform on the filtered signal by the receiver to obtain a module value, includes:
for the filtered signal x filtered (n) performing Fourier transform to obtain X (k);
wherein k =0,1, …, N-1; n =0,1, …, N-1,N is a predetermined value, X (k) is the kth value of the discrete spectrum;
calculating the module value X of the filtered signal after Fourier transformation abs (k)：
Wherein a (k) is the real part of X (k), and b (k) is the imaginary part of X (k).
The receiver is configured to calculate, according to the modulus, an effective value of a frequency spectrum of the filtered signal after fourier transform by:
wherein, X rms K is the total length of the spectrum, K =1,2.
The specific calculations used by the receiver to determine the crest factor from the significant value are:
wherein C is the crest factor, X max Is the maximum value, X, of the frequency spectrum of the filtered signal after Fourier transformation min Is the minimum value of the frequency spectrum of the filtered signal after Fourier transform.
Further, the system further comprises: the device comprises a transmitter, a transducer, a data transmission module, a display terminal and a power supply module;
the original signal is generated by an electric signal sent by a transmitter; the transducer is used for converting the original signal sent by the transmitter into an acoustic signal and coupling the acoustic signal to the turnout steel rail;
a data transmission module: the peak value factor is used for transmitting the peak value factor obtained by calculation in the receiver to a display terminal; the data transmission module in the invention can also adopt cable communication, and can also adopt NB-IoT (Narrow-Band Internet of Things) communication and PLC (Programmable Logic Controller) communication modes.
A display terminal: displaying the peak factor data in the data transmission module, and further judging the health state of the steel rail according to the peak factor;
a power supply module: for powering receivers, transmitters, transducers, filters, sensors, data transmission modules, and display terminals in the system.
The method comprises the steps that a transmitter is used for sending an original electric signal, the original electric signal is converted into an acoustic signal through a transducer and is coupled to a turnout steel rail, the acoustic signal is transmitted in the steel rail and is received by a sensor arranged at the tip end of the turnout steel rail, high-pass filtering is carried out through a Chebyshev filter, fourier transformation is carried out on the filtered signal in a receiver, a module value after the Fourier transformation is calculated, an effective value of a frequency spectrum of the filtered signal after the Fourier transformation is calculated according to the module value, a peak factor is determined according to the effective value, and finally the health state of the steel rail is detected according to the size of the peak factor.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A steel rail online detection method is characterized by comprising the following steps:
collecting an original signal and filtering;
carrying out Fourier transform on the filtered signal to obtain a modulus;
calculating the effective value of the frequency spectrum of the filtered signal after Fourier transform according to the modulus value;
determining a peak factor according to the effective value;
and detecting the health state of the steel rail according to the magnitude of the peak factor.
2. A rail on-line measuring method according to claim 1,
when the peak factor is larger than a first threshold value and not larger than a second threshold value, judging that the steel rail is completely broken off; alternatively, the first and second electrodes may be,
when the peak factor is larger than a second threshold and not larger than a third threshold, judging that the steel rail is broken to a rail waist; alternatively, the first and second electrodes may be,
when the peak factor is larger than a third threshold and not larger than a fourth threshold, judging that the steel rail is broken to a rail head; alternatively, the first and second liquid crystal display panels may be,
and when the peak factor is larger than a fourth threshold value, judging that the steel rail is intact.
3. A rail on-line measuring method according to claim 1,
the specific calculation method for obtaining the modulus value by performing fourier transform on the filtered signal is as follows:
for the filtered signal x filtered (n) performing Fourier transform to obtain X (k);
wherein k =0,1, …, N-1; n =0,1, …, N-1,N is a predetermined value, X (k) is the kth value of the discrete spectrum;
calculating the module value X of the filtered signal after Fourier transformation abs (k)：
Wherein a (k) is the real part of X (k), and b (k) is the imaginary part of X (k).
4. A rail on-line measuring method according to claim 3,
the specific calculation method for calculating the effective value of the frequency spectrum of the filtered signal after Fourier transform according to the modulus value comprises the following steps:
wherein, X rms K is the total length of the spectrum, K =1,2.
5. A rail on-line measuring method according to claim 4,
the specific calculation method for determining the peak factor according to the effective value is as follows:
wherein C is the crest factor, X max Is the maximum value, X, of the frequency spectrum of the filtered signal after Fourier transformation min Is the minimum value of the frequency spectrum of the filtered signal after Fourier transform.
6. An on-line rail detection system, comprising:
a sensor, a filter and a receiver;
the sensor is used for acquiring a raw signal;
the filter is used for filtering the original signal;
the receiver is used for carrying out Fourier transform on the filtered signal to obtain a modulus value;
the receiver is further used for calculating the effective value of the frequency spectrum of the filtered signal after Fourier transform according to the modulus value;
the receiver is further configured to determine a peaking factor based on the effective value;
the receiver is also used for detecting the health state of the steel rail according to the size of the peak value factor.
7. The steel rail online detection system according to claim 6,
the receiver is used for judging that the steel rail is completely broken off when the peak factor is larger than a first threshold value and not larger than a second threshold value; alternatively, the first and second electrodes may be,
when the peak factor is larger than a second threshold and not larger than a third threshold, judging that the steel rail is broken to a rail waist; alternatively, the first and second electrodes may be,
when the peak factor is larger than a third threshold and not larger than a fourth threshold, judging that the steel rail is broken to a rail head; alternatively, the first and second liquid crystal display panels may be,
and when the peak factor is larger than a fourth threshold value, judging that the steel rail is intact.
8. The steel rail online detection system of claim 6,
the specific calculation by the receiver for performing fourier transform on the filtered signal to obtain a modulus value includes:
for the filtered signal x filtered (n) performing Fourier transform to obtain X (k);
wherein k =0,1, …, N-1; n =0,1, …, N-1,N is a predetermined value, X (k) is the kth value of the discrete spectrum;
calculating the module value X of the filtered signal after Fourier transformation abs (k)：
Wherein a (k) is the real part of X (k), and b (k) is the imaginary part of X (k).
9. The steel rail online detection system according to claim 8,
the receiver is configured to calculate, according to the modulus, an effective value of a frequency spectrum of the filtered signal after fourier transform, specifically:
wherein, X rms K is the total length of the spectrum, K =1,2.
10. The steel rail online detection system according to claim 9,
the specific calculation used by the receiver to determine the crest factor from the significant value is:
wherein C is the crest factor, X max Is the maximum value, X, of the frequency spectrum of the filtered signal after Fourier transformation min Is the minimum value of the frequency spectrum of the filtered signal after Fourier transform.
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