CN112697881A - Steel rail guided wave defect identification and positioning method, device and system based on K-S entropy - Google Patents
Steel rail guided wave defect identification and positioning method, device and system based on K-S entropy Download PDFInfo
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Abstract
The invention discloses a method, a device and a system for identifying and positioning guided wave defects of a steel rail based on K-S entropy, wherein the method comprises the following steps: acquiring a time-course signal of guided wave propagation in the steel rail as a guided wave sampling signal; a proper driving force amplitude value is determined through a K-S entropy, and a duffing vibrator chaotic system is established; inputting the guided wave sampling signals into a duffing oscillator chaotic system, scanning the guided wave sampling signals along a time axis by defining a rectangular time-shifting window function, and calculating the K-S entropy of each window; if K-S entropy of all windows between an incident wave and an end face echo of the guided wave sampling signal is constantly equal to 0, the steel rail is free of defects, if a window with K-S entropy larger than 0 exists between the incident wave and the end face echo of the guided wave sampling signal, the steel rail is defective, the time corresponding to the window where the incident wave, the end face echo and the defect echo are located is determined by using a curve peak value of the K-S entropy, and the defects are located through a time-distance proportional relation. The invention improves the robustness and the sensitivity of the steel rail defect detection.
Description
Technical Field
The invention relates to a guided wave detection technology, in particular to a method, a device and a system for identifying and positioning rail guided wave defects based on K-S entropy, and belongs to the technical field of nondestructive testing.
Background
Railway transportation, as one of the most convenient medium and long distance transportation modes in China at present, has gradually developed towards the direction of 'high-speed passenger transportation and heavy-load freight transportation'. In addition, the urban rail transit has the characteristics of quickness and environmental protection, greatly meets the medium and short trip requirements of residents, and is rapidly developed. Therefore, the method is of great importance to the safe maintenance of the track system, and the defect detection of the steel rail is a necessary link in the safe maintenance process. At present, two main modes of steel rail flaw detection in China are adopted, namely a large-scale steel rail flaw detection vehicle and a steel rail flaw detection trolley, which are based on the traditional ultrasonic detection technology, and a flaw detection blind area exists in a steel rail bottom area, so that missed detection and misjudgment are easily caused. The guided wave nondestructive detection technology has the advantages of long detection distance, full-section detection, high detection efficiency and the like, is particularly suitable for detecting slender members, and has important theoretical and practical significance for guaranteeing the safe operation of a railway and rail transit system and improving the detection level of infrastructure if the guided wave nondestructive detection technology can be applied to rail flaw detection.
The guided wave detection technology applied to steel rail defect detection can be divided into four stages: (1) the excitation of guided waves in the steel rail, and the main technology of the stage relates to the design of an excitation device; (2) the propagation of guided waves in a steel rail mainly relates to the research of the dispersion characteristic, the attenuation characteristic and the mode conversion of the guided waves in the steel rail; (3) the guided wave is received in the steel rail, and the main technology relates to the design of a receiving device; (4) the guided wave signals are processed to enable defect identification. The research in the first three stages is mainly to excite non-dispersive single-mode pair guided waves in the steel rail, and related documents show that the research results are richer. The complexity of the actual service environment of the steel rail, the unknown defect size and position, and the attenuation of the guided wave itself all bring a serious challenge on how to extract the weak characteristic signal under the background of strong noise.
Common weak signal detection methods include time-frequency analysis, statistical analysis, correlation analysis and the like, but the methods are all linear methods, most of the methods are noise suppression technologies, and when a weak signal under a noise background is detected, a useful signal is possibly damaged, so that the original weak signal is more difficult to identify, and the detection precision is influenced.
Disclosure of Invention
In view of the above, the invention provides a method, a device, a system, a computer device and a storage medium for identifying and positioning a steel rail guided wave defect based on a K-S entropy, which are used for establishing a duffing oscillator chaotic system by sampling a time-course signal propagated by guided waves in a steel rail, taking a Kolmogorov-Sinai entropy (called K-S entropy for short) as a phase state judgment index of the duffing oscillator chaotic system, and combining a rectangular time shift window function, so that the guided wave signal identification under a strong noise background can be realized, and small defects of different degrees are identified and positioned, and the robustness and the sensitivity of steel rail defect detection are improved.
The invention aims to provide a steel rail guided wave defect identification and positioning method based on K-S entropy.
The invention aims to provide a steel rail guided wave defect identification and positioning device based on K-S entropy.
The invention further aims to provide a steel rail guided wave defect identification and positioning system based on the K-S entropy.
It is a fourth object of the invention to provide a computer apparatus.
It is a fifth object of the present invention to provide a computer-readable storage medium.
The first purpose of the invention can be achieved by adopting the following technical scheme:
acquiring a time-course signal of guided wave propagation in the steel rail as a guided wave sampling signal;
a proper driving force amplitude value is determined through a K-S entropy, and a duffing oscillator chaotic system capable of detecting a specific frequency guided wave signal is established;
inputting the guided wave sampling signal into the established duffing oscillator chaotic system, scanning the guided wave sampling signal along a time axis by defining a rectangular time-shifting window function, and calculating the K-S entropy of each window;
if K-S entropies of all windows between an incident wave and an end face echo of the guided wave sampling signal are constantly equal to 0, the steel rail has no defect, and if a window with the K-S entropies larger than 0 exists between the incident wave and the end face echo of the guided wave sampling signal, the steel rail has defect;
when the steel rail has defects, the curve peak value of the K-S entropy is utilized to determine the incident wave, the end echo and the corresponding time of the window where the defect echo is located, and the defects are positioned through the time-distance proportional relation.
Further, the establishing of the duffing oscillator chaotic system capable of detecting the specific frequency guided wave signal specifically comprises:
selecting a duffing oscillator chaotic system as follows:
wherein F (t) is a driving force term, δ is a damping ratio, -x3+x5Is a non-linear restoring force term;
sampling signals from guided waves
The driving force item of the duffing vibrator chaotic system is rewritten into the same form as the guided wave sampling signal, and the duffing vibrator is usedThe chaotic system is rewritten as follows:
the Duffing oscillator chaotic system is rewritten into a three-dimensional autonomous Duffing oscillator system, which has the following formula:
wherein s (t) is an input guided wave sampling signal, F0The critical value of the system phase state in the chaotic state and the periodic state is determined under the condition of noise or no noise.
Further, the K-S entropy is used for representing the average information loss rate of the duffing oscillator chaotic system and is calculated by the following formula:
where ks denotes K — S entropy, ρ (x) is the density of the state function of the attractor in phase space, ρ (x) is considered constant, i.e. - [ ρ (x) dx ═ 1,
the method is characterized in that the average information quantity increased each time in the multiple iteration processes of the duffing oscillator chaotic system is as follows:
wherein the content of the first and second substances,
the method is the information quantity increased by one iteration of the duffing oscillator chaotic system.
Further, the information quantity increased by the duffing oscillator chaotic system through one iteration is calculated as follows:
for a one-dimensional discrete mapping, the following equation:
xn+1＝f(xn)xn∈[a，b]
wherein f is a non-linear function;
it is assumed that the variation interval of the variable x is divided into n equal parts, and the probability of x in each of the divided equal parts is equal to
If x is known to be within a certain interval, the amount of information obtained is:
if n is reduced, the obtained information quantity is reduced, and the iteration process of the mapping is equivalent to the expansion of the variable change interval
The information quantity of the duffing oscillator chaotic system increased by one iteration is as follows:
further, if the K-S entropy of all windows between the incident wave of the guided wave sampling signal and the end echo is constantly equal to 0, then there is no defect in the steel rail, and if there is a window with a K-S entropy greater than 0 between the incident wave of the guided wave sampling signal and the end echo, then there is a defect in the steel rail, which specifically includes:
if K-S entropies of all windows between an incident wave and an end face echo of the guided wave sampling signal are constantly equal to 0, the duffin oscillator chaotic system makes regular motion, namely the phase state is a periodic state, at the moment, the steel rail has no defect, and if a window with the K-S entropy larger than 0 exists between the incident wave and the end face echo of the guided wave sampling signal, the duffin oscillator chaotic system makes chaotic motion, namely the phase state is a chaotic state, at the moment, the steel rail has defect.
Further, the defect is located according to the time-distance proportional relation, which is as follows:
wherein d isxThe distance between the defect of the steel rail and the excitation position of the guided wave, d is the length of the steel rail, and trFor incident guided wave window corresponding time tdFor the end-face reflection echo window corresponding time, tcThe corresponding time of the defect echo window.
The second purpose of the invention can be achieved by adopting the following technical scheme:
a steel rail guided wave defect identification and positioning device based on K-S entropy, the device comprises:
the acquisition module is used for acquiring a time-course signal of the guided wave propagating in the steel rail as a guided wave sampling signal;
the establishing module is used for establishing a proper driving force amplitude value through a K-S entropy and establishing a duffing oscillator chaotic system capable of detecting a specific frequency guided wave signal;
the calculation module is used for inputting the guided wave sampling signals into the established duffing oscillator chaotic system, scanning the guided wave sampling signals along a time axis by defining a rectangular time-shifting window function, and calculating the K-S entropy of each window;
the identification module is used for enabling the steel rail to have no defect if K-S entropies of all windows between incident waves and end face echoes of the guided wave sampling signals are constantly equal to 0, and enabling the steel rail to have defect if the K-S entropies of all windows between the incident waves and the end face echoes of the guided wave sampling signals are larger than 0;
and the positioning module is used for determining the time corresponding to the incident wave, the end echo and the window where the defect echo is located by utilizing the curve peak value of the K-S entropy when the steel rail has the defect, and positioning the defect through a time-distance proportional relation.
The third purpose of the invention can be achieved by adopting the following technical scheme:
a steel rail guided wave defect identification and positioning system based on K-S entropy comprises an arbitrary waveform generator, a power amplifier, a guided wave excitation transducer, a guided wave receiving transducer, a digital oscilloscope and a computer, wherein the guided wave excitation transducer and the guided wave receiving transducer are arranged on the end face of one side of the bottom of a steel rail;
the computer is used for executing the steel rail guided wave defect identification and positioning method.
The fourth purpose of the invention can be achieved by adopting the following technical scheme:
the computer equipment comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program stored in the memory to realize the rail guided wave defect identification and positioning method.
The fifth purpose of the invention can be achieved by adopting the following technical scheme:
a computer readable storage medium storing a computer program which, when executed by a processor, implements the method for identifying and locating a rail guided wave defect described above.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, a specific frequency guided wave signal is excited at the bottom of a steel rail, so that the guided wave is propagated along the steel rail and passes through all positions of the steel rail, a time-course signal of the guided wave propagated in the steel rail is sampled, a duffing oscillator chaotic system capable of detecting the specific frequency guided wave signal is established, Kolmogorov-Sinai entropy (K-S entropy for short) is used as a phase state judgment index of the duffing oscillator chaotic system, and the guided wave signal identification under the background of strong noise is realized by combining a rectangular time-shift window function, so that the detection range of a pipeline is effectively extended, thus small defects of different degrees are identified and positioned, and the robustness and the sensitivity of steel rail defect detection are improved.
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 description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a steel rail guided wave defect identification and positioning system according to embodiment 1 of the present invention.
Fig. 2 is a flowchart of a method for identifying and positioning a rail guided wave defect according to embodiment 1 of the present invention.
Fig. 3 is a flowchart of a method for identifying and positioning a rail guided wave defect according to embodiment 2 of the present invention.
Fig. 4 is a schematic diagram of a guided wave original time domain signal in the case of no defect in a steel rail in embodiment 2 of the present invention.
Fig. 5 is a schematic diagram of a guided wave original time domain signal in the case of a 3mm crack in a steel rail according to embodiment 2 of the present invention.
Fig. 6 is a schematic diagram of a guided wave original time domain signal in the case of a 4.5mm crack in a steel rail according to embodiment 2 of the present invention.
Fig. 7 is a schematic diagram of a rectangular time-shift window function scanning measured signal according to embodiment 2 of the present invention.
Fig. 8 is a schematic diagram of the K-S entropy calculated by scanning the original time domain signal with the rectangular time shift window function in the case of no defect in the steel rail in embodiment 2 of the present invention.
Fig. 9 is a schematic diagram of the K-S entropy calculated by scanning the original time domain signal with the rectangular time shift window function in the case of a 3mm crack in the steel rail according to embodiment 2 of the present invention.
Fig. 10 is a schematic diagram of the K-S entropy calculated by scanning the original time domain signal with the rectangular time shift window function in the case of a 4.5mm crack in the steel rail according to embodiment 2 of the present invention.
Fig. 11 is a structural block diagram of a rail guided wave defect identification and positioning device according to embodiment 3 of the present invention.
Fig. 12 is a block diagram of a computer device according to embodiment 4 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the protection scope of the present invention.
Example 1:
as shown in fig. 1, the present embodiment provides a steel rail guided wave defect identification and location system, which comprises an arbitrary waveform generator 101, a power amplifier 102, a guided wave excitation transducer 103, a guided wave reception transducer 104, a digital oscilloscope 105 and a computer 106.
Further, the guided wave excitation transducer 103 and the guided wave receiving transducer 104 of the present embodiment are disposed on the end surface of the rail bottom side of the steel rail 107, the arbitrary waveform generator 101, the power amplifier 102 and the guided wave excitation transducer 103 are sequentially connected, the guided wave receiving transducer 104, the digital oscilloscope 105 and the computer 106 are sequentially connected, the arbitrary waveform generator 101 generates a pulse signal modulated by a hanning window, the pulse signal is amplified by the power amplifier 102 and passes through the guided wave excitation transducer 103, a guided wave signal with a specific frequency is excited in the steel rail 107, the guided wave propagates along the steel rail 107 and passes through all positions of the steel rail 107, an echo signal is received by the guided wave receiving transducer 104, the time-course signal of the guided wave propagating in the steel rail 107 is sampled by the digital oscilloscope 105, and the sampled time-course signal is transmitted to the computer 106 by the digital oscilloscope 105.
As shown in fig. 2, the embodiment further provides a method for identifying and positioning a guided wave defect of a steel rail, which is implemented by the computer and includes the following steps:
s201, acquiring a time-course signal of the guided wave propagating in the steel rail as a guided wave sampling signal.
The computer receives signals transmitted by the digital oscilloscope, so that a time-course signal of the sampled guided wave propagating in the steel rail is obtained and used as a guided wave sampling signal.
S202, a proper driving force amplitude value is determined through K-S entropy, and a Duffing vibrator chaotic system capable of detecting a specific frequency guided wave signal is established.
The K-S entropy is an important property for describing chaos, can be used for expressing the average information loss rate of the duffin oscillator chaotic system, and can be used as a phase state judgment index for detecting the duffin oscillator chaotic system.
And S203, inputting the guided wave sampling signal into the established duffing oscillator chaotic system, scanning the guided wave sampling signal along a time axis by defining a rectangular time-shifting window function, and calculating the K-S entropy of each window.
And S204, if K-S entropies of all windows between the incident wave and the end face echo of the guided wave sampling signal are constantly equal to 0, the steel rail has no defect, and if a window with the K-S entropies larger than 0 exists between the incident wave and the end face echo of the guided wave sampling signal, the steel rail has defect.
Specifically, if the K-S entropies of all windows between the incident wave and the end echo of the guided wave sampling signal are constantly equal to 0, the duffing oscillator chaotic system makes regular motion, that is, the phase state is a periodic state, and at this time, the steel rail has no defect, and if a window with a K-S entropy greater than 0 exists between the incident wave and the end echo of the guided wave sampling signal, the duffing oscillator chaotic system makes chaotic motion, that is, the phase state is a chaotic state, and at this time, the steel rail has a defect, and the process proceeds to step S205.
S205, determining the time corresponding to the incident wave, the end echo and the window where the defect echo is located by using the curve peak value of the K-S entropy, and positioning the defect through a time-distance proportional relation.
Example 2:
as shown in fig. 3, in this embodiment, a specific experiment is taken as an example, and the defect identification and location are performed on the steel rail in a laboratory, and the specific implementation process is as follows:
1) the steel rail adopts a 60kg/m standard steel rail, the length of the steel rail is 6m, artificial cracks with the width of 3mm and 4.5mm are respectively manufactured at the position 2.5m away from the end surface of the steel rail, and three working conditions, namely no defect, cracks with the width of 3mm at the bottom of the rail and cracks with the width of 4.5mm at the bottom of the rail are set.
2) The random waveform generator generates a pulse signal modulated by a Hanning window, the pulse signal is amplified by a power amplifier and then passes through a guided wave excitation transducer to excite a guided wave signal with specific frequency in the steel rail, so that the guided wave is propagated along the steel rail and passes through all positions of the steel rail.
3) The measured signals of the three working conditions are received by the guided wave receiving transducer on the steel rail, the time-course signals propagated in the steel rail by the ultrasonic guided waves are sampled by the digital oscilloscope, the sampled time-course signals are transmitted to the computer by the digital oscilloscope, and the original time-domain signals of the three working conditions (the defect-free condition of the steel rail, the 3mm crack condition of the steel rail and the 4.5mm crack condition of the steel rail) are shown in figures 4, 5 and 6.
4) And constructing a duffing oscillator chaotic system to realize the detection of weak signals.
Selecting a duffing oscillator chaotic system, and expressing the duffing oscillator chaotic system in the following form:
wherein F (t) is a driving force term, δ is a damping ratio, -x3+x5Is a non-linear restoring force term.
The guided wave sampling signal adopts a sine signal modulated by a Hanning window, and the following formula is as follows:
the driving force item of the duffing oscillator chaotic system is rewritten into the same form as the guided wave sampling signal, and the duffing oscillator chaotic system can be rewritten into the following form:
in order to facilitate solving, the method is developed and rewritten into a three-dimensional autonomous duffing oscillator chaotic system as follows:
5) and calculating K-S entropy.
For a one-dimensional discrete mapping, the following equation:
xn+1＝f(xn)xn∈[a，b]
where f is a non-linear function.
Assuming that the variation interval of the variable x is divided into n equal parts and the probability of x in each divided equal part is equal, the probability should be equal to
Then, knowing that x is within a certain interval, the amount of information obtained is:
if n is reduced, the obtained information quantity is reduced, and the iteration process of the mapping is equivalent to the expansion of the variable change interval
Therefore, the information quantity of the duffing oscillator chaotic system increased by one iteration is as follows:
therefore, the average information quantity increased each time in the multiple iteration process of the duffing oscillator chaotic system is the information quantity
Comprises the following steps:
the duffin oscillator chaotic system is a three-dimensional autonomous system, and can be decomposed into a low-dimensional system for discussion, wherein the system is
Directions greater than zero contribute positively
Thus, there are:
where ρ (x) is the density of the state function of the attractor in phase space, since λiIs the result of averaging over a long time, in general λiIndependent of x, the density of states ρ (x) in the above formula is considered constant, i.e., [ integral ] ρ (x) dx ═ 1, which can be simplified as:
the K-S entropy ks can then be calculated by:
when ks is 0, the duffing oscillator chaotic system makes regular motion, namely the phase state is a periodic state.
When ks is larger than 0, the duffing vibrator chaotic system does chaotic motion, namely the phase state is chaotic state.
6) And establishing a rectangular time-shift window function to scan the defects of the steel rail under different working conditions.
The rectangular time-shift window function is defined as follows:
Sm＝g(t-nτ)S
wherein S is a time domain signal to be detected, SmFor the signal intercepted by the time shift window, N is the length of the time domain signal to be detectedThe window length of the shift window is 2 delta, the shift interval is tau, and r tau is the time of the center of the shift window.
Scanning the measured signal by a rectangular time-shifting window function, calculating the K-S entropy of each window as shown in figure 7, and comparing the accuracy of the method for identifying and positioning the defects of the steel rail.
Fig. 8 is a K-S entropy curve corresponding to an echo signal under a complete steel rail, and the result shows that in the complete steel rail, the K-S entropy is constantly equal to 0 between an incident wave and an end echo in spite of noise due to no defect.
When the defect of the rail bottom of the steel rail is 3mm, the defect is small, whether the defect exists or not is difficult to judge in a time domain signal, the K-S entropy curve shows that the K-S entropy at the damaged part is larger than 0, as shown in figure 9, the defect can be judged to exist, and as long as a weak signal exists, the system can be converted into a chaotic state, namely the K-S entropy is larger than 0.
As shown in FIG. 10, when the rail bottom defect of the steel rail is 4.5mm, the K-S entropy curve shows that the K-S entropy is more than 0 at the damage position.
7) Determining the time corresponding to the incident wave, the end echo and the window where the defect echo is located by utilizing the curve peak value of the K-S entropy, and positioning the steel rail defect according to the time proportional relation of the incident wave, the end echo and the window where the defect echo is located and the time-distance proportional relation of the incident wave, the end echo and the window where the defect echo is located, wherein the specific expression of the defect position is as follows:
wherein d isxThe distance between the defect of the steel rail and the excitation position of the guided wave, d is the length of the steel rail, and trFor incident guided wave window corresponding time tdFor the end-face reflection echo window corresponding time, tcThe corresponding time of the defect echo window.
The calculation results are shown in table 1 below, and it can be seen that the positioning error of the defect is within an acceptable range.
TABLE 1 Defect location of rails under different conditions
Example 3:
as shown in fig. 11, the present embodiment provides a steel rail guided wave defect identification and positioning apparatus, the apparatus includes an obtaining module 1101, an establishing module 1102, a calculating module 1103, an identifying module 1104, and a positioning module 1105, and specific functions of each module are as follows:
the acquisition module 1101 is configured to acquire a time-course signal of the guided wave propagating in the steel rail as a guided wave sampling signal.
The establishing module 1102 is used for establishing a proper driving force amplitude value through the K-S entropy and establishing the duffing oscillator chaotic system capable of detecting the specific frequency guided wave signal.
And the calculating module 1103 is configured to input the guided wave sampling signal into the established duffing oscillator chaotic system, scan the guided wave sampling signal along a time axis by defining a rectangular time shift window function, and calculate a K-S entropy of each window.
And the identification module 1104 is used for determining that the steel rail has no defect if the K-S entropies of all windows between the incident wave and the end echo of the guided wave sampling signal are constantly equal to 0, and determining that the steel rail has defect if a window with the K-S entropies larger than 0 exists between the incident wave and the end echo of the guided wave sampling signal.
And the positioning module 1105 is configured to determine, when the steel rail has a defect, a time corresponding to a window where an incident wave, an end echo and a defect echo are located by using a curve peak of the K-S entropy, and position the defect through a time-distance proportional relationship.
It should be noted that the system provided in this embodiment is only illustrated by the division of the functional modules, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the internal structure is divided into different functional modules to complete all or part of the functions described above.
Example 4:
as shown in fig. 12, the present embodiment provides a computer apparatus, which is a computer and includes a processor 1202, a memory, an input device 1203, a display 1204 and a network interface 1205 connected by a system bus 1201, where the processor is configured to provide computing and controlling capabilities, the memory includes a nonvolatile storage medium 1206 and an internal storage 1207, the nonvolatile storage medium 1206 stores an operating system, a computer program and a database, the internal storage 1207 provides an environment for the operating system and the computer program in the nonvolatile storage medium to run, and when the processor 1202 executes the computer program stored in the memory, the rail guided wave defect identifying and locating method of the foregoing embodiment 1 is implemented as follows:
acquiring a time-course signal of guided wave propagation in the steel rail as a guided wave sampling signal;
a proper driving force amplitude value is determined through a K-S entropy, and a duffing oscillator chaotic system capable of detecting a specific frequency guided wave signal is established;
inputting the guided wave sampling signal into the established duffing oscillator chaotic system, scanning the guided wave sampling signal along a time axis by defining a rectangular time-shifting window function, and calculating the K-S entropy of each window;
if K-S entropies of all windows between an incident wave and an end face echo of the guided wave sampling signal are constantly equal to 0, the steel rail has no defect, and if a window with the K-S entropies larger than 0 exists between the incident wave and the end face echo of the guided wave sampling signal, the steel rail has defect;
when the steel rail has defects, the curve peak value of the K-S entropy is utilized to determine the incident wave, the end echo and the corresponding time of the window where the defect echo is located, and the defects are positioned through the time-distance proportional relation.
Example 5:
the present embodiment provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for identifying and positioning a guided wave defect of a steel rail according to embodiment 1 is implemented as follows:
acquiring a time-course signal of guided wave propagation in the steel rail as a guided wave sampling signal;
a proper driving force amplitude value is determined through a K-S entropy, and a duffing oscillator chaotic system capable of detecting a specific frequency guided wave signal is established;
inputting the guided wave sampling signal into the established duffing oscillator chaotic system, scanning the guided wave sampling signal along a time axis by defining a rectangular time-shifting window function, and calculating the K-S entropy of each window;
if K-S entropies of all windows between an incident wave and an end face echo of the guided wave sampling signal are constantly equal to 0, the steel rail has no defect, and if a window with the K-S entropies larger than 0 exists between the incident wave and the end face echo of the guided wave sampling signal, the steel rail has defect;
when the steel rail has defects, the curve peak value of the K-S entropy is utilized to determine the incident wave, the end echo and the corresponding time of the window where the defect echo is located, and the defects are positioned through the time-distance proportional relation.
It should be noted that the computer readable storage medium of the present embodiment may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In the present embodiment, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this embodiment, however, a computer readable signal medium may include a propagated data signal with a computer readable program embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable storage medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable storage medium may be written with a computer program for performing the present embodiments in one or more programming languages, including an object oriented programming language such as Java, Python, C + +, and conventional procedural programming languages, such as C, or similar programming languages, or combinations thereof. The program may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods, apparatus, and computer devices according to various embodiments described above. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. The modules described in the above embodiments may be implemented by software or hardware.
The foregoing description is only exemplary of the preferred embodiments of the invention and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure in the embodiments described above is not limited to the particular combination of features described above, and that other embodiments can be made by any combination of features described above or their equivalents without departing from the spirit of the disclosure. For example, the above features and (but not limited to) the features with similar functions disclosed in the above embodiments are mutually replaced to form the technical solution.
It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described above, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A steel rail guided wave defect identification and positioning method based on K-S entropy is characterized by comprising the following steps:
acquiring a time-course signal of guided wave propagation in the steel rail as a guided wave sampling signal;
a proper driving force amplitude value is determined through a K-S entropy, and a duffing oscillator chaotic system capable of detecting a specific frequency guided wave signal is established;
inputting the guided wave sampling signal into the established duffing oscillator chaotic system, scanning the guided wave sampling signal along a time axis by defining a rectangular time-shifting window function, and calculating the K-S entropy of each window;
if K-S entropies of all windows between an incident wave and an end face echo of the guided wave sampling signal are constantly equal to 0, the steel rail has no defect, and if a window with the K-S entropies larger than 0 exists between the incident wave and the end face echo of the guided wave sampling signal, the steel rail has defect;
when the steel rail has defects, the curve peak value of the K-S entropy is utilized to determine the incident wave, the end echo and the corresponding time of the window where the defect echo is located, and the defects are positioned through the time-distance proportional relation.
2. The method for identifying and positioning the guided wave defects of the steel rail according to claim 1, wherein the duffing oscillator chaotic system capable of detecting the guided wave signals with specific frequencies is established, and specifically comprises the following steps:
selecting a duffing oscillator chaotic system as follows:
wherein F (t) is a driving force term, δ is a damping ratio, -x3+x5Is a non-linear restoring force term;
sampling signals from guided waves
The driving force item of the duffing oscillator chaotic system is rewritten into the same form as the guided wave sampling signal, and the duffing oscillator chaotic system is rewritten into the following formula:
the Duffing oscillator chaotic system is rewritten into a three-dimensional autonomous Duffing oscillator system, which has the following formula:
wherein s (t) is an input guided wave sampling signal, F0The critical value of the system phase state in the chaotic state and the periodic state is determined under the condition of noise or no noise.
3. The steel rail guided wave defect identification and positioning method according to claim 1, wherein the K-S entropy is used for representing the average information loss rate of the duffing oscillator chaotic system and is calculated by the following formula:
where ks denotes K — S entropy, ρ (x) is the density of the state function of the attractor in phase space, ρ (x) is considered constant, i.e. - [ ρ (x) dx ═ 1,
the method is characterized in that the average information quantity increased each time in the multiple iteration processes of the duffing oscillator chaotic system is as follows:
wherein the content of the first and second substances,
the method is the information quantity increased by one iteration of the duffing oscillator chaotic system.
4. The steel rail guided wave defect identification and positioning method according to claim 3, wherein the duffing oscillator chaotic system is subjected to an iterative increase of information content, and the calculation process is as follows:
for a one-dimensional discrete mapping, the following equation:
xn+1＝f(xn)xn∈[a，b]
wherein f is a non-linear function;
it is assumed that the variation interval of the variable x is divided into n equal parts, and the probability of x in each of the divided equal parts is equal to
If x is known to be within a certain interval, the amount of information obtained is:
if n is reduced, the obtained information quantity is reduced, and the iteration process of the mapping is equivalent to the expansion of the variable change interval
The information quantity of the duffing oscillator chaotic system increased by one iteration is as follows:
5. a rail guided wave defect identification and positioning method according to any one of claims 1 to 4, wherein if K-S entropy of all windows between an incident wave and an end echo of a guided wave sampling signal is constantly equal to 0, the rail is free of defects, and if a window with K-S entropy greater than 0 exists between the incident wave and the end echo of the guided wave sampling signal, the rail is free of defects, specifically comprising:
if K-S entropies of all windows between an incident wave and an end face echo of the guided wave sampling signal are constantly equal to 0, the duffin oscillator chaotic system makes regular motion, namely the phase state is a periodic state, at the moment, the steel rail has no defect, and if a window with the K-S entropy larger than 0 exists between the incident wave and the end face echo of the guided wave sampling signal, the duffin oscillator chaotic system makes chaotic motion, namely the phase state is a chaotic state, at the moment, the steel rail has defect.
6. A rail guided wave defect identification and location method as claimed in any one of claims 1 to 4 wherein the defect is located by a time-distance proportional relationship as follows:
wherein d isxThe distance between the defect of the steel rail and the excitation position of the guided wave, d is the length of the steel rail, and trFor incident guided wave window corresponding time tdFor the end-face reflection echo window corresponding time, tcThe corresponding time of the defect echo window.
7. A rail guided wave defect identification and positioning device based on K-S entropy is characterized by comprising:
the acquisition module is used for acquiring a time-course signal of the guided wave propagating in the steel rail as a guided wave sampling signal;
the establishing module is used for establishing a proper driving force amplitude value through a K-S entropy and establishing a duffing oscillator chaotic system capable of detecting a specific frequency guided wave signal;
the calculation module is used for inputting the guided wave sampling signals into the established duffing oscillator chaotic system, scanning the guided wave sampling signals along a time axis by defining a rectangular time-shifting window function, and calculating the K-S entropy of each window;
the identification module is used for enabling the steel rail to have no defect if K-S entropies of all windows between incident waves and end face echoes of the guided wave sampling signals are constantly equal to 0, and enabling the steel rail to have defect if the K-S entropies of all windows between the incident waves and the end face echoes of the guided wave sampling signals are larger than 0;
and the positioning module is used for determining the time corresponding to the incident wave, the end echo and the window where the defect echo is located by utilizing the curve peak value of the K-S entropy when the steel rail has the defect, and positioning the defect through a time-distance proportional relation.
8. A steel rail guided wave defect identification and positioning system based on K-S entropy is characterized by comprising an arbitrary waveform generator, a power amplifier, a guided wave excitation transducer, a guided wave receiving transducer, a digital oscilloscope and a computer, wherein the guided wave excitation transducer and the guided wave receiving transducer are arranged on the end face of one side of the bottom of a steel rail;
the computer is used for executing the steel rail guided wave defect identification and positioning method of any one of claims 1 to 6.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor when executing the computer program stored in the memory implements the method of identifying and locating a rail guided wave defect of any one of claims 1 to 6.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the method of steel rail guided wave defect identification and localization according to any one of claims 1-6.
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