CN113532835A - Railway contact net hard spot diagnosis method and device - Google Patents
Railway contact net hard spot diagnosis method and device Download PDFInfo
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Abstract
The invention provides a method and a device for diagnosing hard spots of a railway contact network, wherein the method comprises the following steps: CEEMDAN-SPWVD time-frequency analysis is carried out on the vertical vibration acceleration signal of the pantograph of the railway contact network to be detected, and the time-frequency distribution of the vertical vibration acceleration signal of the pantograph is obtained; determining a mobile filtering marginal spectrum of a railway contact network to be detected; carrying out normalization processing on the mobile filtering marginal spectrum to obtain a contact net marginal index; and determining the marginal index threshold of the contact net for diagnosing the hard spot disease. The high-frequency characteristic that vertical vibration acceleration of the overhead line system is caused by pantograph-catenary impact is depicted from the angle of energy, and the accumulated impact energy is large when hard spots exist, so that the marginal index threshold value of the overhead line system can be rapidly and accurately determined, the problem that the absolute threshold value is difficult to determine when the hard spots are directly diagnosed through the amplitude of the vertical vibration acceleration signal of the pantograph in the prior art is solved, and the accuracy of hard spot diagnosis can be improved.
Description
Technical Field
The invention relates to the technical field of electrified railway power supply, in particular to a method and a device for diagnosing hard spots of a railway contact network.
Background
In a contact network system, the points where the bottom surface of the contact line is not smooth or the vertical elasticity of the contact line changes suddenly are called hard points. The long-term hard spot can also cause the bending stress of the contact line to increase, so that the abnormal abrasion, fatigue and even breakage of the contact line are caused, and in severe cases, the power supply of an operating vehicle can be influenced, and even the running safety of the vehicle can be influenced, so that the analysis of the contact network state, particularly the diagnosis of the hard spot of the railway contact network is very important.
The existing method generally analyzes the contact net state by performing characteristic analysis on bow net dynamic response detection data. The existing method firstly provides a frequency domain analysis method, which takes spectral analysis performed by Fourier transform as a representative, and can calculate the component size of a signal in each frequency component, but the frequency domain analysis cannot obtain the change characteristic of the digital signal frequency along with time space. The time-frequency analysis method solves the problems and can effectively analyze the characteristic that each frequency component of the digital signal changes along with time and space. The WVD has good effect in the time-frequency characteristic extraction of the single-component signal and has the best time-frequency aggregation, but due to typical quadratic form transformation, cross interference terms exist in the time-frequency analysis of the multi-component signal.
On this basis, the conventional method proposes to diagnose a hard spot by judging whether the amplitude of the acceleration original signal exceeds a threshold value based on an integrated Empirical Mode Decomposition (EEMD) method, so as to implement fault diagnosis of the overhead line system.
Disclosure of Invention
The embodiment of the invention provides a hard spot diagnosis method for a railway contact network, which is used for improving the accuracy of hard spot diagnosis and comprises the following steps:
performing CEEMDAN-SPWVD time-frequency analysis on a pantograph vertical vibration acceleration signal of a railway contact network to be detected to obtain time-frequency distribution of the pantograph vertical vibration acceleration signal;
determining a mobile filtering marginal spectrum of the railway contact network to be detected according to the time-frequency distribution of the pantograph vertical vibration acceleration signals;
carrying out normalization processing on the mobile filtering marginal spectrum to obtain a contact net marginal index of the railway contact net to be detected;
and determining a contact net marginal index threshold value of the hard spot disease diagnosis of the railway contact net to be detected according to the contact net marginal index of the railway contact net to be detected.
The embodiment of the invention also provides a device for diagnosing hard spots of a railway contact network, which is used for improving the accuracy of hard spot diagnosis and comprises the following components:
the time-frequency analysis module is used for carrying out CEEMDAN-SPWVD time-frequency analysis on the vertical vibration acceleration signal of the pantograph of the railway contact network to be detected to obtain the time-frequency distribution of the vertical vibration acceleration signal of the pantograph;
the mobile filtering marginal spectrum determining module is used for determining a mobile filtering marginal spectrum of the railway contact network to be detected according to the time-frequency distribution of the pantograph vertical vibration acceleration signals;
the contact network marginal index determining module is used for carrying out normalization processing on the mobile filtering marginal spectrum to obtain a contact network marginal index of the railway contact network to be detected;
and the threshold value determining module is used for determining the contact net marginal index threshold value of the hard spot disease diagnosis of the railway contact net to be detected according to the contact net marginal index of the railway contact net to be detected.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can be run on the processor, wherein the processor executes the computer program to realize the railway contact network hard spot diagnosis method.
The embodiment of the invention also provides a computer readable storage medium, which stores a computer program for executing the method for diagnosing the hard spot of the railway contact network.
In the embodiment of the invention, CEEMDAN-SPWVD time-frequency analysis is carried out on the vertical vibration acceleration signal of the pantograph of the railway contact network to be detected to obtain the time-frequency distribution of the vertical vibration acceleration signal of the pantograph; determining a mobile filtering marginal spectrum of a railway contact network to be detected according to the time-frequency distribution of the pantograph vertical vibration acceleration signals; carrying out normalization processing on the mobile filtering marginal spectrum to obtain a contact net marginal index of the railway contact net to be detected; and determining a contact net marginal index threshold value of the hard spot disease diagnosis of the railway contact net to be detected according to the contact net marginal index of the railway contact net to be detected. The method comprises the steps of determining a mobile filtering marginal spectrum of a railway contact network to be detected according to time-frequency distribution of vertical vibration acceleration signals of the pantograph, normalizing to obtain a contact network marginal index, and realizing high-frequency characteristics of vertical vibration acceleration of the pantograph caused by pantograph-catenary impact from the angle of energy; and hard spot diagnosis is carried out on the railway contact net based on the accurate contact net marginal index threshold value, so that the accuracy of hard spot diagnosis can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a hard spot diagnosis method for a railway contact network in the embodiment of the invention.
Fig. 2 is a schematic diagram of an implementation process of step 101 in the embodiment of the present invention.
Fig. 3 is a schematic diagram of the implementation process of step 102 in the embodiment of the present invention.
Fig. 4 is a schematic diagram of the implementation process of step 103 in the embodiment of the present invention.
Fig. 5 is a schematic diagram illustrating an implementation process of step 104 in an embodiment of the present invention.
FIG. 6 is a schematic diagram illustrating an implementation process of the CEEMDAN-SPWVD time-frequency analysis method in the embodiment of the present invention.
FIG. 7 is a diagram illustrating the detailed calculation steps of CEEMDAN in an embodiment of the present invention.
FIG. 8 is a diagram illustrating an original signal of vertical vibration acceleration of a pantograph according to an embodiment of the present invention.
FIG. 9 is a waveform diagram of the IMF signals and residue signals of various stages according to one embodiment of the present invention.
FIG. 10 is a SPWVD time-frequency plot of the IMF signals and residual signals of each stage in accordance with an embodiment of the present invention.
Fig. 11 is a cemdan-SPWVD time-frequency diagram after the original signal conversion shown in fig. 8 in the embodiment of the present invention.
Fig. 12 is a time-frequency diagram of the transformed WVD of the original signal shown in fig. 8 according to the embodiment of the present invention.
Fig. 13 is a schematic diagram of a calculation flow of a contact net marginal index method in the specific example of the present invention.
FIG. 14 is a graph of the calculation of the signal shift filtering margin spectrum according to the embodiment of the present invention.
FIG. 15 is a diagram illustrating an original vertical acceleration signal of a pantograph according to an embodiment of the present invention.
Fig. 16 is a schematic view of a catenary margin index calculated from the original signal of the vertical acceleration of the pantograph shown in fig. 15 in the embodiment of the present invention.
FIG. 17 is a graph illustrating the original vertical vibration acceleration waveform of a pantograph passing near K1+078 for the first time in an embodiment of the present invention.
FIG. 18 is a graphical representation of the original waveform of the vertical vibratory acceleration of the pantograph near the second pass K1+078 in an embodiment of the present invention.
Fig. 19 is a schematic diagram of the margin index of the catenary near the first pass K1+078 in the embodiment of the present invention.
Fig. 20 is a schematic diagram of the catenary margin index near the second pass K1+078 in the specific example of the invention.
FIG. 21 is a schematic diagram of the margin index of the catenary passing at 130km/h twice in the specific example of the present invention.
Fig. 22 is a schematic diagram of the margin index of the catenary passing at 150km/h twice in the specific example of the invention.
Fig. 23 is a schematic structural diagram of a hard spot diagnostic device of a railway contact system in the embodiment of the invention.
Fig. 24 is a schematic structural diagram of a time-frequency analysis module 2301 in an embodiment of the invention.
FIG. 25 is a block diagram of a module 2302 for determining a mobile filtering margin spectrum according to an embodiment of the present invention.
Fig. 26 is a schematic structural diagram of a contact net marginal index determining module 2303 in the embodiment of the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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 embodiment of the invention provides a hard spot diagnosis method for a railway contact network, which is used for improving the accuracy of hard spot diagnosis and comprises the following steps of:
step 101: CEEMDAN-SPWVD time-frequency analysis is carried out on the vertical vibration acceleration signal of the pantograph of the railway contact network to be detected, and the time-frequency distribution of the vertical vibration acceleration signal of the pantograph is obtained;
step 102: determining a mobile filtering marginal spectrum of a railway contact network to be detected according to the time-frequency distribution of the pantograph vertical vibration acceleration signals;
step 103: carrying out normalization processing on the mobile filtering marginal spectrum to obtain a contact net marginal index of the railway contact net to be detected;
step 104: and determining a contact net marginal index threshold value of the hard spot disease diagnosis of the railway contact net to be detected according to the contact net marginal index of the railway contact net to be detected.
As can be known from the process shown in fig. 1, in the embodiment of the present invention, the time-frequency distribution of the pantograph vertical vibration acceleration signal of the railway contact system to be tested is obtained by performing CEEMDAN-SPWVD time-frequency analysis on the pantograph vertical vibration acceleration signal; determining a mobile filtering marginal spectrum of a railway contact network to be detected according to the time-frequency distribution of the pantograph vertical vibration acceleration signals; carrying out normalization processing on the mobile filtering marginal spectrum to obtain a contact net marginal index of the railway contact net to be detected; and determining a contact net marginal index threshold value of the hard spot disease diagnosis of the railway contact net to be detected according to the contact net marginal index of the railway contact net to be detected. The method comprises the steps of determining a mobile filtering marginal spectrum of a railway contact network to be detected according to time-frequency distribution of vertical vibration acceleration signals of the pantograph, normalizing to obtain a contact network marginal index, and realizing high-frequency characteristics of vertical vibration acceleration of the pantograph caused by pantograph-catenary impact from the angle of energy; and hard spot diagnosis is carried out on the railway contact net based on the accurate contact net marginal index threshold value, so that the accuracy of hard spot diagnosis can be improved.
In specific implementation, CEEMDAN-SPWVD time-frequency analysis is firstly carried out on the pantograph vertical vibration acceleration signal of the railway contact network to be detected, and the time-frequency distribution of the pantograph vertical vibration acceleration signal is obtained. The specific implementation process, as shown in fig. 2, includes:
step 201: CEEMDAN decomposition is carried out on a pantograph vertical vibration acceleration signal of a railway contact network to be detected to obtain a plurality of single component signals;
step 202: carrying out SPWVD conversion on the plurality of single-component signals to obtain the time-frequency distribution of each single-component signal;
step 203: and superposing the time-frequency distribution of each single-component signal to obtain the time-frequency distribution of the vertical vibration acceleration signal of the pantograph.
The CEEMDAN is a Complete Ensemble Empirical Mode Decomposition (CEEMD) method of Adaptive Noise, and is improved by a Complementary Ensemble Empirical Mode Decomposition (CEEMD) method proposed based on j.r.yeh, 2010. The SPWVD transform refers to a Smooth Pseudo Wigner Ville Distribution (Smooth Pseudo Wigner-Ville Distribution).
In the specific embodiment, compared with the traditional WVD (Wigner-Ville distribution), the obtained time-frequency graph better inhibits the interference of cross terms in both the time domain direction and the frequency domain direction and has good time-frequency aggregation performance.
And after the time-frequency distribution of the vertical vibration acceleration signals of the pantograph is obtained, determining the mobile filtering marginal spectrum of the railway contact network to be detected according to the time-frequency distribution of the vertical vibration acceleration signals of the pantograph. The specific implementation process, as shown in fig. 3, includes:
step 301: determining a filtering range according to the time-frequency distribution of the vertical vibration acceleration signals of the pantograph;
step 302: and performing moving window calculation of a specified frequency range on the time-frequency distribution of the vertical vibration acceleration signals of the pantograph based on the filtering range to obtain a moving filtering marginal spectrum.
In the specific implementation of step 301, it is generally determined that the energy of the dynamic response data of the pantograph is mainly distributed at 20 to 800Hz by performing time-frequency analysis on a plurality of hard point data. Therefore, when the moving marginal spectrum is calculated, the band-pass filtering processing is carried out on the time-frequency graph, namely, when the moving marginal spectrum is calculated, only the time-frequency distribution within the range of 20-800 Hz is selected. In specific implementation, band-pass filtering is carried out on the time-frequency distribution of the vertical vibration acceleration signals of the pantograph, and only the range of 20-800 Hz is selected as the specified frequency range, so that the moving window calculation is carried out in the range.
After the moving window calculation is performed to obtain the moving filtering boundary spectrum, in a specific embodiment, the position where energy exists in the time frequency distribution graph can be clearly observed, and the position where energy exists in the time frequency distribution graph is represented in the form of a peak value in the moving filtering boundary spectrum.
After the mobile filtering marginal spectrum is obtained, the mobile filtering marginal spectrum is subjected to normalization processing to obtain a contact net marginal index of the railway contact net to be detected, and a specific process, as shown in fig. 4, includes:
step 401: calculating a movement marginal spectrum of each unit divided by a railway contact network to be tested, and determining a unit marginal value of each unit according to the movement marginal spectrum of each unit;
step 402: determining a calibration parameter according to the cell marginal values of a plurality of cells of the same-speed grade line;
step 403: and carrying out normalization processing on the mobile filtering marginal spectrum by using the calibration parameters to obtain the contact net marginal index of the railway contact net to be detected.
In a specific embodiment, an average value of unit marginal values of all lines with the same speed grade is calculated and recorded as a calibration parameter, and a mobile filtering marginal spectrum of the overhead line system is divided by the calibration parameter to complete normalization, so that an overhead line system marginal index CMI is obtained.
When the contact line has hard points, large impact can occur between the pantograph nets, the vertical vibration acceleration of the pantograph is in a process of increasing and then attenuating, although an extremely large amplitude value does not necessarily occur, the accumulated impact energy is large. Therefore, in a specific embodiment, the threshold of the hard spot diagnosis of the railway contact network to be detected may be determined according to the marginal index of the railway contact network to be detected, and a specific process, as shown in fig. 5, includes:
step 501: determining the waveform of the contact net marginal index of the railway contact net to be detected along with the change of the mileage;
step 502: and analyzing the peak value of the waveform, and determining the threshold value of hard spot diagnosis of the railway contact network to be detected.
The peak value position which is obviously higher than the whole waveform is an area with larger accumulated impact energy, so that the existence of a contact net hard point can be determined, namely, the judgment threshold value for diagnosing the hard point of the railway contact net to be detected can be determined from the oscillogram.
After the judgment threshold value of the diagnosis of the hard spot of the railway contact network to be detected is determined, whether the hard spot exists in the contact network can be judged according to the judgment threshold value, namely, the hard spot of the contact network can be diagnosed as the hard spot of the contact network as long as the point of which the CMI exceeds the judgment threshold value exists in the contact network.
A specific example is given below to illustrate how the embodiment of the present invention performs hard spot diagnosis on a railway contact system.
The inventor finds that the impact energy extracted from the time-frequency distribution of the detection data is not deeply analyzed in the prior art, so that the high-frequency characteristic of vertical vibration acceleration of a contact network caused by pantograph-Catenary impact is described from the angle of the energy, a mobile filtering Marginal spectrum is calculated based on CEEMDAN-SPWVD time-frequency analysis, normalization processing is carried out, the hard point of the contact network is diagnosed by adopting a contact network Marginal Index (CMI), and the problem that the absolute threshold of the hard point is difficult to determine by directly diagnosing the amplitude is solved.
The example is mainly divided into two parts, the first part is a CEEMDAN-SPWVD time-frequency analysis method, and the specific flow is shown in FIG. 6 and comprises the following steps:
performing CEEMDAN decomposition on the bow net dynamic response data to obtain a plurality of groups of IMFs (intrinsic mode functions);
calculating the SPWVD corresponding to each IMF;
and overlapping to obtain time-frequency information of the bow net system vibration.
Wherein, the CEEMDAN detailed calculation steps are shown in FIG. 7:
1) let s (n) represent the original pantograph vertical acceleration signal, wi(n) is the ith added white noise sequence, ε, following a standard normal distributionkFor the kth SNR, define Ek(. cndot.) is the operator that generates the kth IMF by the EMD algorithm. M (-) is the average of the envelope of the upper and lower extreme points, which is the local average function generated by the EMD algorithm. si(n) represents the i-th original signal to which the EMD decomposed noise is added, si(n)＝s(n)+ε0E1[wi(n)]I is the number of experiments, I is 1,2, …, I.
2) All s arei(n) EMD is carried out respectively, and then all modal components are summed and averaged to obtain the residual quantity
And a unique IMF of the 1 st modal component1(n)＝s(n)-r1(n)。
3) To obtain the 2 nd modal component, continue to r1(n)+ε1E2[wi(n)]Performing EMD decomposition to obtain 1 st local mean function, and averaging to obtain 2 nd residue of CEEMDAN
The 2 nd IMF can be expressed as:
IMF2(n)＝r1(n)-r2(n)
4) the K +1 th residual signal is calculated in the same way as in step 3), where K is 2, …, K.
The k +1 th IMF can be expressed as:
IMFk+1(n)＝rk(n)-rk+1(n)
5) step 4) is executed until all residue sequences can not be decomposed continuously, namely the number of extreme points of the residue sequences is less than or equal to two.
The final residual error obtained is:
so that the original pantograph vertical acceleration signal is decomposed into
The SPWVD distribution of the continuous-time signal x (t) is defined as:
SPWVg,h(x；t,f)＝∫∫g(u)h(τ)x(t-u+τ/2)
×x*(t-u-τ/2)e2jπfτdudτ
in the formula: g (u), h (τ) are window functions in the frequency domain and time domain directions, respectively; x (t) is a real signal; t is time; u is the frequency differential; τ is the time differential; f is the frequency;*is a conjugate transpose.
And (3) calculating to obtain the time-frequency distribution of the vertical vibration acceleration of the pantograph by combining the CEEMDAN-SPWVD time-frequency analysis method of the two methods:
in the formula: d (x; t, f) is the calculated time-frequency distribution; SPWVD (-) is the time-frequency distribution obtained after single SPWVD conversion.
For example, for the original signal of vertical vibration acceleration of the pantograph shown in fig. 8, first, CEEMDAN decomposition is performed, the original signal to be studied is decomposed into single-component signals, and after the decomposition, as shown in fig. 9, 8 IMF signals and one residual signal are obtained. And then carrying out SPWVD conversion on the IMF and the residual terms obtained by decomposition to obtain a time-frequency diagram of each single-component signal, as shown in FIG. 10. And superposing the time-frequency graphs of the single-component signals to obtain a CEEMDAN-SPWVD time-frequency graph as shown in FIG. 11.
Compared with the conventional WVD (shown in fig. 12), the time-frequency diagram obtained by the CEEMDAN-SPWVD transform shown in fig. 11 well suppresses the interference of cross terms in both the time domain direction and the frequency domain direction, and has good time-frequency aggregation performance.
The second part of this example is a marginal index method for diagnosing a hard spot of a catenary, and since the vertical acceleration of a pantograph has strong randomness, the marginal index method for the catenary is adopted to convert acceleration data from high frequency to a high-stability marginal index, which is more beneficial to diagnosing the position of the hard spot of the catenary, and a specific process, as shown in fig. 13, includes:
1) calculating the time-frequency distribution D (x; t, f);
2) and determining the main distribution range [ LF, HF ] of the dynamic response data energy of the pantograph through a plurality of hard point time-frequency graphs.
3) Calculating a moving margin spectrum of the time-frequency distribution:
in the formula: k is the time window length of the forward windowing and [ LF, HF ] is the band-pass filtering range.
4) Dividing a contact net into units, wherein the unit length is generally 50 m;
5) calculating the maximum value M of the mobile marginal spectrum of each unitmaxAnd is recorded as a cell margin value;
6) calculating the average value of the unit marginal values of all the lines with the same speed grade, and recording as a calibration parameter
7) Calculating a contact net marginal index;
8) and (4) performing overrun judgment and recording corresponding position information. Based on the computational analysis of a large amount of test data, the inventors took 16.0 for the overrun determination threshold.
In specific implementation, the energy of dynamic response data of the pantograph is mainly distributed at 20-800 Hz by performing time-frequency analysis on the data of the hard points, so that band-pass filtering processing is performed on a time-frequency graph during moving marginal spectrum calculation. Namely, only the time frequency distribution within the range of 20-800 Hz is selected when the moving marginal spectrum is calculated.
Fig. 14 shows a process of obtaining a marginal spectrum by time-frequency distribution and moving window calculation after CEEMDAN-SPWVD conversion and band-pass filtering are performed on an original signal of vertical vibration acceleration of the pantograph shown in fig. 8, where the number of time domain direction window long points K is 420 in this example. The positions of energy in the time frequency distribution graph can be clearly observed, and the positions are represented in a peak value mode in the mobile filtering boundary spectrogram.
Calculating the average value of the unit marginal values of all the lines with the same speed grade, and recording as a calibration parameter
And dividing the mobile filtering marginal spectrum of the contact network by the calibration parameter to complete normalization, thereby obtaining the CMI. As shown in fig. 16, CMI at K1+078 exceeds the set threshold value 16.0, and therefore, the threshold value is determined to be exceeded, and the peaks in fig. 16 are all shown in fig. 15, but strong randomness of the original signal can be observed in fig. 15.
It can be seen that when the contact line has hard points, large impact occurs between the pantograph and pantograph nets, the vertical vibration acceleration of the pantograph is firstly increased and then attenuated, and although an extremely large amplitude value does not necessarily occur, the accumulated impact energy is large.
To verify the validity of the CMI method, the present example also performs a field review of the railway line. Slight contact line hard bending is found at a positioning wire clamp of the T-shaped positioner of the central column, and the contact line is preliminarily judged to be caused by construction non-standardization during the paying off of the contact network according to the introduction of field detection personnel. Meanwhile, two conversion columns on two sides of the field central column respectively correspond to smaller peak values with the interval of about 50m on two sides of the central column in the contact net marginal index diagram in fig. 16.
And analyzing vertical acceleration data of the pantograph passing through the same road section twice by using the contact net marginal index. The original signal of the vertical acceleration of the pantograph and the catenary margin index are shown in fig. 17-20. Comparing fig. 17 and 18, it can be seen that the amplitude of the vertical vibration acceleration signal of the pantograph is random and difficult to determine the judgment threshold, while in the catenary marginal index diagrams of the vibration characteristics of the pantograph, which are plotted in energy angles in fig. 19 and 20, it can be observed that an independent large peak appears at K1+ 078.
To verify the repeatability of CMI at different speed levels, CMI was calculated for 4 total data obtained from 2 runs at 130km/h and 2 runs at 150 km/h. As can be seen from fig. 21 and 22, the marginal index of the catenary calculated from the vertical acceleration of the pantograph has a large value at each time, and the waveforms are similar, and after normalization processing, the evaluation indexes calculated from the detection data of the same road section at different speeds for multiple times tend to be consistent. Therefore, the method for diagnosing the hard spots of the railway contact network provided by the embodiment of the invention is stable and has good repeatability. And the distribution rule is naturally used to easily determine the judgment threshold.
The implementation of the above specific application is only an example, and the rest of the embodiments are not described in detail.
Based on the same inventive concept, embodiments of the present invention further provide a diagnosis apparatus for hard spots of railway contact networks, and because the principle of the problem solved by the diagnosis apparatus for hard spots of railway contact networks is similar to that of the diagnosis method for hard spots of railway contact networks, the implementation of the diagnosis apparatus for hard spots of railway contact networks can refer to the implementation of the diagnosis method for hard spots of railway contact networks, repeated parts are not described again, and the specific structure is shown in fig. 23:
the time-frequency analysis module 2301 is used for performing CEEMDAN-SPWVD time-frequency analysis on the pantograph vertical vibration acceleration signal of the railway contact network to be detected to obtain time-frequency distribution of the pantograph vertical vibration acceleration signal;
the mobile filtering marginal spectrum determining module 2302 is used for determining a mobile filtering marginal spectrum of the railway contact network to be detected according to the time-frequency distribution of the pantograph vertical vibration acceleration signals;
the catenary marginal index determining module 2303 is configured to perform normalization processing on the mobile filtering marginal spectrum to obtain a catenary marginal index of the railway catenary to be tested;
the threshold determination module 2304 is configured to determine a catenary marginal index threshold for diagnosing a hard spot disease of a railway catenary to be detected according to the catenary marginal index of the railway catenary to be detected.
In a specific embodiment, the structure of the time-frequency analysis module 2301 is shown in fig. 24, and includes:
the CEEMDAN decomposition unit 2401 is used for performing CEEMDAN decomposition on a pantograph vertical vibration acceleration signal of a railway contact network to be detected to obtain a plurality of single-component signals;
an SPWVD transform unit 2402, configured to perform SPWVD transform on the multiple single component signals to obtain a time-frequency distribution of each single component signal;
and the superposition unit 2403 is configured to superpose the time-frequency distribution of each single-component signal to obtain the time-frequency distribution of the pantograph vertical vibration acceleration signal.
In a specific embodiment, the structure of the mobile filtering margin spectrum determining module 2302 is shown in fig. 25, and includes:
a filtering range determining unit 2501, configured to determine a filtering range according to the time-frequency distribution of the pantograph vertical vibration acceleration signal;
the mobile filtering margin spectrum determination unit 2502 is configured to perform, based on the filtering range, a mobile window calculation in a specified frequency range on the time-frequency distribution of the pantograph vertical vibration acceleration signal, so as to obtain a mobile filtering margin spectrum.
In specific implementation, the structure of the catenary marginal index determining module 2303 is shown in fig. 26, and includes:
a unit margin value determining unit 2601, configured to calculate a moving margin spectrum of each unit divided by the railway catenary to be tested, and determine a unit margin value of each unit according to the moving margin spectrum of each unit;
a calibration parameter determining unit 2602, configured to determine a calibration parameter according to the cell margin values of the multiple cells of the same-speed-level line;
the normalization processing unit 2603 is configured to perform normalization processing on the mobile filtering marginal spectrum by using the calibration parameters, so as to obtain a catenary marginal index of the railway catenary to be tested.
Specifically, the threshold determination module 2304 is specifically configured to:
determining the waveform of the contact net marginal index of the railway contact net to be detected along with the change of the mileage;
and analyzing the peak value of the waveform, and determining the threshold value of the hard spot diagnosis of the railway contact network to be detected.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can be run on the processor, wherein the processor executes the computer program to realize the railway contact network hard spot diagnosis method.
The embodiment of the invention also provides a computer readable storage medium, which stores a computer program for executing the method for diagnosing the hard spot of the railway contact network.
In summary, the method and the device for diagnosing hard spots of the railway contact network provided by the embodiment of the invention have the following advantages:
CEEMDAN-SPWVD time-frequency analysis is carried out on the vertical vibration acceleration signal of the pantograph of the railway contact network to be detected, so as to obtain the time-frequency distribution of the vertical vibration acceleration signal of the pantograph; determining a mobile filtering marginal spectrum of a railway contact network to be detected according to the time-frequency distribution of the pantograph vertical vibration acceleration signals; carrying out normalization processing on the mobile filtering marginal spectrum to obtain a contact net marginal index of the railway contact net to be detected; and determining a contact net marginal index threshold value of the hard spot disease diagnosis of the railway contact net to be detected according to the contact net marginal index of the railway contact net to be detected. The method comprises the steps of determining a mobile filtering marginal spectrum of a railway contact network to be detected according to time-frequency distribution of vertical vibration acceleration signals of the pantograph, normalizing to obtain a contact network marginal index, and realizing high-frequency characteristics of vertical vibration acceleration of the pantograph caused by pantograph-catenary impact from the angle of energy; and hard spot diagnosis is carried out on the railway contact net based on the accurate contact net marginal index threshold value, so that the accuracy of hard spot diagnosis can be improved.
Although the present invention provides method steps as described in the examples or flowcharts, more or fewer steps may be included based on routine or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, apparatus (system) or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "upper", "lower", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the referred devices or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Unless expressly stated or limited otherwise, the terms "mounted," "connected," and "connected" are intended to be inclusive and mean, for example, that they may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention is not limited to any single aspect, nor is it limited to any single embodiment, nor is it limited to any combination and/or permutation of these aspects and/or embodiments. Moreover, each aspect and/or embodiment of the present invention may be utilized alone or in combination with one or more other aspects and/or embodiments thereof.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (12)
1. A hard spot diagnostic method of a railway contact network is characterized by comprising the following steps:
performing CEEMDAN-SPWVD time-frequency analysis on a pantograph vertical vibration acceleration signal of a railway contact network to be detected to obtain time-frequency distribution of the pantograph vertical vibration acceleration signal;
determining a mobile filtering marginal spectrum of the railway contact network to be detected according to the time-frequency distribution of the pantograph vertical vibration acceleration signals;
carrying out normalization processing on the mobile filtering marginal spectrum to obtain a contact net marginal index of the railway contact net to be detected;
and determining a contact net marginal index threshold value of the hard spot disease diagnosis of the railway contact net to be detected according to the contact net marginal index of the railway contact net to be detected.
2. The method for diagnosing the hard points of the railway contact network as claimed in claim 1, wherein the step of performing CEEMDAN-SPWVD time-frequency analysis on the vertical vibration acceleration signals of the pantograph of the railway contact network to be tested to obtain the time-frequency distribution of the vertical vibration acceleration signals of the pantograph comprises the steps of:
CEEMDAN decomposition is carried out on a pantograph vertical vibration acceleration signal of a railway contact network to be detected to obtain a plurality of single component signals;
carrying out SPWVD conversion on the plurality of single-component signals to obtain the time-frequency distribution of each single-component signal;
and superposing the time-frequency distribution of each single-component signal to obtain the time-frequency distribution of the pantograph vertical vibration acceleration signals.
3. The method for diagnosing the hard spot of the railway contact network of claim 1, wherein the step of determining the mobile filtering marginal spectrum of the railway contact network to be tested according to the time-frequency distribution of the vertical vibration acceleration signal of the pantograph comprises the following steps:
determining a filtering range according to the time-frequency distribution of the pantograph vertical vibration acceleration signals;
and performing moving window calculation of a specified frequency range on the time-frequency distribution of the vertical vibration acceleration signals of the pantograph based on the filtering range to obtain a moving filtering marginal spectrum.
4. The method for diagnosing the hard spot of the railway contact network of claim 1, wherein the mobile filtering marginal spectrum is subjected to normalization processing to obtain a contact network marginal index of the railway contact network to be tested, and the method comprises the following steps:
calculating a movement marginal spectrum of each unit divided by a railway contact network to be tested, and determining a unit marginal value of each unit according to the movement marginal spectrum of each unit;
determining a calibration parameter according to the cell marginal values of a plurality of cells of the same-speed grade line;
and carrying out normalization processing on the mobile filtering marginal spectrum by using the calibration parameters to obtain the contact net marginal index of the railway contact net to be detected.
5. The method for diagnosing the hard spot of the railway contact network as claimed in claim 1, wherein the step of determining the threshold value for diagnosing the hard spot of the railway contact network to be tested according to the contact network marginal index of the railway contact network to be tested comprises the following steps:
determining the waveform of the contact net marginal index of the railway contact net to be detected along with the change of the mileage;
and analyzing the peak value of the waveform, and determining the threshold value of the hard spot diagnosis of the railway contact network to be detected.
6. The utility model provides a railway contact net hard spot diagnostic device which characterized in that includes:
the time-frequency analysis module is used for carrying out CEEMDAN-SPWVD time-frequency analysis on the vertical vibration acceleration signal of the pantograph of the railway contact network to be detected to obtain the time-frequency distribution of the vertical vibration acceleration signal of the pantograph;
the mobile filtering marginal spectrum determining module is used for determining a mobile filtering marginal spectrum of the railway contact network to be detected according to the time-frequency distribution of the pantograph vertical vibration acceleration signals;
the contact network marginal index determining module is used for carrying out normalization processing on the mobile filtering marginal spectrum to obtain a contact network marginal index of the railway contact network to be detected;
and the threshold value determining module is used for determining the contact net marginal index threshold value of the hard spot disease diagnosis of the railway contact net to be detected according to the contact net marginal index of the railway contact net to be detected.
7. The device for diagnosing hard spots of the railway catenary of claim 6, wherein the time-frequency analysis module comprises:
the CEEMDAN decomposition unit is used for performing CEEMDAN decomposition on a pantograph vertical vibration acceleration signal of a railway contact network to be detected to obtain a plurality of single-component signals;
the SPWVD conversion unit is used for carrying out SPWVD conversion on the plurality of single-component signals to obtain the time-frequency distribution of each single-component signal;
and the superposition unit is used for superposing the time-frequency distribution of each single-component signal to obtain the time-frequency distribution of the pantograph vertical vibration acceleration signal.
8. The apparatus for diagnosing hard spot of a railroad catenary of claim 6, wherein the mobile filtering margin spectrum determining module comprises:
the filtering range determining unit is used for determining a filtering range according to the time-frequency distribution of the pantograph vertical vibration acceleration signals;
and the mobile filtering marginal spectrum determining unit is used for performing mobile window calculation of a specified frequency range on the time-frequency distribution of the pantograph vertical vibration acceleration signals based on the filtering range to obtain a mobile filtering marginal spectrum.
9. The device for diagnosing the hard spot of the railway contact network as claimed in claim 6, wherein the contact network marginal index determining module comprises:
the unit marginal value determining unit is used for calculating a moving marginal spectrum of each unit divided by the railway contact network to be tested and determining the unit marginal value of each unit according to the moving marginal spectrum of each unit;
the calibration parameter determining unit is used for determining calibration parameters according to the cell marginal values of the plurality of cells of the same-speed grade line;
and the normalization processing unit is used for performing normalization processing on the mobile filtering marginal spectrum by using the calibration parameters to obtain the contact net marginal index of the railway contact net to be detected.
10. The device for diagnosing hard spots of a railway catenary as claimed in claim 6, wherein the threshold determination module is specifically configured to:
determining the waveform of the contact net marginal index of the railway contact net to be detected along with the change of the mileage;
and analyzing the peak value of the waveform, and determining the threshold value of the hard spot diagnosis of the railway contact network to be detected.
11. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 5 when executing the computer program.
12. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 5.
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