CN110987348A - Catenary hard spot determination method and device based on pantograph-catenary dynamic response - Google Patents

Catenary hard spot determination method and device based on pantograph-catenary dynamic response Download PDF

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CN110987348A
CN110987348A CN201911316976.9A CN201911316976A CN110987348A CN 110987348 A CN110987348 A CN 110987348A CN 201911316976 A CN201911316976 A CN 201911316976A CN 110987348 A CN110987348 A CN 110987348A
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acceleration
vertical vibration
determining
value
vibration acceleration
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CN110987348B (en
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徐晓迪
刘金朝
张文轩
李向东
杨志鹏
秦航远
王婧
丁宇鸣
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China Academy of Railway Sciences Corp Ltd CARS
China State Railway Group Co Ltd
Infrastructure Inspection Institute of CARS
Beijing IMAP Technology Co Ltd
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China Academy of Railway Sciences Corp Ltd CARS
China State Railway Group Co Ltd
Infrastructure Inspection Institute of CARS
Beijing IMAP Technology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M7/00—Vibration-testing of structures; Shock-testing of structures
    • G01M7/08—Shock-testing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting

Abstract

The application discloses a contact net hard spot determining method and device based on pantograph-catenary dynamic response, and the method comprises the following steps: acquiring a vertical vibration acceleration data sequence of the pantograph, determining an abnormal value in the vertical vibration acceleration data sequence according to an acceleration threshold, calculating an average value of the vertical vibration acceleration, and replacing the abnormal value with the average value to obtain an acceleration data sequence; performing EEMD and ADSSTFT on the acceleration data column to determine the time-frequency characteristics of the acceleration data column; determining a filtering frequency range for filtering the acceleration sequence according to the time-frequency characteristics; carrying out band-pass filtering on the acceleration sequence according to the filtering frequency range to obtain a filtered acceleration sequence; calculating a contact net impact index according to the filtered acceleration sequence; and if the impact index of the overhead line system is greater than the given impact index threshold value, determining that hard spots appear on the overhead line system. The method and the device can improve the accuracy of the judgment result of whether the hard spot appears on the contact network, and meanwhile reduce misjudgment.

Description

Catenary hard spot determination method and device based on pantograph-catenary dynamic response
Technical Field
The application relates to the technical field of electrified railway power supply, in particular to a contact network hard spot determination method and device based on pantograph-catenary dynamic response.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
Due to the complexity and instability of point-surface contact, the pantograph-catenary system of the electrified railway becomes a research content which is concerned in the whole electrified railway system. The requirements of the electrified railway on contact network equipment are very strict, and the contact network is erected in the open air, has no reserve and needs to bear the impact from a pantograph, so that the contact network becomes one of weak links of an electrified railway system.
The main detection parameters of the dynamic detection of the contact net comprise contact force between pantograph and catenary, impact acceleration of a pantograph head, offline rate, geometric irregularity of the contact net, a pull-out value, contact line height, contact line abrasion loss and the like.
Along with the continuous improvement of high-speed train operating speed, the interaction between the bow net is more and more unstable, and the vertical acceleration "hard spot" numerical value that represents the bow net mutual motion is showing and is increasing, and this will directly cause bow net contact pressure to change violently, aggravate wearing and tearing or lead to off-line production to draw the arc. The catenary equipment such as the dropper and the supporting device has defects, such as no stress on the dropper, overlarge wire clamp clamping area, bending of a wire clamp bolt at an initial contact point and the like, the influence on the contact pressure of the pantograph and the normal operation of a vehicle is very large, the defects can generally cause the pantograph to generate high-frequency impact vibration, and the corresponding wavelength is usually below 1 meter, so that short-wave impact can be generated. The long-term short-wave impact can cause the bending stress of the contact line to increase, cause the fatigue and even the fracture of the contact line and influence the normal running of the vehicle, so that the analysis of the short-wave state of the contact network is very important. In order to find the short-wave diseases of the contact network equipment in time, the dynamic detection of the contact network is a very effective way except for the regular inspection of the contact network equipment.
The hard point of the contact net is a very important factor influencing the quality of contact net equipment. In order to discover the hard spot of the contact net in time, the hard spot of the contact net can be diagnosed by utilizing the vertical impact acceleration of the pantograph head through a dynamic detection device, or whether the peak value of the contact force exceeds the limit or not can be used for judging the hard spot of the contact net.
The above-described methods are generally amplitude-based diagnostic methods, relatively simple, and generally diagnose hard spots of the touch screen based on a given threshold value. The actual measurement result shows that the judgment criterion can be used for identifying the catenary damage causing the pantograph vibration amplitude to be larger, but the effective diagnosis cannot be carried out on the vibration with higher frequency and relatively unobvious amplitude change. In addition, as the interaction between the pantograph and the overhead line system is high-frequency vibration, the change of the contact position of the pantograph and the overhead line system causes the amplitude of the vibration acceleration to have larger randomness, and the traditional amplitude-based diagnosis method is easy to cause misjudgment, so that the judgment result of whether the overhead line system has hard spots is inaccurate.
Disclosure of Invention
The embodiment of the application provides a contact net hard spot determining method based on pantograph-catenary dynamic response, which is used for improving the accuracy of a judgment result of whether a hard spot appears on a contact net or not and reducing misjudgment, and comprises the following steps:
acquiring a vertical vibration acceleration data sequence of the pantograph, determining an abnormal value in the vertical vibration acceleration data sequence according to an acceleration threshold, calculating an average value of the vertical vibration acceleration, and replacing the abnormal value with the average value of the vertical vibration acceleration to obtain an acceleration data sequence; performing Ensemble Empirical Mode Decomposition (EEMD) and window-Adaptive synchronous pressure-reduction time Fourier transform (ADSSTFT) on the acceleration data column to determine the time-frequency characteristics of the acceleration data column; determining a filtering frequency range for filtering the acceleration sequence according to the energy distribution of the acceleration sequence reflected by the time-frequency characteristics; performing band-pass filtering on the acceleration sequence according to the determined filtering frequency range to obtain a filtered acceleration sequence; calculating a contact net impact index according to the filtered acceleration sequence; and if the impact index of the overhead line system is greater than the given impact index threshold value, determining that hard spots appear on the overhead line system.
The embodiment of the present application still provides a contact net hard spot confirming device based on bow net dynamic response for improve the degree of accuracy to the judgement result that whether the hard spot appears in the contact net, reduce the erroneous judgement simultaneously, the device includes:
the acquisition module is used for acquiring a vertical vibration acceleration data sequence of the pantograph, determining an abnormal value in the vertical vibration acceleration data sequence according to an acceleration threshold, calculating an average value of the vertical vibration acceleration, and replacing the abnormal value with the average value of the vertical vibration acceleration to obtain an acceleration data column; the determining module is used for carrying out EEMD and ADSSTFT on the acceleration data column obtained by the obtaining module and determining the time-frequency characteristics of the acceleration data column; determining a filtering frequency range for filtering the acceleration sequence according to the energy distribution of the acceleration sequence reflected by the time-frequency characteristics; the filtering module is used for carrying out band-pass filtering on the acceleration sequence according to the filtering frequency range determined by the determining module to obtain a filtered acceleration sequence; the calculation module is used for calculating the impact index of the contact network according to the acceleration sequence filtered by the filtering module; the determining module is further configured to determine that a hard spot occurs on the catenary when the catenary impact index calculated by the calculating module is greater than a given impact index threshold.
In the embodiment of the application, EEMD and ADSSTFT are carried out on the acceleration data column after the abnormal value is filtered, the time-frequency characteristic of the acceleration data column is determined, the filtering frequency of the vertical vibration acceleration of the pantograph is determined from the angle of energy, the impact index of the contact network is calculated after the acceleration sequence is filtered, whether the hard spot appears on the contact network is determined according to the impact index, and compared with a method for judging whether the hard spot appears on the contact network through the amplitude in the prior art, the method can reduce the interference of various random factors on the identification result, improves the accuracy of the judgment result whether the hard spot appears on the contact network, and reduces misjudgment at the same time.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the embodiments or the technical solutions in the prior art are briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
fig. 1 is a flowchart of a catenary hard spot determination method based on pantograph-catenary dynamic response in an embodiment of the present application;
FIG. 2 is a schematic diagram of vertical vibration acceleration data of a pantograph with abnormal values filtered out according to an embodiment of the present application;
fig. 3 is a schematic diagram of a calculated catenary impact index in an embodiment of the present application;
fig. 4 is a schematic diagram of a vertical vibration acceleration waveform of a pantograph measured near a certain line upstream K28+450 according to an embodiment of the present application;
fig. 5 is a field diagram of a contact network in an embodiment of the present application;
fig. 6 is a schematic structural diagram of a catenary hard spot determination device based on pantograph-catenary dynamic response in an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present application more apparent, the embodiments of the present application are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present application are provided herein to explain the present application and not to limit the present application.
The inventor finds in the research that: the pantograph is directly connected with a contact network, and the contact network irregularity can cause vertical vibration of the pantograph. The vertical vibration acceleration of the pantograph can directly reflect the influence of external disturbance force caused by the short-wave irregularity of a contact network on the dynamics of the pantograph. The pantograph-catenary relationship and the wheel-rail relationship have strong duality, meanwhile, the rail impact index method can effectively diagnose the rail impact diseases, the axle box acceleration characteristic extraction and diagnosis method is introduced into pantograph acceleration data analysis, and powerful data support is provided for dynamic diagnosis and maintenance of hard points of a contact network.
Based on the above findings, an embodiment of the present application provides a catenary hard spot determination method based on pantograph-catenary dynamic response, as shown in fig. 1, the method includes steps 101 to 105:
step 101, acquiring a vertical vibration acceleration data sequence of the pantograph, determining an abnormal value in the vertical vibration acceleration data sequence according to an acceleration threshold, calculating an average value of the vertical vibration acceleration, and replacing the abnormal value with the average value of the vertical vibration acceleration to obtain an acceleration data sequence.
According to the mathematical statistics principle, the vertical vibration acceleration data sequence x of the pantographpThe method is characterized by varying within a threshold range of acceleration, and if the threshold range of acceleration is exceeded, determining that an abnormal value exists in the vertical vibration acceleration data sequence. The acceleration threshold and outlier are determined using the absolute average method. The basic idea of the absolute average method is: for data sequence xpFirst, the absolute value of the data sample is averaged
Figure BDA0002326091020000041
And standard deviation σ, then by mean of absolute values
Figure BDA0002326091020000042
Multiplying the standard deviation sigma by an empirical coefficient k to determine a threshold value W, and if a preset number of data which are continuously less than or equal to the preset number before and after a certain vertical vibration acceleration meet the requirement
Figure BDA0002326091020000043
Then consider xpIs an abnormal value in the vertical vibration acceleration data sequence. Wherein the predetermined number is a statistical value, which is typically 15.
The acceleration threshold W is calculated according to the following formula:
Figure BDA0002326091020000044
average value of vertical vibration acceleration
Figure BDA0002326091020000045
The calculation is made according to the following formula:
Figure BDA0002326091020000046
after determination of the outliers, use
Figure BDA0002326091020000047
Replacing the outlier; wherein the content of the first and second substances,xpthe data sequence is used for representing the p vertical vibration acceleration in the vertical vibration acceleration data sequence, wherein p is 0,1,2, N-1.
For example, vertical vibration acceleration data of the pantograph before and after filtering the abnormal value is shown in fig. 2.
102, performing EEMD and ADSSTFT on the acceleration data column to determine the time-frequency characteristics of the acceleration data column; and determining a filtering frequency range for filtering the acceleration sequence according to the energy distribution of the acceleration sequence reflected by the time-frequency characteristics.
The time-frequency analysis and the time-frequency combination represent the characteristics of one-dimensional time signals and are one of important tools for analyzing nonlinear and non-stationary signals. Among them, short-time Fourier transform (STFT) is a good calculation method, and its time-frequency accuracy is high, but unfortunately, the window length of the window function is fixed in the calculation process, and the same window length is used for different frequency bands, which cannot effectively characterize the signal for the analysis of the signal. In order to solve the above problems, the embodiment of the present application provides an adaptive time-frequency analysis method for vertical vibration acceleration signals of a pantograph by combining EEMD and ADSSTFT methods, and the method is used to analyze a nonlinear analytic signal and a dynamic response signal of the pantograph, and the result shows that the method can effectively solve the disadvantages in the conventional time-frequency analysis method, such as the problem of fixed STFT window length, and the problem of cross terms in modal aliasing and Wigner Distribution (WVD) in an EMD analysis method, and has the advantages of high time-frequency resolution, small calculation amount and high speed.
The EEMD decomposition method is an improved method which is proposed for overcoming the modal aliasing phenomenon of the traditional EMD decomposition process. The algorithm adds white gaussian noise to a signal each time EMD decomposition is performed, and then ensemble averaging is performed on an Intrinsic Mode Function (IMF) obtained by the decomposition to cancel the added white gaussian noise. The method can inhibit mode aliasing and highlight vibration signal characteristics.
Specifically, in the embodiment of the present application, an EEMD is used to decompose an acceleration data column to obtain at least two IMFs; then setting a window length range, and performing ADSSTFT on at least two IMFs by using each window length in the window length range to obtain discretization expressions of the at least two IMFs corresponding to each window length; calculating a centralized measure of the frequency band in which the IMFs are located from the discrete representations of the at least two IMFs corresponding to each window length; taking the window length which makes the centralized measurement obtain the maximum value as the optimal window length; determining the time-frequency characteristics of the acceleration data column by using the optimal window length and ADSSTFT; determining a main energy distribution range of the acceleration sequence according to the time-frequency characteristics, and taking a frequency range corresponding to the main energy distribution range as a filtering frequency range for filtering the acceleration sequence, wherein a minimum frequency range in which the sum of energy in the frequency range is greater than 50% of the sum of all energy is taken as the main energy distribution range.
The specific implementation process is as follows:
(1) for the signal f (t), i.e. at least two IMFs obtained by decomposition, STFT transformation is performed using a window function g, as shown in the following equation:
Gf(t,η)＝∫f(t)g(t-η)e-iξt
wherein i is used for representing an imaginary unit, t is used for representing time, e is used for representing a natural base number, η is used for representing a translation amount, and ξ is used for representing positive frequency.
(2) The instantaneous frequency ω (t, η) of the signal f (t) is calculated according to the following formula:
Figure BDA0002326091020000051
wherein the content of the first and second substances,
Figure RE-GDA0002392462340000052
means to derive a function with respect to t;
Figure RE-GDA0002392462340000053
to aim at
Figure RE-GDA0002392462340000054
STFT transform results of the function.
(3) According to the calculated instantaneous frequency, performing time-frequency rearrangement, namely performing synchronous compression on the time-frequency plane obtained by STFT calculation, wherein the process is represented as follows:
Figure BDA0002326091020000054
wherein the content of the first and second substances,
Figure BDA0002326091020000055
the method is used for representing discretization expression of at least two IMFs, d is used for representing the lower bound of two continuous separation components, gamma is used for representing a threshold value, gamma is larger than or equal to 0, α is used for representing resolution, mu is used for representing a Lebesgue (Lebesgue) measure on a real number R, and t, ξ epsilon is R multiplied by α N, wherein R is used for representing the real number, and N is used for representing the quantity of the acquired vertical vibration acceleration.
(4) The best window length is then determined using a centralized metric. The effective concentration metric CM can characterize the different distribution characteristics of the signal, the better the concentration of the signal, the more unique the component contained, and therefore it is reasonable to use the maximum concentration metric of the signal on the time-frequency spectrum to determine the optimal transform window length.
Specifically, the concentration metric CM (ξ) of the frequency band in which the IMF is located is calculated according to the following formulaz,l)：
Figure BDA0002326091020000061
Wherein the content of the first and second substances,
Figure BDA0002326091020000062
Figure BDA0002326091020000063
a discretized expression for representing at least two IMFs;
Figure BDA0002326091020000064
for representation normalization
Figure BDA0002326091020000065
d for watchesShowing the lower bound of two consecutive separated components, gamma for a threshold value, gamma ≧ 0, l for a window length, t for a time, ξzFor positive frequencies of the frequency band with the number z, (t, ξ) e R x α N, where R is used to represent real numbers, N is used to represent the number of vertical vibration accelerations acquired, k is used to represent empirical coefficients, α is used to represent resolution, z is used to represent the number of frequency bands, and v, β is used to represent an index, 0 < v < 1 < β.
(5) The above-described steps (1) to (4) are calculated one by one for each window length within the window length range, and the CM (ξ) corresponding to each window length is determined (see above)zL), defining the optimal window length as:
l＝arglmaxCM(ξz,l)
according to the method, the optimal window length can be determined, and the hidden fault characteristics in the signal can be clearly seen by using the time-frequency characteristics determined by the optimal window length.
And 103, performing band-pass filtering on the acceleration sequence according to the determined filtering frequency range to obtain a filtered acceleration sequence.
In the embodiment of the present application, the acceleration sequence is subjected to a piecewise bandpass filtering with a filtering frequency of [ F ] determined in step 102L,FH]And recording the filtered acceleration data as { y }p}。
It should be noted that, since the band-pass filtering is a mature prior art, the process of performing the band-pass filtering on the acceleration sequence by using the filtering range is not described herein again.
And 104, calculating the contact net impact index according to the filtered acceleration sequence.
Because the vertical vibration acceleration of the pantograph is the result of dynamic coupling of the pantograph-catenary, the pantograph exhibits high-frequency and highly nonlinear characteristics. Except contact line short wave irregularity such as contact line hard bending and overlarge clamping area of a dropper wire clamp, the vertical vibration acceleration of the pantograph is greatly influenced by the shapes and materials of the pantograph and the contact line, the roughness of the contact surface, the installation position of the sensor and the like. Diagnosis of contact net hard spots by directly utilizing vertical vibration acceleration amplitude of pantograph can be evaluatedThe randomness of the judgment result is large, and the threshold value is difficult to determine. Provides a new judging method and index for dynamically diagnosing the hard spots of the contact network, namely the impact index (C) of the contact networkII)。
The catenary impact index is defined as the ratio of the moving effective value of the vertical vibration acceleration of the pantograph to the average value thereof, namely C is obtained by normalizing the moving effective value by using the average value of the moving effective value of the vertical vibration acceleration of the pantographII。
Compared with the traditional evaluation method taking the dynamic response amplitude of the pantograph as an index, the new evaluation method utilizes a windowed energy index signal of the dynamic response of the pantograph to replace the original waveform signal, and demodulates the short-wave impact from a high-frequency signal into a low-frequency signal with high stability under the condition of not losing the vibration characteristic, thereby solving the problem of high randomness of the detection result; meanwhile, an appropriate window length is selected according to the impact characteristic of the contact network and the data sampling frequency to calculate the energy index, and a large amount of historical detection data are combined to perform normalization processing, so that the problem that the absolute threshold is difficult to determine is solved.
Specifically, calculating the catenary impact index according to the filtered acceleration sequence comprises the following steps:
(1) computing a filtered sequence of accelerations ypThe effective moving value of each vertical vibration acceleration is marked as { C }m,p}；
(2) Dividing the overhead line system into at least one unit according to the preset unit length, wherein the preset unit length can be manually set, and the preset unit length is set to be 25 m in many cases;
(3) determining the maximum value of the moving effective value of the vertical vibration acceleration in each unit according to the moving effective value of the vertical vibration acceleration, and recording the maximum value as the moving effective value
Figure BDA0002326091020000071
Wherein j is 1,2,3, Nq，NqThe number of the contact net dividing units is represented;
(4) calculating the maximum value of the moving effective value of the vertical vibration acceleration in each unit
Figure BDA0002326091020000072
Is the average value of
Figure BDA0002326091020000073
(5) According to
Figure BDA0002326091020000074
Calculating the impact index C of the contact netII,m；
Wherein, Cm,pIs used for expressing the moving effective value of the mth vertical vibration acceleration in the acceleration sequence,
Figure BDA0002326091020000075
for representing the average value of the maximum values of the moving effective values.
And 105, if the impact index of the overhead line system is larger than a given impact index threshold value, determining that hard spots appear on the overhead line system.
Exemplary, the calculated catenary impact index is shown in fig. 3. As can be seen from fig. 3, near K613+573 km, the impact index of the catenary is relatively large, and hard spots may occur therein; if the catenary impact index here is greater than the index threshold, it may be determined that a hard spot is present here. Therefore, the impact index of the contact net can better depict the impact characteristic of the short-wave irregularity of the contact net on the pantograph-catenary system.
Wherein the index threshold is determined by the cumulative distribution of the catenary impact index. Illustratively, the index threshold for the contact net impact index is taken to be 4.0 in the block section and 10.0 in the branch section.
For example, a vertical vibration acceleration waveform of a pantograph actually measured near a certain line uplink K28+450 is shown in fig. 4, a calculated catenary impact index is 5.6, when a detection vehicle passes through, the catenary high-frequency vibration characteristic is strong, and the catenary at the position is diagnosed to be in a smooth state. The picture of the on-site rechecking is shown in fig. 5, and it can be seen that the dropper is not stressed when being bent. Meanwhile, the phenomenon that the dropper is bent and not stressed near the central anchor rope of the section is obvious, the continuous range is large, the vibration of the pantograph is obvious, and the contact line has large left-right swing amplitude when a vehicle drives through the section. The catenary hard spot can be well characterized by the catenary impact index calculated through the vertical vibration acceleration of the pantograph.
After the hard spot of the contact network is determined, pull-out value information, ledger data and a characteristic peak value can be obtained; determining the position of a positioning point according to the pull-out value information; and determining the type of the hard spot disease of the contact net according to the position of the positioning point, the standing book data and the characteristic peak value.
There are many kinds of hard point diseases, such as unstressed dropper, too large clamp holding area, clamp bolt bending at the initial contact point, arc discharge generated by pantograph off-line, pantograph abrasion caused by contact network short wave disease, etc. The standing book data can help to determine whether the station is near a station yard or a line fork, and the corresponding diseases of the station are generally related to the starting point; the diseases near the positioning points are possibly related to the gradient of the positioning device; the characteristic peak value refers to the problem that the corresponding diseases are generally not stressed in the vicinity of the corresponding peak value of the dropper or the problem that the clamping area of the wire clamp is too large and the like. And the type of the hard spot disease of the contact net can be roughly judged by combining the position of the positioning point, the standing book data, the characteristic peak value and the like.
In the embodiment of the application, EEMD and ADSSTFT are carried out on the acceleration data column after the abnormal value is filtered, the time-frequency characteristic of the acceleration data column is determined, the filtering frequency of the vertical vibration acceleration of the pantograph is determined from the angle of energy, the impact index of the contact network is calculated after the acceleration sequence is filtered, whether the hard spot appears on the contact network is determined according to the impact index, and compared with a method for judging whether the hard spot appears on the contact network through the amplitude in the prior art, the method can reduce the interference of various random factors on the identification result, improves the accuracy of the judgment result whether the hard spot appears on the contact network, and reduces misjudgment at the same time.
The embodiment of the present application further provides a catenary hard spot determination device based on pantograph dynamic response, and as shown in fig. 6, the device 600 includes an obtaining module 601, a determining module 602, a filtering module 603, and a calculating module 604.
The obtaining module 601 is configured to obtain a vertical vibration acceleration data sequence of the pantograph, determine an abnormal value in the vertical vibration acceleration data sequence according to an acceleration threshold, calculate an average value of the vertical vibration acceleration, and replace the abnormal value with the average value of the vertical vibration acceleration to obtain an acceleration data sequence.
A determining module 602, configured to perform EEMD and ADSSTFT on the acceleration data column obtained by the obtaining module 601, and determine a time-frequency characteristic of the acceleration data column; and determining a filtering frequency range for filtering the acceleration sequence according to the energy distribution of the acceleration sequence reflected by the time-frequency characteristics.
The filtering module 603 is configured to perform band-pass filtering on the acceleration sequence according to the filtering frequency range determined by the determining module 602 to obtain a filtered acceleration sequence.
And the calculating module 604 is configured to calculate a catenary impact index according to the acceleration sequence filtered by the filtering module 603.
The determining module 602 is further configured to determine that a hard spot occurs on the catenary when the catenary impact index calculated by the calculating module 604 is greater than a given impact index threshold.
In an implementation manner of the embodiment of the present application, the obtaining module 601 is configured to:
according to
Figure BDA0002326091020000091
Calculating an acceleration threshold value W;
for any xpIf | xp| ≧ W, and in xpIf the absolute value of the vertical vibration acceleration which is less than or equal to the preset number is continuously larger than or equal to W in the front and the back, determining xpIs an abnormal value;
according to
Figure BDA0002326091020000092
Calculating the average value of vertical vibration acceleration
Figure BDA0002326091020000093
And use
Figure BDA0002326091020000094
Replacing the outlier;
wherein x ispThe vertical vibration acceleration data series are used for representing the p-th vertical vibration acceleration in the vertical vibration acceleration data series, p is 0,1,2, N-1, k is used for representing empirical coefficients, and sigma is used for representing the standard deviation of the vertical vibration acceleration data series.
In an implementation manner of the embodiment of the present application, the determining module 602 is configured to:
decomposing the acceleration data column by using the EEMD to obtain at least two intrinsic mode functions IMF;
setting a window length range, and performing ADSSTFT on at least two IMFs by using each window length in the window length range to obtain discretization expressions of the at least two IMFs corresponding to each window length;
calculating a centralized measure of the frequency band in which the IMF is located according to the discretization expression of at least two IMFs corresponding to each window length;
taking the window length which makes the centralized measurement obtain the maximum value as the optimal window length;
determining the time-frequency characteristics of the acceleration data column by using the optimal window length and ADSSTFT;
determining a main energy distribution range of the acceleration sequence according to the time-frequency characteristics, and taking a frequency range corresponding to the main energy distribution range as a filtering frequency for filtering the acceleration sequence, wherein a minimum frequency range in which the sum of energy in the frequency range is greater than 50% of the sum of all energy is taken as the main energy distribution range.
In an implementation manner of the embodiment of the present application, the determining module 602 is configured to:
according to
Figure BDA0002326091020000095
Calculating a concentration metric CM for the frequency band in which the IMF is located (ξ)z,l)。
Wherein the content of the first and second substances,
Figure BDA0002326091020000101
Figure BDA0002326091020000102
a discretized expression for representing at least two IMFs;
Figure BDA0002326091020000103
for representation normalization
Figure BDA0002326091020000104
d for representing the lower bound of two consecutive separated components, γ for representing a threshold value, γ ≧ 0, l for representing a window length, t for representing time, ξzFor positive frequencies of the frequency band with the number z, (t, ξ) e R x α N, where R is used to represent real numbers, N is used to represent the number of vertical vibration accelerations acquired, k is used to represent empirical coefficients, α is used to represent resolution, z is used to represent the number of frequency bands, and v, β is used to represent an index, 0 < v < 1 < β.
In an implementation manner of the embodiment of the present application, the calculating module 604 is configured to:
calculating the moving effective value of each vertical vibration acceleration in the filtered acceleration sequence;
dividing the overhead line system into at least one unit according to the preset unit length;
determining the maximum value of the moving effective value of the vertical vibration acceleration in each unit according to the moving effective value of each vertical vibration acceleration;
calculating the average value of the maximum values of the moving effective values of the vertical vibration acceleration in each unit;
according to
Figure BDA0002326091020000105
Calculating the impact index C of the contact netII,m；
Wherein, Cm，pIs used for expressing the moving effective value of the mth vertical vibration acceleration in the acceleration sequence,
Figure BDA0002326091020000106
for representing the average value of the maximum values of the moving effective values.
In one implementation of an embodiment of the present application,
the obtaining module 601 is further configured to obtain pull-out value information, ledger data, and a characteristic peak value.
The determining module 602 is further configured to determine a position of the positioning point according to the pull-out value information acquired by the acquiring module 601;
the hard spot disease type determining module 605 is configured to determine a type of a hard spot disease of the contact line according to the positioning point position determined by the determining module 602, the ledger data acquired by the acquiring module 601, and the characteristic peak value.
In the embodiment of the application, EEMD and ADSSTFT are carried out on the acceleration data column after the abnormal value is filtered, the time-frequency characteristic of the acceleration data column is determined, the filtering frequency of the vertical vibration acceleration of the pantograph is determined from the angle of energy, the impact index of the contact network is calculated after the acceleration sequence is filtered, whether the hard spot appears on the contact network is determined according to the impact index, and compared with a method for judging whether the hard spot appears on the contact network through the amplitude in the prior art, the method can reduce the interference of various random factors on the identification result, improves the accuracy of the judgment result whether the hard spot appears on the contact network, and reduces misjudgment at the same time.
The embodiment of the present application further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the processor implements any one of the methods in step 101 to step 105.
A computer-readable storage medium is further provided in an embodiment of the present application, and stores a computer program for executing any one of the methods in steps 101 to 105.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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 application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. 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 programs 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 above-mentioned embodiments are further described in detail for the purpose of illustrating the invention, and it should be understood that the above-mentioned embodiments are only illustrative of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (14)

1. A catenary hard spot determination method based on pantograph-catenary dynamic response is characterized by comprising the following steps:
acquiring a vertical vibration acceleration data sequence of the pantograph, determining an abnormal value in the vertical vibration acceleration data sequence according to an acceleration threshold, calculating an average value of vertical vibration acceleration, and replacing the abnormal value with the average value of the vertical vibration acceleration to obtain an acceleration data column;
performing Ensemble Empirical Mode Decomposition (EEMD) and window-adaptive synchronous pressure shortening time-Fourier transform (ADSSTFT) on the acceleration data column, and determining the time-frequency characteristics of the acceleration data column; determining a filtering frequency range for filtering the acceleration sequence according to the energy distribution of the acceleration sequence reflected by the time-frequency characteristics;
performing band-pass filtering on the acceleration sequence according to the determined filtering frequency range to obtain a filtered acceleration sequence;
calculating a contact net impact index according to the filtered acceleration sequence;
and if the impact index of the overhead line system is greater than the given impact index threshold value, determining that hard spots appear on the overhead line system.
2. The method of claim 1, wherein determining outliers in the vertical vibration acceleration data sequence based on the acceleration threshold, calculating an average of the vertical vibration acceleration, and replacing the outliers with the average of the vertical vibration acceleration comprises:
according to
Figure FDA0002326091010000011
Calculating an acceleration threshold value W;
for any xpIf | xp| ≧ W, and in xpIf the absolute value of the vertical vibration acceleration which is less than or equal to the preset number is continuously larger than or equal to W in the front and the back, determining xpIs an abnormal value;
according to
Figure FDA0002326091010000012
Calculating the average value of vertical vibration acceleration
Figure FDA0002326091010000013
And use
Figure FDA0002326091010000014
Replacing the outlier;
wherein x ispThe vertical vibration acceleration data series are used for representing the p-th vertical vibration acceleration in the vertical vibration acceleration data series, p is 0,1,2, N-1, k is used for representing empirical coefficients, and sigma is used for representing the standard deviation of the vertical vibration acceleration data series.
3. The method of claim 1, wherein performing EEMD and ADSSTFT on the acceleration data columns, determining time-frequency characteristics of the acceleration data columns, and determining a filtering frequency range for filtering the acceleration sequences according to energy distribution of the acceleration sequences reflected by the time-frequency characteristics, comprises:
decomposing the acceleration data column by using the EEMD to obtain at least two intrinsic mode functions IMF;
setting a window length range, and performing ADSSTFT on the at least two IMFs by using each window length in the window length range to obtain discretization expressions of the at least two IMFs corresponding to each window length;
calculating a centralized measure of the frequency band in which the IMF is located according to the discretization expression of at least two IMFs corresponding to each window length;
taking the window length which makes the centralized measurement obtain the maximum value as the optimal window length;
determining the time-frequency characteristics of the acceleration data column by using the optimal window length and ADSSTFT;
determining a main energy distribution range of the acceleration sequence according to the time-frequency characteristics, and taking a frequency range corresponding to the main energy distribution range as a filtering frequency range for filtering the acceleration sequence, wherein a minimum frequency range in which the sum of energy in the frequency range is greater than 50% of the sum of all energy is taken as the main energy distribution range.
4. The method of claim 3, wherein computing the centralized measure of the frequency bands in which the IMFs are located from the discretized representations of the at least two IMFs corresponding to each window length comprises:
according to
Figure FDA0002326091010000021
Calculating a concentration metric CM for the frequency band in which the IMF is located (ξ)z,l)；
Wherein the content of the first and second substances,
Figure FDA0002326091010000022
Figure FDA0002326091010000023
a discretized expression for representing at least two IMFs;
Figure FDA0002326091010000024
for representation normalization
Figure FDA0002326091010000025
d for representing the lower bound of two consecutive separated components, γ for representing a threshold value, γ ≧ 0, l for representing a window length, t for representing time, ξzFor positive frequencies of the frequency band with the number z, (t, ξ) e R x α N, where R is used to represent real numbers, N is used to represent the number of vertical vibration accelerations picked up, k is used to represent empirical coefficients, α is used to represent resolution, z is used to represent the number of frequency bands, and v, β is used to represent an index, 0 < v < 1 < β.
5. The method of claim 1, wherein calculating the catenary impact index from the filtered acceleration sequence comprises:
calculating a moving effective value of the filtered acceleration sequence;
dividing the overhead line system into at least one unit according to the preset unit length;
determining the maximum value of the moving effective value of the vertical vibration acceleration in each unit according to the moving effective value of the vertical vibration acceleration;
calculating the average value of the maximum values of the moving effective values of the vertical vibration acceleration in each unit;
according to
Figure FDA0002326091010000026
Calculating the impact index C of the contact netII,m；
Wherein, Cm,pIs used for expressing the moving effective value of the mth vertical vibration acceleration in the acceleration sequence,
Figure FDA0002326091010000027
for representing the average value of the maximum values of the moving effective values.
6. The method of claim 1, wherein after determining that a hard spot has occurred on the catenary, the method further comprises:
acquiring pull-out value information, ledger data and characteristic peak values;
determining the position of a positioning point according to the pull-out value information;
and determining the type of the hard spot disease of the contact net according to the position of the positioning point, the standing book data and the characteristic peak value.
7. An apparatus for determining hard spot of catenary based on dynamic response of pantograph-catenary, comprising:
the acquisition module is used for acquiring a vertical vibration acceleration data sequence of the pantograph, determining an abnormal value in the vertical vibration acceleration data sequence according to an acceleration threshold, calculating an average value of the vertical vibration acceleration, and replacing the abnormal value with the average value of the vertical vibration acceleration to obtain an acceleration data column;
the determining module is used for carrying out EEMD and ADSSTFT on the acceleration data column obtained by the obtaining module and determining the time-frequency characteristics of the acceleration data column; determining a filtering frequency range for filtering the acceleration sequence according to the energy distribution of the acceleration sequence reflected by the time-frequency characteristics;
the filtering module is used for carrying out band-pass filtering on the acceleration sequence according to the filtering frequency range determined by the determining module to obtain a filtered acceleration sequence;
the calculation module is used for calculating the impact index of the contact network according to the acceleration sequence filtered by the filtering module;
the determining module is further configured to determine that a hard spot occurs on the catenary when the catenary impact index calculated by the calculating module is greater than a given impact index threshold.
8. The apparatus of claim 7, wherein the obtaining module is configured to:
according to
Figure FDA0002326091010000031
Calculating an acceleration threshold value W;
for any xpIf | xp| ≧ W, and in xpIf the absolute value of the vertical vibration acceleration which is less than or equal to the preset number is continuously larger than or equal to W in the front and the back, determining xpIs an abnormal value;
according to
Figure FDA0002326091010000032
Calculating the average value of vertical vibration acceleration
Figure FDA0002326091010000033
And use
Figure FDA0002326091010000034
Replacing the outlier;
wherein x ispThe vertical vibration acceleration data series are used for representing the p-th vertical vibration acceleration in the vertical vibration acceleration data series, p is 0,1,2, N-1, k is used for representing empirical coefficients, and sigma is used for representing the standard deviation of the vertical vibration acceleration data series.
9. The apparatus of claim 7, wherein the determining module is configured to:
decomposing the acceleration data column by using the EEMD to obtain at least two intrinsic mode functions IMF;
setting a window length range, and performing ADSSTFT on the at least two IMFs by using each window length in the window length range to obtain discretization expressions of the at least two IMFs corresponding to each window length;
calculating a centralized measure of the frequency band in which the IMF is located according to the discretization expression of at least two IMFs corresponding to each window length;
taking the window length which makes the centralized measurement obtain the maximum value as the optimal window length;
determining the time-frequency characteristics of the acceleration data column by using the optimal window length and ADSSTFT;
determining a main energy distribution range of the acceleration sequence according to the time-frequency characteristics, and taking a frequency range corresponding to the main energy distribution range as a filtering frequency for filtering the acceleration sequence, wherein a minimum frequency range in which the sum of energy in the frequency range is greater than 50% of the sum of all energy is taken as the main energy distribution range.
10. The apparatus of claim 9, wherein the determining module is configured to:
according to
Figure FDA0002326091010000041
Calculating a concentration metric CM for the frequency band in which the IMF is located (ξ)z,l)；
Wherein the content of the first and second substances,
Figure FDA0002326091010000042
Figure FDA0002326091010000043
a discretized expression for representing at least two IMFs;
Figure FDA0002326091010000044
for representation normalization
Figure FDA0002326091010000045
d for representing the lower bound of two consecutive separated components, γ for representing a threshold value, γ ≧ 0, l for representing a window length, t for representing time, ξzFor positive frequencies of the frequency band with the number z, (t, ξ) e R x α N, where R is used to represent real numbers, N is used to represent the number of vertical vibration accelerations picked up, k is used to represent empirical coefficients, α is used to represent resolution, z is used to represent the number of frequency bands, and v, β is used to represent an index, 0 < v < 1 < β.
11. The apparatus of claim 7, wherein the computing module is configured to:
calculating the moving effective value of each vertical vibration acceleration in the filtered acceleration sequence;
dividing the overhead line system into at least one unit according to the preset unit length;
determining the maximum value of the moving effective value of the vertical vibration acceleration in each unit according to the moving effective value of the vertical vibration acceleration;
calculating the average value of the maximum values of the moving effective values of the vertical vibration acceleration in each unit;
according to
Figure FDA0002326091010000046
Calculating the impact index C of the contact netII,m；
Wherein, Cm,pIs used for expressing the moving effective value of the mth vertical vibration acceleration in the acceleration sequence,
Figure FDA0002326091010000047
for representing the average value of the maximum values of the moving effective values.
12. The apparatus of claim 7,
the acquisition module is also used for acquiring pull-out value information, standing book data and a characteristic peak value;
the determining module is further used for determining the position of the positioning point according to the pull-out value information acquired by the acquiring module;
and the hard spot disease type determining module is used for determining the type of the hard spot disease of the contact net according to the positioning point position determined by the determining module, the standing book data acquired by the acquiring module and the characteristic peak value.
13. 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 6 when executing the computer program.
14. 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 6.
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