CN110632483A - Service life estimation method of EPR cable based on multi-scale space spectrogram information - Google Patents

Service life estimation method of EPR cable based on multi-scale space spectrogram information Download PDF

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CN110632483A
CN110632483A CN201911063521.0A CN201911063521A CN110632483A CN 110632483 A CN110632483 A CN 110632483A CN 201911063521 A CN201911063521 A CN 201911063521A CN 110632483 A CN110632483 A CN 110632483A
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郭蕾
曹伟东
张靖康
邢立勐
杨涵
权圣威
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Abstract

The invention discloses a service life estimation method of an EPR cable based on multi-scale space spectrogram information, which comprises the following steps of: collecting partial discharge spectrogram information of an actually-operated cable and a cable to be detected, extracting an aging characteristic factor, and estimating the service life of the cable to be detected according to the aging characteristic factor. The invention can accurately and efficiently estimate the service life of the EPR cable and greatly reduce the maintenance workload.

Description

Service life estimation method of EPR cable based on multi-scale space spectrogram information
Technical Field
The invention belongs to the field of cable insulation state detection, and particularly relates to a multi-scale spatial spectrogram information-based EPR cable service life estimation method.
Background
The vehicle-mounted ethylene propylene rubber cable (EPR) is key high-voltage equipment in high-speed trains and electric locomotives, the connection of each high-voltage electrical equipment in a vehicle-mounted power supply system needs to use the EPR cable, and the use state of the EPR cable directly influences the safety, stability and economic operation of the traction power supply system. EPR insulation is an important component of a cable and is also a part which is easily damaged during the operation of the cable. The reduction of the insulation performance of the cable not only causes a large amount of electric energy loss, but also has serious safety risk, the working current in the pantograph-catenary line is increased if the working current is light, the service life of electrical equipment is shortened, and the loss which is difficult to estimate is caused if the working current is heavy, the train is shut down, the pantograph-catenary accident is caused, and the like. Therefore, the service life estimation of the EPR cable is particularly important.
Under the current large background of rapid development of railway industry, the research on service life estimation of EPR cables is relatively deficient. In consideration of the problem of the operation safety of the insulation deterioration cable, a reliable and safe method is urgently needed to estimate the service life of the vehicle-mounted EPR cable in order to guarantee the complete operation of the train. By estimating the service life of the vehicle-mounted EPR cable, the method has great engineering value and theoretical significance for safe and effective operation and maintenance of a traction power supply system.
Disclosure of Invention
The invention aims to provide an EPR cable service life estimation method based on multi-scale space spectrogram information.
The technical scheme of the invention is as follows:
an EPR cable service life estimation method based on multi-scale space spectrogram information comprises
The first step is as follows: acquiring partial discharge spectrogram information of an actually-operated cable and a cable to be detected:
respectively pressurizing an actual operation cable and a cable to be detected to 25kV, carrying out h-time partial discharge spectrogram acquisition, and marking the spectrogram label acquired from the actual operation cable as A(t,b)To be picked up from cables to be testedSpectrogram label CbT represents the actual cable operation for t years, and b represents the b-th local discharge spectrogram acquisition; t and b are real numbers, t belongs to {1,5,7,10}, b belongs to [1, h }]，h≥100；
The second step is that: extracting aging characteristic factors including
1) For collecting partial discharge spectrogram A(t,b)And CbPerforming graying treatment, and recording the spectrogram after graying treatment as GA(t,b)And GCbThe graying process is as follows:
note A(t,b)The coordinate of the middle red pixel value is r-a(t,b)(i, j) green pixel value coordinate is g-a(t,b)(i, j) blue pixel value coordinate b-a(t,b)(i, j); after graying, GA(t,b)Middle pixel value coordinate ga(t,b)(i, j) is
Figure BDA0002258429830000011
i and j are respectively spectrogram GA(t,b)The horizontal and vertical coordinates of the pixel, i, j are real numbers, i belongs to [1,32 ]]，j∈[1,32]；
Note CbThe coordinate of the middle red pixel value is r-cb(p, q) green pixel value coordinates g-cb(p, q) blue pixel value coordinates b-cb(p, q); after graying, GCbMiddle pixel value coordinate gcb(p, q) is
Figure BDA0002258429830000021
p and q are respectively a spectrogram GCbThe horizontal and vertical coordinates of the pixel, p and q are real numbers, and p belongs to [1,32 ]]，q∈[1,32]；
2) Pair spectrogram GA(t,b)And spectrum GCbPerforming dimension reduction treatment, and recording the spectrograms after dimension reduction treatment as GA(t,b,n)And GC(b,n)N is the dimensionality reduction times, n is a real number, n belongs to {1,2,3}, and GA is recorded(t,b,n)Coordinate of middle pixel value ga(t,b,n)(in,jn) Record GC(b,n)Middle pixel value coordinate is gc(b,n)(pn,qn)；
The dimension reduction process comprises the following steps:
Figure BDA0002258429830000022
Figure BDA0002258429830000023
wherein alpha is1E.g., i, and α1Is an even number; beta is a1E.g. j, and beta1Is an even number; i.e. i1，j1Are respectively a spectrogram GA(t,b,1)Abscissa and ordinate of pixel, i1，j1Are all real, i1∈[1,16]，j1∈[1,16]；ga(t,b,1)(i1,j1) Is a primary dimensionality reduction spectrogram GA(t,b,1)Pixel value coordinates of (a); mu.s1E.g. p, and μ1Is an even number; lambda [ alpha ]1E.g. q, and λ1Is an even number; p is a radical of1，q1Are respectively a spectrogram GC(b,1)Abscissa and ordinate of pixel, p1，q1Are all real numbers, p1∈[1,16]，q1∈[1,16]；gc(b,1)(p1,q1) For a primary dimension reduction spectrogram GC(b,1)Pixel value coordinates of (a);
Figure BDA0002258429830000024
wherein alpha is2∈i1And α is2Is an even number; beta is a2∈j1And β2Is an even number; i.e. i2，j2Are respectively a spectrogram GA(t,b,2)Abscissa and ordinate of pixel, i2，j2Are all real, i2∈[1,8]，j2∈[1,8]；ga(t,b,2)(i2,j2) Is a secondary dimensionality reduction spectrogram GA(t,b,2)Pixel value coordinates of (a); mu.s2∈p1And μ2Is an even number; lambda [ alpha ]2∈q1And λ2Is an even number; p is a radical of2，q2Are respectively a spectrogram GC(b,2)Abscissa and ordinate of pixel, p2，q2Are all real numbers, p2∈[1,8]，q2∈[1,8]；gc(b,2)(p2,q2) For a secondary dimensionality reduction spectrogram GC(b,2)Pixel value coordinates of (a);
Figure BDA0002258429830000026
Figure BDA0002258429830000027
wherein alpha is3∈i2And α is3Is an even number; beta is a3∈j2And β3Is an even number; i.e. i3，j3Are respectively a spectrogram GA(t,b,3)Abscissa and ordinate of pixel, i3，j3Are all real, i3∈[1,4]，j3∈[1,4]；ga(t,b,3)(i3,j3) Is a three-dimensional reduction spectrogram GA(t,b,3)Pixel value coordinates of (a); mu.s3∈p2And μ3Is an even number; lambda [ alpha ]3∈q2And λ3Is an even number; p is a radical of3，q3Are respectively a spectrogram GC(b,3)Abscissa and ordinate of pixel, p3，q3Are all real numbers, p3∈[1,4]，q3∈[1,4]；gc(b,3)(p3,q3) Is a cubic dimensionality reduction spectrogram GC(b,3)Pixel value coordinates of (a);
3) pair spectrogram GA(t,b,n)And spectrum GC(b,n)Respectively carrying out averaging treatment, and respectively recording the averaged spectrograms as mGA(t,n)And mGC(n)Record mGA(t,n)The middle pixel value coordinate is mga(t,n)(in,jn)，mGC(n)Has a pixel value coordinate of mgc(n)(pn,qn)；
The process of the equalization process is as follows:
Figure BDA0002258429830000031
Figure BDA0002258429830000032
4) pair spectrum mGA(t,n)Weighting is carried out, and the weighted spectrogram is recorded as wGA(n)Record wGA(n)Middle pixel value coordinates wga(n)(in,jn) The weighting process is as follows:
Figure BDA0002258429830000033
5) separately calculate spectrum wGA(n)Kernel matrix K of(n)And spectrum mGC(n)Core matrix E of(n)The formula is as follows
Figure BDA0002258429830000041
Figure BDA0002258429830000042
Figure BDA0002258429830000043
Figure BDA0002258429830000044
Wherein ". x" denotes a convolution operation, K(1)Is a 21 × 21 kernel matrix, K(2)Is a 11 × 11 kernel matrix, K(3)A 5 × 5 kernel matrix; e(1)Nuclear moment of 21X 21Array, E(2)Is a 11 × 11 kernel matrix, E(3)A 5 × 5 kernel matrix;
6) separately computing kernel matrices K(n)Coefficient of variation of eta(n)And kernel matrix E(n)Coefficient of variation of (a) < gamma >(n)，
Figure BDA0002258429830000045
Figure BDA0002258429830000046
Figure BDA0002258429830000047
Figure BDA0002258429830000048
Figure BDA0002258429830000049
Figure BDA00022584298300000410
Wherein, "| | | purple sweet∞"represents a matrix norm ∞;
Figure BDA00022584298300000411
is a kernel matrix K(1)、K(2)、K(3)The inverse matrix of (d);
Figure BDA00022584298300000412
Figure BDA00022584298300000413
is a kernel matrix E(1)、E(2)、E(3)The inverse matrix of (d);
7) the aging characteristic factor xi is calculated,
Figure BDA0002258429830000051
Figure BDA0002258429830000052
Figure BDA0002258429830000053
wherein phi is1Representing the coefficient of variation eta(n)The rate of variation of (a); phi is a2Represents the coefficient of variation gamma(n)The rate of variation of (a);
the third step: and estimating the service life of the cable to be detected according to the aging characteristic factor xi.
The invention can accurately and efficiently estimate the service life of the EPR cable and greatly reduce the maintenance workload.
Drawings
Fig. 1 is a schematic diagram of a cable partial discharge spectrum test.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
The service life estimation method of the EPR cable based on the multi-scale space spectrogram information comprises the following steps:
the first step is as follows: building a partial discharge spectrogram test platform
According to the figure 1, a partial discharge spectrogram testing platform is set up and mainly comprises a high-frequency voltage source (1), a No. 1 wire outlet port (2), a high-voltage insulating wire (3), a testing cable (4), a terminal (5), a high-frequency current mutual inductance coil (6), a No. 1 grounding wire (7), a No. 1 signal transmission wire (8), a data acquisition unit (9), a No. 2 signal transmission wire (10), an upper computer (11), a No. 2 wire outlet port (12) and a No. 2 grounding wire (13).
The high-frequency current transformer is characterized in that a No. 1 outgoing line port (2) of a high-frequency voltage source (1) is connected with a terminal (5) of a test cable (4) through a high-voltage insulating wire (3), a No. 1 grounding wire (7) of the test cable (4) is sleeved with a high-frequency current transformer coil (6), the high-frequency current transformer coil (6) is connected with a data collector (9) through a No. 1 signal transmission wire (8), the data collector (9) is connected with an upper computer (11) through a No. 2 signal transmission wire (10), and a No. 2 outgoing line port (12) of the high-frequency voltage source (1) is grounded through a No. 2 grounding wire (13).
The second step is that: collecting partial discharge spectrogram information of actual operation cable and cable to be detected
Respectively taking an actual running cable and a cable to be detected as test cables (4), and turning on a high-frequency voltage source (1) to enable the output voltage of the high-frequency voltage source (1) to be 25 kV; opening a data collector (9) to collect partial discharge spectrograms of the test cable (4) for h times (h is more than or equal to 100); after the acquisition is finished, the high-frequency voltage source (1) is closed, and then the data acquisition unit (9) is closed; marking the spectrogram label A collected from the actual operation cable(t,b)Marking the spectrogram label C collected from the cable to be detectedbT represents the actual cable operation for t years, and b represents the b-th local discharge spectrogram acquisition; t and b are real numbers, t belongs to {1,5,7,10}, b belongs to [1, h }]。
The third step: aging characteristic factor extraction
The aging characteristic factor extraction steps are as follows:
1) for collecting partial discharge spectrogram A(t,b)And CbPerforming graying treatment, and recording the spectrogram after graying treatment as GA(t,b)And GCbThe graying process is as follows:
note A(t,b)The coordinate of the middle red pixel value is r-a(t,b)(i, j) green pixel value coordinate is g-a(t,b)(i, j) blue pixel value coordinate b-a(t,b)(i, j); after graying, GA(t,b)Middle pixel value coordinate ga(t,b)(i, j) is
Figure BDA0002258429830000061
i and j are respectively spectrogram GA(t,b)The horizontal and vertical coordinates of the pixel, i, j are real numbers, i belongs to [1,32 ]]，j∈[1,32]；
Note CbThe coordinate of the middle red pixel value is r-cb(p, q) green pixel value coordinates g-cb(p, q) blue pixel value coordinates b-cb(p, q); after graying, GCbMiddle pixel value coordinate gcb(p, q) is
Figure BDA0002258429830000062
p and q are respectively a spectrogram GCbThe horizontal and vertical coordinates of the pixel, p and q are real numbers, and p belongs to [1,32 ]]，q∈[1,32]。
2) Pair spectrogram GA(t,b)And spectrum GCbPerforming dimension reduction treatment, and recording the spectrograms after dimension reduction treatment as GA(t,b,n)And GC(b,n)N is the dimensionality reduction times, n is a real number, n belongs to {1,2,3}, and GA is recorded(t,b,n)Coordinate of middle pixel value ga(t,b,n)(in,jn) Record GC(b,n)Middle pixel value coordinate is gc(b,n)(pn,qn)；
The dimension reduction process comprises the following steps:
Figure BDA0002258429830000063
Figure BDA0002258429830000064
wherein alpha is1E.g., i, and α1Is an even number; beta is a1E.g. j, and beta1Is an even number; i.e. i1，j1Are respectively a spectrogram GA(t,b,1)Abscissa and ordinate of pixel, i1，j1Are all real, i1∈[1,16]，j1∈[1,16]；ga(t,b,1)(i1,j1) Is a primary dimensionality reduction spectrogram GA(t,b,1)Pixel value coordinates of (a); mu.s1E.g. p, and μ1Is an even number; lambda [ alpha ]1E.g. q, and λ1Is an even number; p is a radical of1，q1Are respectively a spectrogram GC(b,1)Abscissa and ordinate of pixel, p1，q1Are all real numbers, p1∈[1,16]，q1∈[1,16]；gc(b,1)(p1,q1) For a primary dimension reduction spectrogram GC(b,1)Pixel value coordinates of (a);
Figure BDA0002258429830000065
Figure BDA0002258429830000066
wherein alpha is2∈i1And α is2Is an even number; beta is a2∈j1And β2Is an even number; i.e. i2，j2Are respectively a spectrogram GA(t,b,2)Abscissa and ordinate of pixel, i2，j2Are all real, i2∈[1,8]，j2∈[1,8]；ga(t,b,2)(i2,j2) Is a secondary dimensionality reduction spectrogram GA(t,b,2)Pixel value coordinates of (a); mu.s2∈p1And μ2Is an even number; lambda [ alpha ]2∈q1And λ2Is an even number; p is a radical of2，q2Are respectively a spectrogram GC(b,2)Abscissa and ordinate of pixel, p2，q2Are all real numbers, p2∈[1,8]，q2∈[1,8]；gc(b,2)(p2,q2) For a secondary dimensionality reduction spectrogram GC(b,2)Pixel value coordinates of (a);
Figure BDA0002258429830000071
Figure BDA0002258429830000072
wherein alpha is3∈i2And α is3Is an even number; beta is a3∈j2And β3Is an even number; i.e. i3，j3Are respectively a spectrogram GA(t,b,3)Abscissa and ordinate of pixel, i3，j3Are all real, i3∈[1,4]，j3∈[1,4]；ga(t,b,3)(i3,j3) Is a three-dimensional reduction spectrogram GA(t,b,3)Pixel value coordinates of (a); mu.s3∈p2And μ3Is an even number; lambda [ alpha ]3∈q2And λ3Is an even number; p is a radical of3，q3Are respectively a spectrogram GC(b,3)Abscissa and ordinate of pixel, p3，q3Are all real numbers, p3∈[1,4]，q3∈[1,4]；gc(b,3)(p3,q3) Is a cubic dimensionality reduction spectrogram GC(b,3)Pixel value coordinates of (2).
3) Pair spectrogram GA(t,b,n)And spectrum GC(b,n)Respectively carrying out averaging treatment, and respectively recording the averaged spectrograms as mGA(t,n)And mGC(n)Record mGA(t,n)The middle pixel value coordinate is mga(t,n)(in,jn)，mGC(n)Has a pixel value coordinate of mgc(n)(pn,qn)；
The process of the equalization process is as follows:
Figure BDA0002258429830000073
Figure BDA0002258429830000074
4) pair spectrum mGA(t,n)Weighting is carried out, and the weighted spectrogram is recorded as wGA(n)Record wGA(n)Middle pixel value coordinates wga(n)(in,jn) The weighting process is as follows:
Figure BDA0002258429830000075
5) separately calculate spectrum wGA(n)Kernel matrix K of(n)And spectrum mGC(n)Core matrix E of(n)The formula is as follows
Figure BDA0002258429830000081
Figure BDA0002258429830000082
Figure BDA0002258429830000083
Figure BDA0002258429830000084
Figure BDA0002258429830000086
Wherein ". x" denotes a convolution operation, K(1)Is a 21 × 21 kernel matrix, K(2)Is a 11 × 11 kernel matrix, K(3)A 5 × 5 kernel matrix; e(1)A 21 × 21 kernel matrix, E(2)Is a 11 × 11 kernel matrix, E(3)A 5 x 5 kernel matrix.
6) Separately computing kernel matrices K(n)Coefficient of variation of eta(n)And kernel matrix E(n)Coefficient of variation of (a) < gamma >(n)，
Figure BDA0002258429830000088
Figure BDA0002258429830000089
Figure BDA0002258429830000091
Figure BDA0002258429830000093
Wherein, "| | | purple sweet∞"represents a matrix norm ∞;
Figure BDA0002258429830000094
is a kernel matrix K(1)、K(2)、K(3)The inverse matrix of (d);
Figure BDA0002258429830000095
Figure BDA0002258429830000096
is a kernel matrix E(1)、E(2)、E(3)The inverse matrix of (c).
7) Calculating an aging characteristic factor xi
Figure BDA0002258429830000097
Figure BDA0002258429830000098
Figure BDA0002258429830000099
Wherein phi is1Representing the coefficient of variation eta(n)The rate of variation of (a); phi is a2Represents the coefficient of variation gamma(n)The rate of variation of (a).
The fourth step: estimating the service life of the cable to be detected:
if aging characteristic factor xi < epsilon1The service life of the cable to be detected is not more than 1 year;
if aging characteristic factor xi > epsilon1And aging characteristic factor xi is less than or equal to epsilon2The service life of the cable to be detected is more than 1 year and not more than 5 years;
if aging characteristic factor xi > epsilon2And aging characteristic factor xi is less than or equal to epsilon3The service life of the cable to be detected is more than 5 years and not more than 10 years;
if aging characteristic factor xi > epsilon3The service life of the cable to be detected is more than 10 years.
In the above formula: epsilon1＝0.231，ε2＝0.497，ε3＝1。

Claims (1)

1. The service life estimation method of the EPR cable based on the multi-scale space spectrogram information is characterized by comprising the following steps
The first step is as follows: acquiring partial discharge spectrogram information of an actually-operated cable and a cable to be detected:
respectively pressurizing an actual operation cable and a cable to be detected to 25kV, carrying out h-time partial discharge spectrogram acquisition, and marking the spectrogram label acquired from the actual operation cable as A(t,b)Marking the spectrogram label C collected from the cable to be detectedbT represents the actual cable operation for t years, and b represents the b-th local discharge spectrogram acquisition; t and b are real numbers, t belongs to {1,5,7,10}, b belongs to [1, h }]，h≥100；
The second step is that: extracting aging characteristic factors including
1) For collecting partial discharge spectrogram A(t,b)And spectrum CbPerforming graying treatment, and recording the spectrogram after graying treatment as GA(t,b)And GCbThe graying process is as follows:
note A(t,b)The coordinate of the middle red pixel value is r-a(t,b)(i, j) green pixel value coordinate is g-a(t,b)(i, j) blue pixel value coordinate b-a(t,b)(i, j); after graying, GA(t,b)Middle pixel value coordinate ga(t,b)(i, j) is
Figure FDA0002258429820000011
i and j are respectively spectrogram GA(t,b)The horizontal and vertical coordinates of the pixel, i, j are real numbers, i belongs to [1,32 ]]，j∈[1,32]；
Note CbThe coordinate of the middle red pixel value is r-cb(p, q) green pixel value coordinates g-cb(p, q) blue pixel value coordinates b-cb(p, q); after graying, GCbMiddle pixel value coordinate gcb(p, q) is
Figure FDA0002258429820000012
p and q are respectively a spectrogram GCbThe horizontal and vertical coordinates of the pixel, p and q are real numbers, and p belongs to [1,32 ]]，q∈[1,32]；
2) Pair spectrogram GA(t,b)And spectrum GCbPerforming dimension reduction treatment, and recording the spectrograms after dimension reduction treatment as GA(t,b,n)And GC(b,n)N is the dimensionality reduction times, n is a real number, n belongs to {1,2,3}, and GA is recorded(t,b,n)Coordinate of middle pixel value ga(t,b,n)(in,jn) Record GC(b,n)Middle pixel value coordinate is gc(b,n)(pn,qn) (ii) a The dimension reduction process comprises the following steps:
Figure FDA0002258429820000013
Figure FDA0002258429820000014
wherein alpha is1E.g., i, and α1Is an even number; beta is a1E.g. j, and beta1Is an even number; i.e. i1，j1Are respectively a spectrogram GA(t,b,1)Abscissa and ordinate of pixel, i1，j1Are all real, i1∈[1,16]，j1∈[1,16]；ga(t,b,1)(i1,j1) Is a primary dimensionality reduction spectrogram GA(t,b,1)Pixel value coordinates of (a); mu.s1E.g. p, and μ1Is an even number; lambda [ alpha ]1E.g. q, and λ1Is an even number; p is a radical of1，q1Are respectively a spectrogram GC(b,1)Abscissa and ordinate of pixel, p1，q1Are all real numbers, p1∈[1,16]，q1∈[1,16]；gc(b,1)(p1,q1) For a primary dimension reduction spectrogram GC(b,1)Pixel value coordinates of (a);
Figure FDA0002258429820000021
Figure FDA0002258429820000022
wherein alpha is2∈i1And α is2Is an even number; beta is a2∈j1And β2Is an even number; i.e. i2，j2Are respectively a spectrogram GA(t,b,2)Abscissa and ordinate of pixel, i2，j2Are all real, i2∈[1,8]，j2∈[1,8]；ga(t,b,2)(i2,j2) Is a secondary dimensionality reduction spectrogram GA(t,b,2)Pixel value coordinates of (a); mu.s2∈p1And μ2Is an even number; lambda [ alpha ]2∈q1And λ2Is an even number; p is a radical of2，q2Are respectively a spectrogram GC(b,2)Abscissa and ordinate of pixel, p2，q2Are all real numbers, p2∈[1,8]，q2∈[1,8]；gc(b,2)(p2,q2) For a secondary dimensionality reduction spectrogram GC(b,2)Pixel value coordinates of (a);
Figure FDA0002258429820000023
Figure FDA0002258429820000024
wherein alpha is3∈i2And α is3Is an even number; beta is a3∈j2And β3Is an even number; i.e. i3，j3Are respectively a spectrogram GA(t,b,3)Abscissa and ordinate of pixel, i3，j3Are all real, i3∈[1,4]，j3∈[1,4]；ga(t,b,3)(i3,j3) Is a three-dimensional reduction spectrogram GA(t,b,3)Pixel value coordinates of (a); mu.s3∈p2And μ3Is an even number; lambda [ alpha ]3∈q2And λ3Is an even number; p is a radical of3，q3Are respectively a spectrogram GC(b,3)Abscissa and ordinate of pixel, p3，q3Are all real numbers, p3∈[1,4]，q3∈[1,4]；gc(b,3)(p3,q3) Is a cubic dimensionality reduction spectrogram GC(b,3)Pixel value coordinates of (a);
3) pair spectrogram GA(t,b,n)And spectrum GC(b,n)Respectively carrying out averaging treatment, and respectively recording the averaged spectrograms as mGA(t,n)And mGC(n)Record mGA(t,n)The middle pixel value coordinate is mga(t,n)(in,jn)，mGC(n)Has a pixel value coordinate of mgc(n)(pn,qn) (ii) a The process of the equalization process is as follows:
Figure FDA0002258429820000026
4) pair spectrum mGA(t,n)Weighting is carried out, and the weighted spectrogram is recorded as wGA(n)Record wGA(n)Middle pixel value coordinates wga(n)(in,jn) The weighting process is as follows:
Figure FDA0002258429820000031
5) separately calculate spectrum wGA(n)Kernel matrix K of(n)And spectrum mGC(n)Core matrix E of(n)The formula is as follows
Figure FDA0002258429820000032
Figure FDA0002258429820000033
Figure FDA0002258429820000034
Figure FDA0002258429820000035
Figure FDA0002258429820000036
Figure FDA0002258429820000037
Wherein ". x" denotes a convolution operation, K(1)Is a 21 × 21 kernel matrix, K(2)Is a 11 × 11 kernel matrix, K(3)A 5 × 5 kernel matrix; e(1)A 21 × 21 kernel matrix, E(2)Is a 11 × 11 kernel matrix, E(3)A 5 × 5 kernel matrix; 6) separately computing kernel matrices K(n)Coefficient of variation of eta(n)And kernel matrix E(n)Coefficient of variation of (a) < gamma >(n)，
Figure FDA0002258429820000042
Figure FDA0002258429820000043
Figure FDA0002258429820000044
Figure FDA0002258429820000045
Figure FDA0002258429820000046
Wherein, "| | | purple sweet∞Represents a norm of a matrix ∞;is a kernel matrix K(1)、K(2)、K(3)The inverse matrix of (d);
Figure FDA0002258429820000048
is a kernel matrix E(1)、E(2)、E(3)The inverse matrix of (d);
7) the aging characteristic factor xi is calculated,
Figure FDA0002258429820000049
Figure FDA00022584298200000410
wherein phi is1Representing the coefficient of variation eta(n)The rate of variation of (a); phi is a2Represents the coefficient of variation gamma(n)The rate of variation of (a);
the third step: and estimating the service life of the cable to be detected according to the aging characteristic factor xi.
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