CN105512496A - Automatic measurement method of geometric characteristics of randomly-bundled cable bundles - Google Patents
Automatic measurement method of geometric characteristics of randomly-bundled cable bundles Download PDFInfo
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Abstract
The invention discloses an automatic measurement method of geometric characteristics of randomly-bundled cable bundles and belongs to the field of electromagnetic measurement. In order to solve the problem of strict application conditions of the existing method for obtaining parameters of the geometric characteristics of cable bundles, a randomly-bundled cable bundle model based on fractal theory, a cable bundle outer characteristic parameter set based on fractal coefficient, a determinacy forward model for describing relation between the fractal coefficient and impedance characteristic, and a Bayes inverse estimation model of the forward model are respectively built; with combination of a Markov Chain Monte algorithm, the Bayes inverse estimation model is solved by using the actual measurement data of the cable bundle outer impedance characteristic; the calculation is carried out to obtain optimal estimation of the fractal coefficient so as to obtain the distribution characteristic of the geometric characteristics of the randomly-bundled cable bundles. The method is applicable to measurement of the geometric characteristics of various randomly-bundled cable bundles.
Description
Technical Field
The invention belongs to the field of electromagnetic metering.
Background
The problem of cable bundle coupling is a typical problem faced in the electromagnetic compatibility design of aeronautics and astronautics, and this problem is receiving increasing attention. At the beginning of the design of electromagnetic compatibility, the geometric characteristics of the cable bundle must be determined first, and then the electromagnetic compatibility of the cable bundle can be studied. The current approximate description for randomly bundled cable bundles uses a uniform transmission line model, i.e., assuming that the cable bundle cross-sectional geometry does not change axially between transmission line network nodes. This approach is a trade-off between model complexity and accuracy. As a practical matter, due to the limitations of the cable bundling process, even the same type of cable bundle batch produced using the same process has a very random geometric cross-section. In addition, in the using process of the cable bundle, due to the fact that the vibration causes mutual friction between the cable and the fixed frame, temperature change causes aging of the insulating layer and other factors, structural parameters and medium parameters of the cable are changed to a certain extent, and then distributed electrical parameters are influenced, and the electromagnetic coupling effect of the cable bundle is influenced. The traditional uniform transmission line model is not enough to describe the change of the geometric characteristics, so that a measuring method must be adopted to quantify the randomness of the geometric characteristic parameters so as to better study the coupling effect of the cable bundle.
The existing method for acquiring the external characteristics of the cable bundle is mainly acquired by a numerical simulation method, and the method mainly comprises the following three methods:
(1) and (3) cascading the segmented equivalent uniform transmission line models, randomly changing the cross section geometrical structures of adjacent transmission lines, and performing multiple times of simulation by using a Monte Carlo method to obtain the distribution characteristics of the result. The method has the advantages of simple implementation, high consumption of computing resources, and easy cable discontinuity caused by any structural cascade, and influences the accuracy of high-frequency section analysis.
(2) Random midpoint setting method: a set of fractal curves is generated to model the randomness of the cable position, and the fractal dimensions are used to characterize the degree of axial variation in cable position. Based on the RDSI algorithm, the continuity and smoothness of the cable are maintained by using a spline interpolation technology, and the randomness of the cable position is represented by using Gaussian distribution parameters of the position location point and the cable segment number. This method can save calculation amount, but the premise of using the method is that only the same type of wires are contained in the cable bundle, and the condition of bundling a plurality of cables is not considered.
(3) WH (Wire-Hole) model method: the randomness of the cable structure is characterized by controlling the random transmission of each cable (Wire) in a predefined cable bundle position Hole (Hole). This model is still only suitable for modeling a cable bundle consisting of a single cable.
Disclosure of Invention
The invention aims to solve the problem that the existing cable bundle geometric characteristic parameter acquisition method is harsh in model application condition, and provides an automatic measurement method for the geometric characteristic of a randomly bundled cable bundle.
The invention discloses a method for automatically measuring geometrical characteristics of a randomly bundled cable bundle, which comprises the following steps:
the method comprises the following steps: measuring external impedance characteristics of a group of randomly bundled cable bundles to be tested, wherein the external impedance characteristics comprise open-circuit impedance characteristics and short-circuit impedance characteristics, and obtaining impedance characteristic data of the randomly bundled cable bundles to be tested according to the distribution condition of the impedance characteristics of the randomly bundled cable bundles;
step two: establishing a geometric model of the randomly bundled cable bundle based on a fractal theory according to the randomly bundled cable bundle to be detected;
step three: establishing a parameter set for describing the geometric characteristics of the cable bundle based on a cable fractal coefficient according to the cable type in the cable bundle to be bound randomly;
step four: according to the transmission line theory and the geometric model of the randomly bundled cable bundle established in the second step, constructing a mapping relation between a cable fractal coefficient and impedance characteristics to obtain a deterministic forward model;
step five: establishing a Bayesian inverse estimation model according to the impedance characteristic data obtained in the first step and the deterministic forward model established in the fourth step;
step six: solving the Bayesian inverse estimation model obtained in the fifth step by adopting a Markov chain Monte Carlo algorithm to obtain the optimal estimation of the fractal coefficient;
step seven: and substituting the optimal estimation of the fractal coefficient obtained in the sixth step into the parameter set obtained in the third step to obtain the parameters describing the geometric characteristics of the cable bundle.
The first step is a method for measuring the external impedance characteristic of the random bundled cable bundle to be measured, and comprises the following steps:
two ends of the to-be-tested randomly-bundled cable bundle are respectively connected with the vector network analyzer sequentially through an aerial plug, a switching box and a switch matrix; and measuring the open-circuit impedance characteristic and the short-circuit impedance characteristic of the random bundled cable bundle to be measured by utilizing the upper computer to control the vector network analyzer.
The third step, the parameter set describing the geometric characteristics of the cable bundle based on the cable fractal coefficient is as follows:
x＝{x1(θ),x2(θ),...,xn(θ)}；
wherein θ ═ θ1,θ2,...,θm}∈RmIs a cable fractal coefficient;
wherein, thetaiIs the cable fractal coefficient of the ith randomly bundled cable bundle, m is the number of the randomly bundled cable bundle types, RmIs an m-dimensional real number space.
In the fourth step, the deterministic forward model is: f (θ) + v, the model is established based on transmission line theory;
wherein, y ∈ RdImpedance characteristic data for randomly bundling cable bundles, d is impedance characteristicDimension of data, v ∈ RdThe method is characterized in that random errors are introduced during measurement, and F (theta) is a forward model without considering the random errors and represents a mapping function from a cable fractal coefficient to impedance characteristics.
In the fifth step, the Bayesian inverse estimation model is as follows:
wherein Prob (θ) is a priori estimate of the cable fractal coefficient given based on background knowledge; prob (y | θ) is a probability distribution obtained from impedance characteristic data of a set of randomly bundled cable bundles; prob (θ | y) is a likelihood distribution, which is a posterior probability distribution after combining the deterministic forward model and the impedance characterization data of the randomly bundled cable bundle.
In the sixth step, the optimal estimation theta of the fractal coefficient*＝maximizeProb(θ|y)；
Wherein,
the method has the advantages that the randomness of the geometric characteristic parameters of the complex cable bundle model formed by various cables can be quantified, the applicability is strong, the operation of the measuring system is simple and stable, and the repeatability is high, so that the measuring result has high stability and accuracy.
Drawings
FIG. 1 is a schematic diagram of a measuring platform for measuring geometrical characteristics of a randomly bundled cable bundle according to an embodiment.
Fig. 2 is a left side view of the cable support 1 in fig. 1.
Fig. 3 is a T-shaped equivalent circuit of a randomly bundled cable bundle to be tested in an embodiment.
Fig. 4 is a schematic diagram illustrating an impedance measurement principle of the randomly bundled cable bundle to be measured in the embodiment.
Fig. 5 is a schematic diagram of a cross-sectional model of a randomly bundled cable bundle.
Detailed Description
Specifically describing the present embodiment with reference to fig. 1 to 5, the automatic measurement method for geometric characteristics of randomly bundled cable bundles in the present embodiment is implemented based on a test system, where the test system includes a cable bracket 1, two aerial sockets 2, two adapter boxes 3, an SMA interface 50 ohm resistor 4, two switch matrices 5, a vector network analyzer 6, an upper computer 7, and a general interface bus 8.
The randomly bundled cable bundles to be tested can be cable bundles randomly bundled by the same type of cables or cable bundles randomly bundled by different types of cables. In the present embodiment, a cable bundle in which three cables of the same type are bundled is taken as an example;
two ends of a cable bundle to be tested and randomly bundled are respectively connected with two adapter boxes 3 through an aerial plug 2, wherein one adapter box 3 is connected with one switch matrix 5 through an SMA (shape memory alloy) interface 50 ohm resistor 4, the other adapter box 3 is directly connected with the other switch matrix 5, the two switch matrices 5 are connected with a vector network analyzer through a BNC (bayonet nut connector) interface and are simultaneously connected with an upper computer through a USB (universal serial bus) interface, as shown in figure 1.
The measuring method comprises the following steps:
the method comprises the following steps that firstly, an upper computer 7 controls two switch matrixes 5, a vector network analyzer 6 is utilized to measure external impedance characteristics of a group of randomly bundled cable bundles to be measured, and impedance characteristic data of the randomly bundled cable bundles to be measured are obtained according to the impedance characteristic distribution condition of the randomly bundled cable bundles;
in this embodiment, a high-frequency transmission line model of a three-conductor cable is established, the skin effect and the dielectric loss effect of the cable are considered, the RLCG parameter in the basic transmission line model is replaced by a T-shaped equivalent circuit as shown in FIG. 3, according to the schematic diagram of the impedance measurement principle of FIG. 4, the external impedance characteristics of the randomly bundled cable bundle are measured, an open-circuit impedance curve and a short-circuit impedance curve can be obtained respectively, and the measured impedance characteristics are Z ∈ RdAnd (4) showing.
In the embodiment, the external impedance characteristics of a group of to-be-measured randomly bundled cable bundles need to be measured, so that the impedance characteristics of a plurality of to-be-measured randomly bundled cable bundles can present a certain probability distribution rule, and further, the impedance characteristic data of the to-be-measured randomly bundled cable bundles are determined, and the accuracy is improved.
Step two, establishing a geometric model of the randomly bundled cable bundle based on a fractal theory according to the randomly bundled cable bundle to be detected;
in order to accurately describe the randomness of the external characteristics of the randomly bundled cable bundle, a midpoint displacement method in a fractal theory is adopted to model the line elements in the cable bundle. The fractal theory follows the basic fact that the cable bundle is continuous, the randomness description of the cable bundle can be ensured, and the spline interpolation used in the RDSI method is adopted in the modeling to ensure the smoothness of the cable bundle structure.
Step three, establishing a parameter set for describing the geometric characteristics of the cable bundle based on the cable fractal coefficient according to the cable type in the to-be-tested random bundled cable bundle;
in the cross-sectional model of the randomly bundled cable bundle shown in fig. 5, the parameters describing the randomness of the characteristics outside the cable bundle mainly include:
cable center position coordinate (x)i,yi) Radius of cable riThickness of cable insulation layer △ riDistance s between two cablesi,j。
In addition to the parameters described in the cross-sectional model, there is the cable bundle length l.
Therefore, the parameter set for describing the randomness of the characteristics outside the cable bundle based on the fractal coefficients is as follows:
{(xi(θ),yi(θ)),ri(θ),△ri(θ),si,j(θ),l(θ)}。
step four, according to the transmission line theory and the geometric model of the randomly bundled cable bundle established in the step two, constructing a mapping relation between a cable fractal coefficient and impedance characteristics to obtain a deterministic forward model: :
y＝F(θ)+v(5)
wherein, y ∈ RdImpedance characteristic data for randomly bundled cable bundles, d is the dimension of the impedance characteristic data, v ∈ RdIs the random error introduced by the measurement, e.g., the effect of BNC junctions, etc. F (theta) is a forward model without considering random errors and represents a mapping function of the fractal coefficient of the cable to the impedance characteristic.
Step five, establishing a Bayesian inverse estimation model according to the impedance characteristic data obtained in the step one and the deterministic forward model established in the step four:
wherein Prob (θ) is a priori estimate of the cable fractal coefficient given based on background knowledge; prob (y | θ) is a probability distribution obtained from impedance characteristic data of a set of randomly bundled cable bundles; prob (θ | y) is a likelihood distribution, which is a posterior probability distribution after combining the deterministic forward model and the impedance characterization data of the randomly bundled cable bundle.
Step six, solving the Bayesian inverse estimation model obtained in the step five by adopting a Markov chain Monte Carlo algorithm to obtain the optimal estimation of the fractal coefficient:
θ*＝maximizeProb(θ|y)(7)
wherein,
step seven, substituting the optimal estimation of the fractal coefficient obtained in the step six into the parameter set obtained in the step three to obtain a parameter for describing the geometric characteristics of the cable bundle:
{(xi(θ*),yi(θ*)),ri(θ*),△ri(θ*),si,j(θ*),l(θ*)}。
Claims (6)
1. An automated method for measuring geometrical characteristics of a bundle of randomly bundled cables, the method comprising the steps of:
the method comprises the following steps: measuring external impedance characteristics of a group of randomly bundled cable bundles to be tested, wherein the external impedance characteristics comprise open-circuit impedance characteristics and short-circuit impedance characteristics, and obtaining impedance characteristic data of the randomly bundled cable bundles to be tested according to the distribution condition of the impedance characteristics of the randomly bundled cable bundles;
step two: establishing a geometric model of the randomly bundled cable bundle based on a fractal theory according to the randomly bundled cable bundle to be detected;
step three: establishing a parameter set for describing the geometric characteristics of the cable bundle based on a cable fractal coefficient according to the cable type in the cable bundle to be bound randomly;
step four: according to the transmission line theory and the geometric model of the randomly bundled cable bundle established in the second step, constructing a mapping relation between a cable fractal coefficient and impedance characteristics to obtain a deterministic forward model;
step five: establishing a Bayesian inverse estimation model according to the impedance characteristic data obtained in the first step and the deterministic forward model established in the fourth step;
step six: solving the Bayesian inverse estimation model obtained in the fifth step by adopting a Markov chain Monte Carlo algorithm to obtain the optimal estimation of the fractal coefficient;
step seven: and substituting the optimal estimation of the fractal coefficient obtained in the sixth step into the parameter set obtained in the third step to obtain the parameters describing the geometric characteristics of the cable bundle.
2. The method for automatically measuring geometrical characteristics of randomly bundled cable bundles according to claim 1, wherein the first step is a method for measuring external impedance characteristics of randomly bundled cable bundles to be measured:
two ends of the to-be-tested randomly-bundled cable bundle are respectively connected with the vector network analyzer sequentially through an aerial plug, a switching box and a switch matrix; and measuring the open-circuit impedance characteristic and the short-circuit impedance characteristic of the random bundled cable bundle to be measured by utilizing the upper computer to control the vector network analyzer.
3. The method for automatically measuring the geometric characteristics of the randomly bundled cable bundle according to claim 1 or 2, wherein in the third step, the parameter set describing the geometric characteristics of the cable bundle based on the cable fractal coefficients is as follows:
x＝{x1(θ),x2(θ),...,xn(θ)}；
wherein θ ═ θ1,θ2,...,θm}∈RmIs a cable fractal coefficient;
wherein, thetaiIs the cable fractal coefficient of the ith randomly bundled cable bundle, m is the number of the randomly bundled cable bundle types, RmIs an m-dimensional real number space.
4. The method for automatically measuring geometrical characteristics of randomly bundled cables as claimed in claim 3, wherein in the fourth step, the deterministic forward model is: f (θ) + v, the model is established based on transmission line theory;
wherein, y ∈ RdImpedance characteristic data for randomly bundled cable bundles, d is the dimension of the impedance characteristic data, v ∈ RdThe method is characterized in that random errors are introduced during measurement, and F (theta) is a forward model without considering the random errors and represents a mapping function from a cable fractal coefficient to impedance characteristics.
5. The method for automatically measuring the geometric characteristics of the randomly bundled cable bundle as claimed in claim 4, wherein in the step five, the Bayesian inverse estimation model is as follows:
wherein, Prob (θ) gives a priori estimate of the fractal coefficient of the cable; prob (y | θ) is a probability distribution obtained from impedance characteristic data of a set of randomly bundled cable bundles; prob (θ | y) is a likelihood distribution, which is a posterior probability distribution after combining the deterministic forward model and the impedance characterization data of the randomly bundled cable bundle.
6. The method for automatically measuring geometrical characteristics of randomly bundled cables as claimed in claim 5, wherein in step six, the optimal estimation of fractal coefficients θ*＝maximizeProb(θ|y)；
Wherein,
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