Method for checking eccentricity of insulation layer for cable

Abstract

A method for calibrating the eccentricity of an insulation layer for a cable comprises: systematically clustering a data group of machine-measured eccentricity for modeling to obtain a plurality of groups; selecting a group in the plurality of groups whose machine-measured eccentricity for modeling is greater than that of other groups as a target group; for the target group, using a deviation value for modeling as an independent variable and the actually measured eccentricity for modeling as a dependent variable to perform curve estimation, thereby selecting a fitting formula with the maximum fitting degree and greater than 0.5, wherein the deviation value for modeling refers to the value of the actually measured eccentricity for modeling minus the machine-measured eccentricity for modeling; obtaining an estimated value using the instant machine-measured eccentricity as an independent variable through the fitting formula; and adjusting the instant machine-measured eccentricity so that the estimated value is less than a standard value.

Description

Technical Field

[0001] The invention relates to a method for calibrating the eccentricity of an insulating layer of a cable. Background Art

[0002] In the process of producing cables, when the conductor is extruded with an insulating layer, the eccentricity of the extruded insulating layer needs to be measured and adjusted so that the eccentricity of the insulating layer meets the production requirements. However, since there is a large deviation between the machine-measured eccentricity automatically collected by the eccentricity instrument and the actual eccentricity measured by workers using manual tools, workers need to compare the machine-measured eccentricity and the actual eccentricity when starting the machine for each batch of cables, and adjust the actual eccentricity so that the actual eccentricity meets the production requirements. In addition, the actual eccentricity cannot be too small, otherwise it will lead to increased working hours, a sharp increase in cable consumption, and increased costs. Therefore, the actual eccentricity is often kept within a range slightly smaller than the standard value, resulting in the actual measured eccentricity often being close to the standard value and requiring a large amount of manual correction of the machine-measured eccentricity to ensure that the actual eccentricity is less than the standard value.

[0003] Specifically, assuming that the cable standard requires that the standard value of the eccentricity of the insulation layer is less than and equal to 10%, when the machine is turned on to produce cables, the workers first make a preliminary judgment based on experience whether the actual eccentricity implied by the machine-measured eccentricity is close to or even greater than 10%; when the actual eccentricity implied by the machine-measured eccentricity may be close to or even greater than 10%, the workers need to use manual tools to measure and obtain the measured eccentricity many times, and then adjust the insulation layer extrusion equipment according to the difference between the measured eccentricity and the machine-measured eccentricity to reduce the machine-measured eccentricity, and finally ensure that the measured eccentricity (regarded as the actual eccentricity implied by the machine-measured eccentricity) is not greater than 10% and maintains a safe distance from 10%. The above adjustments often consume about 200 meters of cable, resulting in increased consumables, increased energy consumption, and increased costs. In addition, the above adjustments rely on experience and are prone to errors in subjective judgment by workers, which can cause an entire batch of cables to fail to meet the standards. Summary of the invention

[0004] An illustrative aspect of the present invention provides a method for calibrating insulation eccentricity for a cable that can omit the work of a worker holding a manual tool to measure the eccentricity of the insulation layer.

[0005] According to an illustrative aspect of the present invention, a method for calibrating the eccentricity of an insulation layer for a cable comprises: systematically clustering a data group of machine-measured eccentricity for modeling to obtain a plurality of groups; selecting a group among the plurality of groups whose machine-measured eccentricity for modeling is greater than that of other groups as a target group; for the target group, performing curve estimation with a deviation value for modeling as an independent variable and the actually measured eccentricity for modeling as a dependent variable, thereby selecting a fitting formula with the maximum fitting degree and greater than 0.5, wherein the deviation value for modeling refers to the value of the actually measured eccentricity for modeling minus the machine-measured eccentricity for modeling; obtaining an estimated value with the instant machine-measured eccentricity as the independent variable through the fitting formula; and adjusting the instant machine-measured eccentricity so that the estimated value is less than the standard value. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 is a diagram showing the structure of a conductor and an eccentricity detection position of an insulating layer according to an embodiment of the present invention;

[0007] Figure 2 is a graph showing a first set of measured eccentricities and machine-measured eccentricities according to an embodiment of the present invention;

[0008] Figure 3 is a graph showing a second set of measured eccentricities and machine-measured eccentricities according to an embodiment of the present invention;

[0009] Figure 4 is a graph showing a third set of measured eccentricities and machine-measured eccentricities according to an embodiment of the present invention;

[0010] Figure 5 is a graph showing measured eccentricity for modeling and machine-measured eccentricity for modeling for cluster analysis according to an embodiment of the present invention;

[0011] Figure 6 is a clustering pedigree diagram representing an embodiment of the present invention;

[0012] Figure 7 is a graph showing the results of a curve estimation model for a target group according to an embodiment of the present invention;

[0013] Figure 8 is a graph of selected fitting curves representing a target group according to an embodiment of the present invention;

[0014] Fig. 9 is a diagram showing the relationship between a first critical curve fitted with a modeling deviation value data set as an independent variable and a hypothetical curve assumed with a modeling machine-measured eccentricity data set as an independent variable for a target group according to an embodiment of the present invention;

[0015] Fig.10 is a diagram showing a control area of ​​a target group according to an embodiment of the present invention;

[0016] Fig.11 is a diagram showing the relationship between the control area of ​​the target group and the machine-measured eccentricity data group for modeling of other groups according to an embodiment of the present invention; and

[0017] Fig.12 is a diagram showing coefficient adjustment of a fitting formula of an exponential model of a target group in a control region according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The exemplary embodiments of the present invention will be specifically described below in conjunction with the accompanying drawings. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. Exemplary embodiments are provided in the present invention to illustrate various aspects of the present invention and should not be construed as limiting the scope of the present invention. Below, embodiments according to the present invention will be described with reference to the accompanying drawings.

[0019] [Data collection]

[0020] Figure 1 1 and 2 are diagrams showing the structure of the conductor 1 and eccentricity detection positions a, b, c, d, e, f, g, and h of the insulating layer 3 according to an embodiment of the present invention. Figure 2 is a graph showing a first set of measured eccentricities and machine-measured eccentricities of the insulating layer 3 according to an embodiment of the present invention. Figure 3 2 is a graph showing a second set of measured eccentricities and machine-measured eccentricities of the insulating layer 3 according to an embodiment of the present invention. Figure 4 3 is a graph showing a third set of measured eccentricities and machine-measured eccentricities of the insulating layer 3 according to the embodiment of the present invention.

[0021] like Figure 1 As shown, the cable according to the present invention comprises at least one conductor 1, and the conductor 1 is sequentially covered with a conductor shielding layer 2, an insulating layer 3, and an insulating shielding layer 4 in the circumferential direction and radially outward. In addition, the eccentricity of the insulating layer 3 according to the present invention refers to the percentage of the value of the maximum thickness of the insulating layer 3 minus the thinnest thickness of the insulating layer 3 to the maximum thickness of the insulating layer 3. Specifically, as Figure 1 As shown, on a cross section perpendicular to the axial direction of the conductor 1, the insulating layer 3 is evenly provided with 8 eccentricity detection positions a, b, c, d, e, f, g, h in the circumferential direction, and the 8 insulating layer thicknesses at the 8 eccentricity detection positions a, b, c, d, e, f, g, h are measured by an eccentricity instrument or a manual tool. In each test, the maximum value (maximum thickness) and the minimum value (thinnest thickness) of the 8 insulating layer thicknesses at the 8 eccentricity detection positions a, b, c, d, e, f, g, h on the cross section perpendicular to the axial direction of the conductor 1 are used to calculate the eccentricity of the insulating layer 3.

[0022] To achieve the purpose of the present invention, there is provided Figure 2 , Figure 3 ,as well as Figure 4 The three groups of raw data of measured eccentricity and machine-measured eccentricity are shown in Figure 1. Figure 2 The measured conductor of the first set of data shown is a circular conductor with a size of 3*240. Figure 3 The measured conductor of the second set of data shown is a special-shaped conductor with a size of 3*300, and Figure 4 The measured conductor of the third set of data shown is a circular conductor with a size of 3*300. And, Figure 2 , Figure 3 ,as well as Figure 4 The three sets of data shown are all for 8.7 / 15kV voltage. Figure 2 As shown, the first group of measured eccentricity and machine-measured eccentricity of the insulating layer 3 according to the embodiment of the present invention includes 15 detection data from 1-1 to 1-15; Figure 3 As shown, the second group of measured eccentricity and machine-measured eccentricity of the insulating layer 3 according to the embodiment of the present invention includes 15 detection data from 2-1 to 2-15; Figure 4 As shown in FIG. 1 , the third set of measured eccentricity and machine-measured eccentricity of the insulating layer 3 according to the embodiment of the present invention includes 15 test data from 3-1 to 3-15. Figure 2 , Figure 3 ,as well as Figure 4 In the present invention, the machine-measured thinnest thickness, machine-measured average thickness, and machine-measured maximum thickness are automatically collected by grabbing the eccentricity instrument every five minutes; the actually measured thinnest thickness, actually measured average thickness, and actually measured maximum thickness are measured by workers using hand-held manual tools at certain conductor length intervals; and, whether in the axial direction of conductor 1 or in the cross-section perpendicular to the axial direction of conductor 1, the collection positions of the machine-measured thinnest thickness, machine-measured average thickness, and machine-measured maximum thickness are not necessarily the same as the collection positions of the actually measured thinnest thickness, actually measured average thickness, and actually measured maximum thickness.

[0023] In addition, Figure 2 , Figure 3 ,as well as Figure 4 In the figure, the machine-measured average thickness and the actually measured average thickness are the average values ​​of the eight insulation layer thicknesses at eight eccentricity detection positions a, b, c, d, e, f, g, and h on a cross section perpendicular to the axial direction of the conductor 1 each time.

[0024] [Data pre-processing]

[0025] Figure 5 4 is a graph showing the actually measured eccentricity for modeling and the machine-measured eccentricity for modeling used for cluster analysis according to an embodiment of the present invention.

[0026] During data preprocessing, Figure 2 , Figure 3 ,as well as Figure 4 The data are integrated into a chart and sorted in ascending order based on the machine-measured eccentricity. When the machine-measured eccentricities are the same, the data with the largest measured eccentricity is retained. Finally, the data used for modeling is obtained, such as Figure 5 The 32 machine-measured eccentricity data for modeling and the measured eccentricity data for modeling are shown. For example, when the machine-measured eccentricity is 2.33%, there are 4 data, and the measured eccentricities are 5.59%, 6.47%, 3.82%, and 4.06%, respectively. Finally, the data with the measured eccentricity of 6.47% is retained, and the three data with the measured eccentricity of 5.59%, 3.82%, and 4.06% are deleted. The purpose of this processing method is to ensure that the measured eccentricity for modeling does not exceed the standard value when the machine-measured eccentricity for modeling is the largest.

[0027] At the same time, if Figure 5 As shown, the deviation value between the measured eccentricity for modeling and the machine-measured eccentricity for modeling is calculated, that is, the value of the measured eccentricity for modeling minus the machine-measured eccentricity for modeling is taken as the deviation value.

[0028] [Cluster Analysis]

[0029] Figure 6 is a clustering pedigree diagram according to an embodiment of the present invention.

[0030] like Figure 6 As shown, a systematic cluster analysis was performed with the machine-measured eccentricity data group for modeling as a variable. When the square Euclidean distance was greater than 8 and less than 25, the machine-measured eccentricity data group for modeling was exactly divided into two categories (multiple groups), and the values ​​of the machine-measured eccentricity data group for modeling were split exactly between numbers 20 and 21, so that the machine-measured eccentricity for modeling of one category (target group) including numbers 21 to 32 was greater than the machine-measured eccentricity for modeling of another category (other groups) including numbers 1 to 20.

[0031] like Figure 6 As shown, when the squared Euclidean distance is greater than 6 and less than 8, the model-building machine-measured eccentricity data group is divided into three categories (multiple groups), and the values ​​of the model-building machine-measured eccentricity are split between numbers 8 and 9, and between numbers 20 and 21, respectively, so that the numerical ranges of the model-building machine-measured eccentricity of each group are independent of each other and do not overlap. In particular, the model-building machine-measured eccentricity of a class (target group) including numbers 21 to 32 is greater than the model-building machine-measured eccentricity of other classes (other groups) including numbers 1 to 8 and numbers 9 to 20.

[0032] like Figure 6As shown, when the squared Euclidean distance is greater than 3 and less than 6, the modeling machine-measured eccentricity data group is divided into four categories (multiple groups), and the values ​​of the modeling machine-measured eccentricity are split between numbers 8 and 9, between numbers 20 and 21, and between numbers 24 and 25, respectively, so that the numerical ranges of the machine-measured eccentricity of each group are independent of each other and do not overlap. In particular, the modeling machine-measured eccentricity of a class (target group) including numbers 25 to 32 is greater than the modeling machine-measured eccentricity of other classes (other groups) including numbers 1 to 8, numbers 9 to 20, and numbers 21 to 24.

[0033] Similarly, according to Figure 6 The cluster pedigree diagram shown can divide the modeling machine-measured eccentricity data group into multiple categories. However, the number of categories must meet at least two conditions: first, the modeling machine-measured eccentricity of the target group is greater than that of other groups; second, the sample size of the target group is appropriate.

[0034] As can be seen from the above, in the embodiment of the present invention, when the machine-measured eccentricity data group for modeling is divided into 2 or 3 major categories, the target group includes 12 data, such as numbered 21 to 32. However, when the machine-measured eccentricity data group for modeling is divided into 4 major categories, the target group includes 8 data, such as numbered 25 to 32; when the machine-measured eccentricity data group for modeling is divided into more major categories, the amount of data contained in the target group is continuously reduced, which will reduce the accuracy of the curve estimation to be described later. Therefore, preferably, the machine-measured eccentricity data group for modeling is divided into 2 or 3 major categories.

[0035] In particular, when the machine-measured eccentricity data set for modeling is divided into two major categories, Figure 5 The clustering categories corresponding to the machine-measured eccentricity for modeling according to an embodiment of the present invention are presented, wherein the relevant data of clustering category 2 constitute the target group, and the relevant data of clustering category 1 constitute the other groups.

[0036] [Curve estimation]

[0037] Figure 7 is a graph showing the results of a curve estimation model for a target group according to an embodiment of the present invention. Figure 8 is a fitted curve graph representing a target group according to an embodiment of the present invention. Fig. 9 It is a diagram showing the relationship between a first critical curve 5 fitted with a modeling deviation value data set 8 as an independent variable and a hypothetical curve 10 assumed with a modeling machine-measured eccentricity data set 9 as an independent variable for a target group according to an embodiment of the present invention.

[0038] In curve estimation, for the target group, curve estimation is performed with the deviation value for modeling as the independent variable and the measured eccentricity for modeling as the dependent variable, so as to select the fitting formula with the maximum fitting degree (adjusted R square) greater than 0.5; then, in the eccentricity calibration process to be described later, the estimated value with the instant machine-measured eccentricity as the independent variable is obtained through the fitting formula, and the instant machine-measured eccentricity is adjusted so that the estimated value is less than the standard value.

[0039] By estimating the curve, Figure 7 As shown in the figure, the adjusted R-square of the composite model, growth model, exponential model, and logistic model is the largest and greater than 0.5, and the significance is 0.001, indicating that the statistical significance of the model is very significant. However, since the logistic model is often used for binomial distribution (0-1 variable), the logistic model is not used in the present invention. In addition, Figure 8 As shown, the curves of the above selected composite model, growth model, and exponential model almost overlap into one curve. Therefore, preferably, the fitting formula of the target group according to the embodiment of the present invention is one of the composite model, growth model, and exponential model.

[0040] It should be noted that a core point of the present invention is that when estimating the curve, the deviation value for modeling is used as an independent variable to obtain a fitting formula, and then the machine-measured eccentricity is used as an independent variable to calculate the estimated value through the fitting formula. The reason is that the deviation value formula for modeling is: deviation value for modeling = measured eccentricity for modeling - machine-measured eccentricity for modeling. After the position of the formula is changed, it can be changed to: measured eccentricity for modeling = deviation value for modeling + machine-measured eccentricity for modeling. It can be seen from the formula of the deviation value for modeling after the position is replaced that the measured eccentricity for modeling can be obtained by simply adding the deviation value for modeling and the machine-measured eccentricity for modeling, so that the deviation value for modeling and the machine-measured eccentricity for modeling together constitute the equivalent range of the measured eccentricity for modeling, that is, the deviation value for modeling and the machine-measured eccentricity for modeling each fall within the numerical range of the measured eccentricity for modeling. Therefore, when one of the deviation value for modeling and the machine-measured eccentricity for modeling is omitted and the other is selected as the independent variable for curve estimation, the "one" can absorb the "other", so that the roles of the deviation value for modeling and the machine-measured eccentricity for modeling in mathematical operations can be replaced with each other. Of course, preferably, the fitting formula is obtained with the machine-measured eccentricity for modeling as the independent variable when estimating the curve. However, since it is difficult to find a suitable fitting formula through curve estimation for the machine-measured eccentricity for modeling and the actually measured eccentricity for modeling according to an embodiment of the present invention, and the deviation value for modeling and the actually measured eccentricity for modeling according to an embodiment of the present invention can find a suitable fitting formula through curve estimation, the deviation value for modeling is used as the independent variable to obtain the fitting formula during curve estimation, and then the machine-measured eccentricity is used as the independent variable and the estimated value is calculated through the fitting formula.

[0041] It is necessary to further explain that Figure 5 As shown in the figure, in the target group, the machine-measured eccentricity used for modeling is often greater than the deviation value used for modeling; Figure 8 The curve trend of the curve estimation model (compound model, growth model, exponential model) shown in the figure shows that as the deviation value used for modeling increases, the measured eccentricity used for modeling gradually increases. Fig. 9 As shown, when the actual measured eccentricity for modeling is constant, compared with the hypothetical curve 10 obtained by taking the machine-measured eccentricity data group 9 with a larger value as the independent variable, the first critical curve 5 of the composite model, the growth model, or the exponential model obtained by taking the deviation value data group 8 with a smaller value as the independent variable is located upward, so that the estimated value obtained when the instant machine-measured eccentricity with a larger value is taken as the independent variable in the first critical curve 5 is slightly higher. Therefore, when estimating the curve, the fitting formula is obtained by taking the deviation value for modeling as the independent variable, and then the estimated value calculated by the fitting formula by taking the instant machine-measured eccentricity as the independent variable is slightly higher than the actual eccentricity. Therefore, when the estimated value does not exceed the standard value, it is finally ensured that the actual eccentricity of the insulating layer 3 is not greater than the standard value and maintains a safe distance from the standard value.

[0042] [Eccentricity correction]

[0043] Fig.10 is a diagram showing a control area 12 of a target group according to an embodiment of the present invention. Fig.11 It is a diagram showing the relationship between the control area 12 of the target group and the machine-measured eccentricity data group 13 for modeling of other groups according to an embodiment of the present invention. Fig.12 1 is a diagram showing coefficient adjustment of the fitting formula of the target group in the control region 12 according to an embodiment of the present invention.

[0044] The purpose of eccentricity calibration is to adjust the machine-measured eccentricity so that the estimated value is greater than the measured eccentricity and does not exceed the standard value. Specifically, since the curves of the aforementioned selected composite model, growth model, and exponential model almost overlap into one curve, in order to facilitate the description of the calibration method of the eccentricity of the insulating layer 3, the exponential model is selected as an example below.

[0045] like Fig.10As shown, the first critical curve 5 represents the fitting curve of the target group under the exponential model with the deviation value for modeling as the independent variable, the standard value straight line 6 represents the standard value of 10%, and the maximum instant machine-measured eccentricity straight line 7 passing through the intersection of the first critical curve 5 and the standard value straight line 6 represents the maximum instant machine-measured eccentricity of about 5.5%. Similarly, in production practice, when the standard value is 10%, experience shows that when the instant machine-measured eccentricity is about 5%, the measured eccentricity is very close to the standard value. It can be seen that the composite model, growth model, and exponential model represented by the first critical curve 5 are consistent with production practice.

[0046] In addition, the formula of the exponential model is: Estimated value = A*e B*机测偏心度 , where A and B are coefficients. In the above curve estimation, the coefficient A that satisfies the first critical curve 5 is 4.00 and the coefficient B is 0.164. Among them, the coefficient A mainly affects the up and down movement of the first critical curve 5 on the ordinate, while the coefficient B mainly affects the growth rate of the first critical curve 5. When the coefficient A increases, the first critical curve 5 moves upward as a whole on the ordinate, and vice versa. When the coefficient B increases slightly, the first critical curve 5 accelerates upward as the horizontal coordinate increases; and when the coefficient B decreases slightly, the first critical curve 5 accelerates to flatten as the horizontal coordinate increases. Therefore, the adjustment space of the coefficient A is large, while the adjustment space of the coefficient B is small. In order to meet the condition that the estimated value is greater than the measured eccentricity and keep the growth rate of the second critical curve 11 equivalent to the growth rate of the first critical curve 5, when the coefficient B is kept unchanged at 0.164 and the coefficient A is reduced to 3.49, the second critical curve 11 just passes through a point of the measured eccentricity data group 9 for modeling of the target group. Therefore, the area enclosed by the ordinate, the first critical curve 5 , the maximum instantaneous eccentricity straight line 7 , and the second critical curve 11 constitutes an acceptable control area 12 .

[0047] To verify the acceptability of control area 12, such as Fig.11 As shown, the control area 12 runs through the approximate central area of ​​the machine-measured eccentricity data group 13 for modeling of other groups, and the partial data of the measured eccentricity data group 13 for modeling of other groups that do not fall into the control area 12 are also roughly centrally symmetrically distributed on the upper and lower sides of the control area 12; at the same time, the measured eccentricity data group 13 for modeling of other groups is distributed on the left side of the measured eccentricity data group 9 for modeling of the target group, and distributed in the lower left area of ​​the control area 12, thus far away from the standard value straight line 6. It can be seen that the estimated values ​​in the control area 12 are all greater than the machine-measured eccentricity for modeling of the target group, and can also roughly display the values ​​of the measured eccentricity data group 13 for modeling of other groups when they are less than the standard value and ensure a certain safety distance from the standard value, so the control area 12 is acceptable for eccentricity correction.

[0048] Further, in the control area 12, as Fig.11 As shown, when the fitting curve of the exponential model passes through the intersection of the ordinate and the second critical curve 11 and the intersection of the maximum instant machine-measured eccentricity straight line 7 and the first critical curve 5, the coefficient A is 3.49 and the coefficient B is 0.188; when the fitting curve of the exponential model passes through the intersection of the ordinate and the first critical curve 5 and the intersection of the maximum instant machine-measured eccentricity straight line 7 and the second critical curve 11, the coefficient A is 4.00 and the coefficient B is 0.140. Therefore, the coefficient A is greater than or equal to 3.49 and less than or equal to 4.00, and the coefficient B is greater than or equal to 0.140 and less than or equal to 0.188.

[0049] Therefore, in the eccentricity correction process, preferably, the instant machine-measured eccentricity is adjusted to be less than or equal to 5.5%; further preferably, the instant machine-measured eccentricity is adjusted to be less than the smallest machine-measured eccentricity for modeling in the target group; further preferably, as Figure 5 As shown, since the minimum machine-measured eccentricity for modeling of the target group according to the embodiment of the present invention is 2.92%, the instant machine-measured eccentricity is adjusted to be less than 2.92%.

[0050] It should be noted that, under the composite model, Where A1 and B1 are coefficients. Compared with the coefficients A and B of the exponential model, Figure 8 As shown in the figure, since the fitting curves of the exponential model and the composite model are almost the same, the coefficients of the exponential model and the composite model can be treated equivalently, that is, A = A1; B = LN (B1). At the same time, the coefficients A1 and B1 under the composite model can be adjusted as the values ​​of the coefficients A and B under the exponential model are adjusted.

[0051] It should also be noted that under the growth model, the estimated value = e (A2+B2*机测偏心度) =e (A2) *e (B2*机测偏心度) , where A2 and B2 are coefficients. Compared with the coefficients A and B of the exponential model, Figure 8 As shown in Figure 2, since the fitting curves of the exponential model and the growth model are almost the same, the coefficients of the exponential model and the growth model can be treated as equivalent, that is, A = e (A2) ; B = B2. At the same time, the coefficients A2 and B2 under the growth model can be adjusted as the values ​​of the coefficients A and B under the exponential model are adjusted.

[0052] [Technical Effect]

[0053] Here, the following [1] to [7] will briefly summarize and list the features and technical effects of the embodiments of the method for calibrating the eccentricity of the insulation layer for cables according to the present invention described above.

[0054] [1] A method for calibrating the eccentricity of the insulation layer of a cable, comprising: systematically clustering a data group of machine-measured eccentricity for modeling to obtain multiple groups; selecting a group among the multiple groups whose machine-measured eccentricity for modeling is greater than that of other groups as a target group; for the target group, using a deviation value for modeling as an independent variable and the measured eccentricity for modeling as a dependent variable to perform curve estimation, thereby selecting a fitting formula with the maximum fitting degree and greater than 0.5, wherein the deviation value for modeling refers to the value of the measured eccentricity for modeling minus the machine-measured eccentricity for modeling; obtaining an estimated value using the instant machine-measured eccentricity as an independent variable through the fitting formula; and adjusting the instant machine-measured eccentricity so that the estimated value is less than the standard value.

[0055] According to the cable insulation layer eccentricity calibration method [1], the machine-measured eccentricity data group for modeling is systematically clustered to obtain multiple groups, and a group whose machine-measured eccentricity for modeling is greater than that of other groups is selected as the target group. Then, mathematical modeling is performed on the target group and the actual eccentricity is estimated, which can ensure that the actual eccentricity is less than the standard value while the actual eccentricity is close to the standard value. In addition, for the target group, the deviation value for modeling is used as the independent variable and the measured eccentricity for modeling is used as the dependent variable to estimate the curve, so as to select the fitting formula with the maximum fitting degree greater than 0.5, and obtain the estimated value with the machine-measured eccentricity as the independent variable through the fitting formula, thereby avoiding the dilemma of difficulty in fitting a suitable curve with the machine-measured eccentricity as the independent variable; on the other hand, Figure 5 As shown in Figure 2, in the target group, the machine-measured eccentricity for modeling is often greater than the deviation value for modeling. According to the curve trend of the selected models (compound model, growth model, exponential model), as the deviation value for modeling increases, the measured eccentricity for modeling gradually increases. Therefore, Fig. 9 As shown, when the actual measured eccentricity for modeling is constant, compared with the hypothetical curve 10 obtained by taking the machine-measured eccentricity data group 9 with a larger value as the independent variable, the first critical curve 5 of the composite model, the growth model, or the exponential model obtained by taking the deviation value data group 8 with a smaller value as the independent variable is located upward, so that the estimated value obtained when the instant machine-measured eccentricity with a larger value is taken as the independent variable in the first critical curve 5 is slightly higher. Therefore, when estimating the curve, the fitting formula is obtained by taking the deviation value for modeling as the independent variable, and then the estimated value calculated by the fitting formula by taking the instant machine-measured eccentricity as the independent variable is slightly higher than the actual eccentricity. Therefore, when the estimated value does not exceed the standard value, it is finally ensured that the actual eccentricity of the insulating layer 3 is not greater than the standard value and maintains a safe distance from the standard value.

[0056] [2] The method for calibrating the eccentricity of the insulation layer of a cable according to [1] also includes: adjusting the real-time measured eccentricity so that the estimated value falls within the control area.

[0057] According to the cable insulation layer eccentricity calibration method [2], the machine-measured eccentricity is adjusted so that the estimated value falls within the control area, which can ensure that the actual eccentricity of the insulation layer 3 is less than the control area 12, thereby keeping the actual eccentricity of the insulation layer 3 at a safe distance from the standard value straight line 6. Fig.10 As shown, when the maximum real-time machine-measured eccentricity straight line 7 is 5.5%, the estimated value of the intersection with the first critical curve 5 is 9.86%, and the estimated value of the intersection with the second critical curve 11 is 8.60%, and the difference between the two is 1.26%. Therefore, when the real-time machine-measured eccentricity is adjusted so that the estimated value falls into the control area 12, the safety distance between the actual eccentricity of the insulating layer 3 and the standard value is at least 1.26% of eccentricity.

[0058] [3] A method for calibrating the eccentricity of insulation layers for cables according to [1] or [2], wherein the fitting formula is one of a composite model, a growth model, and an exponential model.

[0059] According to the cable insulation eccentricity calibration method [3], the fitting formula is selected as one of the composite model, growth model, and exponential model, so that when the standard value is 10%, the maximum instantaneous eccentricity obtained by any of the composite model, growth model, and exponential model is about 5.5%, which is equivalent to the empirical value of 5% in production practice. Therefore, the aforementioned composite model, growth model, and exponential model are all in line with production practice. In addition, if Figure 5 As shown in Figure 2, in the target group, the machine-measured eccentricity for modeling is often greater than the deviation value for modeling, while the curve trend of any one of the composite model, growth model, and exponential model shows that as the deviation value for modeling increases, the measured eccentricity for modeling gradually increases. Therefore, Fig. 9 As shown, when the actual measured eccentricity for modeling is constant, compared with the hypothetical curve 10 obtained by taking the machine-measured eccentricity data group 9 with a larger value as the independent variable, the first critical curve 5 of the composite model, the growth model, or the exponential model obtained by taking the deviation value data group 8 with a smaller value as the independent variable is located upward, so that the estimated value obtained when the instant machine-measured eccentricity with a larger value is used as the independent variable in the first critical curve 5 is slightly higher. Therefore, when estimating the curve, the fitting formula is obtained by taking the deviation value for modeling as the independent variable, and then the estimated value calculated by the fitting formula by taking the instant machine-measured eccentricity as the independent variable is slightly higher than the actual estimated value. Therefore, when the estimated value does not exceed the standard value, it is finally ensured that the actual eccentricity of the insulating layer 3 is not greater than the standard value and maintains a safe distance from the standard value.

[0060] [4] The cable insulation eccentricity calibration method according to [3], wherein the standard value is 10%, and wherein when the fitting formula is: estimated value = A*e B*机测偏心度When , coefficient A is greater than or equal to 3.49 and less than or equal to 4.00, and coefficient B is greater than or equal to 0.140 and less than or equal to 0.188.

[0061] According to the cable insulation layer eccentricity calibration method [4], on the one hand, the numerical adjustment range of coefficient A and coefficient B facilitates workers to flexibly adjust the fitting curve according to the cable batch or eccentricity instrument in production practice to find a suitable estimated value. On the other hand, the adjustment of coefficient A and coefficient B is carried out within the control area 12, so it can ensure that the actual eccentricity of the insulation layer 3 is less than the control area 12, so that the actual eccentricity of the insulation layer 3 maintains a safe distance from the standard value. Fig.10 As shown, when the maximum instant machine-measured eccentricity straight line 7 is 5.5%, the estimated value of the intersection with the first critical curve 5 is 9.86%, and the estimated value of the intersection with the second critical curve 11 is 8.60%. The difference between the two is 1.26%. Therefore, when the instant machine-measured eccentricity is adjusted so that the estimated value falls into the control area 12, the actual eccentricity of the insulating layer 3 is at least 1.26% away from the standard value.

[0062] [5] A method for calibrating the eccentricity of the insulation layer of a cable according to any one of [1] to [4], wherein the instant machine-measured eccentricity is adjusted to be less than or equal to 5.5%.

[0063] According to the cable insulation layer eccentricity calibration method [5], the machine-measured eccentricity is adjusted to be less than or equal to 5.5%, which can ensure that the actual eccentricity of the insulation layer 3 is less than the control area 12, so that the actual eccentricity of the insulation layer 3 maintains a safe distance from the standard value. Fig.10 As shown, when the maximum instant machine-measured eccentricity straight line 7 is 5.5%, the estimated value of the intersection with the first critical curve 5 is 9.86%, and the estimated value of the intersection with the second critical curve 11 is 8.60%. The difference between the two is 1.26%. Therefore, when the instant machine-measured eccentricity is adjusted to be less than or equal to 5.5% so that the estimated value falls into the control area 12, the actual eccentricity of the insulating layer 3 is at least 1.26% away from the standard value.

[0064] [6] A method for calibrating the eccentricity of the insulation layer of a cable according to any one of [1] to [4], wherein the instant machine-measured eccentricity is adjusted to be smaller than the smallest machine-measured eccentricity for modeling in the target group.

[0065] According to the cable insulation layer eccentricity calibration method [6], when the adjustment is that the machine-measured eccentricity is smaller than the smallest machine-measured eccentricity for modeling in the target group, such as Figure 5As shown, since the minimum eccentricity of the target group according to the embodiment of the present invention is 2.92%, which is a safe distance from the standard value of 10% by eccentricity 7.08%, it can ensure that the actual eccentricity of the insulating layer 3 maintains an absolute safe distance from the standard value.

[0066] [7] The method for calibrating the eccentricity of the insulation layer of a cable according to [6], wherein the instant machine-measured eccentricity is adjusted to be less than 2.9%.

[0067] According to the cable insulation layer eccentricity calibration method [7], when the machine-measured eccentricity is adjusted to be less than 2.9%, the safety distance from the standard value of 10% is 7.08%, thereby ensuring that the actual eccentricity of the insulation layer 3 maintains an absolute safety distance from the standard value.

[0068] In summary, according to the configuration of the above technical solution, when adjusting the eccentricity of the insulation layer, the work of workers measuring the eccentricity of the insulation layer with hand tools can be omitted.

[0069] Although the subject matter of the present invention has been described with reference to exemplary embodiments, the scope of the subject matter of the present invention is not limited to the above-described exemplary embodiments, and those skilled in the art will appreciate that various improvements and modifications may be made therein without departing from the scope of the subject matter of the present invention as defined in the appended claims.

Claims

1. A method for calibrating the eccentricity of an insulation layer for a cable, comprising: A plurality of groups are obtained by systematically clustering the machine-measured eccentricity data set for modeling; Selecting a group among the multiple groups whose machine-measured eccentricity for modeling is greater than that of other groups as a target group; For the target group, when the machine-measured eccentricity for modeling is greater than the deviation value for modeling and when the measured eccentricity for modeling gradually increases with the increase of the deviation value for modeling, a curve is estimated with the deviation value for modeling as the independent variable and the measured eccentricity for modeling as the dependent variable, so as to select a fitting formula with the maximum fitting degree and greater than 0.5, wherein the deviation value for modeling refers to the value of the measured eccentricity for modeling minus the machine-measured eccentricity for modeling; Obtaining an estimated value with the instantaneous measured eccentricity as an independent variable through the fitting formula; and The instant machine-measured eccentricity is adjusted so that the estimated value is smaller than the standard value, Wherein, the fitting formula is one of a composite model, a growth model, and an exponential model; Among them, the standard value is 10%, and Wherein, when the fitting formula is: estimated value = a*e b*机测偏心度 When , the coefficient a is greater than or equal to 3.49 and less than or equal to 4.00, and the coefficient b is greater than or equal to 0.140 and less than or equal to 0.

188.

2. The method for calibrating the eccentricity of the insulation layer of a cable according to claim 1, further comprising: The instantaneous machine-measured eccentricity is adjusted so that the estimated value falls within a control region.

3. The method for calibrating the eccentricity of the insulation layer of a cable according to any one of claims 1 to 2, in, The instant machine-measured eccentricity is adjusted to be less than or equal to 5.5%.

4. The method for calibrating the eccentricity of the insulation layer of a cable according to any one of claims 1 to 2, in, The instant machine-measured eccentricity is adjusted to be smaller than the smallest machine-measured eccentricity for modeling in the target group.

5. The method for calibrating the eccentricity of the insulation layer of a cable according to claim 4, wherein: The instant machine-measured eccentricity is adjusted to be less than 2.92%.

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