CN111638428B - GIS-based ultrahigh frequency partial discharge data processing method and system - Google Patents
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Abstract
The invention provides a processing method and a system based on GIS ultrahigh frequency partial discharge data, wherein the processing method comprises data enhancement and cleaning, and the data enhancement is realized by adopting fault feature extraction, fault feature data correlation analysis, data noise addition and data weighting; and corresponding cleaning factors are obtained according to different labels to realize data cleaning, so that the effectiveness of enhanced data is ensured, and the type diagnosis of GIS ultrahigh frequency partial discharge is realized. The method for processing the data, enhancing the data by increasing noise and weighting processing and cleaning the enhanced data by adopting the cleaning characteristic factor is realized by the correlation coefficient analysis method, and the accuracy and the generalization capability can be improved when the GIS partial discharge data sample is not enough.
Description
Technical Field
The invention relates to the technical field of partial discharge detection, in particular to a GIS-based ultrahigh frequency partial discharge data processing method and system.
Background
With the proposal of 'three-type two-network', the intelligent power grid is rapidly developed, and the intelligent detection terminal is used as the basis for realizing intelligent operation and maintenance and is also the guarantee for ensuring the reliable and safe operation of the power equipment. The GIS is composed of a breaker, a disconnecting switch, a grounding switch, a mutual inductor, a lightning arrester, a bus, a connecting piece, an outgoing line terminal and the like, all of the equipment or components are enclosed in a metal grounded shell, and SF6 insulating gas with certain pressure is filled in the metal grounded shell. The GIS has the advantages of compact structure, small occupied area, high reliability, flexible configuration, convenience in installation, high safety and high environment adaptability.
The fully sealed structure of the GIS also causes that the GIS partial discharge is difficult to position and maintain, the maintenance work is complicated, the average power failure maintenance time after an accident is longer than that of conventional equipment, the power failure range is large, and non-fault elements are often involved. Therefore, power companies often perform partial discharge detection work of the GIS, and in order to accurately identify fault types through partial discharge detection of the GIS, GIS ultrahigh frequency partial discharge data need to be researched. However, the existing GIS ultrahigh frequency partial discharge detection has few data samples, and sufficient data sets lack for characteristic research of GIS, so that GIS fault types cannot be accurately identified and arrangement of maintenance work is caused.
The existing data enhancement method for GIS ultrahigh frequency data is rare, common transformation and shearing in different modes and the like often cause damage to original characteristic data, so that the identification accuracy of fault types is low, and the calculation loss of equipment operation and maintenance strategies is caused.
Disclosure of Invention
The invention provides a GIS-based ultrahigh frequency partial discharge data processing method and system, which are used for solving the problems that the existing GIS ultrahigh frequency partial discharge detection has few data samples and the data enhancement method is unreasonable.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a processing method based on GIS ultrahigh frequency partial discharge data, which comprises the following steps:
acquiring GIS ultrahigh frequency partial discharge data under different labels;
extracting characteristic values of the partial discharge data to form two-dimensional time sequence data A;
performing correlation analysis on the two-dimensional time sequence data A and the corresponding label to obtain a first correlation coefficient;
dividing the two-dimensional time series data A into A1 and A2 according to a correlation threshold, wherein A2 is the two-dimensional time series data with a first correlation coefficient smaller than the correlation threshold;
adding noise processing to A2 to generate two-dimensional time series data B;
performing correlation analysis on the two-dimensional time sequence data B and the corresponding label to obtain a second correlation coefficient;
dividing the two-dimensional time series data B into B1 and B2 according to a correlation threshold, wherein B2 is the two-dimensional time series data of which the second correlation number is less than the correlation threshold;
b2 is subjected to weighted fusion, is spliced with B1 and is synthesized with A1 to obtain two-dimensional time sequence data C;
and performing correlation analysis on the two-dimensional time sequence data C and the corresponding label to obtain a third correlation coefficient, obtaining a cleaning characteristic factor of the corresponding label, and cleaning the C based on the cleaning characteristic factor to obtain a data sample D.
Further, the tag includes air gaps, aerosols, corona, edgelets, and particles.
Further, the characteristic value is obtained by a region mean decomposition method.
Further, the specific process of adding noise processing to a2 is as follows:
a gaussian random variable is added to the two-dimensional time series data a2 and data correction is performed.
Further, the gaussian random variable rv is calculated as follows:
rv＝sqrt(-2.0*log(U 1 ))*cos(2*π*U 2 )
wherein the random variables U1 and U2 are represented as:
in the formula, a random variable U 1 、U 2 Independent of each other and all obey uniform distribution among (0, 1); random variable Z 0 ，Z 1 Obey a standard gaussian distribution and satisfy a normal distribution with a mean of 0 and a variance of 1.
Further, the specific process of performing data correction is as follows:
defining sample data A2 to be enhanced as dstImage [ x ] [ y ], defining sample data B after enhancement as EnhDstImage [ x ] [ y ], defining the value after adding a Gaussian random variable rv as val, and calculating val according to the following formula:
val＝dstImage[x][y]+rv
correcting the range of val:
if val <0, then val is 0;
if val >255, then val-255;
redefining the enhanced sample data B:
EnhDstImage[x][y]＝val
and after all the data in the dstImage array are processed, the enhanced sample data EnhDstimage is formed.
Further, the performing weighted fusion on B2 specifically includes:
B2[x][y]＝a1*W1+a2*W2+…+an*Wn
wherein, W1+ W2+ … + Wn is 1; a1, a2, … an are the x-th row feature data.
Further, the specific process of acquiring the washing characteristic factor of the corresponding label is as follows:
sequencing the obtained third phase relation number according to the numerical value;
segmenting the third phase relation number according to the sequencing result, and respectively calculating the average value of each segment;
and averaging the average values of the segments to obtain the cleaning characteristic factors.
Further, the specific process of cleaning C based on the cleaning characteristic factor is as follows:
comparing the cleaning characteristic factor with a third phase relation number matrix;
if the cleaning characteristic factor is larger than the corresponding third correlation coefficient, discarding the data corresponding to the current third phase relation number, and averaging the previous and subsequent data values corresponding to the data to replace the currently discarded data;
otherwise, keeping the data corresponding to the current third phase relation number.
The invention provides a processing system based on GIS ultrahigh frequency partial discharge data, which comprises:
the data acquisition unit is used for acquiring GIS ultrahigh frequency partial discharge data under different labels;
the first data processing unit is used for extracting a characteristic value of the partial discharge data to form two-dimensional time sequence data A;
the second data processing unit is used for carrying out correlation analysis on the two-dimensional time sequence data A and the corresponding label to obtain a first correlation coefficient;
a first comparing unit that divides the two-dimensional time series data a into a1 and a2 according to a correlation threshold, wherein a2 is the two-dimensional time series data whose first correlation coefficient is smaller than the correlation threshold;
a third data processing unit which adds noise processing to A2 and generates two-dimensional time series data B;
the fourth data processing unit is used for carrying out correlation analysis on the two-dimensional time sequence data B and the corresponding label to obtain a second correlation coefficient;
a second comparing unit which divides the two-dimensional time series data B into B1 and B2 according to a correlation threshold, wherein B2 is the two-dimensional time series data of which the second correlation number is less than the correlation threshold;
the fifth data processing unit is used for performing weighted fusion on the B2, splicing the B2 with the B1, and then synthesizing the B1 with the A1 to obtain two-dimensional time sequence data C;
and the sixth data processing unit is used for carrying out correlation analysis on the two-dimensional time sequence data C and the corresponding label to obtain a third correlation coefficient, acquiring a cleaning characteristic factor of the corresponding label, and cleaning the C based on the cleaning characteristic factor to obtain a data sample D.
The processing system according to the second aspect of the present invention is capable of implementing the methods according to the first aspect and the respective implementation manners of the first aspect, and achieves the same effects.
The effect provided in the summary of the invention is only the effect of the embodiment, not all the effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
the method for processing the data, enhancing the data by increasing noise and weighting processing and cleaning the enhanced data by adopting the cleaning characteristic factor is realized by the correlation coefficient analysis method, and the accuracy and the generalization capability can be improved when the GIS partial discharge data sample is not enough.
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In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of the process of the present invention;
fig. 2 is a schematic diagram of the system of the present invention.
Detailed Description
In order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
The processing method of the GIS ultrahigh frequency partial discharge data comprises data enhancement and cleaning, wherein the data enhancement is realized by adopting fault feature extraction, fault feature data correlation analysis, data noise addition and data weighting; and corresponding cleaning factors are obtained according to different labels to realize data cleaning, so that the effectiveness of enhanced data is ensured, and the type diagnosis of GIS ultrahigh frequency partial discharge is realized.
As shown in fig. 1, the method for processing the ultrahigh frequency partial discharge data based on the GIS of the present invention specifically includes the following steps:
s1, acquiring GIS ultrahigh frequency partial discharge data under different labels;
s2, extracting characteristic values of the partial discharge data to form two-dimensional time sequence data A;
s3, performing correlation analysis on the two-dimensional time sequence data A and the corresponding label to obtain a first correlation coefficient;
s4, dividing the two-dimensional time series data A into A1 and A2 according to a correlation threshold, wherein A2 is the two-dimensional time series data of which the first correlation coefficient is smaller than the correlation threshold;
s5, adding noise processing to the A2 to generate two-dimensional time series data B;
s6, performing correlation analysis on the two-dimensional time sequence data B and the corresponding label to obtain a second correlation coefficient;
s7, dividing the two-dimensional time series data B into B1 and B2 according to a correlation threshold, wherein B2 is the two-dimensional time series data of which the second correlation number is less than the correlation threshold;
s8, performing weighted fusion on the B2, splicing the B2 with the B1, and synthesizing the B2 with the A1 to obtain two-dimensional time sequence data C;
s9, performing correlation analysis on the two-dimensional time sequence data C and the corresponding label to obtain a third correlation coefficient, obtaining a cleaning characteristic factor of the corresponding label, and cleaning the C based on the cleaning characteristic factor to obtain a data sample D.
The labels in step S1 include air gaps, levitation, corona, edgelets and particles. The following examples are illustrated with air gap labels as examples.
The forming of the two-dimensional time sequence array a in the step S2 specifically includes:
s21, the ultrahigh frequency detection collects data for 1 second once, each period is 20ms, 20ms is divided into 60 time slices, and two-dimensional time sequence data of 50 x 60 is obtained;
s22, obtaining the characteristic value of the two-dimensional time sequence data by using a region mean decomposition method, and forming new two-dimensional time sequence data A;
in step S22, the instantaneous value of the envelope function in the envelope map is obtained by the region mean decomposition method, and the envelope function is as follows:
in the formula, n i ，n i+1 Respectively adjacent extreme points, a i Is the average of the neighboring extreme points, i.e. the eigenvalue, a (t), i.e. the instantaneous value of the envelope function. And constructing two-dimensional time sequence data A according to the obtained characteristic values of the group of data.
Setting a correlation coefficient threshold value in step S4, where the threshold value is set to 0.85, separating the two-dimensional time series data a according to the set correlation coefficient threshold value to obtain a1 and a2, specifically:
a1 is two-dimensional time series data having a first correlation coefficient of 0.85 or more;
a2 is two-dimensional time series data with a first correlation coefficient smaller than 0.85.
The specific process of adding noise processing to a2 in step S5 is:
a gaussian random variable is added to the two-dimensional time series data a2 and data correction is performed.
The gaussian random variable rv is calculated as follows:
rv＝sqrt(-2.0*log(U 1 ))*cos(2*π*U 2 )
wherein the random variables U1 and U2 are represented as:
in the formula, a random variable U 1 、U 2 Independent of each other and all obey uniform distribution among (0, 1); random variable Z 0 ，Z 1 Obey a standard gaussian distribution and satisfy a normal distribution with a mean of 0 and a variance of 1.
The specific process of data correction is as follows:
defining sample data A2 to be enhanced as dstImage [ x ] [ y ], defining sample data B after enhancement as EnhDstImage [ x ] [ y ], defining the value after adding Gaussian random variable rv as val, and calculating val according to the following formula:
val＝dstImage[x][y]+rv
correcting the range of val:
if val <0, then val is 0;
if val is greater than 255, then val is 255;
redefining the enhanced sample data B:
EnhDstImage[x][y]＝val
and after all the data in the dstImage array are processed, the enhanced sample data EnhDstimage is formed.
In step S7, setting a correlation coefficient threshold value to 0.8 according to the air gap tag, comparing the correlation coefficient threshold value with the second correlation coefficient, and recording the characteristic data of the correlation coefficient greater than or equal to 0.8 as B1; the data characterizing a correlation coefficient of less than 0.8 is denoted as B2.
In step S8, the weighted fusion of B2 specifically includes:
B2[x][y]＝a1*W1+a2*W2+…+an*Wn
wherein, W1+ W2+ … + Wn is 1; a1, a2, … an are the x-th row feature data.
In step S9, the specific process of obtaining the washing feature factor of the corresponding label is as follows:
sequencing the obtained third phase relation number according to the numerical value; segmenting the third phase relation number according to the sequencing result, and respectively calculating the average value of each segment; and averaging the average values of the segments to obtain the cleaning characteristic factors. In this embodiment, 20% is taken as a segment, that is, the average value of 20% of the correlation coefficients is taken, the average values of 20% are sequentially taken backwards until the average value of the last 20% of the correlation coefficients is reached, and then the average value is removed according to the five values, so as to obtain the cleaning characteristic factor a.
Comparing the cleaning characteristic factor a with a correlation coefficient matrix, and if the cleaning characteristic factor is less than or equal to a corresponding third correlation coefficient, retaining data corresponding to the coefficient; and if the cleaning characteristic factor is larger than the corresponding third correlation coefficient, discarding the data, averaging the values before and after the cleaning characteristic factor is corresponding to the data, and replacing the discarded value.
For example: suppose that the data enhancement sample is Enhance [ x, y ], the Correlation coefficient matrix is Correlation [ m, n ], and the cleaning characteristic factor is a.
If a is less than or equal to Correlation [3,2], reserving the Enhance [3,2 ];
if a > correction [5,6], then Enhance [5,6] is discarded and (Enhance [5,5] + Enhance [5,7])/2 is filled in the position;
and if the continuous values are all smaller than a, taking the average value of the row for filling. And finishing the cleaning of the enhanced data, and further obtaining a final GIS air gap partial discharge data enhanced sample D for training a deep learning network model.
And updating the air gap tag, so that the data cleaning factor is dynamically adjusted, the data cleaning effect and quality are improved, and the accuracy and integrity of data cleaning are ensured.
By using the method, correlation analysis is carried out on different partial discharge types and corresponding labels. The processed samples are data samples processed by the method, and the obtained correlation coefficient average values are shown in the following table.
| Air gap | Suspended in water | Corona discharge | Edge surface | Granules | |
| Raw sample | 0.88 | 0.89 | 0.82 | 0.8 | 0.81 |
| Processing a sample | 0.95 | 0.97 | 0.91 | 0.87 | 0.89 |
Therefore, compared with the method of directly using the sample as the training data, the method can generate the sample with more characteristic parameters with less calculation amount, can solve the problem of insufficient GIS partial discharge data set, increases the number of samples, avoids over-fitting, and improves the identification accuracy of GIS ultrahigh frequency partial discharge fault types.
As shown in fig. 2, the processing system based on GIS ultrahigh frequency partial discharge data of the present invention includes a data acquisition unit, a first data processing unit, a second data processing unit, a first comparison unit, a third data processing unit, a fourth data processing unit, a second comparison unit, a fifth data processing unit, and a sixth data processing unit, which are connected in sequence.
The data acquisition unit is used for acquiring GIS ultrahigh frequency partial discharge data under different labels; the first data processing unit is used for extracting characteristic values of the partial discharge data to form two-dimensional time sequence data A; the second data processing unit performs correlation analysis on the two-dimensional time sequence data A and the corresponding label to obtain a first correlation coefficient; the first comparing unit divides the two-dimensional time series data A into A1 and A2 according to a correlation threshold, wherein A2 is the two-dimensional time series data of which the first correlation coefficient is smaller than the correlation threshold; the third data processing unit adds noise processing to A2 to generate two-dimensional time series data B; the fourth data processing unit performs correlation analysis on the two-dimensional time sequence data B and the corresponding label to obtain a second correlation coefficient; the second comparison unit divides the two-dimensional time series data B into B1 and B2 according to a correlation threshold, wherein B2 is the two-dimensional time series data of which the second correlation number is smaller than the correlation threshold; the fifth data processing unit performs weighted fusion on B2, is spliced with B1 and then is synthesized with A1 to obtain two-dimensional time sequence data C; and the sixth data processing unit performs correlation analysis on the two-dimensional time sequence data C and the corresponding label to obtain a third correlation coefficient, obtains a cleaning characteristic factor of the corresponding label, and cleans the data C based on the cleaning characteristic factor to obtain a data sample D.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (6)
1. A GIS-based ultrahigh frequency partial discharge data processing method is characterized by comprising the following steps:
acquiring GIS ultrahigh frequency partial discharge data under different labels, wherein the labels comprise air gaps, suspension, corona, surfaces and particles;
extracting characteristic values of the partial discharge data to form two-dimensional time sequence data A;
performing correlation analysis on the two-dimensional time sequence data A and the corresponding label to obtain a first correlation coefficient;
dividing the two-dimensional time series data A into A1 and A2 according to a correlation threshold, wherein A2 is the two-dimensional time series data with a first correlation coefficient smaller than the correlation threshold;
adding noise processing to A2 to generate two-dimensional time series data B;
performing correlation analysis on the two-dimensional time sequence data B and the corresponding label to obtain a second correlation coefficient;
dividing the two-dimensional time series data B into B1 and B2 according to a correlation threshold, wherein B2 is the two-dimensional time series data of which the second correlation number is less than the correlation threshold;
b2 is subjected to weighted fusion, is spliced with B1 and is synthesized with A1 to obtain two-dimensional time sequence data C;
performing correlation analysis on the two-dimensional time sequence data C and the corresponding label to obtain a third correlation coefficient, obtaining a cleaning characteristic factor of the corresponding label, and cleaning the C based on the cleaning characteristic factor to obtain a data sample D;
the specific process of extracting the characteristic value of the partial discharge data to form the two-dimensional time sequence data A is as follows:
s21, collecting data of 1 second at a time by ultrahigh frequency detection, dividing 20ms into 60 time slices in each period of 20ms, and obtaining two-dimensional time sequence data of 50 x 60;
s22, obtaining the characteristic value of the two-dimensional time sequence data by using a region mean decomposition method, and forming new two-dimensional time sequence data A;
in step S22, the region mean decomposition method obtains an instantaneous value of the envelope function in the envelope map, where the envelope function is as follows:
in the formula,
are respectively the adjacent extreme points, and are respectively the adjacent extreme points,
the average value of the adjacent extreme points, i.e. the characteristic value,
i.e. the instantaneous value of the envelope function; constructing two-dimensional time sequence data A according to the characteristic values of the group of data;
adding noise to the A2, specifically, adding a Gaussian random variable in the two-dimensional time sequence data A2 and performing data correction;
the specific process of acquiring the cleaning characteristic factor of the corresponding label comprises the following steps:
sequencing the obtained third phase relation number according to the numerical value; segmenting the third phase relation number according to the sequencing result, and respectively calculating the average value of each segment; and averaging the average values of the segments to obtain the cleaning characteristic factors.
2. The method for processing GIS-based ultrahigh frequency partial discharge data according to claim 1, wherein the Gaussian random variable rv is calculated as follows:
wherein the random variables U1 and U2 are represented as:
in the formula, a random variable U 1 、U 2 Independent of each other and all obey uniform distribution among (0, 1); random variable Z 0 ，Z 1 Obey a standard gaussian distribution and satisfy a normal distribution with a mean of 0 and a variance of 1.
3. The method for processing the GIS-based ultrahigh frequency partial discharge data according to claim 2, wherein the specific process of data correction is as follows:
defining sample data A2 to be enhanced as dstImage [ x ] [ y ], defining sample data B after enhancement as EnhDstImage [ x ] [ y ], defining the value after adding a Gaussian random variable rv as val, and calculating val according to the following formula:
correcting the range of val:
redefining the enhanced sample data B:
and after all the data in the dstImage array are processed, the enhanced sample data EnhDstimage is formed.
4. The method for processing the GIS ultrahigh frequency partial discharge data according to claim 1, wherein the weighted fusion of B2 is specifically as follows:
wherein,
is the x-th row characteristic data.
5. The GIS-based ultrahigh frequency partial discharge data processing method according to claim 4, wherein the specific process of cleaning C based on the cleaning characteristic factors is as follows:
comparing the cleaning characteristic factor with a third phase relation number matrix;
if the cleaning characteristic factor is larger than the corresponding third correlation coefficient, discarding data corresponding to the current third phase relation number, averaging previous and next data values corresponding to the discarded data, and replacing the currently discarded data;
otherwise, retaining the data corresponding to the current third phase relation number.
6. A GIS ultrahigh frequency partial discharge data-based processing system is characterized by comprising:
the system comprises a data acquisition unit, a data acquisition unit and a data processing unit, wherein the data acquisition unit is used for acquiring GIS ultrahigh frequency partial discharge data under different labels, and the labels comprise air gaps, suspension, corona, surfaces and particles;
the first data processing unit is used for extracting a characteristic value of the partial discharge data to form two-dimensional time sequence data A;
the second data processing unit is used for carrying out correlation analysis on the two-dimensional time sequence data A and the corresponding label to obtain a first correlation coefficient;
a first comparing unit that divides the two-dimensional time series data a into a1 and a2 according to a correlation threshold, wherein a2 is the two-dimensional time series data whose first correlation coefficient is smaller than the correlation threshold;
a third data processing unit which adds noise processing to A2 and generates two-dimensional time series data B;
the fourth data processing unit is used for carrying out correlation analysis on the two-dimensional time sequence data B and the corresponding label to obtain a second correlation coefficient;
a second comparing unit which divides the two-dimensional time series data B into B1 and B2 according to a correlation threshold, wherein B2 is the two-dimensional time series data of which the second correlation number is less than the correlation threshold;
the fifth data processing unit is used for performing weighted fusion on the B2, splicing the B2 with the B1, and then synthesizing the B1 with the A1 to obtain two-dimensional time sequence data C;
the sixth data processing unit is used for carrying out correlation analysis on the two-dimensional time sequence data C and the corresponding label to obtain a third correlation coefficient, obtaining a cleaning characteristic factor of the corresponding label, and cleaning the C based on the cleaning characteristic factor to obtain a data sample D;
the specific process of extracting the characteristic value of the partial discharge data to form the two-dimensional time sequence data A is as follows:
s21, the ultrahigh frequency detection collects data for 1 second at a time, each period is 20ms, 20ms is divided into 60 time slices, and the data are obtained
Two-dimensional time series data of (a);
s22, obtaining the characteristic value of the two-dimensional time sequence data by using a region mean decomposition method, and forming new two-dimensional time sequence data A;
in step S22, the region mean decomposition method obtains an instantaneous value of the envelope function in the envelope map, where the envelope function is as follows:
in the formula,
are respectively the adjacent extreme points, and are respectively the adjacent extreme points,
the average value of the adjacent extreme points, i.e. the feature value,
i.e. the instantaneous value of the envelope function; constructing two-dimensional time sequence data A according to the characteristic values of the group of data;
adding noise to the A2, specifically, adding a Gaussian random variable in the two-dimensional time sequence data A2 and performing data correction;
the specific process of acquiring the cleaning characteristic factor of the corresponding label comprises the following steps:
sequencing the obtained third phase relation number according to the numerical value; segmenting the third phase relation number according to the sequencing result, and respectively calculating the average value of each segment; and averaging the average values of the segments to obtain the cleaning characteristic factors.
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