CN104715136A - Method for comprehensively evaluating level of spinning process - Google Patents

Abstract

The invention discloses a method for comprehensively evaluating the level of the spinning process. The method includes the steps that first, the indexes and parameters mainly used for expressing the properties of spinning raw materials are detected by the aid of a professional equipment instrument, various principal component numerical values used for expressing different properties of the spinning raw materials are obtained through the mathematical statistical analysis means, and the comprehensive evaluation value used for expressing the overall property of the spinning raw materials is further obtained; the comprehensive evaluation value of yarn quality is obtained in the same way, the comprehensive evaluation value of the yarn quality and the comprehensive evaluation value of the spinning raw materials are subtracted after being standardized, and accordingly the standardized comprehensive evaluation value of the level of the spinning process can be obtained. According to the method, the spinning level evaluation criterion is objectified, and the evaluation process and the method are simplified.

Description

A kind of method of comprehensive evaluation spinning process level

Technical field

The invention belongs to spinning detection technique field, be specifically related to a kind of method of comprehensive evaluation spinning process level.

Background technology

At present in scientific experimentation or production practices, usually need to compare various various yarn quality of different nature and spinning material.But each individual event character of yarn quality and spinning material is measured, often there is each character height different, the conclusion of gained inconsistent situation.When running into the conflicting situation of yarn quality, spinning material every test index parameter values size, engineering and scientific research personnel can only by rule of thumb with feel the quality judging yarn qualities, and the evaluation of estimate of yarn quality evaluation of estimate and spinning material directly affects spinning process assessment of levels value, thus need a kind of simple effective method comprehensive evaluation spinning process level.

Summary of the invention

The present invention arranges operational data by using computer software, by each detection numerical value of spinning material, yarn quality, convert and become each major component being expressed as spinning material, yarn quality, each major component contains the numerical value of former each detection index of different proportion, and by the contribution proportion that each main composition is expressed yarn quality, spinning material, the comprehensive evaluation value of linear expression yarn quality, spinning material, the evaluation of estimate of yarn quality deducts the evaluation of estimate of spinning material, must evaluate the evaluation of estimate of spinning process level.

Its technical solution comprises:

A method for comprehensive evaluation spinning process level, comprises the following steps successively:

Step one, first, uses correlation test instrument to detect each performance index of spinning material, obtains detecting numerical value; Then detection numerical value is compiled; Finally, reduced data is inputted data processing unit and processes, its disposal route comprises following sub-step:

A, collection p tie up random vector X=(x ₁, x ₂..., x _p) ＇, X _i=(x _i1, x _i2..., x _ip) ＇, wherein, i=1,2 ... n, n are the number of times of sample observation and n>p, form sample battle array, carry out standardized transformation, obtain standardization battle array to sample array element according to as shown in the formula (1), (2), (3),

$Z_{\mathrm{ij}}=\frac{x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j}{s_j},---\left(1\right)$

${\stackrel{\‾}{x}}_j=\frac{{\mathrm{\Σ}}_{i=1}^n{x}_{\mathrm{ij}}}{n},---\left(2\right)$

$s_j^2=\frac{{\mathrm{\Σ}}_{i=1}^n{(x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j)}^2}{n-1},---\left(3\right)$

In above formula (1), (2), (3), i=1,2 ..., n; J=1,2 ..., p, wherein, p=9, x ₁, x ₂... x _prepresent the modal length of spinning material, quality length, short fiber content, degree of ripeness, fiber strength, horse value, percentage of impurity, fibre moisture regain rate and raw cotton fault successively;

B, according to formula (4), to standardization battle array, ask correlation matrix;

$R={\left[r_{\mathrm{ij}}\right]}_p\mathrm{xp}=\frac{Z^{T}Z}{n-1},---\left(4\right)$

Wherein, $r_{\mathrm{ij}}=\frac{\mathrm{\Σ}{z}_{\mathrm{kj}}\·z_{\mathrm{ki}}}{n-1},i,j=\mathrm{1,2},...p;$

The secular equation ︱ R-λ I of c, solution sample correlation matrix R _p︱=0 obtains p characteristic root, determines major component, determines m value according to formula (5), make the utilization factor of information reach more than 55%, to each λ in formula (5) _j, j=1,2 ..., m, solve an equation Rb=λ _jb, obtain unit character vector b _j ⁰;

$\frac{{\mathrm{\Σ}}_{j=1}^m{\mathrm{\λ}}_j}{{\mathrm{\Σ}}_{j=1}^p{\mathrm{\λ}}_j}\≥0.55;---\left(5\right)$

D, according to formula (6), the target variable after standardization is converted to major component,

U _ij＝z _i ^Tb _j ⁰,j＝1,2,...,m； (6)

U ₁be called first principal component, U ₂be called Second principal component, ..., U _pbe called p major component;

E, comprehensive evaluation is carried out to m major component,

Be weighted summation to m major component, obtain final evaluation of estimate, flexible strategy are the variance contribution ratio of each major component;

Step 2, first, uses correlation test instrument to detect yarn quality property indices, obtains detecting numerical value; Then, the detection numerical value obtained is compiled; Finally, reduced data is inputted data processing unit and processes, comprise following sub-step:

F, collection p tie up random vector X=(x ₁, x ₂..., x _p) ＇, X _i=(x _i1, x _i2..., x _ip) ＇, wherein, i=1,2 ... n, n are the number of times of sample observation and n>p, form sample battle array, carry out standardized transformation, obtain standardization battle array to sample array element according to as shown in the formula (7), (8), (9),

$Z_{\mathrm{ij}}=\frac{x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j}{s_j},---\left(7\right)$

${\stackrel{\‾}{x}}_j=\frac{{\mathrm{\Σ}}_{i=1}^n{x}_{\mathrm{ij}}}{n},---\left(8\right)$

$s_j^2=\frac{{\mathrm{\Σ}}_{i=1}^n{(x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j)}^2}{n-1},---\left(9\right)$

In above formula (7), (8), (9), i=1,2 ..., n; J=1,2 ..., p, wherein, p=24, x ₁, x ₂... x _pfor describing the variable of yarn quality, it represents weight CV%, deviation of weight, yarn regain %, powerful CV%, fracture strength, ultimate strength, minimum brute force, breaking elongation, elongation CV%, Unevenness CV %, Unevenness CV b, details-50%, slubbing+50%, cotton knot+200%, filoplume, filoplume CV%, cotton knot, cotton assorted, the twist, uneven twist, torque multiplier, empty ingot rate, instantaneous end breakage rate, cylinder yarn regain % successively;

G, according to formula (10), to standardization battle array, ask correlation matrix;

$R={\left[r_{\mathrm{ij}}\right]}_p\mathrm{xp}=\frac{Z^{T}Z}{n-1},---\left(10\right)$

Wherein, $r_{\mathrm{ij}}=\frac{\mathrm{\Σ}{z}_{\mathrm{kj}}\·z_{\mathrm{ki}}}{n-1},i,j=\mathrm{1,2},...p;$

The secular equation ︱ R-λ I of h, solution sample correlation matrix R _p︱=0 obtains p characteristic root, determines major component, determines m value according to formula (11), make the utilization factor of information reach more than 55%, to each λ in formula (11) _j, j=1,2 ..., m, solve an equation Rb=λ _jb, obtain unit character vector b _j ⁰;

$\frac{{\mathrm{\Σ}}_{j=1}^m{\mathrm{\λ}}_j}{{\mathrm{\Σ}}_{j=1}^p{\mathrm{\λ}}_j}\≥0.55;---\left(11\right)$

K, according to formula (12), the target variable after standardization is converted to major component,

U _ij＝z _i ^Tb _j ⁰,j＝1,2,...,m； (12)

U ₁be called first principal component, U ₂be called Second principal component, ..., U _pbe called p major component;

L, comprehensive evaluation is carried out to m major component,

Be weighted summation to m major component, obtain final evaluation of estimate, flexible strategy are the variance contribution ratio of each major component;

The evaluation of estimate of the yarn quality that step 3, above-mentioned steps two obtain deducts the evaluation of estimate of the spinning material that step one obtains, and obtains the initial evaluation value of spinning process level; After yarn quality comprehensive evaluation value and the standardization of spinning material comprehensive evaluation value, subtract each other and obtain the comprehensive evaluation value of spinning process level.

The Advantageous Effects that the present invention brings:

First the present invention carries out overall treatment analysis by the parameter of all technical recorded spinning material testing laboratory of spinnery, main index and the parameter of expressing spinning material performance is filtered out by computer computing, draw the comprehensive evaluation index that spinning material performance is evaluated, use mathematical statistics analysis means to draw and express spinning material each major component numerical value of different nature, and the comprehensive evaluation value obtaining expressing spinning material bulk property further obtains the evaluation of estimate of spinning material; Then by carrying out overall treatment analysis to the quality of spinnery's yarn by the parameter of all technical recorded in testing laboratory, main index and the parameter of expressing yarn quality performance is filtered out by computer computing, draw the comprehensive evaluation index that yarn quality performance is evaluated, use mathematical statistics analysis means to draw and express spinning material each major component numerical value of different nature, and obtain the comprehensive evaluation value of expressing yarn quality bulk property further; Evaluation of estimate finally by yarn quality deducts the evaluation of estimate of spinning material, obtains the evaluation of estimate of spinning process level.

The present invention objectifies spinning assessment of levels standard, simplifies evaluation procedure and method.

Accompanying drawing explanation

Below in conjunction with accompanying drawing, explanation clear, complete is further done to the present invention:

Fig. 1 is the rubble figure that in the present invention, spinning material calculates the major component initial characteristic values of gained;

Fig. 2 is the rubble figure that in the present invention, yarn quality calculates the major component initial characteristic values of gained.

Embodiment

The invention discloses a kind of method of comprehensive evaluation spinning process level, in order to make advantage of the present invention, technical scheme clearly, clearly, below in conjunction with specific embodiment, explanation clear, complete further being done to the present invention.

The inventive method specifically comprises the following steps:

Step one, service test equipment and instrument detect spinning material property indices, obtain detecting numerical value, namely use Y111A fiber type length analyser to record modal length, quality length, the short fiber content of spinning material; Y145C type mic value analyzer and Y175 type cotton fiber pneumatic tester is used to record: degree of ripeness, horse value; YG041 type raw material impurity analytical engine and JA1003 type serial analysis electronic balance is used to record percentage of impurity, raw cotton fault; YG001B type electronic mono-fiber strong force instrument is used to record fiber strength; YG202 type weaving moisture apparatus is used to record fibre moisture regain rate;

Then, use Microsoft Excel to compile data, rejecting abnormalities value, fill and save value;

Finally, the data collected input data processing unit (computing machine) processed, disposal route is as follows:

Step 1, collection p tie up random vector X=(x ₁, x ₂..., x _p) ＇, X _i=(x _i1, x _i2..., x _ip) ＇, wherein, i=1,2 ... n, n are the number of times of sample observation and n>p, form sample battle array, carry out standardized transformation, obtain standardization battle array to sample array element according to as shown in the formula (1), (2), (3),

$Z_{\mathrm{ij}}=\frac{x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j}{s_j},---\left(1\right)$

${\stackrel{\‾}{x}}_j=\frac{{\mathrm{\Σ}}_{i=1}^n{x}_{\mathrm{ij}}}{n},---\left(2\right)$

$s_j^2=\frac{{\mathrm{\Σ}}_{i=1}^n{(x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j)}^2}{n-1},---\left(3\right)$

In above formula (1), (2), (3), i=1,2 ..., n; J=1,2 ..., p, wherein, p=9, x ₁, x ₂... x _prepresent the modal length of spinning material, quality length, short fiber content, degree of ripeness, fiber strength, horse value, percentage of impurity, fibre moisture regain rate and raw cotton fault successively;

Step 2, according to formula (4), to standardization battle array, ask correlation matrix;

$R={\left[r_{\mathrm{ij}}\right]}_p\mathrm{xp}=\frac{Z^{T}Z}{n-1},---\left(4\right)$

Wherein, $r_{\mathrm{ij}}=\frac{\mathrm{\Σ}{z}_{\mathrm{kj}}\·z_{\mathrm{ki}}}{n-1},i,j=\mathrm{1,2},...p;$

Above-mentioned specifically as embodiment 1, first show (1) try to achieve data normalization matrix, by table (2) try to achieve " correlation matrix ";

The secular equation ︱ R-λ I of step 3, solution sample correlation matrix R _p︱=0 obtains p characteristic root, determines major component, and as table 3, table 4, table 5, shown in Fig. 1, (5) determine m value according to the following formula, and m value for the number of the major component calculated, makes the utilization factor of information reach more than 55%, to each λ in formula (5) for determining _j, j=1,2 ..., m, solve an equation Rb=λ _jb, obtain unit character vector b _j ⁰;

$\frac{{\mathrm{\Σ}}_{j=1}^m{\mathrm{\λ}}_j}{{\mathrm{\Σ}}_{j=1}^p{\mathrm{\λ}}_j}\≥0.55;---\left(5\right)$

Step 4, according to formula (6), the target variable after standardization is converted to major component, as shown in table 9, table 10,

U _ij＝z _i ^Tb _j ⁰,j＝1,2,...,m； (6)

U ₁be called first principal component, U ₂be called Second principal component, ..., U _pbe called p major component;

Step 5, comprehensive evaluation is carried out to m major component,

Be weighted summation to m major component, obtain final evaluation of estimate, flexible strategy are the variance contribution ratio of each major component, as shown in table 5.

By testing the routine product of spinnery 159 and n=159, drawing the technical indicator parameters such as modal length, using statistical method to carry out computing by computing machine, drawing following result, as shown in table 1-table 11, it is as shown in table 1 that statistics describes raw data:

Table 1 descriptive statistics

n＝159

Table 2 correlation matrix

Table 3 KMO and bartlett's test

Best results when KMO statistic numerical value is greater than 0.9, more than 0.7 can accept, less than 0.5 should not do factorial analysis, in this example, 0.610 still can accept, the significance value of the sphericity inspection of Bartlett is less than 0.01, negate the null hypothesis that correlation matrix is unit matrix thus, namely think and there is significant correlativity between each variable, the conclusion drawn with table 2 correlation matrix conforms to.

Table 4 communality

Table 5 population variance is explained

Table 6 component matrix

(a. has extracted 2 compositions).

The postrotational component matrix of table 7

(spinning solution: Kaiser standardization varimax, a. restrains after being rotated in 3 iteration).

Table 8 composition transformation matrix

Assembly	1	2
			1	.774	.633
2	-.633	.774

(spinning solution: Kaiser standardization varimax).

Table 9 composition score matrix of coefficients

Table 10 composition score covariance matrix

Assembly	1	2
			1	1.000	.000
2	.000	1.000

(spinning solution: Kaiser standardization varimax).

Table 11 some numerical results data

n	FAC1_1	FAC2_1	ZF
				1	-2.98594	1.85022	-0.67
2	-2.98594	1.85022	-0.67
				3	-2.98594	1.85022	-0.67
4	-2.96336	1.81251	-0.67
				5	-2.91818	1.73709	-0.68
6	-2.87301	1.66167	-0.69
				7	-2.35167	1.59027	-0.48
8	-2.30197	1.50731	-0.49
				9	-2.16273	1.3497	-0.48
10	-2.16273	1.3497	-0.48
				11	-2.08933	1.24963	-0.48
12	-2.08933	1.24963	-0.48
				13	-1.65627	0.92966	-0.41
14	-1.65627	0.92966	-0.41
				15	-1.52904	0.65404	-0.45
16	-1.40656	0.45464	-0.47
				17	-1.36476	0.27805	-0.51
18	-1.33391	0.17754	-0.53
				19	0.14506	-1.35617	-0.43
20	0.14506	-1.35617	-0.43
				21	0.14506	-1.35617	-0.43
22	0.14506	-1.35617	-0.43
				23	0.14506	-1.35617	-0.43

According to the data in table 9 (composition score matrix of coefficients), calculate the value of 2 major components respectively, that is:

FAC1_1=0.233* modal length+0.287* quality length-0.228* short fiber content-0.082* degree of ripeness+0.151* fiber strength+0.053* horse value+0.006* percentage of impurity-0.183* fibre moisture regain rate+0.296* raw cotton fault;

FAC2_1=-0.023* modal length-0.114* quality length+0.381* short fiber content+0.328* degree of ripeness+0.094* fiber strength+0.180* horse value+0.219* percentage of impurity-0.023* fibre moisture regain rate-0.258* raw cotton fault;

According to table 5 (population variance explanation), the numerical value of " variance percentage " of " spin load quadratic sum " the inside, accumulative variance percentage is 81.049%, calculates spinning material quality comprehensive score value:

ZF＝44.81％*FAC1_1+36.239％*FAC2_1。

The property indices of step 2, service test equipment and instrument detection line, obtains detecting numerical value, namely uses YG086 type sample skein winder and Y2101 type weaving electronic scales to record weight CV%, deviation of weight; YG202 type weaving moisture apparatus is used to record yarn regain %, cylinder yarn regain %; YG171 type yarn filoplume tester is used to record filoplume, filoplume CV%; Y331A type yarn twist meter is used to record the twist, uneven twist, torque multiplier; YG023B-I I type fully automatic single thread force-machine is used to record powerful CV%, fracture strength, ultimate strength, minimum brute force, breaking elongation and elongation CV%; Use records YG133B/M fiber strand evenness tester and records Unevenness CV %, Unevenness CV b, details-50%, slubbing+50%, cotton knot+200%, cotton knot; YG072A type yarn defect analyser is used to record cotton assorted; Calculated by touring counting collection: empty ingot rate, instantaneous end breakage rate;

Then, use Microsoft Excel to compile data, rejecting abnormalities value, fill and save value;

Finally, the data collected input data processing unit (computing machine) processed, disposal route is as follows:

Step 1, collection p tie up random vector X=(x ₁, x ₂..., x _p) ＇, X _i=(x _i1, x _i2..., x _ip) ＇, wherein, i=1,2 ... n, n are the number of times of sample observation and n>p, form sample battle array, carry out standardized transformation, obtain standardization battle array to sample array element according to as shown in the formula (1), (2), (3),

$Z_{\mathrm{ij}}=\frac{x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j}{s_j},---\left(1\right)$

${\stackrel{\‾}{x}}_j=\frac{{\mathrm{\Σ}}_{i=1}^n{x}_{\mathrm{ij}}}{n},---\left(2\right)$

$s_j^2=\frac{{\mathrm{\Σ}}_{i=1}^n{(x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j)}^2}{n-1},---\left(3\right)$

In above formula (1), (2), (3), i=1,2 ..., n; J=1,2 ..., p, wherein, p=24, x ₁, x ₂... x _pfor describing the variable of yarn quality, it represents weight CV%, deviation of weight, yarn regain %, powerful CV%, fracture strength, ultimate strength, minimum brute force, breaking elongation, elongation CV%, Unevenness CV %, Unevenness CV b, details-50%, slubbing+50%, cotton knot+200%, filoplume, filoplume CV%, cotton knot, cotton assorted, the twist, uneven twist, torque multiplier, empty ingot rate, instantaneous end breakage rate, cylinder yarn regain % successively;

Step 2, according to formula (4), to standardization battle array, ask correlation matrix;

$R={\left[r_{\mathrm{ij}}\right]}_p\mathrm{xp}=\frac{Z^{T}Z}{n-1},---\left(4\right)$

Wherein, $r_{\mathrm{ij}}=\frac{\mathrm{\Σ}{z}_{\mathrm{kj}}\·z_{\mathrm{ki}}}{n-1},i,j=\mathrm{1,2},...p;$

Above-mentioned specifically as embodiment 1, first show (12) try to achieve data normalization matrix, by table (13) try to achieve " correlation matrix ";

The secular equation ︱ R-λ I of step 3, solution sample correlation matrix R _p︱=0 obtains p characteristic root, and determine major component, as table 14, table 15, table 16, shown in Fig. 2, determines m value according to formula (5), make the utilization factor of information reach more than 55%, to each λ in formula (5) _j, j=1,2 ..., m, solve an equation Rb=λ _jb, obtain unit character vector b _j ⁰;

$\frac{{\mathrm{\Σ}}_{j=1}^m{\mathrm{\λ}}_j}{{\mathrm{\Σ}}_{j=1}^p{\mathrm{\λ}}_j}\≥0.55;---\left(5\right)$

Step 4, according to formula (6), the target variable after standardization is converted to major component, as shown in table 20, table 21,

U _ij＝z _i ^Tb _j ⁰,j＝1,2,...,m； (6)

U ₁be called first principal component, U ₂be called Second principal component, ..., U _pbe called p major component;

Step 5, comprehensive evaluation is carried out to m major component,

Be weighted summation to m major component, obtain final evaluation of estimate, flexible strategy are the variance contribution ratio of each major component, shown in table 16.

By testing the routine product of spinnery 159 and n=159, drawing the technical indicator parameters such as weight CV%, using statistical method to carry out computing by computing machine, drawing following result, as shown in table 12-table 22, it is as shown in table 12 that statistics describes raw data:

Table 12: descriptive statistics

Table 13 correlation matrix

Table 14:KMO and bartlett's test

Best results when KMO statistic numerical value is greater than 0.9, more than 0.7 can accept, and less than 0.5 should not do factorial analysis, and in this example, 0.753 can accept.The significance value of the sphericity inspection of Bartlett is less than 0.01, and negate the null hypothesis that correlation matrix is unit matrix thus, namely think and there is significant correlativity between each variable, the conclusion drawn with table 2 correlation matrix conforms to.

Table 15

Table 16 population variance is explained

Table 17 component matrix

The postrotational component matrix of table 18

Table 19 composition transformation matrix

Table 20 composition score matrix of coefficients

Table 21 composition score covariance matrix

According to the data in table 9 (composition score matrix of coefficients), calculate the value of 6 major components respectively:

FAC1_1=0.087* weight CV%-0.045* deviation of weight+0.071* yarn regain-0.009* yarn brute force+...+0.161* yarn cylinder regain;

FAC6_1=-0.088* weight CV%-0.117* deviation of weight+0.109* yarn regain-0.306* yarn brute force+...-0.114* yarn cylinder regain;

According to table 5 (population variance explanation), the numerical value of " variance percentage " of " spin load quadratic sum " the inside, accumulative variance percentage is 69.033%, as shown in table 11, calculates that yarn qualities is comprehensive must be worth:

ZF＝16.162％*FAC1_1+15.544％*FAC2_1+13.675％1*FAC3_1+8.324％*FAC4_1+7.856％*FAC5_1+7.472％*FAC6_1

Table 22: some numerical results data

n	FAC1_1	FAC2_1	FAC3_1	FAC4_1	FAC5_1	FAC6_1	ZF
								1	-0.4766	0.2499	-0.91127	-0.71282	5.39345	-2.32499	0.03
2	1.88153	0.326	-0.62653	-0.7683	1.37802	-0.19806	0.3
								3	1.64628	0.23674	-0.30144	0.39405	1.56682	-0.16954	0.4
4	1.46364	0.67935	-0.79882	-0.50638	1.35197	-0.79016	0.24
								5	1.54248	0.66793	-1.16942	-1.05779	1.51223	0.2529	0.24
6	1.29117	0.54851	-1.03355	-0.84331	1.75943	-0.25863	0.2
								7	1.56973	0.40643	-0.7895	-0.57826	1.0875	-0.2632	0.23
8	1.35231	0.0064	-0.74793	-1.11965	1.08974	-0.19856	0.09
								9	0.94095	0.12353	-1.16656	-0.67604	1.19249	-1.56106	-0.07
10	1.1911	0.35044	-1.0931	-0.7017	1.36658	-0.51689	0.11

11	1.42535	0.21506	-0.80898	-0.52919	1.59919	-0.69095	0.18
								12	0.62686	0.28112	-1.02827	-0.99892	1.44462	-1.12088	-0.05
13	0.43508	0.03208	-0.95604	-1.29854	1.29397	-0.79427	-0.12
								14	0.60333	-0.22659	-0.69393	-0.59509	0.96134	0.39298	0.02
15	0.07138	0.01953	-0.94049	-0.42393	0.346	0.71954	-0.07
								16	0.69075	-0.31485	-0.28264	-0.16781	0.6831	-0.48346	0.03
17	0.67812	-0.33959	-0.26775	-0.47383	1.0562	-1.00541	-0.01
								18	0.89329	-0.51672	-0.6952	-0.05792	1.20123	0.17019	0.07
19	0.10182	-0.58997	-0.98798	-0.69677	-0.53113	-1.32627	-0.41
								20	0.38067	-0.48699	-0.7178	-0.45405	0.42669	-1.42464	-0.22
21	0.74628	-0.63307	-0.47807	-0.29992	0.94069	-1.17036	-0.08
								22	1.35702	-1.04258	0.03645	-0.90127	-0.13363	-0.17496	-0.04
23	1.26106	-1.20268	-0.1109	-0.05567	-0.72232	-0.29179	-0.08

The evaluation of step 3, spinning process level, if the ZF in step one is ZFo, the ZF in step 2 is ZFyarn, and the initial value of the comprehensive evaluation value of spinning process level is ZFp, then

ZFp _i＝ZFyarn _i-ZFo _i；i＝1,2,...,n；

ZFp _ivalue larger, illustrate that spinning process level is higher.

By ZFo and ZFyarn standardization (formula is the same), the comprehensive evaluation value of spinning process level is ZZFp, then

ZZFp _i＝ZZFyarn _i-ZZFo _i；i＝1,2,...,n

ZZFp _ivalue larger, illustrate that spinning process level is higher.

Table 23: descriptive statistics

Table 24 some numerical results data

n	Zfo	ZFyarn	ZFp	ZZFo	ZZFyarn	ZZFp
							1	-0.67	0.03	0.7	-1.15826	0.09407	1.25
2	-0.67	0.3	0.97	-1.15826	1.00847	2.17
							3	-0.67	0.4	1.07	-1.15826	1.3674	2.53
4	-0.67	0.24	0.91	-1.16441	0.80359	1.97
							5	-0.68	0.24	0.92	-1.1767	0.8202	2
6	-0.69	0.2	0.89	-1.189	0.67987	1.87
							7	-0.48	0.23	0.7	-0.82853	0.76513	1.59
8	-0.49	0.09	0.58	-0.84206	0.32035	1.16
							9	-0.48	-0.07	0.41	-0.8329	-0.22792	0.6
10	-0.48	0.11	0.59	-0.8329	0.36416	1.2
							11	-0.48	0.18	0.67	-0.83876	0.61847	1.46
12	-0.48	-0.05	0.43	-0.83876	-0.16555	0.67
							13	-0.41	-0.12	0.28	-0.70324	-0.4094	0.29
14	-0.41	0.02	0.43	-0.70324	0.07682	0.78
							15	-0.45	-0.07	0.38	-0.77762	-0.23096	0.55

16	-0.47	0.03	0.49	-0.80778	0.09328	0.9
							17	-0.51	-0.01	0.5	-0.88632	-0.03848	0.85
18	-0.53	0.07	0.6	-0.92554	0.24064	1.17
							19	-0.43	-0.41	0.02	-0.74001	-1.38196	-0.64
20	-0.43	-0.22	0.2	-0.74001	-0.75335	-0.01
							21	-0.43	-0.08	0.34	-0.74001	-0.27587	0.46
22	-0.43	-0.04	0.39	-0.74001	-0.12276	0.62
							23	-0.43	-0.08	0.34	-0.74001	-0.27519	0.46

The inventive method is used for evaluation and spins spinning process level, has the advantages such as objective, efficient, simple.

Claims (1)

1. a method for comprehensive evaluation spinning process level, is characterized in that: comprise the following steps successively:

Step one, first, uses correlation test instrument to detect each performance index of spinning material, obtains detecting numerical value; Then detection numerical value is compiled; Finally, reduced data is inputted data processing unit and processes, its disposal route comprises following sub-step:

A, collection p tie up random vector X=(x ₁, x ₂..., x _p) ＇, X _i=(x _i1, x _i2..., x _ip) ＇, wherein, i=1,2 ... n, n are the number of times of sample observation and n>p, form sample battle array, carry out standardized transformation, obtain standardization battle array to sample array element according to as shown in the formula (1), (2), (3),

$Z_{\mathrm{ij}}=\frac{x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j}{s_j},---\left(1\right)$

${\stackrel{\‾}{x}}_j=\frac{{\mathrm{\Σ}}_{i=1}^n{x}_{\mathrm{ij}}}{n},---\left(2\right)$

$s_j^2=\frac{{\mathrm{\Σ}}_{i=1}^n{(x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j)}^2}{n-1},---\left(3\right)$

In above formula (1), (2), (3), i=1,2 ..., n; J=1,2 ..., p _,wherein, p=9, x ₁, x ₂... x _prepresent the modal length of spinning material, quality length, short fiber content, degree of ripeness, fiber strength, horse value, percentage of impurity, fibre moisture regain rate and raw cotton fault successively;

B, according to formula (4), to standardization battle array, ask correlation matrix;

$R={\left[r_{\mathrm{ij}}\right]}_p\mathrm{xp}=\frac{Z^{T}Z}{n-1},---\left(4\right)$

Wherein, i, j=1,2 ... p;

The secular equation ︱ R-λ I of c, solution sample correlation matrix R _p︱=0 obtains p characteristic root, determines major component, determines m value according to formula (5), make the utilization factor of information reach more than 55%, to each λ in formula (5) _j, j=1,2 ..., m, solve an equation Rb=λ _jb, obtain unit character vector b _j ⁰;

D, according to formula (6), the target variable after standardization is converted to major component,

U _ij＝z _i ^Tb _j ⁰,j＝1,2,...,m； (6)

U ₁be called first principal component, U ₂be called Second principal component, ..., U _pbe called p major component;

E, comprehensive evaluation is carried out to m major component,

Be weighted summation to m major component, obtain final evaluation of estimate, flexible strategy are the variance contribution ratio of each major component;

Step 2, first, uses correlation test instrument to detect yarn quality property indices, obtains detecting numerical value; Then, the detection numerical value obtained is compiled; Finally, reduced data is inputted data processing unit and processes, comprise following sub-step:

F, collection p tie up random vector X=(x ₁, x ₂..., x _p) ＇, X _i=(x _i1, x _i2..., x _ip) ＇, wherein, i=1,2 ... n, n are the number of times of sample observation and n>p, form sample battle array, carry out standardized transformation, obtain standardization battle array to sample array element according to as shown in the formula (7), (8), (9),

$Z_{\mathrm{ij}}=\frac{x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j}{s_j},---\left(7\right)$

${\stackrel{\‾}{x}}_j=\frac{{\mathrm{\Σ}}_{i=1}^n{x}_{\mathrm{ij}}}{n},---\left(8\right)$

$s_j^2=\frac{{\mathrm{\Σ}}_{i=1}^n{(x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j)}^2}{n-1},---\left(9\right)$

In above formula (7), (8), (9), i=1,2 ..., n; J=1,2 ..., p, wherein, p=24, x ₁, x ₂... x _pfor describing the variable of yarn quality, it represents weight CV%, deviation of weight, yarn regain %, powerful CV%, fracture strength, ultimate strength, minimum brute force, breaking elongation, elongation CV%, Unevenness CV %, Unevenness CV b, details-50%, slubbing+50%, cotton knot+200%, filoplume, filoplume CV%, cotton knot, cotton assorted, the twist, uneven twist, torque multiplier, empty ingot rate, instantaneous end breakage rate, cylinder yarn regain % successively;

G, according to formula (10), to standardization battle array, ask correlation matrix;

$R={\left[r_{\mathrm{ij}}\right]}_p\mathrm{xp}=\frac{Z^{T}Z}{n-1},---\left(10\right)$

Wherein, i, j=1,2 ... p;

The secular equation ︱ R-λ I of h, solution sample correlation matrix R _p︱=0 obtains p characteristic root, determines major component, determines m value according to formula (11), make the utilization factor of information reach more than 55%, to each λ in formula (11) _j, j=1,2 ..., m, solve an equation Rb=λ _jb, obtain unit character vector b _j ⁰;

K, according to formula (12), the target variable after standardization is converted to major component,

U _ij＝z _i ^Tb _j ⁰,j＝1,2,...,m； (12)

U ₁be called first principal component, U ₂be called Second principal component, ..., U _pbe called p major component;

L, comprehensive evaluation is carried out to m major component,

Be weighted summation to m major component, obtain final evaluation of estimate, flexible strategy are the variance contribution ratio of each major component;

The evaluation of estimate of the yarn quality that step 3, above-mentioned steps two obtain deducts the evaluation of estimate of the spinning material that step one obtains, and obtains the initial evaluation value of spinning process level; After yarn quality comprehensive evaluation value and the standardization of spinning material comprehensive evaluation value, subtract each other and obtain the comprehensive evaluation value of spinning process level.