CN110674468A - Quantitative analysis method for spun yarn breakage factor based on improved rough set algorithm - Google Patents

Quantitative analysis method for spun yarn breakage factor based on improved rough set algorithm Download PDF

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CN110674468A
CN110674468A CN201910910632.4A CN201910910632A CN110674468A CN 110674468 A CN110674468 A CN 110674468A CN 201910910632 A CN201910910632 A CN 201910910632A CN 110674468 A CN110674468 A CN 110674468A
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江豪
张保威
王永华
王锦
宋久祥
魏敬典
冯立增
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Abstract

The invention provides a quantitative analysis method for yarn breakage factors of spun yarns based on an improved rough set algorithm, which comprises the following steps: firstly, acquiring learning sample data of a spun yarn breakage factor, and converting the learning sample data into a binary information table; secondly, discretizing the binary information table according to the data type of the attribute value of the attribute in the binary information table to obtain a discrete data set; then, an improved rough set algorithm is utilized to eliminate redundant attributes in the discrete data set to obtain reduction, and the weight of all the attributes in the reduction is calculated; then, sorting the weights, taking the attribute with the maximum weight as a fault node, and constructing a Bayesian network for the fault node by using a fault tree analysis method; and finally, processing next fault data by using the Bayesian network, and outputting main factors of yarn breakage factors. The invention can determine the weight of the yarn breaking factor, assist the user to quickly position the yarn breaking factor, further improve the factor with larger influence, and reduce the broken ends to improve the production efficiency.

Description

Quantitative analysis method for spun yarn breakage factor based on improved rough set algorithm
Technical Field
The invention relates to the field of detection of spun yarn breakage, in particular to a quantitative analysis method for a spun yarn breakage factor based on an improved rough set algorithm.
Background
In the daily production process, the broken ends of the spun yarns in the spinning process are always a big problem for restricting the production of the spun yarns, and meanwhile, the broken end rate of the spun yarns is one of main economic indexes in textile production and is an important mark for reflecting the management level of the spinning technology. The broken spun yarn not only can cause frequent joint of workers, increased labor intensity, reduced yarn yield and reduced production efficiency, but also can cause a large number of defects on the cloth surface and degraded quality, and directly influences the economic benefit of enterprises. The reduction of broken ends not only can improve the production efficiency and reduce the consumption of raw material power, but also plays an important role in the aspects of improving the yarn forming quality, reducing labor employment, prolonging the service life of equipment and the like.
The reasons for broken ends include raw cotton quality data, equipment state parameters, processing technology, special part states and the like, and at present, various analysis methods for all influence factors are also numerous, but are basically limited to qualitative analysis of the influence factors, quantitative analysis of the influence of all factors on broken ends is not available, and if the quantitative analysis of all the influence factors can be realized, the method has important value for positioning the broken yarn factors and finding the broken yarn reasons.
Disclosure of Invention
Aiming at the defects in the technical background, the invention provides a quantitative analysis method for the yarn breakage factor of the spun yarn based on an improved rough set algorithm, and solves the problem of long time and heavy task of qualitative analysis of the yarn breakage factor.
The technical scheme of the invention is realized as follows:
a quantitative analysis method for spun yarn breakage factors based on an improved rough set algorithm comprises the following steps:
s1, acquiring learning sample data Learn _ OIT of the yarn breakage factor of the spun yarn by using a PLC data acquisition unit;
s2, converting learning sample data Learn _ OIT into a binary information table Learn _ DOIT;
s3, discretizing the binary information table Learn _ DOIT according to the data type of the attribute value of the attribute of the binary information table Learn _ DOIT to obtain a discrete data set D _ Learn _ DOIT;
s4, aiming at each group of discrete data, eliminating redundant attributes of the group of discrete data by using an improved rough set reduction algorithm to obtain reduction of the group of discrete data, and calculating the weight of all attributes in the reduction, wherein a reduction information table comprises K attributes;
s5, sorting the weights of all the attributes in the reduction, and taking the attribute corresponding to the maximum weight as the fault node of the group;
s6, judging whether all the discrete data of the discrete data set are traversed or not, if so, executing a step S7; otherwise, go to step S4;
s7, constructing a Bayesian network for all fault nodes by using a fault tree analysis method;
and S8, judging next fault data by using the Bayesian network, outputting the weight of each yarn breakage factor, and sequencing according to the weight to obtain the main factors of the spun yarn breakage.
The learning sample data Learn _ OIT in the step S1 includes strength, evenness, temperature, humidity, twist, ingot speed, steel wire and steel wire ring.
In step S1, learning sample data, Learn _ OIT, is a binary set, Learn _ OIT ═ IT { >, (ii { >)a|a∈At}), whereinaIs a value range VaThe relation of the upper weak sequence, AtIs attribute set, a is attribute, IT ═ U, At,{Va|a∈At},{Ia|a∈At}) is a standard information table, U is a non-null theory field, VaIs a value range, IaIs notUniverse U to value VaThe mapping function of (2).
The binary information table Learn _ DOIT is as follows:
Learn_DOIT＝(U×U+,At,{Va|a∈At},{Ia|a∈At})，
wherein, U is multiplied by U+＝U×U-{(x,x)|x∈U}＝{(x,y)|x,y∈U；x≠y}，
Figure BDA0002214592220000021
Figure BDA0002214592220000022
The data types of the attribute values of the attributes of the binary information table Learn _ DOIT comprise numerical types and non-numerical types;
when the attribute a of binary information table Learn _ DOITiProperty value of
Figure BDA0002214592220000023
When the value is numerical, the attribute valueDiscretization is as follows:
Figure BDA0002214592220000025
wherein, the attribute ai∈AtRepresenting a set of attributes AtAnd i ═ 1,2, …, OjIs attribute aiAnd j ═ 1,2, …,
Figure BDA0002214592220000026
is attribute aiThe corresponding range of values is set to be,
Figure BDA0002214592220000027
is attribute aiIntermediate entity OjA value of (d);
when the attribute a of binary information table Learn _ DOITiProperty value of
Figure BDA0002214592220000028
When non-numerical, attribute value
Figure BDA0002214592220000029
Discretization is as follows:
Figure BDA00022145922200000210
wherein t is an attribute aiNumber of attribute values in value range, RaiIs attribute aiThe ordered sequence of the value ranges is,
Figure BDA00022145922200000211
the positions of the attribute values in the value range ordering sequence are ordered.
The method for eliminating the redundant attribute in the discrete binary information table D _ Learn _ DOIT by using the improved rough set reduction algorithm to obtain the reduction information table R of the discrete binary information table D _ Learn _ DOIT comprises the following steps:
s41, giving an information system S ═ U, a, V, f, a ═ C ∪ D, C is the conditional attribute set, D is the decision attribute set, for
Figure BDA00022145922200000212
If so:
U/(C- { a }) -, U/C, attribute a is called unnecessary attribute, i.e. redundant attribute,
if U/(C- { a }) > is not equal to U/C, the attribute a is called as a necessary attribute;
if the attribute subset
Figure BDA00022145922200000213
If so: U/P is equal to U/C,
Figure BDA00022145922200000214
then P is called a reduction set of C;
s42, a decision table is given, and piCIs a division of conditional attributes, πDIs the division of decision attributes, e (-) is a generalized reduction metric, pair
Figure BDA00022145922200000215
If so:
e(πD|πp)＝e(πD|πC)，
e(πD|πP-{a})≠e(πD|πC)，
then attribute subset P is said to be a reduced set of conditional attributes C with respect to decision attribute D;
s43, simplifying the discrete binary information table D _ Learn _ DOIT according to the steps S41 and S42 to obtain reduction R.
The method for calculating the weights of all the attributes in the reduction information table R in step S4 includes:wherein, ak∈A′t，A′tTo reduce the set of attributes of R, P, Q are conditional and decision attributes, POS, respectivelyP'Q ' represents the conditional attribute P ' positive field of the decision attribute Q ',
Figure BDA0002214592220000032
for knowledge dependency, U' is the domain of discourse.
The beneficial effect that this technical scheme can produce: the method combines the factors influencing the broken yarn, utilizes the improved rough set algorithm to determine the accurate weight of each influencing factor, realizes the quantitative analysis of the broken yarn factors, assists the user to quickly locate the broken yarn factors, further finds the specific reason of the broken yarn, further improves the factors with larger influencing weight, and realizes the reduction of broken ends so as to improve the production efficiency.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a Bayesian network diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
In order to accomplish the design task of the present invention, the theoretical basis involved in the present invention needs to be elucidated.
Definition 1, in general, in one information table, if U represents an object set and a represents an attribute set, then
Figure BDA0002214592220000033
The binary relation is defined on the information table, and the nature of the binary relation is as follows:
self-reflexibility: to pair
Figure BDA0002214592220000034
If (X, X) is ∈ R, then R is the reflexive relationship on X, or R is reflexive;
symmetry: to pair
Figure BDA0002214592220000035
If (x, y)
Figure BDA0002214592220000036
Then R is symmetric;
transferability: to pair
Figure BDA0002214592220000037
If it is
Figure BDA0002214592220000038
Then R is passed;
continuity: to pair
Figure BDA0002214592220000039
There is at least one y ∈ X such that xRy, then R is continuous.
As shown in fig. 1, a quantitative analysis method for spun yarn breakage factors based on an improved rough set algorithm includes the following steps:
s1, acquiring learning sample data Learn _ OIT of the yarn breakage factor of the spun yarn by using a PLC data acquisition unit; learning sample data Learn _ OIT comprises strength, evenness, temperature, humidity, twist, ingot speed, steel wire and steel wire ring data.
Learning sample data, Learn _ OIT, is a bigram, Learn _ OIT ═ IT, { >, anda|a∈At}), whereinaIs a value range VaThe relation of the upper weak sequence, AtIs attribute set, a is attribute, IT ═ U, At,{Va|a∈At},{Ia|a∈At}) is a standard information table, U is a non-null theory field, VaIs a value range, IaFrom a non-null-theory domain U to a value domain VaThe mapping function of (2).
S2, converting the learning sample data Learn _ OIT into a binary information table Learn _ DOIT:
Learn_DOIT＝(U×U+,At,{Va|a∈At},{Ia|a∈At})，
wherein, U is multiplied by U+＝U×U-{(x,x)|x∈U}＝{(x,y)|x,y∈U；x≠y}，
Figure BDA0002214592220000042
Because the value types of each attribute are different, the attribute is numerical or non-numerical, and if the quality of the order products of textile enterprises is to be integrally evaluated, the attribute needs to be discretized.
S3, discretizing the binary information table Learn _ DOIT according to the data types of the attribute values of the attributes of the binary information table Learn _ DOIT, wherein the data types comprise numerical types and non-numerical types, and obtaining a discrete data set D _ Learn _ DOIT.
When the attribute a of binary information table Learn _ DOITiProperty value of
Figure BDA0002214592220000043
When the value is numerical, the attribute value
Figure BDA0002214592220000044
Discretization is as follows:
Figure BDA0002214592220000045
wherein, the attribute ai∈AtRepresenting a set of attributes AtAnd i ═ 1,2, …, OjIs attribute aiAnd j ═ 1,2, …,
Figure BDA0002214592220000046
is attribute aiThe corresponding range of values is set to be,
Figure BDA0002214592220000047
is attribute aiIntermediate entity OjA value of (d);
when the attribute a of binary information table Learn _ DOITiProperty value ofWhen non-numerical, attribute value
Figure BDA0002214592220000049
Discretization is as follows:
Figure BDA00022145922200000410
wherein t is an attribute aiNumber of attribute values in value range, RaiIs attribute aiThe ordered sequence of the value ranges is,
Figure BDA00022145922200000411
the positions of the attribute values in the value range ordering sequence are ordered.
Because the database is huge, part of information in the discrete binary information table D _ Learn _ DOIT needs to be repeated and deleted, therefore, a rough set theory is introduced to process redundant information in the discrete binary information table D _ Learn _ DOIT to construct a model, and the model constructed after the redundant information is eliminated is relatively simple and has higher efficiency of processing faults.
And S4, for each group of discrete data, eliminating redundant attributes of the group of discrete data by using an improved rough set reduction algorithm to obtain reduction of the group of discrete data, and calculating the weight of all attributes in the reduction, wherein the reduction information table comprises K attributes.
S41, giving an information system S ═ U, a, V, f, a ═ C ∪ D, C is the condition attribute, D is the decision attribute, for
Figure BDA0002214592220000051
If so:
U/(C- { a }) -, U/C, attribute a is called unnecessary attribute, i.e. redundant attribute,
if U/(C- { a }) > is not equal to U/C, the attribute a is called as a necessary attribute;
if the attribute subset
Figure BDA0002214592220000052
If so: U/P is equal to U/C,
Figure BDA0002214592220000053
U/(P- { a }) ≠ U/C, and then P is called a reduction of C.
S42, a decision table is given, and piCIs a division of conditional attributes, πDIs the division of decision attributes, e (-) is a generalized reduction metric, pair
Figure BDA0002214592220000054
If so:
e(πD|πp)＝e(πD|πC)，
e(πD|πP-{a})≠e(πD|πC)，
then P is said to be a reduction of the conditional attribute C with respect to the decision attribute D;
s43, simplifying the discrete binary information table D _ Learn _ DOIT according to the steps S41 and S42 to obtain reduction R.
The method for calculating the weights of all attributes in the reduction R comprises the following steps:
Figure BDA0002214592220000056
wherein, ak∈A′t，A′tTo reduce the set of attributes of the information table R,respectively a conditional attribute and a decision attribute, POSP'Q ' represents the conditional attribute P ' positive field of the decision attribute Q ',
Figure BDA0002214592220000058
and the degree of dependence of the decision attribute Q ' on the condition attribute P ' is shown as the knowledge dependence, and U ' is a domain of discourse. The original fault symptom yarn-breaking nodes are reduced by using a knowledge dependency formula, so that the number of fault nodes is reduced, and a network model is simplified.
And S5, sorting the weights of all the attributes in the reduction, and taking the attribute corresponding to the maximum weight as the fault node of the group.
S6, judging whether all the discrete data of the discrete data set are traversed or not, if so, executing a step S7; otherwise, step S4 is executed.
S7, constructing a Bayesian network for all fault nodes by utilizing a fault tree analysis method, wherein the constructed topological structure diagram of the Bayesian network is shown in FIG. 2. And taking each fault factor as a small node, and constructing the Bayesian network by using a fault tree analysis method. The fault factors comprise human faults, machine faults and yarn faults, and a Bayesian network is built by utilizing a fault tree analysis method according to the types of the fault factors.
And S8, judging next fault data by using the Bayesian network, outputting the weight of each yarn breakage factor, and sequencing according to the weight to obtain the main factors of the spun yarn breakage.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (7)

1. A quantitative analysis method for spun yarn breakage factors based on an improved rough set algorithm is characterized by comprising the following steps:
s1, acquiring learning sample data Learn _ OIT of the yarn breakage factor of the spun yarn by using a PLC data acquisition unit;
s2, converting learning sample data Learn _ OIT into a binary information table Learn _ DOIT;
s3, discretizing the binary information table Learn _ DOIT according to the data type of the attribute value of the attribute of the binary information table Learn _ DOIT to obtain a discrete data set D _ Learn _ DOIT;
s4, aiming at each group of discrete data, eliminating redundant attributes of the group of discrete data by using an improved rough set reduction algorithm to obtain reduction of the group of discrete data, and calculating the weight of all attributes in the reduction, wherein a reduction information table comprises K attributes;
s5, sorting the weights of all the attributes in the reduction, and taking the attribute corresponding to the maximum weight as the fault node of the group;
s6, judging whether all the discrete data of the discrete data set are traversed or not, if so, executing a step S7; otherwise, go to step S4;
s7, constructing a Bayesian network for all fault nodes by using a fault tree analysis method;
and S8, judging next fault data by using the Bayesian network, outputting the weight of each yarn breakage factor, and sequencing according to the weight to obtain the main factors of the spun yarn breakage.
2. The quantitative analysis method for yarn breakage factor of spun yarn based on the improved rough set algorithm according to claim 1, wherein the learning sample data Learn _ OIT in the step S1 includes strength, evenness, temperature, humidity, twist, spindle speed, steel wire and traveler.
3. The method for quantitatively analyzing yarn breakage factor of spun yarn based on improved rough set algorithm as claimed in claim 1, wherein the learning sample data Learn _ OIT in said step S1 is a binary set, Learn _ OIT ═ IT { >, wherea|a∈At}), whereinaIs a value range VaThe relation of the upper weak sequence, AtIs attribute set, a is attribute, IT ═ U, At,{Va|a∈At},{Ia|a∈At}) is a standard information table, U is a non-null theory field, VaIs a value range, IaFrom a non-null-theory domain U to a value domain VaThe mapping function of (2).
4. The quantitative analysis method for the spun yarn breakage factor based on the improved rough set algorithm according to claim 1 or 3, characterized in that the binary information table Learn _ DOIT is as follows:
Learn_DOIT＝(U×U+,At,{Va|a∈At},{Ia|a∈At})，
wherein, U is multiplied by U+＝U×U-{(x,x)|x∈U}＝{(x,y)|x,y∈U；x≠y}，
Figure FDA0002214592210000011
Figure FDA0002214592210000012
5. The quantitative analysis method for the spun yarn breakage factor based on the improved rough set algorithm as claimed in claim 4, wherein the data types of the attribute values of the attributes of the binary information table Learn _ DOIT comprise numerical type and non-numerical type;
when the attribute a of binary information table Learn _ DOITiProperty value of
Figure FDA0002214592210000013
When the value is numerical, the attribute value
Figure FDA0002214592210000014
Discretization is as follows:wherein, the attribute ai∈AtRepresenting a set of attributes AtAnd i ═ 1,2, …, OjIs attribute aiAnd j ═ 1,2, …,
Figure FDA0002214592210000021
is attribute aiThe corresponding range of values is set to be,
Figure FDA0002214592210000022
is attribute aiIntermediate entity OjA value of (d);
when the attribute a of binary information table Learn _ DOITiProperty value of
Figure FDA0002214592210000023
When non-numerical, attribute value
Figure FDA0002214592210000024
Discretization is as follows:
Figure FDA0002214592210000025
wherein t is an attribute aiNumber of attribute values in value range, RaiIs attribute aiThe ordered sequence of the value ranges is,
Figure FDA0002214592210000026
the positions of the attribute values in the value range ordering sequence are ordered.
6. The quantitative analysis method for the spun yarn breakage factor based on the improved rough set algorithm as claimed in claim 5, wherein the method for eliminating the redundant attribute in the discrete binary information table D _ Learn _ DOIT by using the improved rough set reduction algorithm to obtain the reduction information table R of the discrete binary information table D _ Learn _ DOIT comprises the following steps:
s41, giving an information system S ═ U, a, V, f, a ═ C ∪ D, C is the conditional attribute set, D is the decision attribute set, for
Figure FDA0002214592210000027
If so:
U/(C- { a }) -, U/C, attribute a is called unnecessary attribute, i.e. redundant attribute,
if U/(C- { a }) > is not equal to U/C, the attribute a is called as a necessary attribute;
if the attribute subsetIf so: U/P is equal to U/C,
Figure FDA0002214592210000029
U/(P- { a }) > is not equal to U/C, P is called a reduction set of C;
s42, a decision table is given, and piCIs a division of conditional attributes, πDIs the division of decision attributes, e (-) is a generalized reduction metric, pair
Figure FDA00022145922100000210
If so:
e(πD|πp)＝e(πD|πC)，
e(πD|πP-{a})≠e(πD|πC)，
then attribute subset P is said to be a reduced set of conditional attributes C with respect to decision attribute D;
s43, simplifying the discrete binary information table D _ Learn _ DOIT according to the steps S41 and S42 to obtain reduction R.
7. The method for quantitatively analyzing yarn breakage factors of spun yarns based on the improved rough set algorithm as claimed in claim 1, wherein the weights of all the attributes in the information table R are reduced in the step S4The weight calculation method comprises the following steps:
Figure FDA00022145922100000211
wherein, ak∈A't，A'tTo reduce the set of attributes of R, P, Q are conditional and decision attributes, POS, respectivelyP'Q ' represents the conditional attribute P ' positive field of the decision attribute Q ',
Figure FDA00022145922100000212
for knowledge dependency, U' is the domain of discourse.
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CN111598352A (en) * 2020-05-25 2020-08-28 哈尔滨工业大学 Concrete beam type bridge comprehensive evaluation method based on Bayesian network
CN111950940A (en) * 2020-09-24 2020-11-17 山东钢铁集团日照有限公司 Quality data attribute reduction method based on attribute relational product
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