CN112365135A - Fuzzy analytic hierarchy process based wind power blade manufacturing quality evaluation method, system and equipment - Google Patents
Fuzzy analytic hierarchy process based wind power blade manufacturing quality evaluation method, system and equipment Download PDFInfo
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Abstract
The invention discloses a method, a system and equipment for evaluating the manufacturing quality of wind power equipment based on a fuzzy analytic hierarchy process, wherein the method specifically comprises the following steps: establishing a wind power blade manufacturing quality evaluation index system, dividing wind power blade quality evaluation factors into three layers, constructing a plurality of judgment matrixes reflecting the influence degree of evaluation indexes based on the blade manufacturing quality evaluation index system, calculating index weights of all layers by adopting an analytic hierarchy process to obtain index weight matrixes of all layers, carrying out consistency check on the index weight matrixes of all layers, and calculating check coefficients of the judgment matrixes; fuzzy evaluation is used for multilevel indexes; carrying out percentage statistics on the quality problems according to grade evaluation indexes, and taking the quality problems as grade membership to obtain a grade membership evaluation matrix corresponding to the indexes of the criterion layer: and calculating according to the index weight matrix of each level and the level membership degree evaluation matrix to obtain the evaluation result of the criterion layer, and objectively reflecting each production link of the blade and the overall blade manufacturing quality level.
Description
Technical Field
The invention belongs to the technical field of wind power blade manufacturing quality evaluation, and relates to a method, a system and equipment for evaluating wind power blade manufacturing quality based on a fuzzy analytic hierarchy process.
Background
As a key component of a wind power plant, the quality of the blades plays a crucial role in the overall safety and economic benefit of a wind power project. In recent years, the wind power industry is in a rapid development state all the time, the domestic wind power industry has raised the rush installation tide, all project nodes are concentrated and the demand is concentrated, the supply contradiction of all main wind power equipment, especially blade equipment, is in a trend of white heat, the capacity of a provider tends to be saturated, under the supply pressure, part of manufacturers have a relaxation in the blade quality control, the condition of changing the progress of quality generally exists, the manufacturing quality risk is obviously increased, and even if the blade products of the same manufacturer fluctuate frequently due to the stress of the construction period, the supply shortage and the like. If the manufactured blades with unqualified quality are applied to the wind power project site, great potential safety quality accidents are caused.
At present, equipment quality evaluation methods mainly include an expert evaluation method, a statistical survey method, an analytic hierarchy process, a causal analysis method and the like, but an evaluation method aiming at the manufacturing quality of a wind power blade is not formed at present, so that a quality evaluation method which is suitable for the manufacturing characteristics of the wind power blade is urgently needed to be found in practical engineering and is used for scientifically and accurately obtaining the manufacturing level of the blade, enabling a wind power plant to accurately know the manufacturing quality condition of the blade and providing scientific basis for acceptance check of finished blade products.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method for evaluating the manufacturing quality of the wind power blade accurately, which adopts a method of combining quantification and qualification to objectively reflect each production link of the blade and the manufacturing quality level of the overall blade, and provides scientific reference for accurately knowing the manufacturing quality condition of the blade and the acceptance of the finished blade.
In order to achieve the purpose, the invention adopts the technical scheme that the method for evaluating the manufacturing quality of the wind power equipment based on the fuzzy analytic hierarchy process comprises the following steps:
step 1, establishing a wind power blade manufacturing quality evaluation index system according to a wind power blade production manufacturing process and quality evaluation key factors thereof, and dividing the wind power blade quality evaluation factors into three layers, namely a target layer, a criterion layer and an index layer, and a factor B in the criterion layeriAs the factor set of the target layer a, i ═ 1,2, … N, factor C in the index layerjLayer B as criterioniJ ═ 1,2 … … M;
step 2, constructing a plurality of judgment matrixes reflecting the influence degree of a group of evaluation indexes in the t-level layer on one evaluation index in the t-1 level layer corresponding to the evaluation indexes based on a blade manufacturing quality evaluation index system, wherein t is 2 and 3;
step 3, calculating the index weight of each level by adopting an analytic hierarchy process to obtain a weight matrix of each level index: wA，WBi i＝1,2,…N；
Step 4, carrying out consistency check on the index weight matrix of each layer obtained in the step 3, and calculating a check coefficient of the judgment matrix;
step 5, fuzzy evaluation is carried out on the multi-level indexes to construct an evaluation index evaluation grade set;
step 6, carrying out percentage statistics on the quality problems according to the grade evaluation indexes by adopting a percentage statistical method, and taking the percentage statistics as the grade membership to obtain a grade membership evaluation matrix corresponding to the standard layer indexes: mBi，i＝1,2,…N；
And 7, calculating according to the index weight matrix of each layer obtained in the step 3 and the grade membership evaluation matrix obtained in the step 6 to obtain a criterion layer evaluation result: rBi＝WBi·MBi，i＝1,2,…N；
Step 8, calculating a target layer comprehensive evaluation result based on the criterion layer index weight and the criterion layer evaluation result
And (5) combining the evaluation index evaluation grade set in the step 5 to obtain a fuzzy subset of the blade manufacturing quality grade, namely a final evaluation result.
Target layer is wind powerBlade manufacturing quality, the criteria layers include: raw material quality (B)1) Layer and quality of infusion (B)2) Bonding Process (B)3) And acceptance inspection of finished products (B)4) And packaging and storage (B)5) The index layer is a quality certification file (C)1) Appearance quality (C)2) And re-examination of the in-plant Performance (C)3) Examination of layer-filling Process (C)4) And controlling the size and position of the fiber cloth and the core material (C)5) Apparent mass and dimension (C) of the girder6) Vacuum system and degree of infusion cure (C)7) Adhesive process review (C)8) Apparent mass and dimension of web (C)9) And control of mold clamping clearance (C)10) Binder resin formulation and degree of cure (C)11) Blade weight and geometry inspection (C)12) Appearance inspection (C)13) Nondestructive flaw detection and internal inspection (C)14) Lightning protection device detection (C)15) Matched with tool (C)16) Storage conditions (C)17) And mark inspection (C)18) (ii) a N is 4, M is 18, and each factor set in the wind power blade manufacturing quality evaluation index system is as follows:
the first layer is as follows: a ═ B1、B2、B3、B4)
And a second level: b is1＝(C1，C2，C3)；
B2＝(C4，C5，C6，C7)；
B3＝(C8，C9，C10，C11)；
B4＝(C12，C13，C14，C15)；
B5＝(C16，C17，C18)。
In step 2, a judgment matrix is constructed by adopting a 1-9 scaling method.
The index weight matrix calculation process is as follows:
firstly, calculating the index weight of the target layer A to the criterion layer, normalizing each column of the target layer matrix
A matrix U is obtained which is then used,
summing the rows of the matrix U to obtain a matrix V,
the normalization is carried out, and the normalization is carried out,
obtaining the index weight of the target layer A to the criterion layer, namely the matrix WAIs calculated by the same method to obtain WBii＝1,2,…N。
The check coefficient CR in step 4 is calculated as follows:
wherein: consistency check index
In the formula: lambda [ alpha ]maxFor the maximum feature root, n is the construction matrix dimension,
TABLE 1 average random consistency index RI
| n | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| RI | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
The smaller the check coefficient CR value of the judgment matrix is, the better the consistency degree of the judgment matrix is, if the check coefficient CR is less than 0.1, the judgment matrix passes consistency check, and if CR is greater than 0.1, the judgment matrix needs to be reconstructed.
The evaluation grade in the step 5 is specifically as follows:
TABLE 2 wind turbine blade manufacture quality evaluation grade table
L ═ L (L1, L2, L3, L4, L5) corresponded to 5 grades of good, fair, poor and poor, as shown in table 2.
The wind power equipment manufacturing quality evaluation system based on the fuzzy analytic hierarchy process comprises a manufacturing quality evaluation index system module, a judgment matrix construction module, a weight matrix calculation module, a check coefficient calculation module, a criterion layer membership degree evaluation matrix calculation module, a criterion layer evaluation module and a target layer evaluation module; the manufacturing quality evaluation index system module is used for establishing a wind power blade manufacturing quality evaluation index system according to the wind power blade production manufacturing process and quality evaluation key factors thereof, and dividing the wind power blade quality evaluation factors into three layers, namely a target layer, a criterion layer and an index layer;
the judgment matrix building module builds a plurality of judgment matrixes reflecting the influence degree of a group of evaluation indexes in the t-level layer on one evaluation index in the t-1 level layer corresponding to the evaluation indexes on the basis of the blade manufacturing quality evaluation index system;
the weight matrix calculation module calculates each level index weight by adopting an analytic hierarchy process to obtain each level index weight matrix;
the check coefficient calculation module is used for carrying out consistency check on each level index weight matrix and calculating the check coefficient of the judgment matrix;
the criterion layer membership degree evaluation matrix calculation module is used for constructing a fuzzy evaluation index set and an evaluation grade, and carrying out percentage statistics on the quality problems according to the grade evaluation indexes by adopting a percentage statistical method to serve as the grade membership degree so as to obtain a grade membership degree evaluation matrix corresponding to the criterion layer indexes;
the criterion layer evaluation module is used for calculating according to each level index weight and the level membership evaluation matrix to obtain a criterion layer evaluation result;
and the target layer comprehensive evaluation module calculates a target layer comprehensive evaluation result based on each level index weight and the criterion layer evaluation result.
The invention also provides a computer device, which comprises but is not limited to one or more processors and a memory, wherein the memory is used for storing a computer executable program, the processor reads part or all of the computer executable program from the memory and executes the computer executable program, and when the processor executes part or all of the computer executable program, part or all of the steps of the method for evaluating the manufacturing quality of the wind power device based on the fuzzy analytic hierarchy process can be realized.
A computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the method for evaluating the manufacturing quality of a wind power plant based on the fuzzy analytic hierarchy process according to the present invention can be implemented.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention constructs a hierarchical analysis model of a wind power blade quality evaluation system by comprehensively utilizing an analytic hierarchy process and a fuzzy mathematical theory, objectively reflects each production link of the blade and the manufacturing quality level of the overall blade by adopting a method of combining quantification and qualification, provides scientific reference for accurately knowing the manufacturing quality condition of the blade and the acceptance of the finished blade product, is beneficial to the production party to objectively know the production quality of the production party, and is beneficial to analyzing the source of the quality problem.
Detailed Description
The invention will be further described below with reference to actual production quality data of a certain wind turbine blade manufacturing plant and a specific process of the invention:
a wind power equipment manufacturing quality evaluation method based on a fuzzy analytic hierarchy process comprises 5 first-level evaluation factors and 18 second-level evaluation factors of blade manufacturing quality, wherein the evaluation factors are typical factors influencing the blade manufacturing quality and have an important effect on the blade manufacturing quality; dividing the manufacturing quality grades of the wind power blades into five grades of excellent, good, medium, poor and poor, establishing the quality evaluation grades of the wind power blades, constructing a membership matrix of each index to the quality grades, and determining the membership degree of all evaluation factors of the manufacturing quality of the wind power blades to the quality grades; the method comprises the following steps of establishing a wind power blade manufacturing quality evaluation model by adopting a fuzzy analytic hierarchy process, and accurately evaluating the wind power blade manufacturing quality grade, wherein the method specifically comprises the following steps:
the method comprises the following steps: according to the wind power blade production and manufacturing process and quality evaluation key factors thereof, a wind power blade manufacturing quality evaluation index system is established, and the wind power blade quality evaluation factors are divided into three layers, namely a target layer, a criterion layer and an index layer.
Wherein, the target layer is wind-powered electricity generation blade manufacturing quality, and the criterion layer includes: raw material quality (B)1) Layer and quality of infusion (B)2) Bonding Process (B)3) And acceptance inspection of finished products (B)4) And packaging and storage (B)5) The index layer is a quality certification file (C)1) Appearance of the molded articleMass (C)2) And re-examination of the in-plant Performance (C)3) Examination of layer-filling Process (C)4) And controlling the size and position of the fiber cloth and the core material (C)5) Apparent mass and dimension (C) of the girder6) Vacuum system and degree of infusion cure (C)7) Adhesive process review (C)8) Apparent mass and dimension of web (C)9) And control of mold clamping clearance (C)10) Binder resin formulation and degree of cure (C)11) Blade weight and geometry inspection (C)12) Appearance inspection (C)13) Nondestructive flaw detection and internal inspection (C)14) Lightning protection device detection (C)15) Matched with tool (C)16) Storage conditions (C)17) And mark inspection (C)18). See table 1 below for details.
TABLE 1 blade manufacturing quality evaluation index System
According to the evaluation model established in table 1, the factor sets of each layer are:
the first layer is as follows: a ═ B1、B2、B3、B4)
And a second level: b is1＝(C1，C2，C3)；
B2＝(C4，C5，C6，C7)；
B3＝(C8，C9，C10，C11)；
B4＝(C12，C13，C14，C15)；
B5＝(C16，C17，C18)；
Step two: based on a blade manufacturing quality evaluation index system, a plurality of judgment matrixes reflecting the influence degree of a group of evaluation indexes in the t-level layer on one evaluation index in the t-1-level layer corresponding to the evaluation indexes are constructed. The judgment matrix was constructed using the "1-9 scale method", the meaning of which is shown in table 2 below:
TABLE 2 Scale of meanings
The judgment matrix A is constructed by adopting a 1-9 scale method for the manufacturing quality of the wind power blade and 5 corresponding evaluation indexes thereof, and is shown in a table 3:
TABLE 3 blade manufacturing quality decision matrix A
Adopting a 1-9 scaling method to construct a judgment matrix B for the quality indexes of the raw materials and the corresponding 3 evaluation indexes1As shown in table 4:
table 4 raw material quality judgment matrix B1
| Quality certification document | Appearance quality | In-plant performance retest | |
| Quality certification document | a11 | a12 | a13 |
| Appearance quality | a21 | a22 | a23 |
| In-plant performance retest | a31 | a32 | a33 |
Adopts a 1-9 scale method to construct a judgment matrix B for the quality indexes of the laying and the pouring and 4 corresponding evaluation indexes2As shown in table 5:
TABLE 5 layering and perfusion quality determination matrix B2
Adopting a 1-9 scaling method to construct a judgment matrix B for the quality index of the bonding process and 4 corresponding evaluation indexes3As shown in table 6: TABLE 6 bonding Process quality judgment matrix B3
The quality index of finished product inspection and 4 corresponding evaluation indexes are constructed to judge matrix B by 1-9 scale method4As shown in table 7:
TABLE 7 inspection of the finished productQuality judgment matrix B4
A judgment matrix B is constructed by adopting a 1-9 scale method to the package storage quality index and 3 corresponding evaluation indexes5As shown in table 8 below:
TABLE 8 determination matrix B of package storage quality5
| Tooling match | Storage conditions | Mark inspection | |
| Tooling match | a11 | a12 | a13 |
| Storage conditions | a21 | a22 | a23 |
| In-mark inspection | a31 | a32 | a33 |
Step three: calculating the index weight of each layer by adopting an analytic hierarchy process to obtain a weight matrix W of each layer indexA,
Step four: the consistency check is carried out on the index weight matrixes of each level, the check coefficient CR of the judgment matrix is calculated,
wherein: consistency check index
In the formula: lambda [ alpha ]maxFor the maximum feature root, n is the construction matrix dimension,
TABLE 9 average random consistency index RI
| n | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| RI | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
The smaller the check coefficient CR value of the judgment matrix is, the better the consistency degree of the judgment matrix is, if the check coefficient CR is less than 0.1, the judgment matrix passes consistency check, and if CR is greater than 0.1, the judgment matrix needs to be reconstructed.
Step five, fuzzy evaluation is used for multi-level indexes in the evaluation index system, and an evaluation index evaluation grade set L is constructed, wherein L is (L1, L2, L3, L4, L5) corresponding to 5 grades of good, common, poor and poor, as shown in the following table 10.
TABLE 10 wind turbine blade manufacturing quality evaluation grade
Step six: and calculating the grade membership degree of each evaluation index.
Performing percentage statistics on the quality problems according to grade evaluation indexes by adopting a percentage statistical method to obtain grade membership evaluation matrixes corresponding to the indexes of the criterion layer as grade membership
As an example: for index CiThe total number of the quality problems of the equipment discovered by supervision and inspection is y pieces, wherein the characteristic is LmThe quality problem of the grade is x pieces, so that the index C can be knowniL ofmThe degree of membership of the grade is: r isimX/y (i 1,2.. 18; m 1,2.. 5). From this, the index C can be obtainediIs a rank membership matrix of ri＝(ri1,ri2,ri3,ri4,ri5)。
Step seven: the fuzzy comprehensive evaluation result of the criterion layer obtained by calculation according to the weight of each index and the grade membership evaluation matrix is as follows:
step eight: and calculating a target layer comprehensive evaluation result based on the criterion layer index weight and the criterion layer fuzzy evaluation result. And determining the manufacturing quality evaluation grade of the blade according to the maximum membership principle.
The invention also provides a computer device, which comprises but is not limited to one or more processors and a memory, wherein the memory is used for storing a computer executable program, the processor reads part or all of the computer executable program from the memory and executes the computer executable program, and when the processor executes part or all of the computer executable program, part or all of the steps of the method for evaluating the manufacturing quality of the wind power device based on the fuzzy analytic hierarchy process can be realized.
A computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the method for evaluating the manufacturing quality of a wind power plant based on the fuzzy analytic hierarchy process according to the present invention can be implemented.
The computer device may be a laptop, a tablet, a desktop computer, or a workstation.
The processor may be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), or an off-the-shelf programmable gate array (FPGA).
The memory of the invention can be an internal storage unit of a notebook computer, a tablet computer, a desktop computer or a workstation, such as a memory and a hard disk; external memory units such as removable hard disks, flash memory cards may also be used.
Computer-readable storage media may include computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. The computer-readable storage medium may include: read Only Memory (ROM), Random ACCess Memory (RAM), Solid State Drive (SSD), or optical disc, etc. The Random ACCess Memory may include a resistive Random ACCess Memory (ReRAM) and a DynamiC Random ACCess Memory (DRAM).
The following are examples:
the method comprises the following steps: according to the wind power blade production and manufacturing process and quality evaluation key factors thereof, a wind power blade manufacturing quality evaluation index system is established, and the wind power blade quality evaluation factors are divided into three layers, namely a target layer, a criterion layer and an index layer.
Wherein, the target layer is wind-powered electricity generation blade manufacturing quality, and the criterion layer includes: raw material quality (B)1) Layer and quality of infusion (B)2) Bonding Process (B)3) And acceptance inspection of finished products (B)4) And packaging and storing (B)5) The index layer is a quality certification file (C)1) Appearance quality (C)2) And re-examination of the in-plant Performance (C)3) Examination of layer-filling Process (C)4) And controlling the size and position of the fiber cloth and the core material (C)5) Apparent mass and dimension (C) of the girder6) Vacuum system and degree of infusion cure (C)7) Adhesive process review (C)8) Apparent mass and dimension of web (C)9) And control of mold clamping clearance (C)10) Binder resin formulation and degree of cure (C)11) Blade weight and geometry inspection (C)12) Appearance inspection (C)13) Nondestructive flaw detection and internal inspection (C)14) Lightning protection device detection (C)15) Matched with tool (C)16) Storage conditions (C)17) And checking the mark (C)18). See table 1 for details.
According to the evaluation model established in table 1, the factor sets of each layer are:
the first layer is as follows: a ═ B1、B2、B3、B4)
And a second level: b is1＝(C1，C2，C3)；
B2＝(C4，C5，C6，C7)；
B3＝(C8，C9，C10，C11)；
B4＝(C12，C13，C14，C15)；
B5＝(C16，C17，C18)
Step two: by adopting a 1-9 scaling method, each level judgment matrix is constructed and assigned, and the obtained judgment matrix is as follows
1. The judgment matrix A of the target layer is as follows
2. Similarly, a judgment matrix B of a criterion layer is constructed1、B2、B3、B4、B5
Step three: and calculating index weight by using an analytic hierarchy process.
(1) Calculating the weight of each index of the target layer to the criterion layer, and normalizing each column of the matrix A:
the sum of the first column of matrix a is 9.533, the sum of the second column is 4, the sum of the third column is 2.043, the sum of the fourth column is 17, and the sum of the fifth column is 13.
Obtain the following matrix U
(2) Summing the rows of the matrix U;
the following matrix V is obtained.
VT＝[0.811 1.216 2.310 0.310 0.353]
(3) Normalization is carried out
Obtaining the index weight of the target layer A to the criterion layer, namely the matrix WA。
WA＝[0.162 0.243 0.462 0.062 0.071]
Step four: checking consistency, calculating check coefficient CR of the judgment matrix,
wherein: consistency check index
In the formula: lambda [ alpha ]maxFor the maximum feature root, n is the construction matrix dimension.
calculating a consistency index CI:
and (3) determining a corresponding average random consistency index RI by using a table look-up 9, and obtaining R.I-1.12 by using the table look-up for a judgment matrix of 5 th order.
Table average random consistency index RI
| n | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| RI | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
The consistency ratio CR is calculated, the smaller the check coefficient CR value of the judgment matrix is, the better the consistency degree of the judgment matrix is,
can know CR<And 0.1, judging that the matrix passes consistency test, and obtaining a reasonable weight calculation result. If CR is>0.1, the decision matrix needs to be reconstructed.
The same rule layer B can be obtained1、B2、B3、B4、B5The results after normalization for weighting and consistency check are as follows:
step five: fuzzy evaluation is used for multi-level indexes in the evaluation index system, and an evaluation index evaluation grade set L is constructed, wherein L is (L1, L2, L3, L4 and L5) corresponding to 5 grades of good, common, poor and poor, and is shown in Table 10.
Step six: and calculating the grade membership degree of each evaluation index. Suppose for index CiEvaluating, adopting a percentage statistical method to carry out percentage statistics on the quality problem grade evaluation result,as a degree of membership. For example: for index CiThe total number of the quality problems of the equipment discovered by supervision and inspection is y pieces, wherein the characteristic is LmThe quality problem of the grade is x pieces, from which the index C is knowniSubject to LmThe degree of membership of the grade is: r isimX/y (i 1,2.. 18; m 1,2.. 5). From this, the index C can be obtainediIs given as a rank membership matrix of ri ═ i (ri ═ i)1,ri2,ri3,ri4,ri5)。
By mass of raw material B1For example, the evaluation results of the levels of the respective index layers are shown in the following Table 11.
TABLE 11 membership degree of each index of quality of raw materials
| Evaluation index | L1Superior food | L2Good effect | L3In general | L4Is poor | L5Difference (D) |
| C1 | 0.4 | 0.3 | 0.2 | 0.1 | 0 |
| C2 | 0.3 | 0.3 | 0.2 | 0.2 | 0 |
| C3 | 0.3 | 0.2 | 0.3 | 0.2 | 0 |
The evaluation matrix of the grade membership degree corresponding to the quality of the raw materials is obtained from the table
In the same way, B2，B3，B4，B5The index grade membership degree evaluation matrix is
Step seven: and calculating and analyzing the evaluation result of the criterion layer.
B is to be1Index weight coefficient of
And grade membership degree evaluation matrix
Multiplying to obtain B1Index evaluation set
It can be seen that the quality of the raw material is excellent and the degree of grade membership is 31.4%.
Similarly, the evaluation set of other indexes of the criterion layer can be obtained:
the quality of the layer and the pouring is excellent, and the grade membership degree is 57.5%.
The quality of the bonding process is excellent, and the grade membership degree is 65.3%.
The inspection quality of the finished product is excellent, and the grade membership degree is 69.9%.
The packing storage quality condition is excellent, and the grade membership is 72.3 percent.
Step eight: and calculating and analyzing a comprehensive evaluation result of the target layer.
The fuzzy subset of blade manufacturing quality classes is:
determining a blade manufacturing quality pair L based on blade manufacturing quality problem data according to a maximum membership principle1The grade membership degree is the highest, the manufacturing quality evaluation result is excellent, and the grade membership degree is 58.7%.
Claims (9)
1. A wind power equipment manufacturing quality evaluation method based on a fuzzy analytic hierarchy process is characterized by comprising the following steps:
step 1, establishing a wind power blade manufacturing quality evaluation index system according to a wind power blade production manufacturing process and quality evaluation key factors thereof, dividing the wind power blade quality evaluation factors into three layers, namely a target layer, a standard layer and an index layer, wherein factors Bi in the standard layer are a factor set of the target layer A, i is 1,2 and … N, factors Cj in the index layer are a factor set of the standard layer Bi, j is 1 and 2 … … M;
step 2, constructing a plurality of judgment matrixes reflecting the influence degree of a group of evaluation indexes in the t-level layer on one evaluation index in the t-1 level layer corresponding to the evaluation indexes based on a blade manufacturing quality evaluation index system, wherein t is 2 and 3;
step 3, calculating the index weight of each level by adopting an analytic hierarchy process to obtain a weight matrix of each level index: wA，WBii＝1,2,…N；
Step 4, carrying out consistency check on the index weight matrix of each layer obtained in the step 3, and calculating a check coefficient of the judgment matrix;
step 5, fuzzy evaluation is carried out on the multi-level indexes to construct an evaluation index evaluation grade set;
step 6, carrying out percentage statistics on the quality problems according to the grade evaluation indexes by adopting a percentage statistical method, and taking the percentage statistics as the grade membership to obtain a grade membership evaluation matrix corresponding to the standard layer indexes: mBi，i＝1,2,…N；
And 7, calculating according to the index weight matrix of each layer obtained in the step 3 and the grade membership evaluation matrix obtained in the step 6 to obtain a criterion layer evaluation result: rBi＝WBi·MBi，i＝1,2,…N；
Step 8, calculating a target layer comprehensive evaluation result based on the criterion layer index weight and the criterion layer evaluation result
And (5) combining the evaluation index evaluation grade set in the step 5 to obtain a fuzzy subset of the blade manufacturing quality grade, namely a final evaluation result.
2. The fuzzy analytic hierarchy process-based wind power equipment manufacturing quality evaluation method of claim 1, wherein the target layer is wind power blade manufacturing quality, and the criterion layer comprises: raw material quality (B)1) Layer and quality of infusion (B)2) Bonding Process (B)3) And acceptance inspection of finished products (B)4) And packaging and storage (B)5) The index layer is a quality certification file (C)1) Appearance quality (C)2) And re-examination of the in-plant Performance (C)3) Examination of layer-filling Process (C)4) And controlling the size and position of the fiber cloth and the core material (C)5) Apparent mass and dimension (C) of the girder6) Vacuum system and degree of infusion cure (C)7) Adhesive process review (C)8) Apparent mass and dimension of web (C)9) And control of mold clamping clearance (C)10) Binder resin formulation and degree of cure (C)11)、Blade weight and geometry inspection (C)12) Appearance inspection (C)13) Nondestructive flaw detection and internal inspection (C)14) Lightning protection device detection (C)15) Matched with tool (C)16) Storage conditions (C)17) And mark inspection (C)18) (ii) a N is 4, M is 18, and each factor set in the wind power blade manufacturing quality evaluation index system is as follows:
the first layer is as follows: a ═ B1、B2、B3、B4)
And a second level: b is1＝(C1，C2，C3)；
B2＝(C4，C5，C6，C7)；
B3＝(C8，C9，C10，C11)；
B4＝(C12，C13，C14，C15)；
B5＝(C16，C17，C18)。
3. The method for evaluating the manufacturing quality of the wind power equipment based on the fuzzy analytic hierarchy process of claim 1, wherein in the step 2, a judgment matrix is constructed by a '1-9 scaling method'.
4. The fuzzy analytic hierarchy process-based wind power equipment manufacturing quality evaluation method of claim 1, wherein the index weight matrix calculation process is as follows:
firstly, calculating the index weight of the target layer A to the criterion layer, normalizing each column of the target layer matrix
A matrix U is obtained which is then used,
summing the rows of the matrix U to obtain a matrix V,
the normalization is carried out, and the normalization is carried out,
obtaining the index weight of the target layer A to the criterion layer, namely the matrix WAIs calculated by the same method to obtain WBii＝1,2,…N。
5. The fuzzy analytic hierarchy process-based wind power equipment manufacturing quality evaluation method of claim 1, wherein the calibration coefficient CR in step 4 is calculated as follows:
wherein: consistency check index
In the formula: lambda [ alpha ]maxFor the maximum feature root, n is the construction matrix dimension,
the average random consistency index RI is obtained by looking up table 1:
TABLE 1 average random consistency index RI
The smaller the check coefficient CR value of the judgment matrix is, the better the consistency degree of the judgment matrix is, if the check coefficient CR is less than 0.1, the judgment matrix passes consistency check, and if CR is greater than 0.1, the judgment matrix needs to be reconstructed.
6. The fuzzy analytic hierarchy process-based wind power equipment manufacturing quality evaluation method of claim 1, wherein the evaluation grade in step 5 is specifically:
TABLE 2 wind turbine blade manufacture quality evaluation grade table
L ═ L (L1, L2, L3, L4, L5) corresponded to 5 grades of good, fair, poor and poor, as shown in table 2.
7. The wind power equipment manufacturing quality evaluation system based on the fuzzy analytic hierarchy process is characterized by comprising a manufacturing quality evaluation index system module, a judgment matrix building module, a weight matrix calculation module, a check coefficient calculation module, a criterion layer membership degree evaluation matrix calculation module, a criterion layer evaluation module and a target layer evaluation module; the manufacturing quality evaluation index system module is used for establishing a wind power blade manufacturing quality evaluation index system according to the wind power blade production manufacturing process and quality evaluation key factors thereof, and dividing the wind power blade quality evaluation factors into three layers, namely a target layer, a criterion layer and an index layer;
the judgment matrix building module builds a plurality of judgment matrixes reflecting the influence degree of a group of evaluation indexes in the t-level layer on one evaluation index in the t-1 level layer corresponding to the evaluation indexes on the basis of the blade manufacturing quality evaluation index system;
the weight matrix calculation module calculates each level index weight by adopting an analytic hierarchy process to obtain each level index weight matrix;
the check coefficient calculation module is used for carrying out consistency check on each level index weight matrix and calculating the check coefficient of the judgment matrix;
the criterion layer membership degree evaluation matrix calculation module is used for constructing a fuzzy evaluation index set and an evaluation grade, and carrying out percentage statistics on the quality problems according to the grade evaluation indexes by adopting a percentage statistical method to serve as the grade membership degree so as to obtain a grade membership degree evaluation matrix corresponding to the criterion layer indexes;
the criterion layer evaluation module is used for calculating according to each level index weight and the level membership evaluation matrix to obtain a criterion layer evaluation result;
and the target layer comprehensive evaluation module calculates a target layer comprehensive evaluation result based on each level index weight and the criterion layer evaluation result.
8. A computer device, comprising but not limited to one or more processors and a memory, wherein the memory is used for storing a computer executable program, the processor reads part or all of the computer executable program from the memory and executes the computer executable program, and the processor can implement part or all of the steps of the fuzzy analytic hierarchy process based wind power device manufacturing quality evaluation method of claims 1 to 6 when executing the part or all of the computer executable program.
9. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, is capable of implementing the method for assessing the manufacturing quality of a wind power plant based on the fuzzy analytic hierarchy process of claims 1 to 6.
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