CN108021111A - Manufacturing process multivariate quality diagnostic classification device based on chi-square value - Google Patents
Manufacturing process multivariate quality diagnostic classification device based on chi-square value Download PDFInfo
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- CN108021111A CN108021111A CN201711254792.5A CN201711254792A CN108021111A CN 108021111 A CN108021111 A CN 108021111A CN 201711254792 A CN201711254792 A CN 201711254792A CN 108021111 A CN108021111 A CN 108021111A
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
Manufacturing process multivariate quality diagnostic classification device based on chi-square value, collect the initial data of mass property in manufacturing process, carry out data prediction, process analysis procedure analysis is carried out to the Multivariate Quality Characteristics of critical process using hybrid algorithm, the data recorded according to control figure are sentenced steady and whether abnormal phenomenon occur, where finding out process exception source based on chi-square value method, in order to make classification results more accurate, introduce, chi-square value, weight proportion, similarity sentence steady rule between the two.Process of the present invention capacity factor condition is rigorous, decision state is accurate, algorithm complex is low, the time of processing is fast, combines multivariate quality, the erroneous judgement factor, the principal component factor, and applicability is stronger, parameter processing specification, data processing is perfect, reduces probability of miscarriage of justice, solves the problems, such as that data biasing, unit are skimble-scamble, it is higher than support vector machines accuracy, it is possible to achieve abnormity diagnosis technology.
Description
Technical field
The present invention relates to Mechanical Product's Machining manufacturing process quality diagnosis technical field, and in particular to one kind is based on chi-square value
Manufacturing process multivariate quality diagnostic classification device.
Background technology
Modem manufacturing operations are multivariable height correlations, are known as multivariate quality control to the process monitoring of this kind of production process
Make (MQC) or multivariatestatistical process control (MSPC).The process for finding reason out of control is referred to as MSPC diagnosis or anomalous identification.
Mainly there are two class methods：First, Statistics decomposition technology；Second, the technology based on machine learning.Mainstream decomposition technique include it is main into
Analysis (PCA), feature space comparison method, MTY methods, step drop method, multidirectional core principle component analysis method.However, these sides
Method usually all contains complex statistics process, is unfavorable for applying.With the development of computer technology, machine learning becomes this neck
The research hotspot in domain.Artificial neural network (ANN) and decision tree (DT) algorithm are applied to MSPC fields.Due to product matter
The critical role in modern industry is measured, statistical Process Control (SPC) is in machinery, weaving, electronic product, the discrete manufacture of auto lamp
Very ten-strike is achieved in industry, and is gradually permeated to the industry of the interval such as papermaking, oil refining, chemical industry, food and continuous manufacturing industry.In reality
In the manufacturing process on border, being processed parts or product often has multiple mass propertys, and exists between these mass propertys
How certain correlation, determine the Measure of Process Capability of the process and procedure quality diagnosed, be that there is an urgent need to solve
Certainly the problem of, the research of the problem not only have great importance the research of polynary manufacturing process capability analysis, but also to polynary
The quality of manufacturing process, which is monitored and diagnoses, is respectively provided with certain theory significance and practical value.Based on the demand, this hair
The bright manufacturing process multivariate quality diagnostic classification device provided based on chi-square value.
The content of the invention
For deficiency of the multivariate control chart in complex process monitoring and abnormity diagnosis, the present invention provides based on chi-square value
Manufacturing process multivariate quality diagnostic classification device.
To solve the above-mentioned problems, the present invention is achieved by the following technical solutions：
Step 1：The initial data of mass property in manufacturing process is collected, and necessary arrangement, simplification are carried out to the data
And calculate.
Step 2：Process analysis procedure analysis is carried out to the Multivariate Quality Characteristics of critical process；
Step 3：The data observed recorded in oneself control figure through finishing control limit, according to sentencing steady rule judgment mistake
Whether journey there is abnormal phenomenon；
Step 4：According to recognition result, process exception source place is found out based on chi-square value method；
Step 5：Related personnel proposes and implements improved measure for quality problems, solves process exception situation；
Step 6：After implementation is improved, dimension is continuous to carry out procedure quality verification confirmation using control figure, and whether observation still has
It is abnormal, return and asked to (3) if having, if being monitored without control figure is continued with to manufacturing process.
Present invention has the advantages that：
1st, process capability coefficient condition is more rigorous, and decision state result is more accurate.
2nd, algorithm complex is low, and the time of processing is short, has obtained preferable result precision.
3rd, preferable basis has been established for subsequent manufacturing processes diagnostic techniques.
4th, the polynary characteristic between quality is considered, algorithm adaptability is stronger, more meets actual application.
5th, the more normative and reasonable of parameter factors processing, obtained value more meet the result of experience judgement.
6th, consider the erroneous judgement factor and combine principal component analytical method, the further lifting that result precision obtains.
7th, data processing is more perfect, reduces the probability of erroneous judgement.
8th, solve the problems, such as that biasing, the unit of data are skimble-scamble.
9th, abnormity diagnosis technology can be realized.
10th, the decision rule of anomalous mode is more easy to be bright and clear.
Brief description of the drawings
The structure flow chart of the improved manufacturing process multivariate quality diagnostic classification devices of Fig. 1
Fig. 2 workshop data acquisition scheme figures of the present invention
The modified specification region of Fig. 3 two-dimensional process and actual distribution example region figure
Embodiment
In order to solve deficiency of the multivariate control chart in complex process monitoring and abnormity diagnosis, with reference to Fig. 1-Fig. 3 to this hair
Bright to be described in detail, its specific implementation step is as follows：
Step 1：The initial data of mass property in manufacturing process is collected, and necessary arrangement, simplification are carried out to the data
And calculate, its specific calculating process is as follows：
In process of production, when Systematic Errors are not present in process, the quality characteristic value X of product meets normal distribution；
Since Multivariate Quality Characteristics are worth unit disunity, numerical values recited gap is also larger, and data need to be further processed；
The data matrix that production process normal operation is collected is Xn×m, n is the number of sample, and m is sample quality attribute
Number.
Formula X aboven×iFor n-th of sample, i-th kind of quality property value, μiFor i-th kind of qualitative attribute average, σiI-th kind of quality category
Property standard deviation.
To pretreated data X 'n×i, it is as follows to carry out proportion calculating：
Assuming that m ties up normal distribution Nm(μ, ∑), i.e. Xm~Nm(μ, ∑), wherein μ are population mean vector, and ∑ is covariance
Matrix, due to ∑m×mFor symmetrical matrix, therefore there are symmetrical matrix P so that
Wherein λ1,λ2,…,λmFor the characteristic value of covariance matrix, it meets (λ1,λ2,…,λm)>0, i.e. m tie up multivariate quality
Weight distribution can be expressed as following formula：
The contribution rate of accumulative total of k pivot reaches more than 80% before taking, i.e. contribution rate is W：
Then principal component model is
The attribute vector of respectively k pivot quality, E are error.
Step 2：Process analysis procedure analysis is carried out to the Multivariate Quality Characteristics of critical process, its specific calculating process is as follows：
Here the mainly calculating and estimation to E in step 1；
X∈N(μ,σ2), wherein X is quality characteristic value, and μ is population mean, σ2It is population variance.When quality characteristic value is obeyed
During normal distribution, its averageAlso Normal Distribution, wherein, n is sample size.According to the characteristic of normal distribution, then
P(μ-3σ<X<The σ of μ+3)=99.73%
That is, no matter what value μ and σ takes, and the probability that X falls between is 99.73%, that is to say, that is fallen in this distribution
Outside probability there was only 0.27%.
It is a spheroid for the modified specification region of process, its volume calculation formula is：
Ui、LiThe bound of i-th yuan of quality factor respectively in control figure.
Complex process spheroid in actual distribution region under (1- α) confidence level is：
| ∑ | it is the covariance determinant of the multivariate quality factor.
If its correction factor is k；
ε=[(M1-μ1)2+(M2-μ2)2+…+(Mt-μt)2]1/2
Mi、μiRespectively specification figure and the mean location of real process, ε tie up average difference for t.
Another factor of influence is(Uj、Lj) be specification bound intersection point.
I.e.
In conclusion characterization process capability function is as follows：
In order to improve above formula as a result, integrating following method here, detailed process is as follows：
The probability of misjudgement error is divided into two classes, first, slave mode is judged to runaway condition, probability is P1, second, shape out of control
State is judged to slave mode, and probability is P2。
Sample X, when in slave mode.If it is distributed as normal distribution X ∈ N (μ, σ2)；Process is in runaway condition
When, its distribution is changed, and the distribution function after change is F (x).
The upper and lower control limit for remembering control figure is respectively U, L；
P1=2 (1- Φ (λ))
P2=F (U)-F (L)
Overall error probability is P1+P2
Above formula Φ (λ) is value of the distribution function at point λ of standardized normal distribution, λ actual parameters in figure in order to control, this
Concrete condition can be determined specifically.
Unitary correction factor k '：
β1、β2Respectively centre distance difference | λ-μ |, the weight distribution value of probability of miscarriage of justice, β here1+β2=1, (β1, β2)>
0。
Characterize process capability function CP：
CP=min (CPU,CPL)
Multivariate table sign process capability function MCP′：
Characterize E
E=| MCp-MCP′|
According to X 'It is mainModel is the main feature of extractable manufacturing process abnormal quality.
Step 3：The data observed recorded in oneself control figure through finishing control limit, according to sentencing steady rule judgment mistake
Whether journey there is abnormal phenomenon, its specific calculating process is as follows：
The control figure established when if process is in non-statistical controlled process state with sample point controls follow-up production
Journey, does not have good control effect not only, can bring the forecast of mistake to enterprise on the contrary, cause damages to enterprise.
Sentence steady rule：
ε′、ε1、ε2Respectively pre-set parameter.
Only meet that upper three formula just can determine that whether current state is in slave mode, has a rule to be unsatisfactory for, sentences at the same time
It is set to anomalous mode.
Step 4：According to recognition result, where finding out process exception source based on chi-square value method, its specific calculating process
It is as follows：
The chi-square value of attribute is determined according to training dataset, each qualitative attribute corresponds to several components；
Here qualitative attribute definite opinion following formula really：
SijFor attribute SiBelong to the property value of class j, μjFor the average of class j, nijFor attribute SiBelong to the number of class j.
Anomaly source is determined further according to similarity, it is specific as follows：
Above formula ratio (W (S), CPE) for the similarity of attribute S and control figure ideal control line, CPEThe preferable control of figure in order to control
Line processed, λ are unit equivalent coefficient.
ratio(W(S),CPE) bigger or smaller, corresponding qualitative attribute is more unstable, more than threshold range, that is, triggers police
Report, then abnormal component, i.e. anomaly source are determined by above formula.
Step 5：Related personnel proposes and implements improved measure for quality problems, solves process exception situation；
Step 6：After implementation is improved, dimension is continuous to carry out procedure quality verification confirmation using control figure, and whether observation still has
It is abnormal, return and asked to (3) if having, if being monitored without control figure is continued with to manufacturing process.
Claims (2)
1. the manufacturing process multivariate quality diagnostic classification device based on chi-square value, the present invention relates to Mechanical Product's Machining manufacturing process matter
Measure control technology field, and in particular to a kind of manufacturing process multivariate quality diagnostic classification device based on chi-square value, it is characterized in that, bag
Include following steps：
Step 1：The initial data of mass property in manufacturing process is collected, and the data are carried out with necessary arrangement, simplifies and counts
Calculate, its specific calculating process is as follows：
In process of production, when Systematic Errors are not present in process, the quality characteristic value of productMeet normal distribution；Due to
Multivariate Quality Characteristics are worth unit disunity, and numerical values recited gap is also larger, and data need to be further processed；
Production process normal operation collect data matrix be,For the number of sample,For sample quality attribute
Number
Above formulaForA sampleKind quality property value,ForKind qualitative attribute average,TheKind
Qualitative attribute standard deviation
To pretreated data, it is as follows to carry out proportion calculating：
Assuming thatTie up normal distribution, i.e.,, whereinIt is vectorial for population mean,
For covariance matrix, due toFor symmetrical matrix, therefore there are symmetrical matrixSo that
WhereinFor the characteristic value of covariance matrix, it meets, i.e.,Dimension is more
The weight distribution of first quality can be expressed as following formula：
Before takingThe contribution rate of accumulative total of a pivot reaches more than 80%, i.e. contribution rate is：
Then principal component model is
RespectivelyThe attribute vector of a pivot quality,For error
Step 2：Process analysis procedure analysis is carried out to the Multivariate Quality Characteristics of critical process, its specific calculating process is as follows：
Here mainly in step 1Calculating and estimation；
, whereinIt is quality characteristic value,It is population mean,It is population variance, works as mass property
When being worth Normal Distribution, its averageAlso Normal Distribution, wherein, n is sample size, according to the characteristic of normal distribution,
Then
I.e., no matterWithWhat value is taken,The probability fallen between is, that is to say, that fall in this distribution model
Probability outside enclosing only has
It is a spheroid for the modified specification region of process, its volume calculation formula is：
、Respectively in control figureThe bound of first quality factor
Complex process existsThe spheroid in actual distribution region is under confidence level：
For the covariance determinant of the multivariate quality factor
If its correction factor is；
、Respectively specification figure and the mean location of real process,Average difference is tieed up for t
Another factor of influence is,For the intersection point of specification bound
I.e.
In conclusion characterization process capability function is as follows：
In order to improve above formula as a result, integrating following method here, detailed process is as follows：
The probability of misjudgement error is divided into two classes, first, slave mode is judged to runaway condition, probability is, second, runaway condition
Slave mode is judged to, probability is
Sample, when in slave mode, if it is distributed as normal distribution；Process is in runaway condition
When, its distribution is changed, and the distribution function after change is
The upper and lower control of note control figure, which limits, is respectively、；
Overall error probability is
Above formulaFor standardized normal distribution distribution function in pointThe value at place,Actual parameter in figure in order to control, this
Concrete condition can be determined specifically
Unitary correction factor：
、Respectively centre distance difference, probability of miscarriage of justice weight distribution value, here,
Characterize process capability function：
Multivariate table levies process capability function：
Characterization
According toModel is the main feature of extractable manufacturing process abnormal quality
Step 3：The data observed recorded in oneself control figure through finishing control limit, be according to steady rule judgment process is sentenced
No abnormal phenomenon occur, its specific calculating process is as follows：
The control figure established when if process is in non-statistical controlled process state with sample point controls follow-up production process, no
Good control effect is not only had, the forecast of mistake can be brought to enterprise on the contrary, caused damages to enterprise
Sentence steady rule：
、、Respectively pre-set parameter
Only meet that upper three formula just can determine that whether current state is in slave mode, has a rule to be unsatisfactory for, is determined as at the same time
Anomalous mode
Step 4：According to recognition result, process exception source place is found out based on chi-square value method；
Step 5：Related personnel proposes and implements improved measure for quality problems, solves process exception situation；
Step 6：After implementation is improved, dimension is continuous to carry out procedure quality verification confirmation using control figure, and whether observation still has exception,
Returned if having ask to（3）, manufacturing process is monitored if continuing with control figure without if.
2. according to the manufacturing process multivariate quality diagnostic classification device based on chi-square value described in claim 1, it is characterized in that, with
Specific calculating process in the upper step 4 is as follows：
Step 4：According to recognition result, where finding out process exception source based on chi-square value method, its specific calculating process is as follows：
The chi-square value of attribute is determined according to training dataset, each qualitative attribute corresponds to several components；
Here qualitative attribute definite opinion following formula really：
For attributeBelong to the property value of class j,For the average of class j,For attributeBelong to the number of class j
Anomaly source is determined further according to similarity, it is specific as follows：
Above formulaFor the similarity of attribute S and control figure ideal control line,Figure reason in order to control
Think control line,For unit equivalent coefficient
Bigger or smaller, corresponding qualitative attribute is more unstable, more than threshold range, that is, triggers
Alarm, then abnormal component, i.e. anomaly source are determined by above formula.
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Application publication date: 20180511 |