CN107390667A - Manufacturing process multivariate quality diagnostic classification device based on decision tree - Google Patents
Manufacturing process multivariate quality diagnostic classification device based on decision tree Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
Manufacturing process multivariate quality diagnostic classification device based on decision tree, 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 of control figure record see whether anomaly occur according to steady rule is sentenced, where application decision tree method finds out process exception source, in order that classification results are more accurate, qualitative attribute importance functions are constructed.Process of the present invention capacity factor condition is rigorous, decision state is accurate, algorithm complex is low, the time of processing is fast, and anomalous mode decision rule is more easy to be bright and clear, combines multivariate quality, the erroneous judgement factor, the principal component factor, applicability is stronger, parameter processing specification, data processing is perfect, reduces probability of miscarriage of justice, solve data biasing, the skimble-scamble problem of unit, 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 decision tree
Manufacturing process multivariate quality diagnostic classification device.
Background technology
21 century, along with the development of global economic integration, the competition of international market, with time and cost
Equally, quality oneself turn into enterprise's survival and development main factors concerned.Extensively using domestic and international advanced quality method and matter
Amount technology is significant for Enhancing The Product Quality In Enterprises, raising product competitiveness.Good quality is inexpensive, efficient
Rate, low-loss, the guarantee of high yield are also to win customer loyalty for a long time, and enterprise obtains the foundation stone of sustainable development.In although
The nearest focus of state's business circles seems to concentrate on merger, capital management, the market expansion, diversification etc., but in fact, to appointing
The control of management, the production procedure of quality, it is the mostly important " interior of enterprise development for He Yijia manufactures enterprise
One of work("." internal strength " how is perfected, not only needs thought, the ways and means of quality management, with greater need for there is quality engineering skill
The support of art.How quality engineering technology is utilized, design and produce the production in inexpensive, short cycle, high quality, high reliability
Product, it is derived from striving advantage unexpectedly, the problem of oneself turns into domestic and international vast theoretical research person and working people extensive concern.And carry
One technical way of high quality is exactly to carry out effective process monitoring.Because product quality is important in modern industry
Status, statistical Process Control (SPC) achieve very ten-strike in machinery, weaving, electronic product, auto lamp discrete manufacturing business,
And gradually permeated to the industry of the interval such as papermaking, oil refining, chemical industry, food and continuous manufacturing industry.In the manufacturing process of reality, added
How really work parts or product often have multiple mass propertys, and certain correlation between these mass propertys be present,
The Measure of Process Capability of the fixed process and procedure quality is diagnosed, the problem of being in the urgent need to address, the problem is ground
Study carefully and not only have great importance to the research of polynary manufacturing process capability analysis, and the quality of polynary manufacturing process is supervised
Control and diagnosis are respectively provided with certain theory significance and practical value.Based on the demand, the invention provides one kind to be based on decision-making
The manufacturing process multivariate quality diagnostic classification device of tree.
The content of the invention
For deficiency of the multivariate control chart in complex process monitoring and abnormity diagnosis, the invention provides based on decision tree
Manufacturing process multivariate quality diagnostic classification device.
In order to solve the above 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 the control figure for having finished control limit, according to sentencing steady rule judgment mistake
Whether journey there is anomaly；
Step 4：According to recognition result, application decision tree method is found out where process exception source；
Step 5：Related personnel proposes and implemented improved measure for quality problems, solves process exception situation；
Step 6：After implementation is improved, dimension is continuous to carry out checking confirmation to procedure quality using control figure, and whether observation still has
It is abnormal, return and asked to (3) if having, manufacturing process is monitored if continuing with control figure without if.
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 again.
7th, data processing is more perfect, reduces the probability of erroneous judgement.
8th, biasing, the skimble-scamble problem of unit of data are solved.
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 Fig. 1 manufacture process controls and diagnostic techniques
Fig. 2 workshop data acquisition scheme figures of the present invention
The specification region of Fig. 3 two-dimensional process amendments and actual distribution example region figure
The complex process monitoring model schematic diagram of Fig. 4 decision trees
Embodiment
In order to solve deficiency of the multivariate control chart in complex process monitoring and abnormity diagnosis, with reference to Fig. 1-Fig. 4 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；
Because 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, carry out proportion and be calculated as follows：
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 symmetrical matrix P be present so that
Wherein λ1, λ2..., λmFor the characteristic value of covariance matrix, it meets (λ1, λ2..., λm) the polynary matter of ＞ 0, i.e. m dimension
The weight distribution of amount 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 (σ of μ -3 σ ＜ X ＜ 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%.
Specification region for process amendment is a spheroid, and 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 are that t ties up average difference.
Another factor of influence is(Uj、Lj) be specification bound intersection point.
I.e.
In summary, it is as follows to characterize process capability function：
In order to improve the result of above formula, following method is integrated here, and 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 of standardized normal distribution at point λ, and λ is actual parameter in control figure, 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 MC 'P：
Characterize E
E=| MCp-MC′P|
According to X 'It is mainModel is the main feature of extractable manufacturing process abnormal quality.
Step 3：The data observed recorded in the control figure for having finished control limit, according to sentencing steady rule judgment mistake
Whether journey there is anomaly, and 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, good control effect is not had not only, can bring the forecast of mistake to enterprise on the contrary, be caused damage 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 simultaneously
It is set to anomalous mode.
Step 4：According to recognition result, where application decision tree method finds out process exception source, its specific calculating process
It is as follows：
The influence degree of attribute is determined according to training dataset, each qualitative attribute corresponds to several components；
Here qualitative attribute definite opinion following formula really：
Above formula S 'iFor qualitative attribute importance functions, j ∈ (1,2 ..., L) are the component factor of qualitative attribute, a total of L
Individual component, μiFor the average of i-th of qualitative attribute, X 'ijFor the observation of the i-th qualitative attribute component.
S′iThe importance of qualitative attribute, S ' are determined from big to smalliBigger, corresponding qualitative attribute is more important, according to step 3
Judge anomalous mode, then by the more each component value of above formula, it is determined that abnormal component, i.e. anomaly source.
Step 5：Related personnel proposes and implemented improved measure for quality problems, solves process exception situation；
Step 6：After implementation is improved, dimension is continuous to carry out checking confirmation to procedure quality using control figure, and whether observation still has
It is abnormal, return and asked to (3) if having, manufacturing process is monitored if continuing with control figure without if.
Claims (3)
1. the manufacturing process multivariate quality diagnostic classification device based on decision tree, 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 decision tree, 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 formulaForIndividual sampleKind quality property value,ForKind qualitative attribute average,TheGermplasm
It is poor to measure attribute
To pretreated data, carry out proportion and be calculated as follows：
Assuming thatTie up normal distribution, i.e.,, whereinIt is vectorial for population mean,For covariance square
Battle array, due toFor symmetrical matrix, therefore symmetrical matrix be presentSo that
WhereinFor the characteristic value of covariance matrix, it meets, i.e.,Tie up multivariate quality
Weight distribution can be expressed as following formula：
Before takingThe contribution rate of accumulative total of individual pivot reaches more than 80%, i.e. contribution rate is：
Then principal component model is
RespectivelyThe attribute vector of individual 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, when quality characteristic value takes
From during 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
Outside probability only have
Specification region for process amendment is a spheroid, and 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 summary, it is as follows to characterize process capability function：
In order to improve the result of above formula, following method is integrated here, and 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 is sentenced
For slave mode, probability is
Sample, when in slave mode, if it is distributed as normal distribution；When process is in runaway condition,
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,For actual parameter in control figure, this is specific
Situation 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：
Characterize
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
It is no anomaly occur；
Step 4：According to recognition result, application decision tree method is found out where process exception source；
Step 5：Related personnel proposes and implemented improved measure for quality problems, solves process exception situation； steps 6：
After implementation is improved, dimension is continuous to carry out checking confirmation to procedure quality using control figure, and whether observation still has exception, return and ask if having
Extremely（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 decision tree described in claim 1, it is characterized in that, with
Specific calculating process in the upper step 3 is as follows：
Step 3：The data observed recorded in oneself control figure through finishing control limit, be according to steady rule judgment process is sentenced
No anomaly 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, is caused damage 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 simultaneously
Anomalous mode.
3. according to the manufacturing process multivariate quality diagnostic classification device based on decision tree 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 application decision tree method finds out process exception source, its specific calculating process is as follows：
The influence degree of attribute is determined according to training dataset, each qualitative attribute corresponds to several components；
Here qualitative attribute definite opinion following formula really：
Above formulaFor qualitative attribute importance functions,It is a total of for the component factor of qualitative attributeIt is individual
Component,ForThe average of individual qualitative attribute,ForThe observation of qualitative attribute component
The importance of qualitative attribute is determined from big to small,Bigger, corresponding qualitative attribute is more important, is judged according to step 3
Anomalous mode, then by the more each component value of above formula, it is determined that abnormal component, i.e. anomaly source.
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Application publication date: 20171124 |