CN107291065A - The improved manufacturing process multivariate quality diagnostic classification device based on decision tree - Google Patents
The improved 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
- G05B23/0245—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 based on a qualitative model, e.g. rule based; if-then decisions
- G05B23/0248—Causal models, e.g. fault tree; digraphs; qualitative physics
Abstract
The manufacturing process multivariate quality diagnostic classification device of improved 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, the traditional decision-tree of application enhancements is found out where process exception source, in order that classification results are more accurate, constructs qualitative attribute importance functions.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 improved to be based on
The manufacturing process multivariate quality diagnostic classification device of decision tree.
Background technology
Modem manufacturing operations are multivariable height correlations, are referred to 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：One is Statistics decomposition technology；Two be the technology based on machine learning.Main flow 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 generally all contains complex statistics process, is unfavorable for application.With the development of computer technology, machine learning turns into this neck
The study 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, processed parts or product often have to be existed between multiple mass propertys, and these mass propertys
How certain correlation, determine the Measure of Process Capability of the process and procedure quality diagnosed, and is in the urgent need to solution
Certainly the problem of, the research of the problem not only has great importance to the research of polynary manufacturing process capability analysis, and to polynary
The quality of manufacturing process, which is monitored and diagnosed, is respectively provided with certain theory significance and practical value.Based on the demand, this hair
It is bright to provide the improved manufacturing process multivariate quality diagnostic classification device based on decision tree.
The content of the invention
For deficiency of the multivariate control chart in complex process monitoring and abnormity diagnosis, it is based on the invention provides improved
The manufacturing process multivariate quality diagnostic classification device of decision tree.
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：Multivariate Quality Characteristics to critical process carry out process analysis procedure analysis；
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 anomaly；
Step 4：According to recognition result, the traditional decision-tree of application enhancements finds out process exception source place；
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 using control figure to procedure quality, and whether observe 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 meets the result of experience judgement.
6th, consider the erroneous judgement factor and combine principal component analytical method, the further lifting that result precision is obtained 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 the problem of quality control aspect between traditional vehicle is present, the present invention has been carried out in detail with reference to Fig. 1-Fig. 4
Illustrate, 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 normally runs collection 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 is population mean vector, and ∑ is covariance
Matrix, due to ∑m×mFor symmetrical matrix, therefore there is symmetrical matrix P so that
Wherein λ1, λ2..., λmFor the characteristic value of covariance matrix, it meets (λ1, λ2..., λm) ＞ 0, the i.e. polynary matter of m dimensions
The weight distribution of amount can be expressed as following formula：
The contribution rate of accumulative total of k pivot reaches that more than 80%, i.e. contribution rate are W before taking：
Then principal component model is
The attribute vector of respectively k pivot quality, E is error.
Step 2：Multivariate Quality Characteristics to critical process carry out process analysis procedure analysis, and its specific calculating process is as follows：
Here main 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 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, process capability function is characterized as follows：
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, and one is that slave mode is judged to runaway condition, and probability is P1, two be 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, here β1+β2=1, (β1, β2)
＞ 0.
Characterize process capability function CP：
CP=min (CPU, CPL)
Multivariate table levies 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 oneself control figure through finishing control limit, according to sentencing steady rule judgment mistake
Whether journey there is anomaly, and its specific calculating process is as follows：
If process is in the control figure set up during non-statistical controlled process state with sample point and controls follow-up production
Journey, good control effect is not had not only, can be brought the forecast of mistake to enterprise on the contrary, be caused damage to enterprise.
Sentence steady rule：
ε′、ε1、ε2Respectively pre-set parameter.
Upper three formula is only met simultaneously and just can determine that whether current state is in slave mode, is had a rule to be unsatisfactory for, is sentenced
It is set to anomalous mode.
Step 4：According to recognition result, the traditional decision-tree of application enhancements finds out process exception source place, its specific meter
Calculation 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 formula S 'iFor qualitative attribute importance functions, j ∈ (1,2 ..., L) are the component factor of qualitative attribute, a total of L
Individual component, niFor qualitative attribute i sample number, nij' it is the sample number that qualitative attribute i respective components are j, p (j/Z) is j component
Belong to controlled probability of state, p (j/Y) is that j component belongs to abnormal probability of state.
Si' from small to large determine qualitative attribute importance, S 'iBigger, corresponding qualitative attribute is more inessential, according to step
Rapid 3 judge anomalous mode, then compare each component value by 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 using control figure to procedure quality, and whether observe still has
It is abnormal, return and asked to (3) if having, manufacturing process is monitored if continuing with control figure without if.
Claims (2)
1. the improved manufacturing process multivariate quality diagnostic classification device based on decision tree, the present invention relates to Mechanical Product's Machining manufacture
A kind of process quality control technical field, and in particular to improved manufacturing process multivariate quality diagnostic classification based on decision tree
Device, it is characterized in that, comprise the 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；Because Multivariate Quality Characteristics are worth unit disunity, numerical values recited gap is also larger, data need to be done into
The processing of one step；
The data matrix that production process normally runs collection is,For the number of sample,For sample quality attribute
Number
Above formulaForIndividual sampleQuality property value is planted,ForQualitative attribute average is planted,The
Plant quality attribute poor
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 matrix, due toFor symmetrical matrix, therefore there is 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 individual pivot reaches that more than 80%, i.e. contribution rate are：
Then principal component model is
RespectivelyThe attribute vector of individual pivot quality,For error
Step 2：Multivariate Quality Characteristics to critical process carry out process analysis procedure analysis, and its specific calculating process is as follows：
Here it is main 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
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, process capability function is characterized as follows：
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, and one is that slave mode is judged to runaway condition, and probability is, two be runaway condition
Slave mode is judged to, probability is
Sample, when in slave mode, if it is distributed as normal distribution；Process is in shape out of control
During state, its distribution is changed, and the distribution function after change is
The upper and lower control of note control figure, which is limited, 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
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：
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
No anomaly occur, its specific calculating process is as follows：
If process is in the control figure set up during non-statistical controlled process state with sample point and 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
Upper three formula is only met simultaneously and just can determine that whether current state is in slave mode, is had a rule to be unsatisfactory for, is determined as
Anomalous mode
Step 4：According to recognition result, the traditional decision-tree of application enhancements finds out process exception source place；
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 using control figure to procedure quality, and whether still have exception, return and ask if having if observing
Extremely（3）, manufacturing process is monitored if continuing with control figure without if.
2. the improved manufacturing process multivariate quality diagnostic classification device based on decision tree according to claim 1, it is special
Levying is, the specific calculating process in step 4 described above is as follows：
Step 4：According to recognition result, the traditional decision-tree of application enhancements finds out process exception source place, and it was specifically calculated
Journey 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,For the component factor of qualitative attribute, always
It is sharedIndividual component,For qualitative attributeSample number,For qualitative attributeRespective components areSample number,ForComponent belongs to controlled probability of state,ForComponent belongs to abnormal probability of state
The importance of qualitative attribute is determined from small to large,Bigger, corresponding qualitative attribute is more inessential, according to step 3
Judge anomalous mode, then each component value is compared by above formula, it is determined that abnormal component, i.e. anomaly source.
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Application publication date: 20171024 |