CN109345060B  Product quality characteristic error traceability analysis method based on multisource perception  Google Patents
Product quality characteristic error traceability analysis method based on multisource perception Download PDFInfo
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 CN109345060B CN109345060B CN201810955315.XA CN201810955315A CN109345060B CN 109345060 B CN109345060 B CN 109345060B CN 201810955315 A CN201810955315 A CN 201810955315A CN 109345060 B CN109345060 B CN 109345060B
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Abstract
A product quality characteristic error tracing analysis method based on multisource perception comprises the following steps: establishing a causal relationship between the processes by using historical data; screening the historical data, so that the screened historical data form a sample space; taking the sample space as an analysis standard and adopting T^{2}The control chart method monitors realtime data; according to the cause and effect relationship, the outofrange T is treated^{2}Carrying out orthogonal decomposition on the values to obtain decomposition terms; t to the decomposition term^{2}And performing outofbound analysis on the statistical values so as to locate the problem process. The invention utilizes a large amount of multivariate historical data to complete the analysis of the incidence relation among the working procedures, thereby reducing the influence caused by subjective factors; for outofbounds T^{2}The value is decomposed according to the relationship digraph between the processes obtained by the analysis process of the incidence relationship between the processes, so that the problem process is determined, the process is accurately grasped, and the purpose of problem tracing is achieved.
Description
Technical Field
The method relates to the field of process analysis and model static and/or dynamic analysis, in particular to a product quality characteristic error traceability analysis method based on multisource perception, which is used for traceability of process problems.
Background
The Statistical Process Control (Statistical Process Control) concept originated in the 20 th century, with Control charts proposed by morse houhart usa as the main mark. Since this concept was proposed, it has been widely used in industry and service industry. The fluctuation of the production process is analyzed and monitored by means of mathematical statistical knowledge, and a precautionary measure is provided, so that the production process is in a controlled state only influenced by random factors. The control chart is the most important tool in statistical process control, and can be divided into an analysis control chart and a control chart according to different purposes of use. The analysis control chart is mainly used for analyzing whether the process is in a statistical control state. The process can only be monitored (control map for control) when the process reaches a desired steady state.
With the development of modern sensor technology, the difficulty of collecting relevant data in the production process is greatly reduced, and the multisource data in the production process is obtained to analyze the production process, thereby forming the advantage of modern statistical process control. The acquisition of a large amount of historical and realtime data allows us to better analyze and monitor the process in real time. For complex processing systems, the cause of the failure of the final product is not negligible in addition to the various potential failure factors.
In the multiprocess machining and manufacturing process, failure modes are various, and the problem that error sources corresponding to the failure modes are difficult to accurately position exists. Starting from the source, the method is an effective method for avoiding faults and failures. In the prior art, T is utilized^{2}When the control chart monitors the actual processing process, the phenomenon that each procedure is respectively monitored and is not out of control, but the whole processing process is monitored and is out of control often occurs, so that an error source is difficult to accurately position; in practical situations, most of causal relationship networks among the processing procedures are established according to methods such as process rule files, expert evaluation and the like, the proportion of artificial subjective factors is large, and the established network relationships cannot scientifically and effectively reflect the association relationships among the procedures.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a product quality characteristic error traceability analysis method based on multisource perception, which is realized by the following technical scheme and comprises the following steps: establishing a causal relationship between the processes by using historical data; screening the historical data, so that the screened historical data form a sample space; taking the sample space as an analysis standard and adopting T^{2}The control chart method monitors realtime data; according to the cause and effect relationship, the outofrange T is treated^{2}Carrying out orthogonal decomposition on the values to obtain decomposition terms; t to the decomposition term^{2}And performing outofbound analysis on the statistical values so as to locate the problem process.
Further, the historical data includes: index factors corresponding to the process.
Further, the index factors are one or more, and each index factor is one or more, acquired by a plurality of corresponding sensors.
Further, the method for establishing the causal relationship network among the processes according to the index factors comprises the following steps: calculating the average value of the index factors in each procedure to obtain a covariance matrix of the index factors of the procedure; and obtaining a correlation coefficient matrix between the working procedures according to the covariance matrix.
Further, said using T^{2}The analysis method in the control chart is used for screening the historical data and comprises the following steps: computing a multivariate simple value T^{2}Counting the value; calculating T^{2}Controlling the upper control limit and the lower control limit of the map; will be the T^{2}Is compared with the upper control limit and the lower control limit, and the outofbound T is determined^{2}Removing historical data corresponding to the statistical value; recalculating the average value of the index factors and the covariance matrix of the index factors of the rejected historical data, and repeating the steps until the T which is not out of bounds^{2}Until the statistical value of (2) is generated.
Further, the calculating the multivariate single value T^{2}The statistical values of (a) include: calculating a multivariate single value T according to the average value of the index factors and the covariance matrix of the index factors^{2}The statistical value of (1).
Further, said calculating T^{2}The upper and lower control limits of the control map include: by giving a significance level value, T is calculated^{2}Controlling an upper control limit of the map; let T^{2}The lower control limit of the control map is 0.
Further, said T^{2}The outofbounds condition of the statistical value of (1) includes: the T is^{2}Is greater than or equal to the upper control limit, or T^{2}Is less than or equal to the lower control limit.
Further, said using T^{2}The control chart method for monitoring the realtime data comprises the following steps: according to the realtime data, calculating the corresponding T^{2}Counting the value; t corresponding to the^{2}And comparing the statistical value with the upper control limit and the lower control limit so as to monitor the realtime data.
Further, the outofrange T is determined according to the causal relationship between the processes^{2}Performing orthogonal decomposition on the statistical values comprises: according to between said proceduresEstablishing a process relation directed graph according to the causal relation; according to the process relation directed graph, the outofbounds T is determined^{2}The statistical values are subjected to orthogonal decomposition.
The invention has the advantages that:
i. compared with the traditional method for constructing the causal model, the method for constructing the incidence relation between the working procedures by using the correlation coefficient matrix based on the historical data utilizes a large amount of multisource online perception data acquired by the internet of things technology to construct the causal model, so that the influence of artificial subjective factors is reduced to a great extent, and the established causal model has high credibility.
Conventional T^{2}The control chart has the conditions that the monitoring of a single process is controlled, and the control chart in the whole process is out of control, so that the error source is difficult to position. Aiming at the situation, the invention provides a method for positioning the error source by carrying out orthogonal decomposition on the outofbounds abnormal points in the whole process control chart based on the causal model, so that the problem procedures and the problem procedures with interaction can be accurately and effectively found out, and the method is used for providing accurate and effective guidance for the improvement of the procedures.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 shows a block diagram of a traceability analysis method according to an embodiment of the present invention.
FIG. 2 illustrates a traceability analysis workflow diagram according to an embodiment of the present invention.
FIG. 3 is a schematic diagram illustrating interprocess relationships according to an embodiment of the present invention.
Fig. 4 is a schematic process correlation diagram of a thinwall part partial machining example according to an embodiment of the invention.
FIG. 5 shows a g1 procedure according to an example of an embodiment of the inventionT^{2}Control a chart.
FIG. 6 shows a g2 procedure T according to an example of an embodiment of the present invention^{2}Control a chart.
FIG. 7 shows a g3 procedure T according to an example of an embodiment of the present invention^{2}Control a chart.
FIG. 8 shows a g4 procedure T according to an example of an embodiment of the invention^{2}Control a chart.
FIG. 9 shows a g5 procedure T according to an example of an embodiment of the invention^{2}Control a chart.
FIG. 10 shows a g6 procedure T according to an example of an embodiment of the invention^{2}Control a chart.
FIG. 11 shows realtime monitoring T according to an embodiment of the invention^{2}Control a chart.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
According to the embodiment of the invention, a product quality characteristic error tracing method based on multisource perception information is provided. Aiming at the problem that products are deviated in the multiprocess machining and manufacturing process of an intelligent production line but the source of the problem is difficult to determine, a causal relationship network among the processes is established by using historical data. Utilizing T for realtime multisource perceptual data^{2}The control chart monitors the processing process in real time and detects the outofbounds abnormal T^{2}The statistical values are orthogonally decomposed based on causal relationship between processes, and the decomposed items are then T^{2}And controlling the chart so as to locate the problem process and further carry out targeted improvement.
Fig. 1 is a block diagram of a tracing analysis method according to an embodiment of the present invention. The source tracing analysis method comprises the following steps: s1, LiEstablishing a causal relationship between the processes by using historical data; s2, screening the historical data, and forming a sample space by the screened historical data; s3, taking the sample space as an analysis standard and adopting T^{2}The control chart method monitors realtime data; s4, according to the causal relationship, the outofrange T is processed^{2}Carrying out orthogonal decomposition on the values to obtain decomposition terms; s5, T for the decomposition term^{2}And performing outofbound analysis on the statistical values so as to locate the problem process.
Specifically, the historical data includes: index factors corresponding to the process. The index factors may be one or more, and each index factor may be one or more, acquired by a plurality of corresponding sensors. The method for establishing the causal relationship network among the procedures comprises the following steps: calculating the average value of the index factors in each procedure, and further obtaining a covariance matrix of the index factors of the procedures; according to the covariance matrix, a correlation coefficient matrix between the working procedures is further obtained; wherein the correlation coefficient matrix represents a causal relationship between processes.
Said adoption of T^{2}The analysis method in the control chart is used for screening the historical data and comprises the following steps: computing a multivariate simple value T^{2}The statistical value of (a); calculating T^{2}Controlling the upper control limit and the lower control limit of the map; will be the T^{2}Is compared with the upper and lower control limits, thereby determining the outofbound T^{2}Removing historical data corresponding to the statistical value; recalculating the average value of the index factors and the covariance matrix of the index factors of the rejected historical data, and repeating the steps until the T which is not out of bounds^{2}Until the statistical value of (2) is generated. Wherein the calculating the multivariate single value T^{2}The statistical values of (a) include: calculating a multivariate single value T according to the average value of the index factors and the covariance matrix of the index factors^{2}The statistical value of (1). The calculation of T^{2}The upper and lower control limits of the control map include: by giving a significance level value, T is calculated^{2}Controlling an upper control limit of the map; and is provided with T^{2}The lower control limit of the control map is 0. The T is^{2}Out of bounds of the statistical value ofThe situations include: the T is^{2}Is greater than or equal to the upper control limit, or T^{2}Is less than or equal to the lower control limit. Said adoption of T^{2}The control chart method for monitoring the realtime data comprises the following steps: according to the realtime data, calculating the corresponding T^{2}Counting the value; and further corresponding to the T^{2}And comparing the statistical value with the upper control limit and the lower control limit so as to monitor the realtime data. The outofrange T is determined according to the causal relationship between the processes^{2}Performing orthogonal decomposition on the statistical values comprises: establishing a process relation directed graph according to the causeandeffect relation among the processes; and according to the process relation directed graph, the outofbound T is processed^{2}The statistical values are subjected to orthogonal decomposition. The present invention will be further described with reference to specific work flows.
Fig. 2 is a flowchart illustrating a trace analysis work flow according to an embodiment of the present invention. The invention discloses a product quality characteristic error tracing method based on multisource perception. Firstly, obtaining a correlation coefficient matrix between processes based on historical data, and establishing a causal relationship network between the processes; then based on T in MSPC (multiple statistical process control) multivariate statistical process control^{2}The control chart respectively establishes T for each process and the whole processing process^{2}Controlling the realtime monitoring of the graph; second for T^{2}Exception T out of bounds in control graph^{2}Orthogonal decomposition of the statistics, and T again for the decomposed quantities^{2}A control chart; finally T according to the decomposition amount^{2}And controlling the image to determine an error source process and provide prevention and control measures. The specific work flow is as follows:
(1) and establishing an association relation between the procedures.
If the number of production processes of a certain product in actual production is m, and the type of index factors (such as acceleration, noise and the like) acquired by a sensor is p, X is_{i}＝(X_{i1},X_{i2},…,X_{ip}) And indicating Ptype index factor data collected in the ith process, wherein i is 1,2, …, m. Wherein the set of Ptype indicators is:
X＝(X_{1},X_{2},…,X_{p})^{T}～N_{p}(μ，Σ) (1)
wherein X follows a pdimensional normal distribution, wherein mu is the average value of each type of index factor, and Σ is the covariance of each type of index factor.
The average value of the ith process can be expressed as follows:
wherein the content of the first and second substances,step i is 1,2, …, m; index factor type j is 1,2, …, p; the sample size k for each index factor is 1,2, …, n.
The covariance of the ith pass can be expressed as follows:
further calculation is performed from the covariance matrix, and a correlation coefficient matrix R between processes can be obtained.
Optionally, a strong association relationship exists between two processes with the correlation coefficient  ρ  ≧ 0.6 defined, and then an association relationship model between the processes can be established according to the correlation coefficient matrix.
(2) And screening historical data to obtain the average value and covariance of stable index factors for realtime monitoring.
T^{2}The first stage of the control chart is an analysis control chart stage, and the filtered historical data is mainly used as a sample to provide stable mean and covariance for realtime monitoring of the second stage. At T^{2}In the control chart method, T corresponding to each index factor is calculated^{2}The statistic value is paired with the upper control limit and the lower control limit thereofThe method of ratio monitors the index factor, but again, at T^{2}The analysis stage of the control chart can also adopt the method to screen historical data, and the process is as follows:
multiple unit value T^{2}The calculation formula of the statistical value of (a) is as follows:
wherein the content of the first and second substances,is T of the k index factor of the ith procedure^{2}Statistical value, X_{ik}Is the k index factor of the ith process,the average value of the index factors of the ith process,is the covariance of index factors of the ith process.
Next, T is calculated by giving a significance level value α according to the kind P and the number n of the index factors^{2}The upper control limit of the control map is:
wherein, beta_{α}Beta distribution, F, representing the significance level value alpha obeyed_{α}Representing the F distribution to which the significance level value a obeys.
Optionally, setting a lower control limit LCL of the control chart_{i}When the index factor is equal to 0, the T corresponding to each index factor is judged^{2}Statistics, and further, optionally, for T^{2}Is greater than or equal to the upper control limit, or T^{2}The corresponding historical data with the statistical value less than or equal to the lower control limit are removed, and then the average value of the index factors is recalculated according to the stepsAnd the covariance matrix of the index factors, and repeating the above steps until there is no outofbounds T^{2}Until the statistical value of (2) is generated. And record the timeAnd S_{i}The value of (c).
T^{2}The second phase of the control sum plot is the control phase, which uses the mean of the remaining n' stable samples of the first phaseCovariance S_{i}' realtime process data is monitored. At this time T^{2}Statistics are as follows:
wherein, X_{f}Is a data matrix to be monitored in real time. The upper control limit is as follows:
(3) t for each step^{2}Control chart for judging whether each process is controlled
In the realtime monitoring process, the invention firstly carries out T of each process^{2}The control chart is used for independently verifying whether each process is controlled or not in the process; if not controlled (T)^{2}Value out of bounds), the process is an abnormal process, and problem analysis is performed on the process to solve the solution. If each process is controlled (no T)^{2}Out of bounds), then T is taken as the overall process^{2}And if no uncontrolled condition occurs, the control chart shows that the machining process is normal.
(4) T as a whole^{2}Control chart for judging whether the working procedure is controlled or not in the whole process
If all the working procedures are controlled, the controlled conditions in the whole processing process are analyzed next, and if all the working procedures are controlled, the whole processing process is carried outIf the process is not controlled, the abnormal node (outofbound T) is selected^{2}Value), the analysis content includes the independent item and condition item of the problem node, the specific process is as follows:
and carrying out orthogonal decomposition on the abnormal points based on the causal relationship graph among the processes. Will be provided withThe expression for the orthogonal decomposition is:
wherein the content of the first and second substances,referred to as the independent item,referred to as condition term, PA (g)_{j}) Is a process g_{j}Of all parent nodes. The independent items are calculated in the following mode:
the condition terms are calculated as follows:
wherein d is the number of condition factors, and when no condition item exists, d is 0; j is g1, g2 … … gm is process 1, process 2 … …, and m is the number of processes.
As can be seen from the calculation formula of orthogonal decomposition, the calculation amount of the calculation mode is relatively large, and the correlation relation model obtained by combining the first part of the step defines the T^{2}The values are decomposed as follows:
fig. 3 is a schematic diagram illustrating the correlation between the processes according to the embodiment of the present invention. The method comprises 6 steps of g1 to g6 and the like, and the strength relation degree among the steps is obtained according to the correlation coefficient matrix R, so that the schematic diagram shown in FIG. 3 is obtained. In the relation shown in FIG. 3, g1 and g2 are parents of g3, g4 and g5 share a common parent node of g3, and g5 is a parent node of g 6. The decomposition is as follows:
wherein the content of the first and second substances,
…
if the class I error probability is α, thenAndthe determination limit of (1) is:
the judging method comprises the following steps: if it isThen indicate g_{1}(step 1) is a main cause of error; if it isThen indicate g_{i}Is the main cause of error; if it isAndall are simultaneously greater than the control limit, indicating g_{1}And g_{i}Are all causes of errors.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
Taking a part of a processing process of the xx thinwall cylinder as an example, because the processing process of the thinwall cylinder is a complex multiprocedure process, the example intercepts six continuous procedures of the processing process of the thinwall cylinder, namely, rough turning of an end face, rough turning of an outer circle, rough turning of an inner circle, rough milling of a square hole, rough milling of a round hole and rough milling of a long round hole, wherein g is used for each procedure_{1},g_{2},…,g_{6}And representing and utilizing an external sensor to acquire acceleration signals and sound pressure signals in three directions in real time. The probability α of making a type I error is 0.05. Using the history data, an absolute value matrix of correlation coefficients ρ between six processes is obtained as shown in the following table:
TABLE 1 interProcess correlation coefficient Table
The causal relationship between these six steps is constructed as shown in FIG. 4, with the constraint of  ρ  ≧ 0.6.
Fig. 4 is a schematic diagram illustrating a process relationship of a partial processing example of a thinwall part according to an embodiment of the present invention. The strong association relationship is highlighted by blackening, so that the parent nodes of g3 are g1 and g2, the parent nodes of g4 are g2 and g3, the parent nodes of g5 are g3 and g4, and the parent nodes of g6 are g4 and g 5. By T^{2}The control chart monitors g 1g 6, and the results are shown in fig. 410, and the results of the chart show that T of 6 processes^{2}The control map is in a controlled state. Next, the entire process of these six steps is monitored, T^{2}As shown in fig. 11, the calculated upper control limit UCL is 9.482797, and the calculated lower control limit LCL is 0. Computing discovery T^{2}The statistics are out of bounds at point 2932. For T here^{2}The statistics are decomposed. According to the causal relationship model of FIG. 4, the decomposition of the outofbounds point is as follows:
the calculated reason for runaway for the 2932 th sample is shown in table 2 below:
TABLE 2 error diagnosis information Table
In table 2, "√" indicates that the decomposition term is an error source, and "x" indicates that the decomposition term is not an error source.
From the error diagnosis information table in table 2, the 2932 th sample that is the 1 st runaway sample was diagnosed, and it can be seen that the root process causing the 2932 th sample to runaway is process 1 and process 2. In combination with the actual production process, problems such as knife breakage and the like easily occur in the working procedure 1 and the working procedure 2, so that the quality of the product is abnormally fluctuated, which is approximately the same as the result analyzed by the method provided by the invention.
It should be noted that the contents of the detailed description and the examples are all an alternative of the present invention, wherein, for example, the correlation coefficient, the error probability α, etc. are obtained according to practical experience, and are not limited to the above values, and the present invention can be used for analysis of multiple types of errors, and is not limited to only one type.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (6)
1. A product quality characteristic error tracing analysis method based on multisource perception is characterized by comprising the following steps:
establishing a causal relationship among the processes by using historical data, wherein the historical data comprises one or more index factors corresponding to the processes, each index factor is one or more, the index factors are acquired by a plurality of corresponding sensors, the average value of the index factors in each process is calculated, a covariance matrix of the index factors of the processes is acquired, and a correlation coefficient matrix among the processes is acquired according to the covariance matrix;
screening the historical data, so that the screened historical data form a sample space;
monitoring realtime data by using the sample space as an analysis standard and adopting a T ^2 control graph method;
establishing a process relation directed graph according to the causeandeffect relation among the processes, and performing orthogonal decomposition on the outofbounds T ^2 statistic according to the process relation directed graph to obtain a decomposition item;
and carrying outofbound analysis on the T ^2 statistic value of the decomposition item so as to locate the problem process.
2. The traceability analysis method of claim 1, wherein the screening of the historical data by using the analysis method in the T ^2 control graph comprises:
calculating the statistic value of the multivariate single value T ^ 2;
calculating the upper control limit and the lower control limit of the T ^2 control graph;
comparing the statistic value of T ^2 with the upper control limit and the lower control limit, and eliminating historical data corresponding to the statistic value of T ^2 out of bounds;
and recalculating the average value of the index factors and the covariance matrix of the index factors of the rejected historical data, and repeating the steps until the statistic value of the T ^2 which is not out of range is generated.
3. The traceability analysis method of claim 2, wherein the calculating the statistical value of the multivariate value T ^2 comprises:
and calculating the statistic value of the multivariate single value T ^2 according to the average value of the index factors and the covariance matrix of the index factors.
4. The traceability analysis method of claim 2, wherein the calculating the upper control limit and the lower control limit of the T ^2 control map comprises:
calculating the control upper limit of the T ^2 control graph by giving a significance level value;
the lower control limit of the T ^2 control chart is set to be 0.
5. The traceability analysis method of claim 2, wherein the outofbounds condition of the statistical value of T ^2 comprises:
the statistic value of T ^2 is greater than or equal to the upper control limit, or the statistic value of T ^2 is less than or equal to the lower control limit.
6. The traceability analysis method of claim 1, wherein the monitoring of the realtime data by using the T ^2 control graph method comprises:
calculating a corresponding T ^2 statistic value according to the realtime data;
and comparing the corresponding T ^2 statistic with the upper control limit and the lower control limit, thereby monitoring the realtime data.
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