CN107944491B - Quality characteristic symbolization mapping control chart construction method - Google Patents
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Abstract
The invention discloses a method for constructing a symbolic mapping control chart of quality characteristics; the method comprises the steps of constructing a quality characteristic value symbolization sequence, constructing a quality characteristic influence factor symbolization sequence, establishing a mapping relation and constructing a quality characteristic symbolized mapping control chart. The quality characteristic symbolized mapping control chart establishes the mapping relation between the quality characteristic value and the influence factors thereof, namely establishes the mapping relation between the quality characteristic data fluctuation rule and the influence factors causing fluctuation, on one hand, the fluctuation rule of the quality characteristic data can be reflected, on the other hand, the fault source can be found in advance through the mapping relation, so that the error caused by artificial subjective judgment is realized, the influence factors of the quality characteristic are predicted and adjusted, the quality characteristic is in an effective stable state, and the aim of preventive control is fulfilled.
Description
Technical Field
The invention belongs to the technical field of quality diagnosis and prevention, and particularly relates to a method for constructing a symbolic mapping control chart of quality characteristics.
Background
From a mathematical point of view, the process of quality diagnosis and pre-control is actually mapping the vectors of the symptom space to the fault source space, i.e. implementing the mapping F (mapping relation) from the space X (symptom space) to the space Y (fault source). The mapping relation F is unknown, and the essence of quality diagnosis is to integrate various knowledge and methods, find the mapping relation F and then apply the relation. When quality problems occur later, the problem root can be quickly found out so as to facilitate real-time control;
the traditional control chart can only judge whether the generation process is in a stable state according to the change condition of the quality characteristic data along with time, but can not finish the diagnosis and tracing of abnormal fluctuation in the production process; the traditional control chart can only describe the fluctuation rule of the quality characteristic data and cannot describe the incidence relation between the fluctuation rule and the fluctuation source. In the practical application process, the fault source and the reason of the abnormal fluctuation can be judged only by the experience knowledge and the subjectivity of engineering technicians.
Disclosure of Invention
The invention aims to: in order to solve the problems in the prior art, the invention provides a quality characteristic symbolized mapping control chart construction method, which avoids errors caused by artificial subjective judgment and realizes the prediction of quality characteristic influence factors.
The technical scheme of the invention is as follows: a quality characteristic symbolization mapping control chart construction method comprises the following steps:
s1, equally dividing the Huhart steady-state region and the upper and lower control outer limit regions into a plurality of regions, expressing each equally divided region by adopting a multi-component symbolic sequence, constructing a quality characteristic value symbolized sequence, and forming a gene sequence single chain;
s2, performing principal component analysis on the observed value of the quality characteristic influence factor in each time domain at each sampling moment, establishing a mapping relation between the quality characteristic value and the influence factor thereof, obtaining a quality characteristic influence factor sequence according to the quality characteristic value symbolization sequence established in the step S1, performing normalization processing on each quality characteristic influence factor, establishing a quality characteristic influence factor symbolization sequence, and forming a gene sequence single chain;
s3, combining the quality characteristic value symbolized sequence and the quality characteristic influence factor symbolized sequence by adopting an associated double-chain gene mode to construct a quality characteristic symbolized mapping control chart.
Further, the step S1 equally divides the houttt stable region and the upper and lower control limit regions into a plurality of regions, represents each of the equally divided regions by using a multi-element symbol sequence, constructs a mass characteristic value symbolized sequence, and forms a gene sequence single chain, specifically including the following substeps:
s11, dividing the Huhart steady-state region and the upper and lower control outer limit regions equally into eight regions with the central line μ line as the starting point, and expressing as { (∞, μ -3 σ),[μ-3σ,μ-2σ],(μ-2σ,μ-σ],(μ-σ,μ],(μ,μ+σ],(μ+σ,μ+2σ]，(μ+2σ,μ+3σ](μ +3 σ, + ∞) }, where the Huhart steady-state region is μ ± 3 σ;
s12, representing the eight equally divided areas in the step S11 by using symbols { D, C, B, A, a, B, C, D } octave group respectively;
s13, representing the quality characteristic data points at different sampling time points in each time domain as a symbol sequence QD＝{xiAnd obtaining mass characteristic value symbolized sequences to form gene sequence single chains, wherein the sequences are { …, …, A, B, C, a, B, C, D, D, … and … }.
Further, the step S2 performs principal component analysis on the observed value of the quality characteristic influencing factor in each time domain at each sampling time, establishes a mapping relationship between the quality characteristic value and the influencing factor, obtains a quality characteristic influencing factor sequence according to the quality characteristic value symbolization sequence established in the step S1, performs normalization processing on each quality characteristic influencing factor, establishes a quality characteristic influencing factor symbolization sequence, and forms a gene sequence single chain, specifically including the following sub-steps:
s21, extracting influence factor pivot information according to the observed value of the quality characteristic influence factor in each time domain at each sampling moment, and expressing the quality characteristic influence factor pivot sequence as { V }1,V2,V3,…VBIn which the quality characteristic influencing factor is represented as [ V ]1,V2,V3,…VN]The observed value is represented as [ x ]1,…,xN]；
S22, establishing a mapping for a quality characteristic value and its influencing factor
Establishing a mapping between a quality characteristic value and its influence factor pivot
Obtaining a quality characteristic influence factor sequence according to the quality characteristic value symbolization sequence constructed in step S1:
obtaining a quality characteristic influence factor pivot element sequence:
s23, classifying the influence of different quality characteristic influence factors at different times into grades, normalizing each quality characteristic influence factor and dividing the score after normalization into 10 grades [0-9 ]]The quality characteristics influencing factor state space S is defined by setting a steady state level threshold value, representing the quality characteristics influencing factor state with a score in a steady region as 0, and representing the quality characteristics influencing factor state with a score in an unstable region as 1NFExpressed as:
influencing the quality characteristics into a factor state space SKFExpressed as:
obtaining the quality characteristic influence factor symbolization sequence to form a gene sequence single chain.
Further, in step S3, the quality characteristic value symbolized sequence and the quality characteristic influencing factor symbolized sequence are combined by using a correlated double-stranded gene pattern to construct a quality characteristic symbolized mapping control chart, which specifically includes: and combining the gene sequence single chain formed in the step S1 and the gene sequence single chain formed in the step S2 into a double-chain gene sequence by adopting an associated double-chain gene mode according to the mapping relation between the quality characteristic value and the influence factor thereof established in the step S2, thereby obtaining a quality characteristic symbolized mapping control chart.
The invention has the beneficial effects that: the quality characteristic symbolized mapping control chart establishes the mapping relation between the quality characteristic value and the influence factors thereof, namely establishes the mapping relation between the quality characteristic data fluctuation rule and the influence factors causing fluctuation, on one hand, the fluctuation rule of the quality characteristic data can be reflected, on the other hand, the fault source can be found in advance through the mapping relation, so that the error caused by artificial subjective judgment is realized, the influence factors of the quality characteristic are predicted and adjusted, the quality characteristic is in an effective stable state, and the aim of preventive control is fulfilled.
Drawings
FIG. 1 is a flow chart of a quality characteristic symbolized mapping control chart construction method of the present invention.
Fig. 2 is a schematic diagram of the construction of the quality characteristic value symbolized sequence of the present invention.
FIG. 3 is a schematic diagram of the quality characteristic influencing factor principal component extraction and mapping structure infrastructure construction of the present invention.
FIG. 4 is a schematic diagram of the construction of the quality characteristic influencing factor symbolized sequence of the present invention.
FIG. 5 is a schematic diagram of a symbolic chemical control map for quality characteristics according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, a flow chart of the quality characteristic symbolization mapping control chart construction method of the present invention is schematically shown. A quality characteristic symbolization mapping control chart construction method comprises the following steps:
s1, equally dividing the Huhart steady-state region and the upper and lower control outer limit regions into a plurality of regions, expressing each equally divided region by adopting a multi-component symbolic sequence, constructing a quality characteristic value symbolized sequence, and forming a gene sequence single chain;
s2, performing principal component analysis on the observed value of the quality characteristic influence factor in each time domain at each sampling moment, establishing a mapping relation between the quality characteristic value and the influence factor thereof, obtaining a quality characteristic influence factor sequence according to the quality characteristic value symbolization sequence established in the step S1, performing normalization processing on each quality characteristic influence factor, establishing a quality characteristic influence factor symbolization sequence, and forming a gene sequence single chain;
s3, combining the quality characteristic value symbolized sequence and the quality characteristic influence factor symbolized sequence by adopting an associated double-chain gene mode to construct a quality characteristic symbolized mapping control chart.
In step S1, the shehatt control map focuses the process steady controlled state quality characteristic data points in the μ ± 3 σ region, indicating that the process is controlled if 99.73% of the data points fall within this region. The criterion for judging the effective control according to whether the data point falls within the present region is fuzzy, but the control map can reflect any change in the process by a change in the position of the data point. The trend, chain, period, out-of-range and the like of the position distribution condition of the data points in the control chart can indicate whether an abnormality occurs in the process.
Equally dividing a Huhart steady-state region and an upper and lower control outer limit region into a plurality of regions, expressing each equally divided region by adopting a multi-component symbolic sequence, constructing a quality characteristic value symbolic sequence, and forming a gene sequence single chain, wherein the method specifically comprises the following steps:
s11, equally dividing the Huhart steady-state region and the upper and lower control outer limit regions into eight regions represented by { (∞, μ -3 σ), [ μ -3 σ, μ -2 σ ], (μ -2 σ, μ - σ ], (μ - σ, μ ], (μ, μ + σ ], (μ + σ, μ +2 σ ], (μ +2 σ, μ +3 σ ], (μ +3 σ, and + ∞) }, with the center line μ line as the starting point, wherein the Huhart steady-state region is μ ± 3 σ, μ is the mathematical expectation of the mass characteristic value, and σ is the variance of the mass characteristic value;
s12, representing the eight equally divided areas in the step S11 by using symbols { D, C, B, A, a, B, C, D } octave group respectively;
s12, representing the quality characteristic data points at different sampling time points in each time domain as a symbol sequence QD＝{xiObtaining a mass characteristic value symbolized sequence to form a gene sequence list { …, …, A, B, C, a, B, C, D, D, …, … }, and forming a gene sequence listAnd (3) a chain.
Symbol sequence QDReflecting the change process of the quality characteristic data points at different sampling moments in each time domain. For example, when the character sequence { …, C, B, a, A, B, C, … } appears continuously, it indicates that the ascending trend exception occurs; when the character sequence { …, A, A, B, B, C, C, … } appears, it indicates that the ascending trend on the center side is abnormal; when the character sequence { …, C, B, A, a, B, C, … } appears, the descending trend is abnormal; when the character sequence { …, a, a, b, b, c, c, … } appears, it indicates that the center side downward trend abnormality occurs; when { …, D, … } or { …, D, … } appears in the character sequence, it represents an out-of-bounds exception; when character sequences appear consecutively
When the repeated character sequences which are the same in interval and rise or fall are arranged in a certain time, the periodic symptoms are shown; when more than 12 character sequences { …, A, a, A, a, A, a, … } appear in succession centered on the centerline, then a tightly centered anomaly is indicated.
The character sequence characteristic change of the invention can reflect the process change of data. The character sequences of different local features indicate the existence of different abnormal patterns, which are far larger than the traditional eight-large abnormal pattern features, and can be beneficial to expanding more practical and more specific abnormal patterns through an information identification technology. The traditional eight-large abnormal mode only gives a basic abnormal judgment criterion, but more abnormal rules need to be judged manually. The invention is based on the traditional abnormal mode, can improve and supplement more abnormal mode libraries by using the character sequence characteristic rule and combining the fault information and applying the information identification technology of the character, and is more beneficial to the diagnosis and the pre-control of the quality characteristic.
Fig. 2 is a schematic diagram of the construction of the quality characteristic value symbolization sequence according to the present invention. Wherein, 1,2.. I respectively represent a time domain, the first sequence is a quality characteristic value sequence, and the quality characteristic value sequence is symbolized to form a gene sequence single chain. The converted quality characteristic character sequence contains known and unknown quality characteristic data abnormal modes, and the quality characteristic character sequence in the whole time domain is collected along with the evolution operation of the quality characteristic to form a quality characteristic mass data information base.
In step S2, the factors that influence the fluctuation law of the quality characteristic data are the quality factors in the manufacturing process, and these factors constitute the quality characteristic fluctuation source information set. The result of the variation in the quality characteristics has a certain regularity with the manifestation of the quality-affecting factors. If quality influence factors in the manufacturing process are similar (namely operators and inspectors in the same level, equipment systems in the same type and economic precision, workpieces with the same processing difficulty, processing methods and quality characteristic values in the same type with the same economic precision, detection methods of the same detection errors, similar processing environments and the like), the processed quality characteristic data also meet the distribution rules in the same type. This lays the foundation of the mapping relationship from data fluctuation to element state change.
The step S2 is to perform principal component analysis on the observed value of the quality characteristic influencing factor in each time domain at each sampling time, establish a mapping relationship between the quality characteristic value and the influencing factor, obtain a quality characteristic influencing factor sequence according to the quality characteristic value symbolization sequence established in the step S1, perform normalization processing on each quality characteristic influencing factor, establish a quality characteristic influencing factor symbolization sequence, and form a gene sequence single chain, and specifically includes the following sub-steps:
s21, extracting pivot information according to the observed value of the quality characteristic influence factor in each time domain at each sampling moment, and expressing the quality characteristic influence factor pivot sequence as { V }1,V2,V3,…VBIn which the quality characteristic influencing factor is represented as [ V ]1,V2,V3,…VN]The observed value is represented as [ x ]1,…,xN]，VBIs the B-th quality characteristic influence factor principal element, VNIs the Nth quality characteristic factor, xNIs the observed value of the Nth influencing factor;
s22, establishing a mapping for a quality characteristic value and its influencing factor
Wherein XNFor the nth value of the quality characteristic,
is XNThe corresponding Nth influencing factor; establishing a mapping between a quality characteristic value and its influence factor pivot
Wherein XKFor the kth quality characteristic value after the extraction of the pivot,
for X after extraction of principal elementKThe corresponding B-th influencing factor; obtaining a quality characteristic influence factor sequence according to the quality characteristic value symbolization sequence constructed in step S1:
obtaining a quality characteristic influence factor sequence:
s23, classifying the influence of different quality characteristic influence factors at different times into grades, normalizing each quality characteristic influence factor and dividing the score after normalization into 10 grades [0-9 ]]The quality characteristics influencing factor state space S is defined by setting a steady state level threshold value, representing the quality characteristics influencing factor state with a score in a steady region as 0, and representing the quality characteristics influencing factor state with a score in an unstable region as 1NFExpressed as:
principal component state space S of quality characteristic influencing factorsKFExpressed as:
obtaining the quality characteristic influence factor symbolization sequence to form a gene sequence single chain.
As shown in fig. 3, a schematic diagram of the quality characteristic influence factor principal component extraction and mapping structure infrastructure is constructed.
In step S23, since the quality characteristic factors relate to several aspects of 5M1E, some factors are measurable, some are not measurable, some are qualitatively described, and some are quantitatively described, so to describe the state change uniformly, it is necessary to describe and analyze uniformly by a certain knowledge method. The quantitative values of different influencing factors have no comparability and cannot be compared and analyzed, so that the quantitative values of all the factors are normalized, all dimensionless elements have similarity, and further analysis can be performed. The product quality characteristic level is the result of the comprehensive action of different factors, so different influence factors are given to each factor, the influence factor represents the influence degree of the factor on the product quality, and the result of the comprehensive action of the factors is finally embodied in the form of product quality data.
Grading the influence of different quality characteristic influence factors at different time or time interval, and grading each quality characteristic influence factor VN∈[0,1]Performing normalization processing and dividing the score after the normalization processing into 10 grades [0-9 ]]By setting steady state level thresholds [ alpha, beta ]]The quality characteristics influencing factor state space S is represented by 0 representing the quality characteristics influencing factor state where the score is in the stable region and 1 representing the quality characteristics influencing factor state where the score is in the unstable regionNFExpressed as:
principal component state space S of quality characteristic influencing factorsKFExpressed as:
obtaining the quality characteristic influence factor symbolization sequence to form a gene sequence single chain. The steady state grade threshold [ α, β ] represents the maximum range of the steady state grade, and the grade ranges in the steady state of the influencing factors of different quality characteristics of different products are different and can be obtained empirically.
FIG. 4 is a schematic diagram of the construction of the quality characteristic influencing factor symbolizing sequence of the present invention. If the number of influencer principal elements is B, the combined state of the influencer principal element symbol sequence has 2B. The converted quality characteristic influence factor character sequence contains normal and abnormal information of steady and unsteady quality characteristic influence factors.
In step S3, the quality characteristic character sequence of the present invention includes known and unknown quality characteristic abnormal patterns, and a mapping relationship between the quality characteristic data character sequence and the influencing factor information character sequence is established on a time axis according to a control chart, and the basic structure of the quality characteristic symbolized mapping control chart is collectively established by using an associated double-stranded gene pattern by using mapping relationships between character sequence spaces containing different data fluctuation laws, character sequence spaces containing different fault factor combinations, and time sequences therebetween as three components of the quality characteristic symbolized mapping control chart. Quality characteristics two sequence spaces contain character sequences of a normal mode and an abnormal mode, the quality characteristic sequence space is the basis of predicting and diagnosing quality abnormity, and the character sequence of the abnormal mode contained in the sequence space represents the main characteristic of fluctuation variation of the quality characteristics. The combination of data abnormal fluctuation and fault factors in a sequence space has a fixed corresponding relation in a quality characteristic character confidence library established by a time axis.
Fig. 5 is a schematic diagram of a symbolic chemical control diagram of quality characteristics according to the present invention. The quality characteristic symbolic control chart takes production process quality characteristic data and fault information as analysis objects, takes a statistical method and a control chart as an auxiliary conversion tool, takes a database as a carrier, takes a computer as a tool to store, retrieve, identify, process and analyze a large amount of quality characteristic data generated in a quality control process, and explains results by quality control knowledge, so that a practical and empirical quality characteristic fluctuation rule in a sequence of the stored quality characteristic data and fault information is finally revealed.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (2)
1. A method for constructing a quality characteristic symbolized mapping control chart is characterized by comprising the following steps:
s1, equally dividing the Huhart steady-state region and the upper and lower control outer limit regions into a plurality of regions, expressing each equally divided region by adopting a multi-component symbolic sequence, constructing a quality characteristic value symbolized sequence, and forming a gene sequence single chain; equally dividing a Huhart steady-state region and an upper and lower control outer limit region into a plurality of regions, expressing each equally divided region by adopting a multi-component symbolic sequence, constructing a quality characteristic value symbolic sequence, and forming a gene sequence single chain, wherein the method specifically comprises the following steps:
s11, equally dividing the Huhart steady-state region and the upper and lower control outer limit regions into eight regions with the central line mu line as the starting point, wherein the eight regions are expressed as { (∞, mu-3 sigma), [ mu-3 sigma, mu-2 sigma ], (mu-2 sigma, mu-sigma ], (mu-sigma, mu ], (mu, mu + sigma ], (mu + sigma, mu +2 sigma ], (mu +2 sigma, mu +3 sigma ], (mu +3 sigma, + ∞) }, and the Huhart steady-state region is mu + -3 sigma;
s12, representing the eight equally divided areas in the step S11 by using symbols { D, C, B, A, a, B, C, D } octave group respectively;
s13, representing the quality characteristic data points at different sampling time points in each time domain as a symbol sequence QD＝{xiObtaining mass characteristic value symbolized sequences and forming gene sequence single chains, wherein the mass characteristic value symbolized sequences are { …, …, A, B, C, a, B, C, D, D, … and … };
s2, performing principal component analysis on the observed value of the quality characteristic influence factor in each time domain at each sampling moment, establishing a mapping relation between the quality characteristic value and the influence factor thereof, obtaining a quality characteristic influence factor sequence according to the quality characteristic value symbolization sequence established in the step S1, performing normalization processing on each quality characteristic influence factor, establishing a quality characteristic influence factor symbolization sequence, and forming a gene sequence single chain; performing principal component analysis on the observed value of the quality characteristic influence factor in each time domain at each sampling moment, establishing a mapping relation between the quality characteristic value and the influence factor thereof, obtaining a quality characteristic influence factor sequence according to the quality characteristic value symbolization sequence established in the step S1, performing normalization processing on each quality characteristic influence factor, establishing a quality characteristic influence factor symbolization sequence, and forming a gene sequence single chain, wherein the method specifically comprises the following sub-steps:
s21, extracting influence factor pivot information according to the observed value of the quality characteristic influence factor in each time domain at each sampling moment, and expressing the quality characteristic influence factor pivot sequence as { V }1,V2,V3,…VBIn which the quality characteristic influencing factor is represented as [ V ]1,V2,V3,…VN]The observed value is represented as [ x ]1,…,xN]；
S22, establishing a mapping for a quality characteristic value and its influencing factor
Establishing a mapping between a quality characteristic value and its influence factor pivot
Obtaining the quality characteristic from the quality characteristic value symbolized sequence constructed in step S1Influence factor sequence:
obtaining a quality characteristic influence factor pivot element sequence:
s23, classifying the influence of different quality characteristic influence factors at different times into grades, normalizing each quality characteristic influence factor and dividing the score after normalization into 10 grades [0-9 ]]The quality characteristics influencing factor state space S is defined by setting a steady state level threshold value, representing the quality characteristics influencing factor state with a score in a steady region as 0, and representing the quality characteristics influencing factor state with a score in an unstable region as 1NFExpressed as:
principal component state space S of quality characteristic influencing factorsKFExpressed as:
obtaining a quality characteristic influence factor symbolization sequence to form a gene sequence single chain;
s3, combining the quality characteristic value symbolized sequence and the quality characteristic influence factor symbolized sequence by adopting an associated double-chain gene mode to construct a quality characteristic symbolized mapping control chart.
2. The method for constructing a quality characteristic symbolized mapping control map as claimed in claim 1, wherein said step S3 combines the quality characteristic value symbolized sequence with the quality characteristic influencing factor symbolized sequence by using a related double-stranded gene pattern to construct a quality characteristic symbolized mapping control map, specifically: and combining the gene sequence single chain formed in the step S1 and the gene sequence single chain formed in the step S2 into a double-chain gene sequence by adopting an associated double-chain gene mode according to the mapping relation between the quality characteristic value and the influence factor thereof established in the step S2, thereby obtaining a quality characteristic symbolized mapping control chart.
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