CN114969140B - Method for detecting and analyzing performance data of fluent strip products - Google Patents
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Abstract
The invention discloses a method for detecting and analyzing product performance data of fluent strips, which relates to the technical field of product performance data detection and analysis, solves the technical problem that the product performance of the fluent strips cannot be accurately detected and analyzed in the prior art, accurately judges the qualified interval of the production data of the fluent strips, thereby improving the supervision of the fluent strips, effectively and accurately judging the product performance of the fluent strips, and improving the production quality of the fluent strips; abnormal characteristics of the disqualified fluency strips are analyzed, so that the influence degree of the fluency strip faults is reasonably controlled, the correction timeliness of the fault fluency strips is improved, and meanwhile, the occurrence of fluency strip faults is effectively prevented; and the influence factors of the main fault characteristics are judged by analyzing the influence factors of the main fault characteristics of the historical unqualified fluent strips, and meanwhile, the influence of the influence factors on the main fault characteristics can be controlled, so that the production efficiency of the fluent strips is improved, and meanwhile, the fault frequency of the fluent strips can be reduced.
Description
Technical Field
The invention relates to the technical field of product performance data detection and analysis, in particular to a method for detecting and analyzing product performance data of fluent strips.
Background
The fluency strip is an abbreviation of an aluminum alloy sliding rail, articles placed above the fluency strip are fluently and cleanly operated due to the fact that the fluency strip is convenient and quick to install, the fluency strip is used for workshop material frames and the like as the name implies, in recent years, with the penetration of lean production modes, the fluency strip of plastic products appears for reducing cost, wherein a smooth cylinder is called a second generation fluency strip, a quick sliding strip is called a third generation fluency strip, and with the increasing use amount of the fluency strip, the product performance detection of the fluency strip is extremely important;
however, in the prior art, a production data qualification interval of qualified fluency strips cannot be obtained through historical production data analysis, so that accuracy of quality detection is reduced, influence factors of unqualified fluency strips cannot be analyzed, production efficiency of fluency strips cannot be improved, and failure frequency of fluency strips can be reduced;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to solve the problem by providing a fluent strip product performance data detection and analysis method, which is used for carrying out data analysis on the fluent strip produced in history and accurately judging the qualified interval of the production data of the fluent strip, so that the supervision of the fluent strip is improved, the product performance of the fluent strip is effectively and accurately judged, and the production quality of the fluent strip is improved; abnormal characteristics of the disqualified fluency strips are analyzed, abnormal characteristics of the failed fluency strips are analyzed, so that the influence degree of the faults of the fluency strips is reasonably controlled, the correction timeliness of the failed fluency strips is improved, meanwhile, the occurrence of the faults of the fluency strips is effectively prevented, and the fault rate of the fluency strips is reduced; and analyzing the influence factors of the main fault characteristics of the historical unqualified fluent strips, and judging the influence factors of the main fault characteristics, so that the influence caused by the main fault characteristics is controlled, the influence of the influence factors on the main fault characteristics can be controlled, the production efficiency of the fluent strips is improved, and the fault frequency of the fluent strips can be reduced.
The aim of the invention can be achieved by the following technical scheme:
a method for detecting and analyzing performance data of fluent strip products comprises the following steps:
step one, historical data analysis, namely judging a qualified interval of fluency strip production data by carrying out data analysis on the fluency strips produced in the history;
analyzing abnormal characteristics of the fluency strip with faults, and judging the abnormal characteristics of the fluency strip;
thirdly, analyzing influence factors, and acquiring influence factors of the fluency strips according to the abnormal characteristics of the fluency strips so as to predict the abnormality of the fluency strips;
and step four, real-time detection, wherein quality analysis detection is carried out on the fluent strip produced in real time.
As a preferred embodiment of the present invention, the historical data analysis process in the first step is as follows:
marking the historically produced fluency strips as finished fluency strips, setting the labels i and i as natural numbers larger than 1, collecting the failure times and the use frequency of the finished fluency strips, and marking the corresponding finished fluency strips as qualified fluency strips if the failure times of the finished fluency strips do not exceed a failure time threshold and the use frequency exceeds a use frequency threshold; if the failure times of the finished fluency strips exceed the failure times threshold value and the using frequency does not exceed the using frequency threshold value, marking the corresponding finished fluency strips as unqualified fluency strips;
analyzing the qualified fluent strip to obtain environment data and equipment data of the qualified fluent strip, wherein the environment data comprises environment temperature and environment humidity, the equipment data comprises equipment operation time length and equipment operation frequency, the environment data and the equipment data of the production process of the qualified fluent strip are collected, numerical statistics is carried out on the environment temperature and the environment humidity in the environment data, the highest numerical value and the lowest numerical value of the environment temperature are collected, and an environment temperature interval is obtained through the highest numerical value and the lowest numerical value of the environment temperature; collecting the highest value of the environmental humidity and the lowest value of the environmental humidity, and obtaining an environmental humidity interval through the highest value of the environmental humidity and the lowest value of the environmental humidity;
carrying out numerical statistics on equipment operation time length and equipment operation frequency in the equipment data, collecting the highest numerical value of the equipment operation time length and the lowest numerical value of the equipment operation time length, and acquiring an equipment operation time length interval through the highest numerical value of the equipment operation time length and the lowest numerical value of the equipment operation time length; acquiring the highest numerical value of the equipment operating frequency and the lowest numerical value of the equipment operating frequency, and acquiring an equipment operating frequency interval through the highest numerical value of the equipment operating frequency and the lowest numerical value of the equipment operating frequency; storing an environment temperature interval, an environment humidity interval, an equipment operation duration interval and an equipment operation frequency interval of the qualified fluent strip.
As a preferred embodiment of the present invention, the abnormality characteristic analysis process of the second step is as follows:
marking the historical unqualified fluency strip as a feature analysis object, collecting fault features of the feature analysis object, and setting the fault features of the feature analysis object as a natural number of which the number o is more than 1; collecting the fault feature occurrence frequency and the total maintenance time consumption of the feature analysis object, and marking the fault feature occurrence frequency and the total maintenance time consumption of the feature analysis object as PLo and SCo respectively; acquiring the increasing speed of the occurrence frequency of the fault characteristics of the characteristic analysis object, and marking the increasing speed of the occurrence frequency of the fault characteristics of the characteristic analysis object as SDo;
analyzing and obtaining an analysis coefficient Xo of the fault feature, and comparing the analysis coefficient of the fault feature with an analysis coefficient threshold value:
if the analysis coefficient of the fault feature exceeds the analysis coefficient threshold, marking the corresponding fault feature as a main fault feature; if the analysis coefficient of the fault feature does not exceed the analysis coefficient threshold value, marking the corresponding fault feature as a secondary fault feature;
and updating and storing the main fault characteristics in real time, and if the main fault characteristics of the fluent strip occur in the production process, immediately rectifying the production line of the fluent strip.
As a preferred embodiment of the present invention, the influence factor analysis process of the third step is as follows:
setting an influence factor acquisition time period, wherein the occurrence time of the main fault characteristic of the history unqualified fluent strip is the middle time of the influence factor acquisition time period; dividing an influence factor acquisition time period into a front time period and a rear time period at the occurrence time of the main fault characteristic; acquiring numerical data of fluency strip production, and analyzing the numerical data;
acquiring a floating value of the numerical data in a front time period, and marking the corresponding numerical data as an influence factor if the floating value of the numerical data exceeds a corresponding floating value threshold; if the floating value of the numerical data does not exceed the corresponding floating value threshold value, marking the corresponding numerical data as an irrelevant influence factor; acquiring a floating value of the numerical data in the rear time period, and marking the numerical value of the corresponding data as a factor influence factor if the floating value of the numerical data is converted from not exceeding the corresponding floating value threshold value to exceeding the corresponding floating value threshold value; if the floating value of the numerical data does not exceed the corresponding floating value threshold value and the floating amplitude does not exceed the corresponding floating amplitude threshold value, the corresponding numerical data is expressed as an irrelevant influence factor;
the influence factors and the factor change influence factors are stored, the influence factors are monitored in real time when the main fault characteristics do not appear, and the factor change absorbed factors are timely adjusted and controlled when the main fault characteristics appear.
As a preferred embodiment of the present invention, the real-time detection process of the fourth step is as follows:
marking the fluency strip subjected to survival in real time as a real-time detection fluency strip, collecting the sampling inspection qualification rate and the frequently used longest qualification time of the real-time detection fluency strip, and comparing the sampling inspection qualification rate and the frequently used longest qualification time of the real-time detection fluency strip with a qualification rate threshold and a qualification time threshold respectively:
if the sampling inspection qualification rate of the real-time detection fluency strip exceeds a qualification rate threshold value and the longest frequently used qualification time exceeds a qualification time threshold value, judging that the quality of the corresponding real-time detection fluency strip is qualified, and marking the quality as the real-time qualified fluency strip; if the sampling inspection qualification rate of the real-time detection fluency strip does not exceed the qualification rate threshold value or the longest frequently used qualification time does not exceed the qualification time threshold value, judging that the quality of the corresponding real-time detection fluency strip is unqualified, and marking the quality as the real-time unqualified fluency strip.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, data analysis is carried out on the fluency strips produced in history, and the qualified interval of the production data of the fluency strips is accurately judged, so that the supervision of the fluency strips is improved, the product performance of the fluency strips is effectively and accurately judged, and the production quality of the fluency strips is improved; abnormal characteristics of the disqualified fluency strips are analyzed, abnormal characteristics of the failed fluency strips are analyzed, so that the influence degree of the faults of the fluency strips is reasonably controlled, the correction timeliness of the failed fluency strips is improved, meanwhile, the occurrence of the faults of the fluency strips is effectively prevented, and the fault rate of the fluency strips is reduced;
2. according to the method, the influence factors of the main fault characteristics of the historical unqualified fluent strips are analyzed, and the influence factors of the main fault characteristics are judged, so that the influence caused by the main fault characteristics is controlled, the influence of the influence factors on the main fault characteristics can be controlled, the production efficiency of the fluent strips is improved, and the fault frequency of the fluent strips can be reduced; the quality analysis and detection are carried out on the fluent strips which are produced in real time, and the quality of the production of the fluent strips is controlled, and meanwhile, whether the data analysis is monitored to be qualified can be judged, so that the quality monitoring efficiency of the fluent strips is prevented from being reduced due to the abnormal analysis steps, and the risk of faults of the fluent strips is increased.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a functional block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a method for detecting and analyzing performance data of fluent strip products includes the following steps:
step one, historical data analysis, namely judging a qualified interval of fluency strip production data by carrying out data analysis on the fluency strips produced in the history;
analyzing abnormal characteristics of the fluency strip with faults, and judging the abnormal characteristics of the fluency strip;
thirdly, analyzing influence factors, and acquiring influence factors of the fluency strips according to the abnormal characteristics of the fluency strips so as to predict the abnormality of the fluency strips;
fourth, real-time detection is carried out, and quality analysis detection is carried out on the fluency strips produced in real time;
step one, carrying out data analysis on fluency strips produced in a history way, accurately judging a qualified interval of production data of the fluency strips, thereby improving supervision of the fluency strips, effectively and accurately judging product performance of the fluency strips, improving production quality of the fluency strips, and specifically carrying out the analysis process of the historical data as follows:
marking the historically produced fluency strips as finished fluency strips, setting the labels i and i as natural numbers larger than 1, collecting the failure times and the use frequency of the finished fluency strips, and marking the corresponding finished fluency strips as qualified fluency strips if the failure times of the finished fluency strips do not exceed a failure time threshold and the use frequency exceeds a use frequency threshold; if the failure times of the finished fluency strips exceed the failure times threshold value and the using frequency does not exceed the using frequency threshold value, marking the corresponding finished fluency strips as unqualified fluency strips;
analyzing the qualified fluent strip to obtain environment data and equipment data of the qualified fluent strip, wherein the environment data comprises environment temperature and environment humidity, the equipment data comprises equipment operation time length and equipment operation frequency, the environment data and the equipment data of the production process of the qualified fluent strip are collected, numerical statistics is carried out on the environment temperature and the environment humidity in the environment data, the highest numerical value and the lowest numerical value of the environment temperature are collected, and an environment temperature interval is obtained through the highest numerical value and the lowest numerical value of the environment temperature; collecting the highest value of the environmental humidity and the lowest value of the environmental humidity, and obtaining an environmental humidity interval through the highest value of the environmental humidity and the lowest value of the environmental humidity;
carrying out numerical statistics on equipment operation time length and equipment operation frequency in the equipment data, collecting the highest numerical value of the equipment operation time length and the lowest numerical value of the equipment operation time length, and acquiring an equipment operation time length interval through the highest numerical value of the equipment operation time length and the lowest numerical value of the equipment operation time length; acquiring the highest numerical value of the equipment operating frequency and the lowest numerical value of the equipment operating frequency, and acquiring an equipment operating frequency interval through the highest numerical value of the equipment operating frequency and the lowest numerical value of the equipment operating frequency;
storing an environment temperature interval, an environment humidity interval, an equipment operation duration interval and an equipment operation frequency interval of the qualified fluent strip;
step two, carrying out abnormal characteristic analysis on unqualified fluency strips, analyzing the fluency strips with faults, and analyzing the abnormal characteristics of the fluency strips, so that the influence degree of the faults of the fluency strips is reasonably controlled, the rectifying and modifying timeliness of the faulty fluency strips is improved, meanwhile, the faults of the fluency strips are effectively prevented, the fault rate of the fluency strips is reduced, and the specific abnormal characteristic analysis process is as follows:
marking the historical unqualified fluency strip as a feature analysis object, and collecting fault features of the feature analysis object, wherein the fault features in the summary are expressed as related faults such as abnormality, blocking and the like when the fluency strip breaks down; setting the fault characteristics of the characteristic analysis object as a natural number of which the number o is more than 1; collecting the fault feature occurrence frequency and the total maintenance time consumption of the feature analysis object, and marking the fault feature occurrence frequency and the total maintenance time consumption of the feature analysis object as PLo and SCo respectively; acquiring the increasing speed of the occurrence frequency of the fault characteristics of the characteristic analysis object, and marking the increasing speed of the occurrence frequency of the fault characteristics of the characteristic analysis object as SDo;
by the formula
Obtaining analysis coefficients Xo of fault characteristics, wherein a1, a2 and a3 are preset proportionality coefficients, and a1 is more than a2 and more than a3;
comparing the analysis coefficients of the fault signature with analysis coefficient thresholds:
if the analysis coefficient of the fault feature exceeds the analysis coefficient threshold, marking the corresponding fault feature as a main fault feature; if the analysis coefficient of the fault feature does not exceed the analysis coefficient threshold value, marking the corresponding fault feature as a secondary fault feature;
updating and storing main fault characteristics in real time, and if the main fault characteristics of the fluent strip occur in the production process, immediately rectifying the production line of the fluent strip;
in the third step, the main fault characteristics of the historical unqualified fluent strips are subjected to influence factor analysis, and influence factors of the main fault characteristics are judged, so that influence caused by the main fault characteristics is controlled, meanwhile, influence of the influence factors on the main fault characteristics can be controlled, the production efficiency of the fluent strips is improved, meanwhile, the fault frequency of the fluent strips can be reduced, and the specific influence factor analysis process is as follows:
setting an influence factor acquisition time period, wherein the occurrence time of the main fault characteristic of the history unqualified fluent strip is the middle time of the influence factor acquisition time period; dividing an influence factor acquisition time period into a front time period and a rear time period at the occurrence time of the main fault characteristic; acquiring numerical data of the fluent strip production, and analyzing the numerical data, wherein the numerical data is represented as production related data such as environment data, equipment data and the like in the production process of the fluent strip;
acquiring a floating value of the numerical data in a front time period, and marking the corresponding numerical data as an influence factor if the floating value of the numerical data exceeds a corresponding floating value threshold; if the floating value of the numerical data does not exceed the corresponding floating value threshold value, marking the corresponding numerical data as an irrelevant influence factor; acquiring a floating value of the numerical data in the rear time period, and marking the numerical value of the corresponding data as a factor influence factor if the floating value of the numerical data is converted from not exceeding the corresponding floating value threshold value to exceeding the corresponding floating value threshold value; if the floating value of the numerical data does not exceed the corresponding floating value threshold value and the floating amplitude does not exceed the corresponding floating amplitude threshold value, the corresponding numerical data is expressed as an irrelevant influence factor;
the method comprises the steps of storing the influence factors and the factor change influence factors, monitoring the influence factors in real time when main fault characteristics do not appear, and performing timely adjustment control on the factor change absorbed factors when the main fault characteristics appear;
in the fourth step, quality analysis and detection are carried out on the fluent strips which are produced in real time, the production quality of the fluent strips is controlled, meanwhile, whether data analysis is monitored to be qualified or not can be judged, the quality monitoring efficiency of the fluent strips is prevented from being reduced due to abnormal analysis steps, and therefore the risk of faults of the fluent strips is increased, and the concrete real-time detection process is as follows:
marking the fluency strip subjected to survival in real time as a real-time detection fluency strip, collecting the sampling inspection qualification rate and the frequently used longest qualification time of the real-time detection fluency strip, and comparing the sampling inspection qualification rate and the frequently used longest qualification time of the real-time detection fluency strip with a qualification rate threshold and a qualification time threshold respectively:
if the sampling inspection qualification rate of the real-time detection fluency strip exceeds a qualification rate threshold value and the longest frequently used qualification time exceeds a qualification time threshold value, judging that the quality of the corresponding real-time detection fluency strip is qualified, and marking the quality as the real-time qualified fluency strip; if the sampling inspection qualification rate of the real-time detection fluency strip does not exceed the qualification rate threshold value or the longest frequently used qualification time does not exceed the qualification time threshold value, judging that the quality of the corresponding real-time detection fluency strip is unqualified, and marking the quality as the real-time unqualified fluency strip.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions;
when the method is used, the historical data is analyzed, and the qualified interval of the fluency strip production data is judged by carrying out data analysis on the fluency strips produced in the history, so that the supervision of the fluency strips is improved, the product performance of the fluency strips is effectively and accurately judged, and the production quality of the fluency strips is improved; analyzing abnormal characteristics, namely analyzing the fluency strip with the fault, and judging the abnormal characteristics of the fluency strip; abnormal characteristics of the fluency strip are analyzed, so that the influence degree of the fluency strip fault is reasonably controlled, the correction timeliness of the fault fluency strip is improved, meanwhile, the occurrence of the fluency strip fault is effectively prevented, and the failure rate of the fluency strip is reduced; influence factor analysis, namely acquiring influence factors of the fluent strip according to abnormal characteristics of the fluent strip, so as to predict the abnormality of the fluent strip, judge the influence factors of main fault characteristics, and control the influence caused by the main fault characteristics, and simultaneously control the influence of the influence factors on the main fault characteristics, thereby improving the production efficiency of the fluent strip and reducing the fault frequency of the fluent strip; and the quality analysis detection is carried out on the fluent strips which are produced in real time, and the quality of the production of the fluent strips is controlled, and meanwhile, whether the data analysis is monitored to be qualified can be judged, so that the quality monitoring efficiency of the fluent strips is prevented from being reduced due to abnormal analysis steps, and the risk of faults of the fluent strips is increased.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (1)
1. The method for detecting and analyzing the performance data of the fluent strip product is characterized by comprising the following steps of:
step one, historical data analysis, namely judging a qualified interval of fluency strip production data by carrying out data analysis on the fluency strips produced in the history;
analyzing abnormal characteristics of the fluency strip with faults, and judging the abnormal characteristics of the fluency strip;
thirdly, analyzing influence factors, and acquiring influence factors of the fluency strips according to the abnormal characteristics of the fluency strips so as to predict the abnormality of the fluency strips;
fourth, real-time detection is carried out, and quality analysis detection is carried out on the fluency strips produced in real time;
the historical data analysis process in the first step is as follows:
marking the historically produced fluency strips as finished fluency strips, setting the labels i and i as natural numbers larger than 1, collecting the failure times and the use frequency of the finished fluency strips, and marking the corresponding finished fluency strips as qualified fluency strips if the failure times of the finished fluency strips do not exceed a failure time threshold and the use frequency exceeds a use frequency threshold; if the failure times of the finished fluency strips exceed the failure times threshold value and the using frequency does not exceed the using frequency threshold value, marking the corresponding finished fluency strips as unqualified fluency strips;
analyzing the qualified fluent strip to obtain environment data and equipment data of the qualified fluent strip, wherein the environment data comprises environment temperature and environment humidity, the equipment data comprises equipment operation time length and equipment operation frequency, the environment data and the equipment data of the production process of the qualified fluent strip are collected, numerical statistics is carried out on the environment temperature and the environment humidity in the environment data, the highest numerical value and the lowest numerical value of the environment temperature are collected, and an environment temperature interval is obtained through the highest numerical value and the lowest numerical value of the environment temperature; collecting the highest value of the environmental humidity and the lowest value of the environmental humidity, and obtaining an environmental humidity interval through the highest value of the environmental humidity and the lowest value of the environmental humidity;
carrying out numerical statistics on equipment operation time length and equipment operation frequency in the equipment data, collecting the highest numerical value of the equipment operation time length and the lowest numerical value of the equipment operation time length, and acquiring an equipment operation time length interval through the highest numerical value of the equipment operation time length and the lowest numerical value of the equipment operation time length; acquiring the highest numerical value of the equipment operating frequency and the lowest numerical value of the equipment operating frequency, and acquiring an equipment operating frequency interval through the highest numerical value of the equipment operating frequency and the lowest numerical value of the equipment operating frequency; storing an environment temperature interval, an environment humidity interval, an equipment operation duration interval and an equipment operation frequency interval of the qualified fluent strip;
the abnormal characteristic analysis process of the second step is as follows:
marking the historical unqualified fluency strip as a feature analysis object, collecting fault features of the feature analysis object, and setting the fault features of the feature analysis object as a natural number of which the number o is more than 1; collecting the fault feature occurrence frequency and the total maintenance time consumption of the feature analysis object, and marking the fault feature occurrence frequency and the total maintenance time consumption of the feature analysis object as PLo and SCo respectively; acquiring the increasing speed of the occurrence frequency of the fault characteristics of the characteristic analysis object, and marking the increasing speed of the occurrence frequency of the fault characteristics of the characteristic analysis object as SDo;
by the formula
Obtaining analysis coefficients Xo of the fault characteristics, wherein a1, a2 and a3 are preset proportionality coefficients, a1 is larger than a2 and a3, and comparing the analysis coefficients of the fault characteristics with analysis coefficient thresholds:
if the analysis coefficient of the fault feature exceeds the analysis coefficient threshold, marking the corresponding fault feature as a main fault feature; if the analysis coefficient of the fault feature does not exceed the analysis coefficient threshold value, marking the corresponding fault feature as a secondary fault feature;
updating and storing main fault characteristics in real time, and if the main fault characteristics of the fluent strip occur in the production process, immediately rectifying the production line of the fluent strip;
the influence factor analysis process of the third step is as follows:
setting an influence factor acquisition time period, wherein the occurrence time of the main fault characteristic of the history unqualified fluent strip is the middle time of the influence factor acquisition time period; dividing an influence factor acquisition time period into a front time period and a rear time period at the occurrence time of the main fault characteristic; acquiring numerical data of fluency strip production, and analyzing the numerical data;
acquiring a floating value of the numerical data in a front time period, and marking the corresponding numerical data as an influence factor if the floating value of the numerical data exceeds a corresponding floating value threshold; if the floating value of the numerical data does not exceed the corresponding floating value threshold value, marking the corresponding numerical data as an irrelevant influence factor; acquiring a floating value of the numerical data in the rear time period, and marking the numerical value of the corresponding data as a factor influence factor if the floating value of the numerical data is converted from not exceeding the corresponding floating value threshold value to exceeding the corresponding floating value threshold value; if the floating value of the numerical data does not exceed the corresponding floating value threshold value and the floating amplitude does not exceed the corresponding floating amplitude threshold value, the corresponding numerical data is expressed as an irrelevant influence factor;
the method comprises the steps of storing the influence factors and the factor change influence factors, monitoring the influence factors in real time when main fault characteristics do not appear, and performing timely adjustment control on the factor change absorbed factors when the main fault characteristics appear;
the real-time detection process of the fourth step is as follows:
marking the fluency strip subjected to survival in real time as a real-time detection fluency strip, collecting the sampling inspection qualification rate and the frequently used longest qualification time of the real-time detection fluency strip, and comparing the sampling inspection qualification rate and the frequently used longest qualification time of the real-time detection fluency strip with a qualification rate threshold and a qualification time threshold respectively:
if the sampling inspection qualification rate of the real-time detection fluency strip exceeds a qualification rate threshold value and the longest frequently used qualification time exceeds a qualification time threshold value, judging that the quality of the corresponding real-time detection fluency strip is qualified, and marking the quality as the real-time qualified fluency strip; if the sampling inspection qualification rate of the real-time detection fluency strip does not exceed the qualification rate threshold value or the longest frequently used qualification time does not exceed the qualification time threshold value, judging that the quality of the corresponding real-time detection fluency strip is unqualified, and marking the quality as the real-time unqualified fluency strip.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2078945A2 (en) * | 2008-01-14 | 2009-07-15 | AVL List GmbH | Method and device for analysing and evaluating measurement data of a measurement system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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