CN111240286A - System and method for reducing misjudgment of line stop reason - Google Patents
System and method for reducing misjudgment of line stop reason Download PDFInfo
- Publication number
- CN111240286A CN111240286A CN202010059528.1A CN202010059528A CN111240286A CN 111240286 A CN111240286 A CN 111240286A CN 202010059528 A CN202010059528 A CN 202010059528A CN 111240286 A CN111240286 A CN 111240286A
- Authority
- CN
- China
- Prior art keywords
- data
- reason
- stop
- line
- unit
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/4183—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/31—From computer integrated manufacturing till monitoring
- G05B2219/31282—Data acquisition, BDE MDE
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses a method for reducing misjudgment of a wire stopping reason. The method comprises the steps of collecting line stop data of a process of manufacturing a product through a data acquisition unit; judging whether the reason of the wire stopping is the reason of material shortage and/or the quality reason through a wire stopping reason judging unit; collecting historical stop line data and recent change records of the process, historical operation data and maintenance records of equipment, material inspection records and offline records, recent change records of manufactured products, stop line data before and after change, stop line data of process posts and training record information of operating staff; by combining the analysis unit with the steps S1-S3, the data acquisition unit collects the information of the data unit, personnel operation factors, equipment factors, material factors, design changes and process adjustment influence on the stop line, and comprehensively estimates the key reasons of the stop line. Therefore, the method solves the problem of low efficiency of manually judging the reason of the wire stop, and improves the accuracy of judging the reason of the wire stop.
Description
Technical Field
The invention relates to the technical field of industrial production, in particular to a system and a method for reducing misjudgment of line stop reasons.
Background
In the production process of the production line of a manufacturing enterprise, the times of key point stop (temporary line stop) in each link of each post of the production line are many due to various abnormal reasons, and the point stop is one of important reasons influencing the productivity and the production efficiency of a shift. In order to reduce production waste, production experience is summarized to improve efficiency, time points and times of line stopping are collected through a point stopping switch on a linkage production line, line stopping reasons are required to be clearly selected every time of line stopping, the aspects of product producibility or production procedures and the like are improved in a targeted mode, and production quality and efficiency are improved.
At present, production line workers or quality inspectors select the reason for line stop, and the method is limited by the influence of cognitive factors or other subjective factors, and has low data accuracy and usability and high cleaning difficulty. A large amount of production line data are classified and counted only by means of the line stopping reasons which are subjectively judged and selected by staff, so that wrong directional guidance is easily generated for subsequent production improvement, and key reasons are not easy to find in time and adjust in time. Therefore, the data needs to be comprehensively analyzed according to the collected line stopping reasons, line stopping time points, duration, times, positions and other original data, the data is combined with streaming data, quality test data, production progress data and the like, and effective data is identified to provide production improvement bases.
In order to solve the problems in the prior art, the invention provides a system and a method for reducing misjudgment of line stopping reasons, which are based on big data analysis, and are combined with production line field data (including offline records, test data, offline rates of various procedures and the like), product change records, material inspection records and the like to perform comprehensive operation analysis so as to predict and judge the line stopping reasons; meanwhile, through historical data training, models of production line data are continuously optimized, accuracy of prediction and judgment is improved, misjudgment of production line stop reasons is effectively reduced, and automatic pushing improvement suggestions are realized.
Disclosure of Invention
The invention aims to solve the problem that the manual judgment of the wire stopping reason in the prior art is inaccurate and not efficient. The invention provides a system and a method for reducing misjudgment of a stop line reason. The method comprises the steps of collecting line stop data of a process of manufacturing a product through a data acquisition unit; judging the reason of the wire stopping by a wire stopping reason judging unit, wherein the reason of the wire stopping is whether the reason is the reason of material shortage and/or the reason of quality; collecting historical stop line data and recent change records of the process, historical operation data and maintenance records of equipment, material inspection records and offline records of the process, recent change records of a manufactured product and stop line data before and after change, and stop line data of process posts and training record information of operating staff by a data collecting unit; by combining the analysis unit with the steps S1-S3, the data acquisition unit is integrated, the information of the data acquisition unit is collected, and the key reasons of line stop are comprehensively estimated according to the occupation ratio of the influence of personnel operation factors, equipment factors, material factors, design change and process adjustment on line stop. Therefore, the method and the device can solve the problem that the reason for line stop selected by the person in charge is inaccurate in the prior art. According to the method for reducing misjudgment of the line stopping reasons, the line stopping reasons are automatically classified and identified, problem points are pushed and suggestions are improved based on big data analysis through classification linkage based on statistics of production data, material data, design data and the like of a production line.
In order to solve the above technical problem, a first aspect of the present invention provides a system for reducing misjudgment of a wire stopping reason, including:
the data acquisition unit is used for judging the wire stopping reason unit, the data collection unit, the analysis unit and the pushing improvement suggestion unit.
The data acquisition unit, the wire stopping reason judgment unit, the data collection unit, the analysis unit and the pushing improvement suggestion unit of the system are sequentially connected.
Specifically, the data acquisition unit is used for acquiring stop line data of a process for manufacturing a product.
The wire stopping reason judging unit is used for judging the reason of wire stopping, and the reason of wire stopping is judged whether the reason is the reason of material shortage and/or the reason of quality.
The data collecting unit is used for collecting historical stop line data and recent change records of the process, historical operation data and maintenance records of equipment of the process, material inspection records and offline records, recent change records of manufactured products and stop line data before and after change, and stop line data of process posts and training record information of operators.
The analysis unit is used for integrating the data acquisition unit, the information of the data acquisition unit, personnel operation factors, equipment factors, material factors, design changes, influence of process adjustment on line stop occupation and comprehensively estimating key reasons of line stop.
Optionally, the system further comprises an expert system unit.
Optionally, the system further includes a correction unit for correcting the production line shutdown data.
In order to solve the above technical problem, a second aspect of the present invention provides a method for reducing misjudgment of a wire stopping reason based on the above system, where the method includes the following steps:
s1, acquiring the stop line data of the process of manufacturing the product through the data acquisition unit;
s2, judging the reason of the wire stopping by the wire stopping reason judging unit, wherein the reason of the wire stopping is whether the reason is the reason of material shortage and/or the reason of quality;
s3, collecting historical stop line data and recent change records of the process, historical operation data and maintenance records of equipment of the process, material inspection records and offline records, recent change records of a manufactured product and stop line data before and after change, and training record information of operation staff by a data collecting unit;
and S4, combining the steps S1-S3 through an analysis unit, integrating the data acquisition unit, the information of the data acquisition unit, personnel operation factors, equipment factors, material factors, design changes, influence of process adjustment on stop line occupation, and comprehensively estimating key reasons of stop line.
Optionally, in step S1, the wire stopping data acquired through the data acquisition unit includes a wire stopping point, a wire opening time, and a wire stopping reason.
Optionally, in step S2, the determining whether the reason for the line stop is the reason for the short-of-material and/or the reason for the quality includes, if the reason is classified as the reason for the short-of-material, determining whether there is a record of the short-of-material and a record of material withdrawal in combination with a record of material delivery of the order; if the material is classified as a non-shortage reason, the point position where the stopping occurs needs to be further determined for other reasons; if the quality reason is judged, collecting the test data of the previous section of the stop line and the historical manufacturing test data of the product, judging whether the consistency of the product performance deviates, combining historical statistical data, optimizing a production line data model through historical data training, and converting the proportion of all factors possibly causing the deviation.
Optionally, in step S3, the historical stop line data of the process and the latest change record include pre-change data and post-change data, which are converted into an influence ratio based on the production line stop line model;
the historical equipment operating data and the maintenance record of the process comprise the historical equipment operating data and the maintenance record, wherein the equipment influence ratio is converted based on a production line stop model;
the material inspection record and the offline record comprise the material inspection record and the offline record, wherein the material inspection record and the offline record are obtained by converting a material influence ratio based on a production line stop model;
the recent change record and the before-and-after-change stop line data of the manufactured product comprise the recent change record and the before-and-after-change stop line data of the manufactured product, which are obtained by calculating the ratio of influence factors of the change of the product design based on the production line stop line model;
the process post stop line data and the operator training record information comprise the process post stop line data and the operator training record information, and the post influence factor ratio is converted based on the production line stop line model.
Optionally, the method further includes step S5, pushing possible reason for stopping line through the pushing improvement suggesting unit by combining the data analysis given by the analyzing unit, predicting the reason for stopping line that is likely to happen soon, preventing in advance, and reducing the occurrence of stopping line.
Optionally, the method further includes step S6, where the expert system unit periodically modifies the existing production line shutdown model based on experts to ensure the accuracy of the model.
Optionally, the method further includes step S6, where the existing production line shutdown model is periodically corrected by the correction unit to ensure the accuracy of the model.
By the system and the method, production data pre-estimation of each process is given based on linear and nonlinear embodiment of point stop data by combining production line historical data and change records; multi-dimensional data intercommunication is realized, and the comprehensive analysis and evaluation of the line stop reasons are realized; continuous optimization of the stop-line mathematical model is realized through an expert system and historical data training; the system combines the statistical data to realize the improvement suggestion pushing of the production line; the production line production data, the material data, the design data and the like are linked based on the classification of statistics, and the line stopping reasons, the problem points and the improvement suggestions are automatically classified and identified based on big data analysis. And regularly correcting the production line stop model based on an expert system. The method is based on big data analysis, and combines production line field data (including offline records, test data, offline rates of all procedures and the like), product change records, material inspection records and the like to perform comprehensive operation analysis and give prediction and judgment of the line stop reason; meanwhile, through historical data training, models of production line data are continuously optimized, accuracy of prediction and judgment is improved, misjudgment of production line stop reasons is effectively reduced, and automatic pushing improvement suggestions are realized. And the production line stop model is corrected by regularly combining experts with the production line stop model or other correction units, so that the system and the method for judging the stop reason by mistake are better perfected and reduced, and the accuracy rate of judging the stop reason is improved.
Drawings
In order to make the technical problems solved by the present invention, the technical means adopted and the technical effects obtained more clear, the following will describe in detail the embodiments of the present invention with reference to the accompanying drawings. It should be noted, however, that the drawings described below are only illustrations of exemplary embodiments of the invention, from which other embodiments can be derived by those skilled in the art without inventive step.
Fig. 1 is a schematic diagram illustrating a system for reducing misjudgment caused by a wire stopping reason according to an embodiment of the present invention.
Fig. 2 is a schematic diagram illustrating a misjudgment method based on a reason for reducing wire stopping according to an embodiment of the invention.
Fig. 3 is a flow chart illustrating a method for reducing misjudgment caused by a wire stopping reason according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the invention may be embodied in many specific forms, and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art.
The structures, properties, effects or other characteristics described in a certain embodiment may be combined in any suitable manner in one or more other embodiments, while still complying with the technical idea of the invention.
In describing particular embodiments, specific details of structures, properties, effects, or other features are set forth in order to provide a thorough understanding of the embodiments by one skilled in the art. However, it is not excluded that a person skilled in the art may implement the invention in a specific case without the above-described structures, performances, effects or other features.
The flow chart in the drawings is only an exemplary flow demonstration, and does not represent that all the contents, operations and steps in the flow chart are necessarily included in the scheme of the invention, nor does it represent that the execution is necessarily performed in the order shown in the drawings. For example, some operations/steps in the flowcharts may be divided, some operations/steps may be combined or partially combined, and the like, and the execution order shown in the flowcharts may be changed according to actual situations without departing from the gist of the present invention.
The block diagrams in the figures generally represent functional entities and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The same reference numerals denote the same or similar elements, components, or parts throughout the drawings, and thus, a repetitive description thereof may be omitted hereinafter. It will be further understood that, although the terms first, second, third, etc. may be used herein to describe various elements, components, or sections, these elements, components, or sections should not be limited by these terms. That is, these phrases are used only to distinguish one from another. For example, a first device may also be referred to as a second device without departing from the spirit of the present invention. Furthermore, the term "and/or", "and/or" is intended to include all combinations of any one or more of the listed items.
Fig. 1 is a schematic diagram illustrating a system for reducing misjudgment caused by a wire stopping reason according to an embodiment of the present invention.
A system for reducing misjudgment of line stopping reasons comprises:
the data acquisition unit is used for judging the line stop reason unit, collecting the data unit, the analysis unit and the pushing improvement suggestion unit;
the data acquisition unit, the wire stopping reason judgment unit, the data collection unit, the analysis unit and the pushing improvement suggestion unit of the system are sequentially connected; wherein the content of the first and second substances,
the data acquisition unit is used for acquiring the stop line data of the process of manufacturing the product;
the wire stopping reason judging unit is used for judging the reason of wire stopping, and the reason of wire stopping is judged whether the reason is the reason of material shortage and/or the reason of quality;
the data collecting unit is used for collecting historical stop line data and recent change records of the process, historical operation data and maintenance records of equipment of the process, material inspection records and offline records, recent change records of manufactured products and stop line data before and after change, and stop line data of process posts and training record information of operators;
the analysis unit is used for integrating the data acquisition unit, the information of the data acquisition unit, personnel operation factors, equipment factors, material factors, design changes, influence of process adjustment on line stop occupation and comprehensively estimating key reasons of line stop.
Optionally, the system further comprises an expert system unit. The expert system unit corrects the existing production line stop line model regularly based on experts so as to ensure the accuracy of the model. The expert system unit corrects the existing production line stop line model based on expert manual work so as to avoid deviation generated by artificial intelligence data training. Experts are those experienced in the art and familiar with the production process. The expert system unit is not set in the method for judging the reason of stopping the wire every time.
Optionally, the system further includes a correction unit for correcting the production line shutdown data. The data model can also be corrected and improved by using a correction unit instead of an expert system.
Fig. 2 is a schematic diagram illustrating a misjudgment method based on a reason for reducing wire stopping according to an embodiment of the invention. The method comprises the following steps:
s1, acquiring the stop line data of the process of manufacturing the product through the data acquisition unit; the stop line data acquired through the data acquisition unit comprises stop line point positions, start line time and stop line reasons. The data acquisition unit can detect the stop point position through monitoring equipment such as a camera and the like, record the line opening time, strictly monitor the logistics state, master various flow information on the production line and track the data and control points of the product in each link. Further, the data acquisition unit stores the acquired data in the data storage unit.
Fig. 3 is a flow chart illustrating a method for reducing misjudgment caused by a wire stopping reason according to an embodiment of the invention. Referring to fig. 3, after the collection of the wire stopping data is completed, the reason for the wire stopping is analyzed.
And S2, judging the reason of the wire stopping by the wire stopping reason judging unit, wherein the reason of the wire stopping is whether the reason is the reason of material shortage and/or the reason of quality. In the step S2, the determining whether the reason for stopping the line is the reason for short-of-material and/or the reason for quality includes, if the reason is classified as the reason for short-of-material, determining whether there is a record of short-of-material and a record of material withdrawal by combining with a record of material sending of the order; if the material is classified as a non-shortage reason, the point position where the stopping occurs needs to be further determined for other reasons; if the quality reason is judged, collecting the test data of the previous section of the stop line and the historical manufacturing test data of the product, judging whether the consistency of the product performance deviates, combining historical statistical data, optimizing a production line data model through historical data training, and converting the proportion of all factors possibly causing the deviation.
Optionally, basic data such as historical line stopping reason data are input into the line stopping reason judging unit, and line stopping reasons are classified, such as material shortage reasons or quality reasons.
And S3, collecting the historical stop line data and the recent change records of the process, the historical operation data and maintenance records of the equipment of the process, the material inspection records and the off-line records, the recent change records of the manufactured products and the stop line data before and after the change, and the stop line data of the process posts and the training record information of the operating staff through a data collecting unit. The historical stop line data and the latest change record of the process comprise the data before the change and the data after the change are converted into influence ratios based on the production line stop line model; the historical equipment operating data and the maintenance record of the process comprise the historical equipment operating data and the maintenance record, wherein the equipment influence ratio is converted based on a production line stop model; the material inspection record and the offline record comprise the material inspection record and the offline record, wherein the material inspection record and the offline record are obtained by converting a material influence ratio based on a production line stop model; the recent change record and the before-and-after-change stop line data of the manufactured product comprise the recent change record and the before-and-after-change stop line data of the manufactured product, which are obtained by calculating the ratio of influence factors of the change of the product design based on the production line stop line model; the process post stop line data and the operator training record information comprise the process post stop line data and the operator training record information, and the post influence factor ratio is converted based on the production line stop line model. And (5) converting the ratio of each factor according to a historical data training model. For example, the location where the failure occurred, the failure was confirmed, the machine model, the symptoms, the process, the trend, the maintenance measures, the defective parts, the inspection process, and the like.
The historical data training model is a data set obtained by summarizing reasons of historical line stop, and the historical data training model is finally obtained through artificial intelligence algorithm optimization. The historical data training model is obtained by machine learning and data training based on the historical data such as procedure positioning and offline reasons of bad occurrence, line stop time, equipment operation records, maintenance records, personnel training records, post training records and the like determined by personnel inspection. And establishing a reason analysis algorithm model, mining industrial production parameters in a bad occurrence mode from massive historical production data, and analyzing reasons according to the relation among various parameters. The cause analysis algorithm model can also be adjusted at any time according to the process adjustment.
And S4, combining the steps S1-S3 through an analysis unit, integrating the data acquisition unit, the information of the data acquisition unit, personnel operation factors, equipment factors, material factors, design changes, influence of process adjustment on stop line occupation, and comprehensively estimating key reasons of stop line. The analysis unit gradually refines and analyzes the factors, the analysis unit analyzes the stop line reasons, comprehensively analyzes data such as material detection records, historical offline records, product change records, post stop line records, personnel training records, product test data, stop line historical data, process adjustment records, equipment operation data, historical maintenance records and the like, comprehensively evaluates the stop line reasons by combining the occupation ratio of each data factor, sorts the stop line reasons according to the sequence of key factors, performs uniform speed design change on personnel operation factors, equipment factors and materials according to the importance degree, and displays the reasons to comprehensive management personnel. Compared with the traditional method of only giving reasons, the method and the device not only comprehensively analyze various factors and give multi-dimensional analysis, but also further give the sequencing of the stop line reasons according to the importance degree.
Optionally, the method further includes step S5, pushing possible reason for stopping line through the pushing improvement suggesting unit by combining the data analysis given by the analyzing unit, predicting the reason for stopping line that is likely to happen soon, preventing in advance, and reducing the occurrence of stopping line. The push improvement suggestion unit also stores improvement schemes of different stop line reasons and a scheme set obtained according to historical data. And sequencing according to the stop line reasons given in the step S4 to give a proper improvement scheme. By pushing the improvement suggestion unit, a trend graph of defects or defects, such as a defect occurrence rate, a detection rate, a line stopping reason with large fluctuation and the like, can be obtained, so that the reason for possibly generating line stopping can be predicted, measures and improvement schemes can be taken in time, and the line stopping can be reduced.
Optionally, the method further includes step S6, where the expert system unit periodically modifies the existing production line shutdown model based on experts to ensure the accuracy of the model.
Optionally, the method further includes step S6, where the existing production line shutdown model is periodically corrected by the correction unit to ensure the accuracy of the model.
Compared with the prior art, the invention has the beneficial effects that:
the production line production data, the material data, the design data and the like are linked based on the classification of statistics, and the line stopping reasons, the problem points and the improvement suggestions are automatically classified and identified based on big data analysis. And regularly correcting the production line stop model based on an expert system.
Compared with the prior art, the invention has the following improved technical points:
1. based on linear and nonlinear embodiment of the point stop data, production line historical data and change records are combined to give production data prediction of each procedure;
2. multi-dimensional data intercommunication is realized, and the comprehensive analysis and evaluation of the line stop reasons are realized;
3. continuous optimization of the stop-line mathematical model is realized through an expert system and historical data training;
4. the system combines the statistical data to realize the improvement suggestion push of the production line.
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.
Claims (10)
1. A system for reducing misjudgment of line stopping reasons comprises:
the data acquisition unit is used for judging the line stop reason unit, collecting the data unit, the analysis unit and the pushing improvement suggestion unit;
the data acquisition unit, the wire stopping reason judgment unit, the data collection unit, the analysis unit and the pushing improvement suggestion unit of the system are sequentially connected; wherein the content of the first and second substances,
the data acquisition unit is used for acquiring the stop line data of the process of manufacturing the product;
the wire stopping reason judging unit is used for judging the reason of wire stopping, and the reason of wire stopping is judged whether the reason is the reason of material shortage and/or the reason of quality;
the data collecting unit is used for collecting historical stop line data and recent change records of the process, historical operation data and maintenance records of equipment of the process, material inspection records and offline records, recent change records of manufactured products and stop line data before and after change, and stop line data of process posts and training record information of operators;
the analysis unit is used for integrating the data acquisition unit, the information of the data acquisition unit, personnel operation factors, equipment factors, material factors, design changes, influence of process adjustment on line stop occupation and comprehensively estimating key reasons of line stop.
2. The system of claim 1, further comprising an expert system unit.
3. The system of claim 1, further comprising a correction unit for correcting production line shutdown data.
4. A method for reducing misjudgment of a reason for wire stop based on the system of any one of claims 1 to 3, the method comprising the steps of:
s1, acquiring the stop line data of the process of manufacturing the product through the data acquisition unit;
s2, judging the reason of the wire stopping by the wire stopping reason judging unit, wherein the reason of the wire stopping is whether the reason is the reason of material shortage and/or the reason of quality;
s3, collecting historical stop line data and recent change records of the process, historical operation data and maintenance records of equipment of the process, material inspection records and offline records, recent change records of a manufactured product and stop line data before and after change, and training record information of operation staff by a data collecting unit;
and S4, combining the steps S1-S3 through an analysis unit, integrating the data acquisition unit, the information of the data acquisition unit, personnel operation factors, equipment factors, material factors, design changes, influence of process adjustment on stop line occupation, and comprehensively estimating key reasons of stop line.
5. The method of claim 4, wherein:
in the step S1, the stop data acquired through the data acquisition unit includes stop point, start time, and stop reason.
6. The method of claim 4, wherein:
in the step S2, the determining whether the reason for stopping the line is the reason for short-of-material and/or the reason for quality includes, if the reason is classified as the reason for short-of-material, determining whether there is a record of short-of-material and a record of material withdrawal by combining with a record of material sending of the order; if the material is classified as a non-shortage reason, the point position where the stopping occurs needs to be further determined for other reasons; if the quality reason is judged, collecting the test data of the previous section of the stop line and the historical manufacturing test data of the product, judging whether the consistency of the product performance deviates, combining historical statistical data, optimizing a production line data model through historical data training, and converting the proportion of all factors possibly causing the deviation.
7. The method of claim 4, wherein:
in step S3, the historical stop line data and the latest change record of the process include data before the change and data after the change, which are converted into the influence ratio based on the production line stop line model;
the historical equipment operating data and the maintenance record of the process comprise the historical equipment operating data and the maintenance record, wherein the equipment influence ratio is converted based on a production line stop model;
the material inspection record and the offline record comprise the material inspection record and the offline record, wherein the material inspection record and the offline record are obtained by converting a material influence ratio based on a production line stop model;
the recent change record and the before-and-after-change stop line data of the manufactured product comprise the recent change record and the before-and-after-change stop line data of the manufactured product, which are obtained by calculating the ratio of influence factors of the change of the product design based on the production line stop line model;
the process post stop line data and the operator training record information comprise the process post stop line data and the operator training record information, and the post influence factor ratio is converted based on the production line stop line model.
8. Method according to one of claims 4 to 7, characterized in that:
the method further comprises a step S5 of pushing possible line stopping reasons through the pushing improvement suggestion unit by combining the data analysis given by the analysis unit, predicting the probable line stopping reasons to be generated, preventing in advance and reducing the line stopping.
9. The method of claim 8, wherein:
the method further includes step S6, where the expert system unit periodically corrects the existing production line shutdown model based on experts to ensure the accuracy of the model.
10. The method of claim 8, wherein:
the method further includes step S6, where the existing production line shutdown model is periodically corrected by the correction unit to ensure the accuracy of the model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010059528.1A CN111240286B (en) | 2020-01-19 | 2020-01-19 | System and method for reducing misjudgment of line stop reason |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010059528.1A CN111240286B (en) | 2020-01-19 | 2020-01-19 | System and method for reducing misjudgment of line stop reason |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111240286A true CN111240286A (en) | 2020-06-05 |
| CN111240286B CN111240286B (en) | 2021-02-26 |
Family
ID=70876414
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010059528.1A Active CN111240286B (en) | 2020-01-19 | 2020-01-19 | System and method for reducing misjudgment of line stop reason |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111240286B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112288389A (en) * | 2020-10-20 | 2021-01-29 | 苏州浪潮智能科技有限公司 | SCADA system based production control method, program, device and medium |
| CN112561173A (en) * | 2020-12-18 | 2021-03-26 | 安徽巨一科技股份有限公司 | Optimization method for rapidly improving production capacity of welding line |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10202482A (en) * | 1997-01-20 | 1998-08-04 | Honda Motor Co Ltd | Production line control method |
| US6027022A (en) * | 1996-06-29 | 2000-02-22 | Samsung Electronics Co., Ltd. | Quality control apparatus and method using a bar code data entry system |
| JP2008140014A (en) * | 2006-11-30 | 2008-06-19 | Sharp Corp | Quality failure improvement system, quality failure improvement method, quality failure improvement program and recording medium |
| CN101943908A (en) * | 2010-09-25 | 2011-01-12 | 华中科技大学 | Wireless Andon system |
| CN103914026A (en) * | 2013-01-06 | 2014-07-09 | 上海西门子医疗器械有限公司 | Production state monitoring system and method |
| CN103984326A (en) * | 2014-05-28 | 2014-08-13 | 杭州迈可思法电气工程有限公司 | Production management system and method |
| CN105867337A (en) * | 2016-05-23 | 2016-08-17 | 河源中光电通讯技术有限公司 | Management system of automatic production line of backlight modules and method of management system |
| CN106444659A (en) * | 2016-09-21 | 2017-02-22 | 广东省自动化研究所 | Production management method and system for stamping workshop |
| CN107368053A (en) * | 2017-08-23 | 2017-11-21 | 上海云统信息科技有限公司 | A kind of production line stop reponse system based on Distributed Control System |
| CN108279653A (en) * | 2018-01-29 | 2018-07-13 | 安徽江淮汽车集团股份有限公司 | The areas producing line Ji Pei material flow quantity control method and system |
| CN108845544A (en) * | 2018-06-15 | 2018-11-20 | 合肥齐信信息技术有限公司 | A kind of scarce material intelligent alarm method of production line based on surplus monitoring |
| CN109041456A (en) * | 2018-09-13 | 2018-12-18 | 格力电器（武汉）有限公司 | The detection device of material state and the control device of welding procedure assembly line |
| CN110597196A (en) * | 2019-08-21 | 2019-12-20 | 格力电器（武汉）有限公司 | Data acquisition system and data acquisition method |
-
2020
- 2020-01-19 CN CN202010059528.1A patent/CN111240286B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6027022A (en) * | 1996-06-29 | 2000-02-22 | Samsung Electronics Co., Ltd. | Quality control apparatus and method using a bar code data entry system |
| JPH10202482A (en) * | 1997-01-20 | 1998-08-04 | Honda Motor Co Ltd | Production line control method |
| JP2008140014A (en) * | 2006-11-30 | 2008-06-19 | Sharp Corp | Quality failure improvement system, quality failure improvement method, quality failure improvement program and recording medium |
| CN101943908A (en) * | 2010-09-25 | 2011-01-12 | 华中科技大学 | Wireless Andon system |
| CN103914026A (en) * | 2013-01-06 | 2014-07-09 | 上海西门子医疗器械有限公司 | Production state monitoring system and method |
| CN103984326A (en) * | 2014-05-28 | 2014-08-13 | 杭州迈可思法电气工程有限公司 | Production management system and method |
| CN105867337A (en) * | 2016-05-23 | 2016-08-17 | 河源中光电通讯技术有限公司 | Management system of automatic production line of backlight modules and method of management system |
| CN106444659A (en) * | 2016-09-21 | 2017-02-22 | 广东省自动化研究所 | Production management method and system for stamping workshop |
| CN107368053A (en) * | 2017-08-23 | 2017-11-21 | 上海云统信息科技有限公司 | A kind of production line stop reponse system based on Distributed Control System |
| CN108279653A (en) * | 2018-01-29 | 2018-07-13 | 安徽江淮汽车集团股份有限公司 | The areas producing line Ji Pei material flow quantity control method and system |
| CN108845544A (en) * | 2018-06-15 | 2018-11-20 | 合肥齐信信息技术有限公司 | A kind of scarce material intelligent alarm method of production line based on surplus monitoring |
| CN109041456A (en) * | 2018-09-13 | 2018-12-18 | 格力电器（武汉）有限公司 | The detection device of material state and the control device of welding procedure assembly line |
| CN110597196A (en) * | 2019-08-21 | 2019-12-20 | 格力电器（武汉）有限公司 | Data acquisition system and data acquisition method |
Non-Patent Citations (1)
| Title |
|---|
| 孙浩清，等: "面向OPE管理的单元生产线制造执行系统研究", 《武汉理工大学学报（信息与管理工程版）》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112288389A (en) * | 2020-10-20 | 2021-01-29 | 苏州浪潮智能科技有限公司 | SCADA system based production control method, program, device and medium |
| CN112561173A (en) * | 2020-12-18 | 2021-03-26 | 安徽巨一科技股份有限公司 | Optimization method for rapidly improving production capacity of welding line |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111240286B (en) | 2021-02-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20200371858A1 (en) | Fault Predicting System and Fault Prediction Method | |
| CN111240286B (en) | System and method for reducing misjudgment of line stop reason | |
| CN113077172A (en) | Equipment state trend analysis and fault diagnosis method | |
| CN109711659B (en) | Yield improvement management system and method for industrial production | |
| CN109741927B (en) | Intelligent prediction system for equipment faults and potential defective products of miniature transformer production line | |
| CN113084388B (en) | Welding quality detection method, system, device and storage medium | |
| KR20080070543A (en) | Early warning method for estimating inferiority in automatic production line | |
| CN116028887B (en) | Analysis method of continuous industrial production data | |
| CN110262460B (en) | Concrete piston fault prediction method for extracting features by combining clustering idea | |
| CN109523030B (en) | Telemetering parameter abnormity monitoring system based on machine learning | |
| CN116244765A (en) | Equipment maintenance management method based on industrial Internet | |
| CN110134040B (en) | Method and system for processing operation data of industrial equipment | |
| CN111427330A (en) | Equipment maintenance data-based equipment fault mode and rule analysis method | |
| CN109816191A (en) | The qualitative forecasting method and its system of Multi-workstation System | |
| JP2002236511A (en) | System and method for production control | |
| CN116859838B (en) | Early warning system for monitoring equipment operation condition | |
| CN112884212A (en) | Cigarette single gram weight deviation analysis and prediction method | |
| CN117393076A (en) | Intelligent monitoring method and system for heat-resistant epoxy resin production process | |
| KR102353574B1 (en) | Tool-related abnormal data detection system of CNC machines | |
| CN116205623A (en) | Equipment maintenance method, device, system, electronic equipment and storage medium | |
| EP3483685A1 (en) | Data processing device and method for performing problem diagnosis in a production system with a plurality of robots | |
| CN112215503A (en) | Reliability monitoring method based on SPC | |
| CN109828146A (en) | A method of equipment working condition is judged by device electrical parameters AD sampling | |
| CN117520999B (en) | Intelligent operation and maintenance method and system for edge data center equipment | |
| Hilzbrich et al. | Analyzing and Maximizing Line Productivity—Bottleneck Identification and Predictive Maintenance |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |