CN116700192A - Intelligent monitoring system of adhesive tape production line - Google Patents
Intelligent monitoring system of adhesive tape production line Download PDFInfo
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- CN116700192A CN116700192A CN202310888418.XA CN202310888418A CN116700192A CN 116700192 A CN116700192 A CN 116700192A CN 202310888418 A CN202310888418 A CN 202310888418A CN 116700192 A CN116700192 A CN 116700192A
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 375
- 239000002390 adhesive tape Substances 0.000 title claims abstract description 39
- 238000012544 monitoring process Methods 0.000 title claims abstract description 34
- 238000011156 evaluation Methods 0.000 claims abstract description 127
- 238000004458 analytical method Methods 0.000 claims abstract description 48
- 238000000034 method Methods 0.000 claims abstract description 31
- 238000003860 storage Methods 0.000 claims abstract description 15
- 238000007667 floating Methods 0.000 claims description 29
- 238000012545 processing Methods 0.000 claims description 22
- 230000002159 abnormal effect Effects 0.000 claims description 17
- 230000008569 process Effects 0.000 claims description 17
- 238000005070 sampling Methods 0.000 claims description 15
- 238000001035 drying Methods 0.000 description 7
- 230000009471 action Effects 0.000 description 5
- 230000001070 adhesive effect Effects 0.000 description 5
- 239000000853 adhesive Substances 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 230000007423 decrease Effects 0.000 description 3
- 230000002950 deficient Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 230000005856 abnormality Effects 0.000 description 2
- 230000006378 damage Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000003111 delayed effect Effects 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 239000011248 coating agent Substances 0.000 description 1
- 238000000576 coating method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 238000010223 real-time analysis Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000035939 shock Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- 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
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/31—From computer integrated manufacturing till monitoring
- G05B2219/31282—Data acquisition, BDE MDE
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- 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 application discloses an intelligent monitoring system of an adhesive tape production line, which comprises an acquisition module, a storage module, an analysis module, a comparison module and an early warning module, wherein the acquisition module is used for acquiring the adhesive tape; the acquisition module acquires production line information, including production line power information, production line software information, production line state information and production line environment information; the storage module receives and stores the information acquired by the acquisition module; the method comprises the steps that production line power information and production line software information of an adhesive tape production line are analyzed to generate a preheating evaluation index, and production line state information and production line environment information of the adhesive tape production line are analyzed to generate a production evaluation coefficient; the comparison module compares the preheating evaluation index with a preheating evaluation threshold value and compares the production evaluation coefficient with a production evaluation threshold value; and the early warning module comprehensively evaluates the production line in the production process. According to the application, through comprehensively analyzing the preheating running state and the production running state of the production line, intelligent monitoring of the production line is realized, and the fault risk is reduced.
Description
Technical Field
The application relates to the technical field of intelligent monitoring, in particular to an intelligent monitoring system of an adhesive tape production line.
Background
The adhesive tape is a film or paper material with adhesive property, is widely used for bonding, packaging, insulation, marking, fixing and other applications, has the characteristics of easy use, tearing, good flexibility, adjustable adhesive force, high temperature resistance, low temperature resistance and the like, and is widely applied in the fields of families, offices, industries, medical treatment and the like.
The monitoring system of the adhesive tape production line can realize the acquisition and monitoring of the data of the production process by using a sensor and a data acquisition device.
The prior art has the following defects: under the continuous development of network technology, summarizing various data in the adhesive tape production line and carrying out real-time monitoring, but the existing monitoring system collects a large amount of data information in the monitoring process, and only carries out monitoring display on a large amount of data information, but cannot carry out risk prediction and optimization of the production line, only when the production is completed, the judgment of the production line is carried out according to the mode of comparing the expected output with the actual output, and the production line cannot be analyzed according to the collected data information before the production, for example, the production line lacks evaluation standard in a preheating stage, and cannot carry out real-time analysis and evaluation according to the collected real-time data information in the production process, so that excessive adhesive tape defective products are produced in the production process, and further the production benefit is affected.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The application aims to provide an intelligent monitoring system of an adhesive tape production line, which is used for analyzing the running state of the production line by analyzing the stable state of the production line before production, and in the production process, when the running state of the production line is poor, the production line with the poor running state is maintained in advance, so that the number of defective products is effectively reduced, and the problems in the background technology are solved.
In order to achieve the above object, the present application provides the following technical solutions: an intelligent monitoring system of an adhesive tape production line comprises an acquisition module, a storage module, an analysis module, a comparison module and an early warning module;
the acquisition module acquires production line information, including production line power information, production line software information, production line state information and production line environment information, and transmits the information to the storage module after acquisition;
the storage module receives and stores the information acquired by the acquisition module, stores historical data information and threshold information of the production line, and sends data to the analysis module;
the analysis module receives the data sent by the storage module and the comparison module, generates a preheating evaluation index according to the production line power information and the production line software information of the adhesive tape production line, transmits the preheating evaluation index to the comparison module, analyzes the production line information and the production line environment information according to the information sent by the comparison module, and sends an analysis result to the comparison module;
the comparison module receives the data sent by the analysis module, compares the preheating evaluation index with the preheating evaluation threshold, respectively sends the marked normal preheating production line and the marked abnormal preheating production line to the analysis module and the early warning module, compares the received analysis results, and sends the comparison results meeting the requirements to the early warning module;
and the early warning module receives the data sent by the comparison module and comprehensively evaluates the production line in the production process according to the data.
Preferably, the line power information includes the total harmonic distortion rate and the frequency offset coefficient, and is calibrated as、/>The production line software information comprises a software error processing time length ratio and is marked as +.>The production line information includes the production rate deviation value and is marked as +.>The production line environment information includes the air flow rate floating value and is marked as +.>。
Preferably, the logic for obtaining the frequency offset coefficient is as follows:
obtaining nominal frequency of power gridThe frequency is used as a standard frequency P, the average value of frequency data in the moment T is calculated, the average frequency is calibrated to be Pavg, the standard frequency of a sampling period is compared with the average frequency, the frequency offset coefficient is calculated, and the frequency offset coefficient is calculated through a formula according to the following formula:。
preferably, the logic for total harmonic distortion rate acquisition is as follows:
acquiring an effective value H1 of a fundamental wave in electric power on a production line, acquiring a real-time sum of effective values of all harmonic waves at different moments in t time, and calibrating the sum of the effective values of all harmonic waves as Hn, wherein the expression for acquiring the total harmonic distortion rate is as follows:。
preferably, the logic for acquiring the software error processing duration ratio is as follows:
acquiring the software error reporting times of different moments in t time, marking the software error reporting times of different moments in t time as x, recording the software error reporting occurrence time as Xi, solving the problem that the software error reporting time is recorded as Ji, i is the time sequence number of the occurrence of different software error reporting, i is a positive integer, recording the processed time length in t time, and acquiring the software error reporting processing time length ratio by the expression:。
preferably, the production line state information and the production line environment information are analyzed, and the production evaluation coefficient is generated according to the production rate deviation value in the production line state information and the airflow velocity floating value in the production line environment information.
Preferably, the acquisition logic for the production rate deviation value is as follows:
obtaining a preset standard production rate of a product, marking the standard production rate as V, obtaining the actual production rate of the product after the production and marking the actual production rate as VI, and obtaining an expression of the deviation value of the production rate as follows:。
preferably, the logic for obtaining the air flow rate float value is as follows:
the time interval for collecting the airflow velocity data is taken as a sampling period, in each sampling period, the actual value of the airflow velocity is obtained through a sensor or a flowmeter and other equipment, the airflow velocity data in a certain time is averaged to obtain an average airflow velocity value Qavg, the maximum floating value and the minimum floating value of the airflow velocity in each sampling period are compared with the average airflow velocity value, the maximum floating value and the minimum floating value of the airflow velocity are respectively marked as Qmax and Qmin, and the obtained expression of the airflow velocity floating value is:。
preferably, the preheating evaluation index is compared with the preheating evaluation threshold, and the marked normal preheating production line and abnormal preheating production line are respectively sent to the analysis module and the early warning module, wherein the specific process is as follows:
if the preheating evaluation index is larger than the preheating evaluation threshold, marking the production line as an abnormal preheating production line through the comparison module, and sending the abnormal preheating production line to the early warning module for early warning;
if the preheating evaluation index is smaller than or equal to the preheating evaluation threshold, marking the production line as a normal preheating production line through the comparison module, and sending the normal preheating production line to the analysis module.
Preferably, the received analysis results are compared, and the comparison results meeting the requirements are sent to the early warning module, and the specific process is as follows:
if the production evaluation coefficient is smaller than or equal to the production evaluation threshold value, generating a running state stable signal;
if the production evaluation coefficient is larger than the production evaluation threshold, generating an operation state danger signal, and transmitting the operation state danger signal to the early warning module.
Preferably, the data sent by the comparison module is received, and the production line in the production process is comprehensively evaluated according to the data, and the specific process is as follows;
when the early warning module receives the running state dangerous signal, acquiring production evaluation coefficients generated at corresponding moments and a plurality of production evaluation coefficients generated at subsequent moments to establish an analysis set, and calculating an average value and a discrete degree value of the production evaluation coefficients;
if the average value of the production evaluation coefficients is greater than or equal to the production evaluation threshold value, generating a production line fault signal;
if the average value of the production evaluation coefficients is smaller than the production evaluation threshold value and the discrete degree value is larger than the discrete degree reference threshold value, generating a signal of unstable operation of the production line;
if the average value of the production evaluation coefficients is smaller than the production evaluation threshold value and the discrete degree value is smaller than the discrete degree reference threshold value, generating a signal of stable operation of the production line.
In the technical scheme, the application has the technical effects and advantages that:
the method comprises the steps of collecting production line power information and production line software information to generate a preheating evaluation index, comparing the preheating evaluation index with a preheating evaluation threshold, marking the production line as an abnormal preheating production line and a normal preheating production line, maintaining the abnormal preheating production line, putting the normal preheating production line into production, generating a production evaluation coefficient through the collected production line state information and production line environment information in the production process, analyzing the production line in real time, performing multi-set predictive analysis when the production evaluation coefficient of the production line is larger than the production evaluation threshold, performing different actions according to analysis results, maintaining the production line with poor running state in advance, improving the monitoring frequency of unstable running state, and effectively reducing the risk of the production line.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for those skilled in the art.
FIG. 1 is a schematic block diagram of an intelligent monitoring system for an adhesive tape production line according to the present application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Example 1: the application provides an intelligent monitoring system of an adhesive tape production line shown in fig. 1, which comprises an acquisition module, a storage module, an analysis module, a comparison module and an early warning module;
the acquisition module acquires production line information, including production line power information, production line software information, production line state information and production line environment information, and transmits the information to the storage module after acquisition;
the storage module receives and stores the information acquired by the acquisition module, stores historical data information and threshold information of the production line, and sends data to the analysis module;
in the adhesive tape production line, the aim of preheating the production line is to optimize the production process and improve the product quality, by preheating the adhesive tape production line, the activity and the fluidity of the adhesive can be improved, the coating uniformity is improved, the volatile matters are removed, and the curing process of the adhesive is promoted, so that the product quality and the production efficiency are improved, and the preheating is an important ring in the optimization production process, so that the analysis is performed on the stage of the preheating production line;
the analysis module is used for generating a preheating evaluation index from production line power information and production line software information of the adhesive tape production line and transmitting the preheating evaluation index to the comparison module;
the production line power information comprises a total harmonic distortion rate and a frequency offset coefficient, and after the collection, the collection module respectively calibrates the total harmonic distortion rate and the frequency offset coefficient intoAnd->；
The frequency refers to the periodic variation of the alternating current in the power grid, which represents the number of cycles occurring per unit time, in a power system, the frequency is usually in hertz, which represents the number of cycles occurring per second, for example, the power grid frequency is 50Hz, which means that 50 complete cycles occur per second in the power grid, each cycle consisting of a positive half-cycle, which represents the positive variation of the current or voltage, and a negative half-cycle, which represents the negative variation of the current or voltage, the frequency of the power grid being determined by the rotational speed of the generator, which generates the alternating current by mechanical rotation, which rotational speed determines the frequency of the power grid;
the frequency offset coefficient of the power grid has an important influence on the operation of the production line, the frequency offset refers to the difference or deviation degree between the actual power grid frequency and the reference power grid frequency, and a higher frequency offset indicates that the deviation degree of the power grid frequency is larger, that is, the actual power grid frequency is larger than the standard power grid frequency, which causes the following influence:
clock and timing device errors: higher frequency offsets can lead to reduced accuracy of the clock and timing equipment, e.g., the clock may be fast or slow, resulting in inaccurate time, thereby affecting the punctuality of the production;
the generator and motor are not stable in operation: frequency offset has an important influence on the operation stability of the generator and the motor, and higher frequency offset can cause fluctuation of the rotation speed of the generator or the motor to make the operation unstable;
the power quality is degraded: higher frequency shifts can lead to power quality degradation, such as voltage fluctuations, current imbalance, and voltage distortion, thereby affecting power equipment and product quality;
the risk of failure and damage increases: frequency offset may increase the risk of failure and damage to equipment in the power system, e.g., the power equipment may be damaged by overvoltage or overcurrent, resulting in shortened life of the equipment;
it should be noted that, in a production plant, since the power supply to which each production line may be connected is different and the load characteristics of each production line are different, the stability of the power supply and the power quality may be different, so that the frequency offset of each production line may be different;
therefore, the frequency offset coefficient of the production line is obtained, and the state of the production line can be analyzed;
the logic for frequency offset coefficient acquisition is as follows:
the method comprises the steps of obtaining the nominal frequency of a power grid as a standard frequency P, calculating the average value of frequency data in a moment T, calibrating the average frequency to be Pavg, comparing the standard frequency of a sampling period with the average frequency, calculating a frequency offset coefficient of the sampling period, and calculating the frequency offset coefficient through a formula according to the formula:；
it should be noted that, the frequency of the power grid is measured by using a frequency measuring instrument, a sampling period is set for data acquisition, a smaller frequency offset coefficient indicates that the power grid operates close to the nominal frequency, and a larger frequency offset coefficient may indicate that the power grid has a problem or is unstable;
the total harmonic distortion rate refers to the total distortion degree of all harmonic voltages or currents in a power system, and is used for evaluating an important index of power quality, and when nonlinear loads, harmonic pollution, voltage shock and flicker occur in power, power waveform distortion easily occurs;
the total harmonic distortion rate of the electric power has a certain influence on a production line needing the electric power, and the following problems can occur due to the fact that the total harmonic distortion rate is too high:
the power quality is degraded: the harmonic current and the harmonic voltage cause problems of voltage fluctuation, current imbalance, voltage distortion and the like in the power system. This can lead to reduced power quality, affecting the normal operation of the device and product quality;
further harmonic diffusion: higher harmonic distortion rates can further exacerbate harmonic dispersion effects, when harmonics are present in the power system, they can propagate through the grid to other power devices, affecting the stability and power quality of the overall power system;
interfering with other devices: the harmonic current and the harmonic voltage can interfere other sensitive equipment and systems, including communication equipment, computers, measuring instruments and the like, which can cause misoperation of the equipment, data loss or measurement errors and influence the quality of products produced by a production line;
therefore, the total harmonic distortion rate of the electric power of the production line is obtained, and the state of the production line can be analyzed;
the logic for total harmonic distortion rate acquisition is as follows:
acquiring an effective value H1 of a fundamental wave in electric power on a production line, acquiring a real-time sum of effective values of all harmonic waves at different moments in t time, and calibrating the sum of the effective values of all harmonic waves as Hn, wherein the expression for acquiring the total harmonic distortion rate is as follows:；
it should be noted that, the effective value of the fundamental wave refers to the amplitude value of the sine wave component with the lowest frequency in the power system, when the total harmonic distortion rate is actually calculated, the order range of the harmonic component may be limited, so as to eliminate the influence of the higher order harmonic on the result, and the specific calculation method may be different according to different standards or specifications; the method comprises the steps of measuring electric quantity and electric energy of a production line in real time by using instruments such as a harmonic analyzer, an ammeter and a voltmeter, wherein the measuring equipment can be arranged at a power input position or a key node of the production line so as to monitor harmonic voltage and current of a power grid in real time, record frequency and amplitude of harmonic waves, record and analyze data by using a data acquisition system or an automatic monitoring system, analyze the harmonic waves by using acquired data, and identify frequency, amplitude and phase information of each harmonic component in the analysis process;
the production line software information comprises a software error processing time length ratio, and after acquisition, the acquisition module marks the software error processing time length ratio as；
When the ratio of the software error processing time length of the production line is too large, the production is affected, and the following aspects are affected:
production line downtime extension: too long software error processing time may cause the production line to be stopped for a long time, and the production efficiency and the productivity are affected. The normal operation of the production line needs the stability and the reliability of the software, if the software error processing time is too long, the downtime is increased, and the production plan can not be completed on time;
the production cost is increased: too long software error processing time may cause an increase in production cost, and an increase in downtime may cause a decrease in production efficiency and a decrease in yield, thereby increasing production cost. In addition, delayed error reporting may require additional human and resource investment to solve the problem, further increasing costs;
the quality problem increases: the software error reporting processing time is too long, so that quality problems can be increased, if the software error reporting leads to control or monitoring failure of a production line, unstable product quality or defects can be caused, the delayed error reporting processing means that the problems can not be solved in time, the number of defective products can be increased, and the cost of quality control and reworking is increased;
therefore, the software error processing time length ratio of the production line is obtained, and the state of the production line can be analyzed;
the logic for acquiring the software error processing time length ratio is as follows:
acquiring the software error reporting times of different moments in t time, marking the software error reporting times of different moments in t time as x, recording the software error reporting occurrence time as Xi, solving the problem that the software error reporting time is recorded as Ji, i is the time sequence number of the occurrence of different software error reporting, i is a positive integer, recording the processed time length in t time, and acquiring the software error reporting processing time length ratio by the expression:；
the software error reporting times in the production line can be obtained through log record information of a storage module in the system, the log record information comprises error reporting codes, error description, error reporting time, solving time and other information, and the software error reporting processing time length ratio is obtained through analysis according to the log record information;
according to the power information of the production lines and the software information of the production lines, analyzing the production lines in a preheating stage, screening out the production lines which do not meet the production standard, and overhauling the production lines so as to avoid problems in the subsequent production process;
the analysis module obtains the total harmonic distortion rateFrequency offset coefficient->Software error processing duration ratio +.>After that, a preheating evaluation index is generated, and the preheating evaluation index is scaled to +.>The formula according to is:
in (1) the->、/>、/>Total harmonic distortion rate->Frequency offset coefficient->Software error processing duration ratio +.>Is a preset proportionality coefficient of>、/>、/>Are all greater than 0;
as shown by the formula, the larger the total harmonic distortion rate is, the larger the frequency offset coefficient is, the larger the ratio of the software error processing duration is, namely the preheating evaluation indexThe larger the expression value of (2) is, the higher the probability of occurrence of abnormality of the adhesive tape production line is, the smaller the total harmonic distortion rate is, the smaller the frequency offset coefficient is, the smaller the ratio of the software error processing duration is, namely the preheating evaluation index is->The smaller the expression value of (c) is, the lower the probability of occurrence of an abnormality in the adhesive tape production line is;
the comparison module is used for comparing the generated preheating evaluation index with a preheating evaluation threshold, marking the adhesive tape production line as a normal preheating production line and an abnormal preheating production line, and transmitting the marked adhesive tape production line information to the analysis module and the early warning module;
after the comparison module obtains the preheating evaluation index generated by the adhesive tape production line, comparing the generated preheating evaluation index with a preheating evaluation threshold, if the preheating evaluation index is larger than the preheating evaluation threshold, indicating that the production line has high abnormal probability, marking the production line as an abnormal preheating production line through the comparison module, transmitting the abnormal preheating production line information to the early warning module, and notifying workshop maintenance personnel to detect and maintain the production line;
if the preheating evaluation index is smaller than or equal to the preheating evaluation threshold, indicating that the occurrence probability of the production line is low, marking the production line as a normal preheating production line through a comparison module, and transmitting the normal preheating production line information to an analysis module for further analysis;
the threshold value information of the storage module stores threshold value data required by a preheating evaluation threshold value, a production evaluation threshold value and the like, when an abnormal preheating production line is maintained, preheating analysis is performed again, whether the preheating evaluation threshold value standard is reached or not is judged, and if the preheating evaluation threshold value standard is reached, the normal preheating production line is used for subsequent analysis.
The analysis module is used for selecting the normal preheating production line as an adhesive tape production line for production after receiving the normal preheating production line information sent by the comparison module, and analyzing the running state of the production line in real time;
the production line information comprises a production rate deviation value, the production line environment information comprises an airflow speed floating value, and the acquisition module respectively marks the production rate deviation value and the airflow speed floating value as、/>；
The analysis module is used for generating a production evaluation coefficient according to the production line state information and the production line environment information of the adhesive tape production line and transmitting the production evaluation coefficient to the comparison module;
the production rate deviation value has an important influence on the quality of the produced product, wherein the production rate deviation value refers to the difference between the actual production rate and an expected standard value, and the larger the production rate deviation value is, the larger the difference between the actual production rate and the expected production rate is, so that the following influence can be generated on the quality of the produced product;
too fast a production rate may lead to quality problems: if the production is too fast, operators may not be able to sufficiently control and monitor the quality of each process, so that the quality of the product is reduced, and the production is too fast, so that the problems of overload of equipment, insufficient process, untimely adjustment of process parameters and the like are also caused, so that the quality of the product is further influenced;
too slow a production rate may result in wasted resources: if the production rate is too slow, the resources of the production line may not be fully utilized, for example, equipment idle time is increased, personnel resources are idle, etc., which results in resource waste, production cost rise and benefit decline;
deviation value of production rateThe acquisition logic of (a) is as follows:
obtaining a preset standard production rate of a product, marking the standard production rate as V, obtaining the actual production rate of the product after production, marking the actual production rate as VI, calculating a production rate deviation value through a formula, and obtaining the actual production rate by the formula:；
the floating value of the airflow velocity has an important influence on the product quality of the adhesive tape, and in the production process of the adhesive tape, the airflow velocity influences the curing speed of the adhesive in the drying process of the adhesive tape, so that the product quality of the adhesive tape can be influenced;
uneven drying: the larger air flow speed floating value can cause uneven air flow distribution in the drying process, partial areas can be subjected to excessive high air flow speed, and other areas can be influenced by the excessive low air flow speed, so that uneven temperature distribution of a product in the drying process is caused, and the quality of the product is influenced;
the production efficiency is reduced: a larger air flow speed floating value can cause instability and fluctuation of the drying process, increase the drying time and reduce the productivity and efficiency of the production line;
air flow rate floating valueThe acquisition logic of (a) is as follows:
the time interval for collecting airflow velocity data is taken as a sampling period, an airflow sensor is arranged in a drying area in each sampling period, the actual value of the airflow velocity is obtained through equipment such as the airflow sensor or a flowmeter, the airflow velocity data in a certain time is averaged to obtain an average airflow velocity value Qavg, the maximum floating value and the minimum floating value of the airflow velocity in each sampling period are compared with the average airflow velocity value, the maximum floating value and the minimum floating value of the airflow velocity are respectively marked as Qmax and Qmin, and the airflow velocity floating value is calculated by adopting the following formula:；
the analysis module obtains the deviation value of the production rateAir flow rate floating value->And generating a production evaluation coefficient, and calibrating the production evaluation coefficient to be +.>The formula according to is:
in (1) the->、/>Production rate deviation values ∈>Air flow rate floating value->Is a preset proportionality coefficient of>、/>Are all greater than 0;
as can be seen from the formula, the larger the production rate deviation value is, the larger the airflow speed floating value is, namely the production evaluation coefficientThe larger the expression value of (2) is, the more unstable the operation of the adhesive tape production line is, the smaller the deviation value of the production rate is, the smaller the floating value of the airflow velocity is, namely the preheating evaluation index +.>The smaller the expression value of (2) is, the more stable the adhesive tape production line runs;
the comparison module is used for comparing the acquired production evaluation coefficient with a production evaluation threshold value and analyzing the running state of the production line;
after the comparison module obtains the production evaluation coefficient generated by the adhesive tape production line, comparing the generated production evaluation coefficient with a production evaluation threshold;
if the production evaluation coefficient is smaller than or equal to the production evaluation threshold value, generating a running state stable signal, and indicating that the production line runs stably;
if the production evaluation coefficient is larger than the production evaluation threshold, generating an operation state danger signal, indicating that the operation state of the production line is poor, and transmitting the operation state danger signal to the early warning module;
after receiving the running state dangerous signal sent by the comparison module, the early warning module acquires a production evaluation coefficient generated at a corresponding moment and a production evaluation coefficient set generated at a subsequent moment, comprehensively evaluates a production line in the production process, and analyzes the production state of the production line;
when the early warning module receives the running state dangerous signal, acquiring production evaluation coefficients generated at corresponding moments and a plurality of production evaluation coefficients generated at subsequent moments to establish an analysis set, and changing the data set into JH, thenX is a positive integer, and x production evaluation coefficients are calculated +.>Average and discrete degree values;
if the average value of the production evaluation coefficients is greater than or equal to the production evaluation threshold value, generating a production line fault signal, indicating that the production evaluation coefficients in the data set have a large number of conditions that the production evaluation coefficients are greater than the production evaluation threshold value, sending the production line fault signal, prompting maintenance personnel to monitor the running state deterioration of the production line, and needing to maintain in time, and maintaining the production line with the deteriorated running state in advance, thereby effectively reducing the loss;
if the average value of the production evaluation coefficients is smaller than the production evaluation threshold value and the discrete degree value is larger than the discrete degree reference threshold value, generating a signal of unstable operation of the production line, wherein the signal indicates that the production evaluation coefficients in the data set have less production evaluation coefficients and are larger than the production evaluation threshold value, the stability of the production line is poor, and the monitoring frequency of the production line needs to be improved so as to discover the situation that the operation state of the monitored production line is poor in time;
if the average value of the production evaluation coefficients is smaller than the production evaluation threshold value and the discrete degree value is smaller than the discrete degree reference threshold value, generating a signal for stable operation of the production line, wherein the signal indicates that the production evaluation coefficients in the data set are generally under the condition that the production evaluation coefficients are smaller than or equal to the production evaluation threshold value, and the condition that the production evaluation coefficients are larger than the production evaluation threshold value is accidental and maintenance is not needed;
the calculation formula of the average value of the production evaluation coefficients in the analysis set is as follows:
the calculation formula for producing the discrete degree value of the evaluation coefficient in the analysis set is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the In the method, in the process of the application,evaluation coefficient for analysis of production within a collection>Average value of>Evaluating coefficients for intra-set productionIs a discrete degree value of (a).
According to the method, the preheating evaluation index is established by collecting the electric power information of the production line and the software information of the production line, the production line is marked as an abnormal preheating production line and a normal preheating production line by comparing the preheating evaluation index with the preheating evaluation threshold, the abnormal preheating production line is maintained, the normal preheating production line is put into production, the production evaluation coefficient is established by the collected production line state information and the production line environment information in the production process, the production line is analyzed in real time, the multi-set predictive analysis is performed when the production evaluation coefficient of the production line is larger than the production evaluation threshold, different actions are implemented according to the analysis result, the production line with poor running state is maintained in advance, the monitoring frequency of unstable running state is improved, and the risk of the production line is effectively reduced.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
While certain exemplary embodiments of the present application have been described above by way of illustration only, it will be apparent to those of ordinary skill in the art that modifications may be made to the described embodiments in various different ways without departing from the spirit and scope of the application. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive of the scope of the application, which is defined by the appended claims.
It is noted that relational terms such as first and second, and the like, if any, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (11)
1. The intelligent monitoring system of the adhesive tape production line is characterized by comprising an acquisition module, a storage module, an analysis module, a comparison module and an early warning module;
the acquisition module acquires production line information, including production line power information, production line software information, production line state information and production line environment information, and transmits the information to the storage module after acquisition;
the storage module receives and stores the information acquired by the acquisition module, stores historical data information and threshold information of the production line, and sends data to the analysis module;
the analysis module receives the data sent by the storage module and the comparison module, generates a preheating evaluation index according to the production line power information and the production line software information of the adhesive tape production line, transmits the preheating evaluation index to the comparison module, analyzes the production line information and the production line environment information according to the information sent by the comparison module, and sends an analysis result to the comparison module;
the comparison module receives the data sent by the analysis module, compares the preheating evaluation index with the preheating evaluation threshold, respectively sends the marked normal preheating production line and the marked abnormal preheating production line to the analysis module and the early warning module, compares the received analysis results, and sends the comparison results meeting the requirements to the early warning module;
and the early warning module receives the data sent by the comparison module and comprehensively evaluates the production line in the production process according to the data.
2. The intelligent monitoring system of an adhesive tape production line according to claim 1, wherein the line power information includes a total harmonic distortion rate and a frequency offset coefficient, and is calibrated as、/>The production line software information comprises a software error processing time length ratio and is marked as +.>The production line information includes the production rate deviation value and is marked as +.>The production line environment information includes the air flow rate floating value and is marked as +.>。
3. The intelligent monitoring system of an adhesive tape production line according to claim 2, wherein the logic for obtaining the frequency offset coefficient is as follows:
the method comprises the steps of obtaining the nominal frequency of a power grid as a standard frequency P, calculating the average value of frequency data in a moment T, calibrating the average frequency to be Pavg, comparing the standard frequency of a sampling period with the average frequency, calculating a frequency offset coefficient of the sampling period, and calculating the frequency offset coefficient through a formula according to the formula:。
4. the intelligent monitoring system of an adhesive tape production line according to claim 2, wherein the logic for obtaining the total harmonic distortion is as follows:
acquiring an effective value H1 of a fundamental wave in electric power on a production line, acquiring a real-time sum of effective values of all harmonic waves at different moments in t time, and calibrating the sum of the effective values of all harmonic waves as Hn, wherein the expression for acquiring the total harmonic distortion rate is as follows:。
5. the intelligent monitoring system of claim 2, wherein the logic for obtaining the ratio of the software error handling time length is as follows:
acquiring the software error reporting times of different moments in t time, marking the software error reporting times of different moments in t time as x, recording the software error reporting occurrence time as Xi, solving the problem that the software error reporting time is recorded as Ji, i is the time sequence number of the occurrence of different software error reporting, i is a positive integer, recording the processed time length in t time, and acquiring the software error reporting processing time length ratio by the expression:。
6. the intelligent monitoring system of claim 5, wherein the analysis of the production line status information and the production line environment information is based on an analysis of a production rate deviation value in the production line status information and an airflow rate floating value in the production line environment information to generate a production evaluation coefficient.
7. The intelligent monitoring system of claim 6, wherein the logic for obtaining the rate deviation value is as follows:
obtaining a preset standard production rate of a product, marking the standard production rate as V, obtaining the actual production rate of the product after the production and marking the actual production rate as VI, and obtaining an expression of the deviation value of the production rate as follows:。
8. the intelligent monitoring system of claim 7, wherein the logic for obtaining the air flow rate float value is as follows:
the time interval for collecting the airflow velocity data is taken as a sampling period, in each sampling period, the actual value of the airflow velocity is obtained through a sensor or a flowmeter and other equipment, the airflow velocity data in a certain time is averaged to obtain an average airflow velocity value Qavg, the maximum floating value and the minimum floating value of the airflow velocity in each sampling period are compared with the average airflow velocity value, the maximum floating value and the minimum floating value of the airflow velocity are respectively marked as Qmax and Qmin, and the obtained expression of the airflow velocity floating value is:。
9. the intelligent monitoring system of claim 8, wherein the preheating evaluation index is compared with the preheating evaluation threshold, and the marked normal preheating production line and abnormal preheating production line are respectively sent to the analysis module and the early warning module, and the specific process is as follows:
if the preheating evaluation index is larger than the preheating evaluation threshold, marking the production line as an abnormal preheating production line through the comparison module, and sending the abnormal preheating production line to the early warning module for early warning;
if the preheating evaluation index is smaller than or equal to the preheating evaluation threshold, marking the production line as a normal preheating production line through the comparison module, and sending the normal preheating production line to the analysis module.
10. The intelligent monitoring system of claim 9, wherein the intelligent monitoring system compares the received analysis results and sends the comparison result meeting the requirement to the early warning module, and the intelligent monitoring system comprises the following steps:
if the production evaluation coefficient is smaller than or equal to the production evaluation threshold value, generating a running state stable signal;
if the production evaluation coefficient is larger than the production evaluation threshold, generating an operation state danger signal, and transmitting the operation state danger signal to the early warning module.
11. The intelligent monitoring system of an adhesive tape production line according to claim 10, wherein the data sent by the comparison module is received, and the production line in the production process is comprehensively evaluated according to the data, and the specific process is as follows;
when the early warning module receives the running state dangerous signal, acquiring production evaluation coefficients generated at corresponding moments and a plurality of production evaluation coefficients generated at subsequent moments to establish an analysis set, and calculating an average value and a discrete degree value of the production evaluation coefficients;
if the average value of the production evaluation coefficients is greater than or equal to the production evaluation threshold value, generating a production line fault signal;
if the average value of the production evaluation coefficients is smaller than the production evaluation threshold value and the discrete degree value is larger than the discrete degree reference threshold value, generating a signal of unstable operation of the production line;
if the average value of the production evaluation coefficients is smaller than the production evaluation threshold value and the discrete degree value is smaller than the discrete degree reference threshold value, generating a signal of stable operation of the production line.
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