CN116843320A - Textile equipment abnormality sensing method based on Internet of Things - Google Patents

Textile equipment abnormality sensing method based on Internet of Things Download PDF

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CN116843320A
CN116843320A CN202310902839.3A CN202310902839A CN116843320A CN 116843320 A CN116843320 A CN 116843320A CN 202310902839 A CN202310902839 A CN 202310902839A CN 116843320 A CN116843320 A CN 116843320A
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parameters
equipment
textile equipment
production data
production
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吴玺
高倍佳
任祥斌
刘进
黄涛
石珏
徐梦晓
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Zhejiang Sanxiang Data Co ltd
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    • G06Q10/00—Administration; Management
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    • G—PHYSICS
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
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    • G16Y10/25—Manufacturing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/10—Detection; Monitoring
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/30—Control

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Abstract

The application relates to the technical field of textile equipment, in particular to a textile equipment abnormality sensing method based on the Internet of things. The method specifically comprises the following steps: and acquiring production data of the current textile equipment. Acquiring operation production data standard parameters of textile equipment; the operation standard parameters of the textile equipment are in one-to-one correspondence with the production data. The influencing parameters and standard influencing parameters of the current equipment are obtained.And constructing a deviation model of each production combination project through the production data, the production data standard parameters, the influence parameters and the standard influence parameters. Obtaining influencing parameters through a deviation model of production data and production combination items of current textile equipment, and judging whether the influencing parameters are larger than a preset delta Y 0 j mark If yes, sending an alarm signal, and if not, not sending the alarm signal. Whether the equipment is abnormal or not can be judged in advance by influencing the parameters, the equipment can be maintained to be in an optimal state for operation by adjusting, production delay is avoided, and production efficiency is improved.

Description

Textile equipment abnormality sensing method based on Internet of things
Technical Field
The application relates to the technical field of textile equipment, in particular to a textile equipment abnormality sensing method based on the Internet of things.
Background
The original spinning is a generic name of spinning and weaving, but with the continuous development and perfection of a textile knowledge system and a subject system, especially after the production of technologies such as non-woven textile materials, three-dimensional composite weaving and the like, the current spinning is not only traditional hand spinning and weaving, but also comprises non-woven fabrics technology, modern three-dimensional weaving technology, modern electrostatic nano-networking technology and the like to produce textiles for clothing, industry and decoration. Textile equipment such as knitting machines and the like are equipment for knitting cloth, which mainly includes a power supply circuit, an operation box, a main control circuit board, a machine head, a motor, a driver, and a machine tool for mounting the above elements.
In the industrial control system device of the textile electromechanical equipment, the system generally comprises a computer (PLC) industrial control system, a mechanical system, an electromagnetic valve and air cylinder executing system and a sensor detection and quality monitoring system, and the functions have the functions of real-time monitoring, quality control, power output, data display and the like, and all the components are controlled to perform standard actions according to design steps by on-line control of all the systems. The man-machine interface and the Programmable Logic Controller (PLC) intelligently control the whole machine, so that the mechanical automation technology is rapidly improved, the equipment operation is simpler and more convenient, the full-automatic operation capability is realized, the system has the functions of fault self-breaking and alarming, the working state is displayed in real time, the data adjustment is performed in a visualized and intelligent way, and the self-recovery capability after the system fails is enhanced.
Aiming at the abnormality detection of textile equipment, the main mode adopted at present is to set the monitoring variable threshold of each component, monitor the monitoring variable threshold in real time by a sensor and perform visual abnormality judgment according to the monitoring variable threshold and the data monitored in real time. The mode ignores the potential abnormality of the whole operation of the equipment, and the equipment is often required to be damaged or the quality of the product is often in question to discover, and then the equipment is maintained and adjusted, so that the equipment is required to be shut down for overhauling and maintaining, the smooth operation of production work is affected, and the loss of the equipment and the product is caused.
Disclosure of Invention
In order to solve the technical problems, the application provides a textile equipment abnormality sensing method based on the internet of things, which is used for detecting textile equipment, and the textile equipment abnormality sensing method based on the internet of things comprises the following steps:
s1, acquiring production data X of current textile equipment i j Wherein i is time-ordered for production data, j is ordered for each production data;
s2, acquiring operation production data standard parameters X of textile equipment 0 j The method comprises the steps of carrying out a first treatment on the surface of the The operation standard parameters of the textile equipment are in one-to-one correspondence with the production data;
s3, obtaining the influence parameter Y of the equipment i j And a standard influencing parameter Y 0 j ；
S4, through production data X i j Standard parameters of production data X 0 j Influence parameter Y i j And a standard influencing parameter Y 0 j Constructing a deviation model u- { u } of each production combination item, wherein u is the number of combination items, and { u } is each item of the combination item u;
s5, production data X of current textile equipment i j Deviation model u- { u } of the production combination projectObtain the influence parameter Y i j -Y 0 j And judge the influencing parameter Y i j -Y 0 j Whether or not it is greater than a preset delta Y 0 j mark If yes, sending an alarm signal, and if not, not sending the alarm signal.
Preferably: s6, when judging the influence parameter Y i j -Y 0 j Is greater than a preset delta Y i j mark When the alarm signal is received, the production data X of the current textile equipment in u- { u } is obtained i j And judge X i j -X 0 j Whether or not it is greater than a predetermined X 0 j mark If so, the production data item corresponding to the mark j' is used as the maintenance item.
Preferably: s7, when the number of maintenance items is greater than 1, searching a preset maintenance item difficulty coefficient table through the current maintenance item to obtain difficulty coefficients p corresponding to each maintenance implementation j’ The difficulty value is obtained by calculating the difficulty coefficient of each maintenance itemWherein M is j' The adjustment amount of the event is maintained for j'.
Preferably: the production data comprise raw material performance parameters, technological parameters in the production process of textile equipment, processing parameters of the equipment and the like.
Preferably: the production data standard parameters comprise raw material performance standard parameters, standard process parameters in the production process of textile equipment, standard processing parameters of equipment and the like, and correspond to the raw material performance parameters, the process parameters in the production process of the textile equipment, the processing parameters of the equipment and the like one by one.
Preferably: the influencing parameters of the equipment comprise equipment state deviation parameters and product quality parameters.
Preferably: the bias model construction comprises multiple equationsWherein i' is the number corresponding to the combined item, a i' The equipment with the number i' affects the weight coefficient, m is the corresponding interference frequency, b i Is indexing.
Preferably: indexing of
Preferably: s8, according to Min { P ] j Maintenance items and corresponding adjustment amounts in the device, and controlling the device according to the maintenance items and the corresponding adjustment amounts.
The application has the technical effects and advantages that: by presetting delta Y 0 j mark The device can be adjusted under the condition that defects do not occur, whether the device is abnormal or not can be judged in advance, the device abnormality is not the same concept as the conventional device abnormality, the conventional device abnormality is that the device is damaged, the device can be found only after the device is damaged, and the maintenance and replacement of the components are carried out after the device is damaged, so that the component cost is caused, and meanwhile, the line stop maintenance, the production delay and the production loss are also required. The u- { u }, in the output deviation model, can decide to carry out parameter adjustment and equipment maintenance to the production data, indicate the adjustment direction for maintainer, because the equipment is not really damaged, the data deviation standard value is used for sensing, and the equipment can be maintained in the optimal state to the maximum extent.
Drawings
Fig. 1 is a schematic flow chart of a textile equipment abnormality sensing method based on the internet of things.
Detailed Description
The application will be described in further detail with reference to the drawings and the detailed description. The embodiments of the application have been presented for purposes of illustration and description, and are not intended to be exhaustive or limited to the application in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments were chosen and described in order to best explain the principles of the application and the practical application, and to enable others of ordinary skill in the art to understand the application for various embodiments with various modifications as are suited to the particular use contemplated.
Example 1
Referring to fig. 1, in this embodiment, a textile equipment abnormality sensing method based on the internet of things is provided, which is used for performing predictive abnormality sensing on textile equipment, where the textile equipment abnormality sensing method based on the internet of things includes:
s1, acquiring production data Xi of current textile equipment j Wherein i is time-ordered for production data and j is ordered for each production data. The production data may include raw material performance parameters, process parameters during the production of textile equipment, processing parameters of the equipment, etc. The feedstock performance parameters may include the type, model, feedstock characteristics, etc. of the feedstock. For example, the use of a polyvinyl chloride yarn has a hygroscopicity of 0%, a heat resistance of poor, and a fiber density of 1.4g/cm 3 The strength is 3 g/denier, the breaking elongation is 12-28%, and the product is flame-retardant and flame-retardant. The process parameters here are process parameters which can be adjusted during the production of the textile apparatus and which cannot be changed are not considered here. For example, the non-changeable raw material fiber is processed, spun, woven, dyed, etc., and the chemical fiber multifilament linear density is 16.5tex/30f, which means that the multifilament bus density is 16.5tex, and the number of monofilaments is 30. Specifically, this is not listed here. The CJ18.4tex yarn is spun on a vertical production line provided with a C4 carding machine, the cotton distribution level is lower, a cover plate which is arranged in a wave-shaped seven-point gauge process wave-shaped mode is adopted between the cover plate and the cylinder of the C4 carding machine, the gauge of the cylinder carding area is not in smooth transition, and the other five points except the inlet point 1 and the outlet point 7 are in a wave state, namely 1.2mm, 0.35mm, 0.25mm, 0.3mm, 0.25mm, 0.28mm and 0.6mm. The processing parameters of the equipment are abnormal noise of the equipment frequency converter, heating vibration frequency and amplitude, injection flow velocity, flow rate and the like of the water jet loom, and detailed description is omitted here. Parameters such as technological parameters in the production process of textile equipment, processing parameters of the equipment and the like can be sensed through the equipment through the Internet of things setting, and of course, the automatic operation can be realized through the Internet of thingsThe adjustment is the prior art, and detailed description thereof is omitted here.
S2, acquiring operation production data standard parameters X of textile equipment 0 j The method comprises the steps of carrying out a first treatment on the surface of the The operation standard parameters of the textile equipment are in one-to-one correspondence with the production data; the production data standard parameters also comprise raw material performance standard parameters, standard process parameters in the production process of textile equipment, standard processing parameters of equipment and the like, and correspond to the raw material performance parameters, the process parameters in the production process of the textile equipment, the processing parameters of the equipment and the like one by one. The production data standard parameters can be obtained through tests according to actual production requirements, and are optimal raw materials, equipment processes and equipment processing states. For example, it is experimentally obtained that the desired fiber density is 1.6g/cm 3 And the like, the requirements of different raw materials are different, and detailed description is omitted herein.
S3, obtaining the influence parameter Y of the current equipment i j And a standard influencing parameter Y 0 j The method comprises the steps of carrying out a first treatment on the surface of the The influence parameters of the equipment comprise equipment state deviation parameters, product quality parameters and the like, wherein the equipment state deviation parameters and the product quality parameters are parameters for influencing the use of the equipment and the product quality, and the equipment state deviation parameters can be equipment yarn broken line density, whether the action of an equipment electromagnetic valve is normal or not, and whether the associated pneumatic components have internal leakage and external leakage or not. The product quality parameter may be the wool ball density of the fabric, etc. Each influencing parameter is a parameter that influences the use of the apparatus, the quality of the product, etc. The production data, the standard parameters, the influence parameters and the standard influence parameters all need data quantization and cannot be set qualitatively. For the production data, the standard parameters, the influence parameters, and the qualitative parameters in the standard influence parameters, the standard can be defined as 0, the maximum deviation is listed as 10, and the grade classification is performed, and detailed description is omitted here. The influencing parameter for the criterion may be an optimal state criterion, e.g. a hair bulb density of zero. And is not described in detail herein.
S4, producing data X through history i j Standard parameters of production data X 0 j Influence parameter Y i j And standard shadowResponse parameter Y 0 j A bias model u- { u } for each production combination item is constructed, where u is the number of combination items and { u } is each item of combination item u. The bias model construction comprises multiple equationsWherein i' is the number corresponding to the combined item, a i' The equipment with the number i' affects the weight coefficient, m is the corresponding interference frequency, b i For indexing, it can be in particular +.>Through multiple groups of history Y i j 、X i j And Y 0 j 、X 0 j Calculating a of the corresponding combination item i' And m, whereby a deviation model u- { u } for each production assembly item can be obtained. The deviation model can construct each production data, which of course includes single, two, three … … up to all production data n, the corresponding numbers are respectivelyFor example, for the raw material parameters of yarn fiber length and wool ball density, a plurality of lengths of single production data yarn fibers are obtained, and under the condition that other items have no deviation, a deviation model is obtained through calculation of a plurality of parameters, which is not described in detail herein.
S5, production data X of current textile equipment i j Obtaining the influence parameter Y by a deviation model u- { u } of the production combination project i j -Y 0 j And judge the influencing parameter Y i j -Y 0 j Whether or not it is greater than a preset delta Y 0 j mark If so, an alarm signal is sent and u- { u } in the deviation model u- { u } is displayed, thereby facilitating the countermeasure against the bad countermeasure, and if not, the alarm signal is not sent. DeltaY 0 j mark May be product quality or equipment deviation result standard, deltaY 0 j mark Is affirmed to be inThe product qualification standard or the standard when the equipment is abnormal can be obtained through experience or experiment. For example for the appearance of folds in textiles, if the product quality standard is a fold number of 10/m 2 And each pleat length is not greater than 10cm, then ΔY here 0 j mark Can be the number of folds is less than 2/m 2 And the length of the folds is not more than 5cm. By presetting delta Y 0 j mark The device can adjust each production data under the condition that defects do not occur, whether the device is abnormal or not can be judged in advance, the abnormality of the device is not the same concept as the abnormality of the conventional device, the abnormality of the conventional device is that the device is damaged, the device can be found only after the device is damaged, and the maintenance and replacement of the parts are carried out after the device is damaged, so that the cost of the parts is caused, and meanwhile, the line stop maintenance is required, the production delay is caused, and the production loss is caused. The application obtains an alarm before equipment is damaged or products are bad, thereby taking countermeasures. The u- { u }, in the deviation model, can be used for directionally carrying out parameter adjustment and equipment maintenance on production data, indicating an adjustment direction for maintenance personnel, and can furthest maintain the equipment to operate in an optimal state because the equipment is not really damaged and is sensed through a data deviation standard value.
S6, when judging the influence parameter Y i j -Y 0 j Is greater than a preset delta Y i j mark When the alarm signal is received, the production data X of the current textile equipment in u- { u } is obtained i j And judge X i j -X 0 j Whether or not it is greater than a predetermined X 0 j mark If so, the production data item corresponding to the mark j' is used as the maintenance item. For example by determining the production data X of the current textile machine in the output u- { u }, for example by determining the production data X of the current textile machine in the output u- { u } i 1 The fiber density was 1.9g/cm 3 ，X 0 1 The fiber density was 1.6g/cm 3 ，X 0 1 mark 0.2g/cm 3 It is found by judgment that the fiber density needs to be changed to be close to 1.6g/cm 3 Is a raw material of (a) a powder. X is X 0 j mark Can be based on practical production experienceThe production standards of the products are obtained, and detailed description is omitted here.
S7, when the number of maintenance items is greater than 1, searching a preset maintenance item difficulty coefficient table through the current maintenance item to obtain difficulty coefficients p corresponding to each maintenance implementation j’ The difficulty value is obtained by calculating the difficulty coefficient of each maintenance itemWherein M is j' The adjustment amount for the maintenance item of j' is calculated and obtained Min { P } j }. The maintenance item difficulty coefficient table can be set according to the actual production condition, and can be used for setting no difficulty, it is impossible to finish the classification of 10 grades, without difficulty of 0, it is impossible to achieve +.. The difficulty coefficient may be production cost, equipment loss, etc. For example, the M at this time can also be calculated by cost j' Is the mass of the raw material, p j’ Is a cost difficulty. Disposable completed M j' 1, and is not described in detail herein. Through sequencing the difficulty values, the difficulty level can be adjusted, so that the maintenance cost can be reduced to the greatest extent, and the maintenance efficiency can be improved.
S8, obtaining Min { P } j Maintenance items and corresponding adjustment amounts in the device, and controlling the device according to the maintenance items and the corresponding adjustment amounts. By applying to Min { P j The maintenance items and the corresponding adjustment amounts in the process are adjusted, so that the adjustment cost and the adjustment difficulty can be reduced to the greatest extent, the adjustment is not performed based on one maintenance item, the maintenance items are adjusted in an organic matching way, and the adjustment with the minimum difficulty can be performed. The application senses and controls various parameters of the equipment based on the Internet of things, and realizes sensing, acquisition and control of automatic data. Intelligent regulation is realized.
It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art and which are included in the embodiments of the present application without the inventive step, are intended to be within the scope of the present application. Structures, devices and methods of operation not specifically described and illustrated herein, unless otherwise indicated and limited, are implemented according to conventional means in the art.

Claims (9)

1. The textile equipment abnormality sensing method based on the Internet of things is characterized by comprising the following steps of:
s1, acquiring production data X of current textile equipment i j Wherein i is time-ordered for production data, j is ordered for each production data;
s2, acquiring operation production data standard parameters X of textile equipment 0 j The method comprises the steps of carrying out a first treatment on the surface of the The operation standard parameters of the textile equipment are in one-to-one correspondence with the production data;
s3, obtaining the influence parameter Y of the equipment i j And a standard influencing parameter Y 0 j ；
S4, through production data X i j Standard parameters of production data X 0 j Influence parameter Y i j And a standard influencing parameter Y 0 j Constructing a deviation model u- { u } of each production combination item, wherein u is the number of combination items, and { u } is each item of the combination item u;
s5, production data X of current textile equipment i j Obtaining the influence parameter Y by a deviation model u- { u } of the production combination project i j -Y 0 j And judge the influencing parameter Y i j -Y 0 j Whether or not it is greater than a preset delta Y 0 j mark If yes, sending an alarm signal, and if not, not sending the alarm signal.
2. The method for sensing abnormality of textile equipment based on the internet of things according to claim 1, wherein the production data includes raw material performance parameters, process parameters in the production process of the textile equipment, processing parameters of the equipment, and the like.
3. The method for sensing abnormal conditions of textile equipment based on the internet of things according to claim 2, wherein the production data standard parameters comprise raw material performance standard parameters, standard process parameters in the production process of the textile equipment, standard processing parameters of the equipment and the like, and are in one-to-one correspondence with the raw material performance parameters, the process parameters in the production process of the textile equipment, the processing parameters of the equipment and the like.
4. The textile equipment abnormality sensing method based on the internet of things according to claim 3, wherein the influence parameters of the equipment comprise equipment state deviation parameters and product quality parameters.
5. The method for sensing abnormality of textile equipment based on the Internet of things according to claim 1, wherein the deviation model is constructed by a multiple equationConstructing, wherein i' is the number corresponding to the combined item, a i' The equipment with the number i' affects the weight coefficient, m is the corresponding interference frequency, b i Is indexing.
6. The method for sensing abnormality of textile equipment based on the internet of things according to claim 5, wherein the indexing is
7. The textile equipment abnormality sensing method based on the internet of things according to claim 1, characterized in that the textile equipment abnormality sensing method based on the internet of things further comprises: s6, when judging the influence parameter Y i j -Y 0 j Is greater than a preset delta Y i j mark When the alarm signal is received, the production data X of the current textile equipment in u- { u } is obtained i j And judge X i j -X 0 j Whether or not it is greater than a predetermined X 0 j mark If so, the production data item corresponding to the mark j' is used as the maintenance item.
8. The textile equipment abnormality sensing method based on the internet of things according to claim 7, further comprising: s7, when the number of maintenance items is greater than 1, searching a preset maintenance item difficulty coefficient table through the current maintenance item to obtain difficulty coefficients p corresponding to each maintenance implementation j’ The difficulty value is obtained by calculating the difficulty coefficient of each maintenance itemWherein M is j' The adjustment amount of the event is maintained for j'.
9. The textile equipment abnormality sensing method based on the internet of things according to claim 8, further comprising: s8, according to Min { P ] j Maintenance items and corresponding adjustment amounts in the device, and controlling the device according to the maintenance items and the corresponding adjustment amounts.
CN202310902839.3A 2023-07-21 2023-07-21 Textile equipment abnormality sensing method based on Internet of Things Pending CN116843320A (en)

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CN202310902839.3A CN116843320A (en) 2023-07-21 2023-07-21 Textile equipment abnormality sensing method based on Internet of Things

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Application Number Priority Date Filing Date Title
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CN116843320A true CN116843320A (en) 2023-10-03

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