A method, device and system for online detection of ultra-fine polytetrafluoroethylene fiber yarn

Abstract

The present invention belongs to the technical field of yarn online detection, specifically, an online detection method, equipment and system for ultrafine polytetrafluoroethylene fiber yarn, the method includes judging whether the heating performance of the heating box is up to standard, if up to standard, obtaining image data collected by the image detection module, judging whether the image collected by the image detection module is qualified based on the image data, if qualified, allowing the ultrafine polytetrafluoroethylene fiber yarn to be heated and rolled; in the process of heating and rolling the ultrafine polytetrafluoroethylene fiber yarn, judging whether the equipment is stable, if stable, judging whether the yarn diameter is qualified, if qualified, the equipment continues to operate. The present invention can timely warn and prompt when abnormalities occur in each link, thereby effectively ensuring the quality and production stability of the ultrafine polytetrafluoroethylene fiber yarn during the heating and rolling process, and ensuring that the final product meets the relevant quality standards.

Description

Technical Field

[0001] The invention belongs to the technical field of yarn online detection, and specifically relates to an ultra-fine polytetrafluoroethylene fiber yarn online detection method, equipment and system. Background Art

[0002] Ultrafine PTFE fiber yarn is a fiber yarn made of PTFE (PTFE). PTFE is a polymer material with excellent chemical stability and can withstand corrosion from strong acids, strong alkalis and various organic solvents. Because its fibers are very fine, they can be woven into filter materials with tiny pores, which can effectively filter tiny particles. For example, in the waste gas treatment system of chemical companies, filter bags made of this yarn can effectively filter out harmful dust particles while resisting corrosion from chemical gases.

[0003] Existing online detection methods for ultrafine polytetrafluoroethylene fiber yarns include appearance detection, diameter detection, and tension detection, among which diameter detection is a crucial link and can be performed by optical projection method, etc. However, existing online monitoring methods are inconvenient to judge whether the heating performance of the heating box is stable, whether the image detection module collects images accurately, whether the equipment is stable during operation, and whether the yarn diameter is prone to deviation, which may lead to substandard detection quality. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an online detection method, equipment and system for ultrafine polytetrafluoroethylene fiber yarn, which can effectively ensure the quality and production stability of the ultrafine polytetrafluoroethylene fiber yarn during the heating and winding process.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] An online detection device for ultra-fine polytetrafluoroethylene fiber yarns comprises a winding roller and a wire sparser, wherein a winding drum is mounted on the winding roller, and the wire sparser is fixedly connected to a reciprocating rod;

[0007] A heating box is arranged at the bottom of the winding roller, a support plate is fixedly connected to the surface of the heating box, a wire guide is fixedly connected to the support plate, a first wire hole and a second wire hole are opened on the surface of the wire guide, a tensioning wheel is installed on the top of the first wire hole, and the tensioning wheel is connected to the support plate;

[0008] A first guide wheel and a second guide wheel are installed on the support plate, and a wire spreading wheel is also installed on the support plate, and two wire spreading grooves are arranged on the wire spreading wheel;

[0009] A wire inlet hole is provided at the top of the heating box, a first reversing wheel and a second reversing wheel are installed at the bottom of the heating box, and a heater is installed inside the heating box;

[0010] Also includes:

[0011] Data processors;

[0012] A laser diameter detection module is arranged inside the heating box, an image detection module and a capacitive diameter detection module are arranged on the wire guide, and a temperature detection module is arranged inside the heating box, wherein:

[0013] The laser diameter detection module detects the diameter of the yarn based on the principle of laser diffraction;

[0014] The image detection module is used to obtain the yarn image through the image acquisition device, and the capacitive diameter detection module is used to detect the diameter of the yarn based on the capacitance principle;

[0015] When the laser diameter detection module fails, the yarn diameter is determined based on the yarn image and the capacitive diameter detection module;

[0016] The temperature detection module is used to obtain the heating data inside the heating box;

[0017] The data processor is respectively connected to the laser diameter detection module, the image detection module, the capacitive diameter detection module, the data acquisition module and the temperature detection module for detection and communication.

[0018] An online detection method for ultra-fine polytetrafluoroethylene fiber yarn, based on the above-mentioned equipment, comprises the following steps:

[0019] Determine whether the heating performance of the heating box meets the standards:

[0020] If the standard is not met, an early warning will be issued;

[0021] If the standard is met, the image data collected by the image detection module is obtained, and based on the image data, it is determined whether the image collected by the image detection module is qualified:

[0022] If it fails, an early warning will be issued;

[0023] If qualified, heating and winding of ultra-fine polytetrafluoroethylene fiber yarn is allowed;

[0024] During the heating and winding process of the ultra-fine polytetrafluoroethylene fiber yarn, determine whether the equipment is running stably:

[0025] If it is unstable, an early warning will be issued;

[0026] If it is stable, then judge whether the yarn diameter is qualified:

[0027] If it fails, an early warning will be issued;

[0028] If qualified, the device continues to operate.

[0029] Preferably, judging whether the heating performance of the heating box meets the standard comprises the following steps:

[0030] Acquire temperature characteristic data inside the heating box, the temperature characteristic data including temperature distribution characteristics, time required for the temperature to rise to the set temperature, and temperature fluctuation characteristics;

[0031] Acquire the parameterized temperature characteristic data stored in the database, the parameterized temperature characteristic data including the temperature distribution parameterized characteristics, the parameterized time required for the temperature to rise to the set temperature, and the temperature parameterized fluctuation characteristics;

[0032] Combine the temperature characteristic data with the temperature parameter fluctuation characteristics to obtain the temperature evaluation coefficient;

[0033] If the temperature assessment coefficient is greater than the temperature assessment threshold stored in the database, it does not meet the standard;

[0034] If the temperature evaluation coefficient is not greater than the temperature evaluation threshold stored in the database, the standard is met.

[0035] Preferably, judging whether the image collected by the image detection module is qualified based on the image data comprises the following steps:

[0036] Obtain image quality data of the ultra-fine polytetrafluoroethylene fiber yarn online detection equipment before heating and winding, the image quality data including image resolution, contrast, image brightness and color accuracy;

[0037] Obtaining image quality parameter data stored in a database, the image quality parameter data including parameter image resolution, parameter contrast, parameter image brightness and parameter color accuracy;

[0038] Obtaining an image quality assessment coefficient based on the image quality data and the image quality parameter data;

[0039] If the image quality assessment coefficient is greater than the image quality assessment threshold stored in the database, it is unqualified;

[0040] If the image quality assessment coefficient is not greater than the image quality assessment threshold stored in the database, it is qualified.

[0041] Preferably, determining whether the device is running stably comprises the following steps:

[0042] Acquire diffraction light data of the laser diameter detection module, and acquire the diffraction quality coefficient based on the diffraction light data, wherein the diffraction light data includes the diffraction light signal intensity fluctuation value, the diffraction light intensity distribution and the center bright spot intensity;

[0043] If the diffraction quality coefficient is greater than the diffraction quality threshold stored in the database, the device is unstable;

[0044] If the diffraction quality coefficient is not greater than the diffraction quality threshold stored in the database, the yarn edge profile data is obtained, and the yarn motion stability coefficient is obtained based on the yarn edge profile data, and the yarn motion stability coefficient is used to evaluate whether the yarn moves stably outside the heating box;

[0045] If the yarn motion stability coefficient is greater than the yarn motion stability threshold stored in the database, the equipment is unstable;

[0046] If the yarn motion stability coefficient is not greater than the yarn motion stability threshold stored in the database, the diffraction quality coefficient and the yarn motion stability coefficient are weighted and summed to obtain a comprehensive stability coefficient;

[0047] If the comprehensive stability coefficient is greater than the comprehensive stability threshold stored in the database, the device is unstable;

[0048] If the comprehensive stability coefficient is not greater than the comprehensive stability threshold stored in the database, the device is running stably.

[0049] Preferably, obtaining the diffraction quality coefficient based on the diffraction light data comprises the following steps:

[0050] Acquire the diffraction light parameter data stored in the database, the diffraction light parameter data including the parameterized diffraction light intensity distribution and the parameterized central bright spot intensity;

[0051] Combining the diffraction light parameter data with the diffraction light data, we can get the diffraction quality coefficient:

[0052] ;

[0053] Where yz is the diffraction quality coefficient, is the intensity fluctuation value of the diffraction light signal, sY is the intensity distribution of the diffraction light, sL is the intensity of the central bright spot, cY is the parameterized diffraction light intensity distribution, cL is the parameterized central bright spot intensity, e is a natural constant, is the similarity function between sY and cY.

[0054] Preferably, obtaining the yarn motion stability coefficient based on the yarn edge profile data comprises the following steps:

[0055] Acquiring yarn edge profile data at multiple moments;

[0056] When the image collected by the image detection module is qualified, the image quality data is compared with each image quality matching data stored in the database one by one to obtain the image comparison coefficient. The image quality matching data includes the image resolution matching value, the contrast matching value, the image brightness matching value and the color accuracy matching value;

[0057] Determine the image comparison coefficient with the smallest value, and obtain the corresponding deviation allowable value from the database based on the image comparison coefficient with the smallest value;

[0058] Calculate the contour deviation of the yarn edge contour data at any two adjacent moments based on the Euclidean distance, sum up multiple contour deviations and perform average processing to obtain an averaged contour deviation;

[0059] The yarn motion stability coefficient is calculated by calculating the ratio of the averaged profile deviation to the allowable deviation value.

[0060] Preferably, judging whether the yarn diameter is qualified comprises the following steps:

[0061] When the diffraction quality coefficient is not greater than the diffraction quality threshold stored in the database, obtaining a first yarn diameter based on a laser diameter detection module;

[0062] Determine whether the absolute value of the difference between the first yarn diameter and the standard yarn diameter is greater than the diameter deviation threshold stored in the database:

[0063] If it is greater than, the yarn diameter is unqualified;

[0064] If it is not greater than, the yarn diameter is qualified;

[0065] In the case where the diffraction quality coefficient is greater than the diffraction quality threshold stored in the database and the yarn motion stability coefficient is not greater than the yarn motion stability threshold stored in the database:

[0066] Obtaining a second yarn diameter based on the yarn edge profile data and a minimum circumscribed rectangle method;

[0067] acquiring a third yarn diameter based on a capacitive diameter detection module;

[0068] The diameter of the yarn to be evaluated is obtained by weighted summing the second yarn diameter and the third yarn diameter;

[0069] Determine whether the absolute value of the difference between the yarn diameter to be evaluated and the standard yarn diameter is greater than the diameter deviation threshold stored in the database:

[0070] If it is greater than, the yarn diameter is unqualified;

[0071] If it is not greater than, the yarn diameter is qualified;

[0072] In case the diffraction quality coefficient is greater than the diffraction quality threshold stored in the database and the yarn motion stability coefficient is greater than the yarn motion stability threshold stored in the database:

[0073] acquiring a third yarn diameter based on a capacitive diameter detection module;

[0074] Determine whether the absolute value of the difference between the third yarn diameter and the standard yarn diameter is greater than the diameter deviation threshold stored in the database:

[0075] If it is greater than, the yarn diameter is unqualified;

[0076] If it is not greater than, the yarn diameter is qualified.

[0077] Preferably, obtaining the third yarn diameter based on the capacitive diameter detection module comprises the following steps:

[0078] The diameter of the yarn to be corrected is obtained based on the capacitive diameter detection module;

[0079] Acquire the environmental data of the capacitive diameter detection module, the environmental data including the ambient temperature, ambient humidity and ambient electromagnetic intensity;

[0080] The environmental data is compared with each environmental matching data stored in the database one by one to obtain an environmental matching coefficient, where the environmental matching data includes environmental matching temperature, environmental matching humidity and environmental matching electromagnetic intensity;

[0081] Determine the environmental comparison coefficient with the minimum value, and obtain the corresponding diameter correction factor from the database based on the environmental comparison coefficient with the minimum value;

[0082] The third yarn diameter is obtained by multiplying the yarn diameter to be corrected by the diameter correction factor.

[0083] An online detection system for ultra-fine polytetrafluoroethylene fiber yarn, used to implement the above method, includes a heating index detection module, an image quality detection module, an equipment permission state detection module and a yarn diameter detection module, wherein:

[0084] The heating index detection module is used to determine whether the heating performance of the heating box meets the standard. If not, an early warning prompt will be issued;

[0085] An image quality detection module is used to obtain image data collected by the image detection module when the heating performance meets the standard, and judge whether the image collected by the image detection module is qualified based on the image data. If it is unqualified, an early warning prompt is issued;

[0086] If qualified, heating and winding of ultra-fine polytetrafluoroethylene fiber yarn is allowed;

[0087] The equipment allowed status detection module is used to determine whether the equipment is running stably during the heating and winding process of the ultra-fine polytetrafluoroethylene fiber yarn. If it is not stable, an early warning prompt will be issued;

[0088] The yarn diameter detection module is used to determine whether the yarn diameter is qualified when the equipment is running stably:

[0089] If it fails, an early warning will be issued;

[0090] If qualified, the device continues to operate.

[0091] The present invention has the following beneficial effects:

[0092] The present invention first performs a self-inspection of the equipment before winding, and then performs a yarn diameter detection after the detection is qualified. The heating performance of the heating box, the image acquisition situation, the equipment operation stability and the yarn diameter are accurately detected and judged in turn, and timely early warning prompts are given when abnormalities occur in each link, thereby effectively ensuring the quality and production stability of the ultrafine polytetrafluoroethylene fiber yarn in the heating and winding process, and ensuring that the final product meets the relevant quality standards. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Figure 1 It is a schematic diagram of the overall structure of the online detection equipment of the present invention;

[0094] Figure 2 This is a schematic diagram of the structure of the wire thinning wheel of the online detection equipment of the present invention;

[0095] Figure 3 It is a schematic diagram of the process of the online detection method of the present invention;

[0096] Figure 4 It is a schematic diagram of the structural connection of the online detection system of the present invention.

[0097] In the figure, 1. winding roller; 2. wire spreader; 3. heating box; 4. support plate; 5. wire guide; 6. first wire hole; 7. second wire hole; 8. tensioning wheel; 9. first guide wheel; 10. second guide wheel; 11. wire spreader wheel; 12. wire spreader groove; 13. wire entry hole; 14. first reversing wheel; 15. second reversing wheel; 16. heater. DETAILED DESCRIPTION

[0098] The present invention is described clearly and completely below in conjunction with the accompanying drawings.

[0099] Example 1: Figure 1-Figure 2As shown, an online detection device for ultra-fine polytetrafluoroethylene fiber yarn comprises a winding roller 1 and a wire sparser 2, the winding roller 1 is provided with a winding drum, and the wire sparser 2 is fixedly connected to the reciprocating rod; a heating box 3 is arranged at the bottom of the winding roller 1, a support plate 4 is fixedly connected to the surface of the heating box 3, a wire guide 5 is fixedly connected to the support plate 4, a first wire hole 6 and a second wire hole 7 are opened on the surface of the wire guide 5, a tensioning wheel 8 is installed on the top of the first wire hole 6, and the tensioning wheel 8 is connected to the support plate 4; a first guide wheel 9 and a second guide wheel 10 are installed on the support plate 4, a wire sparser 11 is also installed on the support plate 4, and two wire sparser grooves 12 are arranged on the wire sparser 11; a wire inlet hole 13 is opened at the top of the heating box 3, a first reversing wheel 14 and a second reversing wheel 15 are installed at the bottom of the heating box 3, and a heater 16 is installed inside the heating box 3;

[0100] Also includes:

[0101] A data processor; a laser diameter detection module arranged inside the heating box 3, an image detection module and a capacitive diameter detection module arranged on the wire guide 5, and a temperature detection module arranged inside the heating box 3, wherein: the laser diameter detection module detects the diameter of the yarn based on the laser diffraction principle; the image detection module is used to obtain the yarn image through the image acquisition device, and the capacitive diameter detection module is used to detect the diameter of the yarn based on the capacitance principle; when the laser diameter detection module fails, the diameter of the yarn is determined based on the yarn image and the capacitive diameter detection module; the temperature detection module is used to obtain the heating data inside the heating box 3; the data processor detects and communicates with the laser diameter detection module, the image detection module, the capacitive diameter detection module, the data acquisition module and the temperature detection module respectively.

[0102] When in use, the ultrafine polytetrafluoroethylene fiber yarn that needs to be tested first passes through the first wire hole 6, then passes through the tensioning wheel 8, and then passes through the first guide wheel 9 and is connected to the second guide wheel 10, bypasses the first wire sorting groove 12 of the wire sorting wheel 11, and passes through the wire entry hole 13 opened on the top of the heating box 3, enters the heating box 3, and is heated by the heater 16 in the heating box 3. A temperature detection module is arranged in the heater 16 area inside the heating box 3, and the temperature detection module includes a temperature sensor and a timer arranged in an array.

[0103] After passing through the laser diameter detection module, the yarn passes through the heating box 3, passes through the first reversing wheel 14 and the second reversing wheel 15, passes through the second wire hole 7, and then passes through the capacitive diameter detection module. An image detection module is arranged above the capacitive diameter detection module to obtain the image of the yarn. After passing through the first guide wheel 9, the yarn passes through another wire sparse groove 12 of the wire sparse wheel 11, and then passes through the wire sparser 2. The wire sparser 2 is connected to the reciprocating rod. The reciprocating rod is connected to a power device, such as an electric push rod, which can realize the reciprocating motion of the wire sparser 2. During the rotation of the winding roller 1, the yarn can be evenly wound on the winding drum.

[0104] The laser diameter detection module, the image detection module, the capacitive diameter detection module and the temperature detection module are all connected to the processor by wireless communication to transmit data to the processor.

[0105] Example 2: An online detection method for ultra-fine polytetrafluoroethylene fiber yarn, based on the equipment in Example 1, such as Figure 3 As shown, the following steps are included: judging whether the heating performance of the heating box 3 is up to standard: if not, giving an early warning prompt;

[0106] Judging whether the heating performance of the heating box 3 meets the standard includes the following steps: obtaining the temperature characteristic data inside the heating box 3, the temperature characteristic data including the temperature distribution characteristics, the time required for the temperature to rise to the set temperature and the temperature fluctuation characteristics; obtaining the parameterized temperature characteristic data stored in the database, the parameterized temperature characteristic data including the temperature distribution parameterized characteristics, the parameterized time required for the temperature to rise to the set temperature and the temperature parameter fluctuation characteristics; combining the temperature characteristic data with the temperature parameter fluctuation characteristics to obtain the temperature evaluation coefficient; if the temperature evaluation coefficient is greater than the temperature evaluation threshold stored in the database, it does not meet the standard; if the temperature evaluation coefficient is not greater than the temperature evaluation threshold stored in the database, it meets the standard.

[0107] The temperature evaluation coefficient is obtained as follows:

[0108] ;

[0109] Wherein, wx is the temperature evaluation coefficient, sF is the temperature distribution characteristic, sT is the time required for the temperature to rise to the set temperature, sB is the temperature fluctuation characteristic, expressed by the temperature mean square error, cF is the temperature distribution parameter characteristic, cT is the parameter time required for the temperature to rise to the set temperature, cB is the temperature parameter fluctuation characteristic, e is a natural constant, is the similarity function between sF and cF, where sF can be represented by the temperature distribution eigenvector, ,in, is the dot product of sF and cF, and are the moduli of vectors sF and cF respectively.

[0110] By acquiring temperature characteristic data including temperature distribution characteristics, heating time and temperature fluctuation characteristics, the heating efficiency of the heating box 3 can be comprehensively and accurately measured. By comparing with the reference temperature characteristic data in the database and combining them to obtain the temperature evaluation coefficient, it is possible to quantitatively evaluate whether the heating performance meets the standard, avoiding the one-sidedness of judging by a single temperature value. For example, the temperature distribution characteristics can reflect the uniformity of heating, the heating time affects the production efficiency and the heating process of the yarn, and the temperature fluctuation characteristics are related to the thermal stability of the yarn.

[0111] By using the formula to calculate the temperature evaluation coefficient, the differences and similarities between various characteristics and parameter values ​​are comprehensively considered, which can determine the heating performance more scientifically and ensure that the heating box 3 operates under ideal conditions, thereby providing reliable guarantee for the stable heating of the ultra-fine polytetrafluoroethylene fiber yarn, avoiding the impact of yarn quality due to poor heating performance, such as physical property differences due to uneven heating or abnormal temperature rise affecting the internal structure of the yarn, thereby effectively solving the above-mentioned technical problems related to heating and improving the stability of the entire production process and product quality.

[0112] Based on the image data, determine whether the image collected by the image detection module is qualified: if it is unqualified, issue an early warning prompt;

[0113] The image quality data of the ultrafine polytetrafluoroethylene fiber yarn online detection equipment before heating and winding is performed is obtained, and the image quality data includes image resolution, contrast, image brightness and color accuracy; the image quality parameter data stored in the database is obtained, and the image quality parameter data includes parameter image resolution, parameter contrast, parameter image brightness and parameter color accuracy; an image quality assessment coefficient is obtained based on the image quality data and the image quality parameter data; if the image quality assessment coefficient is greater than the image quality assessment threshold stored in the database, it is unqualified; if the image quality assessment coefficient is not greater than the image quality assessment threshold stored in the database, it is qualified.

[0114] The method for obtaining the image quality index is as follows:

[0115] ;

[0116] In the formula, tz is the image quality index, F is the image resolution, D is the contrast, L is the image brightness, S is the color accuracy, To determine the image resolution, To determine the contrast, To determine the image brightness, To determine color accuracy, ln is a logarithmic function expressed as a logarithm with base e.

[0117] Acquiring multi-dimensional image quality data including image resolution, contrast, image brightness and color accuracy can fully reflect the actual situation of the image collected by the image detection module. Resolution affects the ability to capture yarn details, contrast helps to highlight the differences between the yarn and the background and the yarn's own structure, appropriate brightness can ensure that the image is clearly visible without information loss caused by too bright or too dark areas, and color accuracy is important for judging the color-related characteristics of the yarn.

[0118] By comparing with the image quality parameter data in the database and calculating the image quality evaluation coefficient, the image quality can be quantified, and the image can be accurately judged whether it is qualified, avoiding the uncertainty and inaccuracy of manual subjective judgment. The formula comprehensively considers the differences between each image quality parameter and the parameter value, and constructs the evaluation coefficient calculation method in the form of a combination of exponential and logarithmic functions, which can more carefully reflect the weight of the influence of different parameter deviations on the overall image quality. For example, the larger the deviation of parameters such as resolution, the faster the value of the exponential part grows, and the greater the impact on the evaluation coefficient, so as to strictly screen out images that meet the requirements, ensure that the images collected by the image detection module can be accurately used for subsequent ultra-fine polytetrafluoroethylene fiber yarn quality judgment, effectively solve the problem of yarn detection errors or inaccuracies due to poor image quality, and ensure the reliability and effectiveness of the entire production inspection process.

[0119] If qualified, the heating and winding of the ultra-fine polytetrafluoroethylene fiber yarn is allowed; during the heating and winding process of the ultra-fine polytetrafluoroethylene fiber yarn, determine whether the equipment is running stably: if not, issue an early warning prompt;

[0120] The diffraction light data of the laser diameter detection module is obtained, and the diffraction quality coefficient is obtained based on the diffraction light data, the diffraction light data includes the diffraction light signal intensity fluctuation value, the diffraction light intensity distribution and the central bright spot intensity; if the diffraction quality coefficient is greater than the diffraction quality threshold stored in the database, the equipment operation is unstable; if the diffraction quality coefficient is not greater than the diffraction quality threshold stored in the database, the yarn edge profile data is obtained, and the yarn motion stability coefficient is obtained based on the yarn edge profile data, and the yarn motion stability coefficient is used to evaluate whether the yarn movement outside the heating box 3 is stable.

[0121] The diffraction light data of the laser diameter detection module includes information such as the diffraction light signal intensity fluctuation value, diffraction light intensity distribution and central bright spot intensity. This information is closely related to the stability of equipment operation. For example, when the equipment is unstable, it may cause a slight movement or vibration of the yarn position, which will cause the diffraction light signal intensity to fluctuate and the diffraction light intensity distribution to change. By monitoring the changes in these physical quantities, the stability of the equipment operation can be preliminarily judged.

[0122] It may not be possible to completely and accurately judge the operating status of the equipment only by relying on diffraction light data, because there may be some complex situations, such as local optical interference, etc. The yarn edge profile data can provide direct information about the movement of the yarn, such as whether the yarn moves on the normal track, whether there is obvious swing during the movement, etc. By obtaining the yarn movement stability coefficient from the yarn edge profile data, it is possible to make a supplementary judgment on the stability of the equipment operation and further improve the accuracy of the judgment.

[0123] If the yarn motion stability coefficient is greater than the yarn motion stability threshold stored in the database, the equipment operation is unstable; if the yarn motion stability coefficient is not greater than the yarn motion stability threshold stored in the database, the diffraction quality coefficient and the yarn motion stability coefficient are weighted and summed to obtain a comprehensive stability coefficient; if the comprehensive stability coefficient is greater than the comprehensive stability threshold stored in the database, the equipment operation is unstable; if the comprehensive stability coefficient is not greater than the comprehensive stability threshold stored in the database, the equipment operation is stable.

[0124] Taking into account that both the diffraction quality coefficient and the yarn motion stability coefficient reflect the operating status of the equipment from different angles, in order to more comprehensively evaluate the overall stability of the equipment, it is necessary to perform a weighted summation of these two coefficients to obtain a comprehensive stability coefficient, which can comprehensively consider the influence of different factors and is more in line with the complex situation of equipment operation in actual production, thereby more effectively determining whether the equipment operation is stable.

[0125] The diffraction quality coefficient is obtained based on the diffraction light data, including the following steps: obtaining the diffraction light parameter data stored in the database, the diffraction light parameter data including the parameterized diffraction light intensity distribution and the parameterized central bright spot intensity.

[0126] The data related to diffraction light is accurately quantified into diffraction quality coefficients through formulas. In actual production inspections, quantitative evaluation indicators can more objectively measure the operating status of equipment. Compared with simple qualitative judgments, the coefficients obtained by calculation can provide a clear numerical standard, which is convenient for comparison with the thresholds in the database, thereby more accurately judging whether the equipment is running stably.

[0127] Combining the diffraction light parameter data with the diffraction light data, we can get the diffraction quality coefficient:

[0128] ;

[0129] Where yz is the diffraction quality coefficient, is the intensity fluctuation value of the diffraction light signal, which can be expressed by the mean square error of the diffraction light signal, sY is the diffraction light intensity distribution, sL is the central bright spot intensity, cY is the parameterized diffraction light intensity distribution, cL is the parameterized central bright spot intensity, is the similarity function between sY and cY, which can be used as mentioned above The calculation method of .

[0130] The formula includes the intensity of the central bright spot, the intensity distribution of the diffracted light, and the intensity fluctuation value of the diffracted light signal. The intensity of the central bright spot and the intensity distribution of the diffracted light reflect the basic characteristics of light, which are closely related to the physical state of the yarn and the stability of the optical system of the equipment. For example, changes in the intensity of the central bright spot may indicate changes in the yarn diameter or alignment problems of the optical system. At the same time, considering the intensity fluctuation value of the diffracted light signal can capture dynamic changes during the operation of the equipment, such as vibration of the equipment or jitter of the yarn.

[0131] A similarity function of the diffraction light intensity distribution is introduced, which can effectively measure the similarity between the actual diffraction light intensity distribution and the parameterized diffraction light intensity distribution. In this way, it is possible to more accurately determine whether the diffraction light intensity distribution has changed in a way that is not conducive to the stable operation of the equipment. For example, if the equipment is operating normally, the actual diffraction light intensity distribution should be similar to the parameterized distribution, and the value of the similarity function will be relatively high; conversely, if there is a problem with the equipment, such as interference with the optical path or displacement of the yarn position, the value of the similarity function will decrease, resulting in an increase in the diffraction quality coefficient, accurately reflecting the unstable state of the equipment operation.

[0132] Obtaining the yarn motion stability coefficient based on the yarn edge profile data includes the following steps: obtaining the yarn edge profile data at multiple moments; obtaining the yarn edge profile data at multiple moments can reflect the dynamic process of the yarn motion. The motion state of the yarn during the heating and winding process may change over time, and the data at a single moment cannot fully reflect its motion stability. By analyzing the data at multiple moments, the changing trend of the yarn motion can be captured, for example, whether there is a gradually increasing jitter or periodic unstable motion.

[0133] When the image collected by the image detection module is qualified, the image quality data is compared one by one with each image quality matching data stored in the database to obtain the image comparison coefficient. The image quality matching data includes a picture resolution matching value, a contrast matching value, a picture brightness matching value and a color accuracy matching value. The specific method for obtaining the image comparison coefficient can be obtained through the Euclidean distance, that is, the variance of each parameter in the image quality data and each parameter in the image quality matching data is calculated and then added.

[0134] Determine the image comparison coefficient with the minimum value, and obtain the corresponding deviation allowable value from the database based on the image comparison coefficient with the minimum value; calculate the contour deviation of the yarn edge contour data at any two adjacent moments based on the Euclidean distance, sum up multiple contour deviations and perform average processing to obtain the averaged contour deviation; calculate the ratio of the averaged contour deviation to the deviation allowable value to obtain the yarn motion stability coefficient.

[0135] The image quality data is compared with the image quality matching data and the image comparison coefficient is obtained. This design is to find the data that best matches the current image quality, so as to obtain the corresponding deviation tolerance value from the database. Because under different image quality conditions, the tolerance for yarn edge profile deviation may be different. For example, when the image resolution is high and the contrast is good, the yarn edge profile data can be obtained more accurately, and the corresponding deviation tolerance value may be relatively small; conversely, when the image quality is poor, the deviation tolerance value may be appropriately increased. In this way, the allowable range of yarn edge profile deviation can be more reasonably determined according to the actual image quality.

[0136] The Euclidean distance is used to calculate the profile deviation of the yarn edge profile data at any two adjacent moments, and the average profile deviation is obtained by summing up multiple profile deviations and averaging them. This is to comprehensively consider the changes in the yarn edge profile over the entire time period. The averaging method can avoid the excessive influence of abnormal data at individual moments on the overall judgment, more objectively reflect the average deviation degree during the yarn movement process, and thus more accurately evaluate the stability of the yarn movement.

[0137] The yarn motion stability coefficient is calculated by calculating the ratio of the averaged profile deviation to the allowable deviation value. This design enables the yarn motion stability coefficient to intuitively reflect whether the yarn motion is stable. When the averaged profile deviation exceeds the allowable deviation value, the yarn motion stability coefficient is greater than 1, indicating that the yarn motion is unstable; conversely, when the yarn motion stability coefficient is not greater than 1, it means that the yarn motion is stable within the allowable deviation range. The quantitative method is convenient for comparison with the threshold in the database, thereby realizing effective judgment of the yarn motion stability.

[0138] Determine whether the yarn diameter is qualified: if it is unqualified, an early warning prompt will be issued; if it is qualified, the equipment will continue to operate.

[0139] When the diffraction quality coefficient is not greater than the diffraction quality threshold stored in the database, the first yarn diameter is obtained based on the laser diameter detection module; whether the absolute value of the difference between the first yarn diameter and the standard yarn diameter is greater than the diameter deviation threshold stored in the database is determined: if greater, the yarn diameter is unqualified; if not greater, the yarn diameter is qualified;

[0140] When the diffraction quality coefficient is not greater than the diffraction quality threshold stored in the database, the equipment runs relatively stably, and the laser diameter detection module can measure the yarn diameter, i.e., the first yarn diameter, more accurately. At this time, the diameter is directly used for judgment, because laser detection is less subject to interference factors in a stable state, and its measurement results have a high degree of credibility, and can quickly and effectively judge whether the yarn diameter is qualified.

[0141] When the diffraction quality coefficient is greater than the diffraction quality threshold stored in the database and the yarn motion stability coefficient is not greater than the yarn motion stability threshold stored in the database: obtain the second yarn diameter based on the yarn edge profile data and the minimum circumscribed rectangle method; obtain the third yarn diameter based on the capacitive diameter detection module; obtain the yarn diameter to be evaluated by weighted summing the second yarn diameter and the third yarn diameter; determine whether the absolute value of the difference between the yarn diameter to be evaluated and the standard yarn diameter is greater than the diameter deviation threshold stored in the database: if greater, the yarn diameter is unqualified; if not, the yarn diameter is qualified;

[0142] When the diffraction quality coefficient is greater than the threshold and the yarn motion stability coefficient is not greater than the threshold, the device operation is unstable to a certain extent, but the yarn movement is relatively stable. At this time, the measurement results of the yarn edge profile data and the capacitive diameter detection module are combined because the yarn edge profile data can reflect the actual shape of the yarn to a certain extent. Although the diffraction quality of the device is problematic, the second yarn diameter obtained by the minimum circumscribed rectangle method can be used as a supplementary information. At the same time, the capacitive diameter detection module is relatively less affected by the diffraction quality of the device. The weighted sum of the two to obtain the yarn diameter to be evaluated can combine the advantages of the two methods to more accurately evaluate the yarn diameter.

[0143] When the diffraction quality coefficient is greater than the diffraction quality threshold stored in the database and the yarn movement stability coefficient is greater than the yarn movement stability threshold stored in the database: obtain the third yarn diameter based on the capacitive diameter detection module; determine whether the absolute value of the difference between the third yarn diameter and the standard yarn diameter is greater than the diameter deviation threshold stored in the database: if greater than, the yarn diameter is unqualified; if not greater than, the yarn diameter is qualified.

[0144] When the diffraction quality coefficient and the yarn motion stability coefficient are both greater than the threshold, the device operation is unstable and the yarn motion is also unstable. At this time, the capacitive diameter detection module is mainly used to obtain the third yarn diameter for judgment, because the capacitive diameter detection module is relatively less dependent on the stability of device operation and yarn motion, and can provide relatively more reliable diameter detection data in such complex unstable situations.

[0145] The capacitive diameter detection module mainly measures the yarn diameter based on the change of capacitance. When the yarn is between the capacitor plates, the presence of the yarn will change the capacitance value between the plates. As a medium, the change of the yarn's diameter will cause the change of the effective dielectric constant and electric field distribution between the plates, thereby causing the change of the capacitance value. For example, when the yarn diameter increases, the space occupied by the yarn between the plates increases, which is equivalent to a change in the dielectric constant, and the capacitance value will also increase accordingly.

[0146] Obtaining a third yarn diameter based on a capacitive diameter detection module comprises the following steps: obtaining the yarn diameter to be corrected based on detection by the capacitive diameter detection module; obtaining environmental data where the capacitive diameter detection module is located, the environmental data comprising environmental temperature, environmental humidity and environmental electromagnetic intensity; comparing the environmental data with each environmental matching data stored in a database one by one to obtain an environmental matching coefficient, the environmental matching data comprising environmental matching temperature, environmental matching humidity and environmental matching electromagnetic intensity;

[0147] Similarly, the environment comparison coefficient can be calculated using the above-mentioned image comparison coefficient calculation method.

[0148] The environmental comparison coefficient with the minimum value is determined, and the corresponding diameter correction factor is obtained from the database based on the environmental comparison coefficient with the minimum value; the diameter of the yarn to be corrected is multiplied by the diameter correction factor to obtain the third yarn diameter.

[0149] The working principle of the capacitive diameter detection module is based on the change of capacitance, and factors such as ambient temperature, humidity and electromagnetic intensity will affect the capacitance, and environmental factors need to be considered and corrected. For example, temperature changes may cause thermal expansion or contraction of the capacitor plates, thereby changing the distance between the plates and the capacitance value; humidity changes will affect the dielectric constant of the medium, and changes in electromagnetic intensity may interfere with the signal of the detection circuit.

[0150] The purpose of comparing environmental data with environmental matching data one by one and calculating the environmental comparison coefficient is to find the database record that is closest to the current environment. Since different combinations of environmental conditions may have different degrees of impact on capacitive detection, the most appropriate diameter correction factor can be determined through this comparison. For example, in an environment with high temperature, moderate humidity, and low electromagnetic interference, by comparing and finding the most matching environmental record, obtaining the corresponding diameter correction factor to correct the yarn diameter to be corrected, the actual diameter of the yarn can be obtained more accurately.

[0151] In the actual production process, environmental conditions may change continuously. This embodiment can dynamically make corrections according to environmental changes, so that the capacitive diameter detection module can adapt to environmental changes in real time. Each time the yarn diameter is detected, the environmental data is re-acquired and compared and corrected, ensuring the accuracy and reliability of the measurement results at different times and in different environments.

[0152] Embodiment 3: An online detection system for ultra-fine polytetrafluoroethylene fiber yarn, used to implement the method in Embodiment 2, such as Figure 4 As shown, it includes a heating index detection module, an image quality detection module, an equipment permission state detection module and a yarn diameter detection module, wherein: the heating index detection module is used to determine whether the heating performance of the heating box 3 is up to standard, and if not, an early warning is given; the image quality detection module is used to obtain the image data collected by the image detection module when the heating performance is up to standard, and determine whether the image collected by the image detection module is qualified based on the image data, and if not, an early warning is given; if qualified, the heating and winding of the ultra-fine polytetrafluoroethylene fiber yarn is allowed; the equipment permission state detection module is used to determine whether the operation of the equipment is stable during the heating and winding of the ultra-fine polytetrafluoroethylene fiber yarn, and if not, an early warning is given; the yarn diameter detection module is used to determine whether the yarn diameter is qualified when the equipment is running stably: if unqualified, an early warning is given; if qualified, the equipment continues to run.

Claims

1. An online detection method for ultra-fine polytetrafluoroethylene fiber yarn, characterized in that: The detection method is based on an online detection device for ultra-fine polytetrafluoroethylene fiber yarns, comprising a winding roller (1) and a wire sparser (2), wherein a winding drum is mounted on the winding roller (1), and the wire sparser (2) is fixedly connected to a reciprocating rod; A heating box (3) is provided at the bottom of the winding roller (1); a support plate (4) is fixedly connected to the surface of the heating box (3); a wire guide (5) is fixedly connected to the support plate (4); a first wire hole (6) and a second wire hole (7) are provided on the surface of the wire guide (5); a tensioning wheel (8) is installed on the top of the first wire hole (6); and the tensioning wheel (8) is connected to the support plate (4); A first guide wheel (9) and a second guide wheel (10) are mounted on the support plate (4); a wire spreading wheel (11) is also mounted on the support plate (4); and two wire spreading grooves (12) are provided on the wire spreading wheel (11); A wire inlet hole (13) is provided at the top of the heating box (3), a first reversing wheel (14) and a second reversing wheel (15) are installed at the bottom of the heating box (3), and a heater (16) is installed inside the heating box (3); Also includes: Data processors; A laser diameter detection module is arranged inside the heating box (3), an image detection module and a capacitive diameter detection module are arranged on the wire guide (5), and a temperature detection module is arranged inside the heating box (3), wherein: The laser diameter detection module detects the diameter of the yarn based on the principle of laser diffraction; The image detection module is used to obtain the yarn image through the image acquisition device, and the capacitive diameter detection module is used to detect the diameter of the yarn based on the capacitance principle; When the laser diameter detection module fails, the yarn diameter is determined based on the yarn image and the capacitive diameter detection module; The temperature detection module is used to obtain heating data inside the heating box (3); The data processor is respectively connected to the laser diameter detection module, the image detection module, the capacitive diameter detection module, the data acquisition module and the temperature detection module for detection and communication; The detection method includes the following steps: Determine whether the heating performance of the heating box (3) meets the standards: If the standard is not met, an early warning will be issued; If the standard is met, the image data collected by the image detection module is obtained, and based on the image data, it is determined whether the image collected by the image detection module is qualified: If it fails, an early warning will be issued; If qualified, heating and winding of ultra-fine polytetrafluoroethylene fiber yarn is allowed; During the heating and winding process of the ultra-fine polytetrafluoroethylene fiber yarn, determine whether the equipment is running stably: If it is unstable, an early warning will be issued; If it is stable, then judge whether the yarn diameter is qualified: If it fails, an early warning will be issued; If qualified, the equipment continues to operate; Determining whether the heating performance of the heating box (3) meets the standard comprises the following steps: Acquiring temperature characteristic data inside the heating box (3), the temperature characteristic data including temperature distribution characteristics, time required for the temperature to rise to a set temperature, and temperature fluctuation characteristics; Acquire the parameterized temperature characteristic data stored in the database, the parameterized temperature characteristic data including the temperature distribution parameterized characteristics, the parameterized time required for the temperature to rise to the set temperature, and the temperature parameterized fluctuation characteristics; Combine the temperature characteristic data with the temperature parameter fluctuation characteristics to obtain the temperature evaluation coefficient; If the temperature assessment coefficient is greater than the temperature assessment threshold stored in the database, it does not meet the standard; If the temperature assessment coefficient is not greater than the temperature assessment threshold stored in the database, the standard is met; Judging whether the image collected by the image detection module is qualified based on the image data includes the following steps: Obtain image quality data of the ultra-fine polytetrafluoroethylene fiber yarn online detection equipment before heating and winding, the image quality data including image resolution, contrast, image brightness and color accuracy; Obtaining image quality parameter data stored in a database, the image quality parameter data including parameter image resolution, parameter contrast, parameter image brightness and parameter color accuracy; Obtaining an image quality assessment coefficient based on the image quality data and the image quality parameter data; If the image quality assessment coefficient is greater than the image quality assessment threshold stored in the database, it is unqualified; If the image quality assessment coefficient is not greater than the image quality assessment threshold stored in the database, it is qualified; Determining whether the equipment is running stably includes the following steps: Acquire diffraction light data of the laser diameter detection module, and acquire the diffraction quality coefficient based on the diffraction light data, wherein the diffraction light data includes the diffraction light signal intensity fluctuation value, the diffraction light intensity distribution and the center bright spot intensity; If the diffraction quality coefficient is greater than the diffraction quality threshold stored in the database, the device is unstable; If the diffraction quality coefficient is not greater than the diffraction quality threshold stored in the database, the yarn edge profile data is obtained, and the yarn motion stability coefficient is obtained based on the yarn edge profile data, and the yarn motion stability coefficient is used to evaluate whether the yarn moves stably outside the heating box (3); If the yarn motion stability coefficient is greater than the yarn motion stability threshold stored in the database, the equipment is unstable; If the yarn motion stability coefficient is not greater than the yarn motion stability threshold stored in the database, the diffraction quality coefficient and the yarn motion stability coefficient are weighted and summed to obtain a comprehensive stability coefficient; If the comprehensive stability coefficient is greater than the comprehensive stability threshold stored in the database, the device is unstable; If the comprehensive stability coefficient is not greater than the comprehensive stability threshold stored in the database, the equipment is operating stably; Obtaining a diffraction quality coefficient based on diffraction light data includes the following steps: Acquire the diffraction light parameter data stored in the database, the diffraction light parameter data including the parameterized diffraction light intensity distribution and the parameterized central bright spot intensity; Combining the diffraction light parameter data with the diffraction light data, we can get the diffraction quality coefficient: ； Where yz is the diffraction quality coefficient, is the intensity fluctuation value of the diffraction light signal, sY is the intensity distribution of the diffraction light, sL is the intensity of the central bright spot, cY is the parameterized diffraction light intensity distribution, cL is the parameterized central bright spot intensity, e is a natural constant, is the similarity function between sY and cY.

2. The method for online detection of ultra-fine polytetrafluoroethylene fiber yarn according to claim 1, characterized in that: The method for obtaining the yarn motion stability coefficient based on the yarn edge profile data includes the following steps: Acquiring yarn edge profile data at multiple moments; When the image collected by the image detection module is qualified, the image quality data is compared with each image quality matching data stored in the database one by one to obtain the image comparison coefficient. The image quality matching data includes the image resolution matching value, the contrast matching value, the image brightness matching value and the color accuracy matching value; Determine the image comparison coefficient with the smallest value, and obtain the corresponding deviation allowable value from the database based on the image comparison coefficient with the smallest value; Calculate the contour deviation of the yarn edge contour data at any two adjacent moments based on the Euclidean distance, sum up multiple contour deviations and perform average processing to obtain an averaged contour deviation; The yarn motion stability coefficient is calculated by calculating the ratio of the averaged profile deviation to the allowable deviation value.

3. The method for online detection of ultra-fine polytetrafluoroethylene fiber yarn according to claim 1, characterized in that: Judging whether the yarn diameter is qualified includes the following steps: When the diffraction quality coefficient is not greater than the diffraction quality threshold stored in the database, obtaining a first yarn diameter based on a laser diameter detection module; Determine whether the absolute value of the difference between the first yarn diameter and the standard yarn diameter is greater than the diameter deviation threshold stored in the database: If it is greater than, the yarn diameter is unqualified; If it is not greater than, the yarn diameter is qualified; In the case where the diffraction quality coefficient is greater than the diffraction quality threshold stored in the database and the yarn motion stability coefficient is not greater than the yarn motion stability threshold stored in the database: Obtaining a second yarn diameter based on the yarn edge profile data and a minimum circumscribed rectangle method; acquiring a third yarn diameter based on a capacitive diameter detection module; The diameter of the yarn to be evaluated is obtained by weighted summing the second yarn diameter and the third yarn diameter; Determine whether the absolute value of the difference between the yarn diameter to be evaluated and the standard yarn diameter is greater than the diameter deviation threshold stored in the database: If it is greater than, the yarn diameter is unqualified; If it is not greater than, the yarn diameter is qualified; In case the diffraction quality coefficient is greater than the diffraction quality threshold stored in the database and the yarn motion stability coefficient is greater than the yarn motion stability threshold stored in the database: acquiring a third yarn diameter based on a capacitive diameter detection module; Determine whether the absolute value of the difference between the third yarn diameter and the standard yarn diameter is greater than the diameter deviation threshold stored in the database: If it is greater than, the yarn diameter is unqualified; If it is not greater than, the yarn diameter is qualified.

4. The method for online detection of ultra-fine polytetrafluoroethylene fiber yarn according to claim 1, characterized in that: Obtaining the third yarn diameter based on the capacitive diameter detection module, The following steps are involved: The diameter of the yarn to be corrected is obtained based on the capacitive diameter detection module; Acquire the environmental data of the capacitive diameter detection module, the environmental data including the ambient temperature, ambient humidity and ambient electromagnetic intensity; The environmental data is compared with each environmental matching data stored in the database one by one to obtain an environmental matching coefficient, where the environmental matching data includes environmental matching temperature, environmental matching humidity and environmental matching electromagnetic intensity; Determine the environmental comparison coefficient with the minimum value, and obtain the corresponding diameter correction factor from the database based on the environmental comparison coefficient with the minimum value; The third yarn diameter is obtained by multiplying the yarn diameter to be corrected by the diameter correction factor.

5. An online detection system for ultra-fine polytetrafluoroethylene fiber yarn, used to implement the method described in any one of claims 1 to 4, characterized in that: It includes a heating index detection module, an image quality detection module, an equipment permission state detection module and a yarn diameter detection module, wherein: A heating index detection module, used to determine whether the heating performance of the heating box (3) meets the standard, and if not, to issue an early warning prompt; An image quality detection module is used to obtain image data collected by the image detection module when the heating performance meets the standard, and judge whether the image collected by the image detection module is qualified based on the image data. If it is unqualified, an early warning prompt is issued; If qualified, heating and winding of ultra-fine polytetrafluoroethylene fiber yarn is allowed; The equipment allowed status detection module is used to determine whether the equipment is running stably during the heating and winding process of the ultra-fine polytetrafluoroethylene fiber yarn. If it is not stable, an early warning prompt will be issued; The yarn diameter detection module is used to determine whether the yarn diameter is qualified when the equipment is running stably: If it fails, an early warning will be issued; If qualified, the device continues to operate.

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