Knitting machine anomaly detection method and system

Abstract

The invention provides a knitting machine anomaly detection method and system. The detection method comprises the steps that the state of yarn on a bobbin is monitored through a first monitoring module; when the yarn state meets a preset first alarm condition, first alarm information is output, and the spindle is controlled to stop at a preset first position; wherein the first monitoring module is arranged on one side of the bobbin. According to the knitting machine anomaly detection method and system, detection of the raw materials of the knitting machine is achieved, so that whether the raw materials are abnormal or not is determined, and then the product quality is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment detection, and in particular to a method and system for detecting abnormalities in a knitting machine. Background Art

[0002] The textile industry processes natural and chemical fibers into various yarns, threads, tapes, fabrics, and dyed and finished products. As a key sector of light industry, the textile industry boasts low investment, rapid capital turnover, short construction cycles, and a high level of employment. The loom, a widely used piece of machinery in the textile industry, has significantly advanced the industry. With the advancement of technology, looms are becoming increasingly efficient and intelligent. For the weaving of three-dimensional objects, a three-dimensional braiding machine is usually used; the main structure of the three-dimensional braiding machine is a large circular ring, and the spindle moves in an 8-shaped manner along the large circular ring of the braiding machine during operation, that is, the two layers of spindles exchange with each other and move in opposite directions, and the inner and outer layers continuously exchange positions; a yarn tube is installed on the spindle, and the yarn is wound on the yarn tube; during operation, the yarn passes through the spindle threading hole and is connected to the mold side by the constant force device; the threading guide wheel is movable; the braiding machine main unit in the existing patent CN218932504U special-shaped high-density carbon fiber preform three-dimensional braiding machine has a similar structure to the braiding machine used in this application; when the braiding machine is operating normally, it is of great significance to monitor the abnormal operation of the braiding machine, which can ensure the production of products and avoid the formation of waste.

[0003] Existing patent CN114164556B discloses a tension-controllable yarn feeding device for a three-dimensional braiding machine and its use method, which discloses a motor, a yarn bobbin coaxially connected to the motor output shaft for rotation, a buffer device located next to the yarn bobbin, a visual sensor, a tension sensor, and a linear guide extending from bottom to top; the buffer device includes a slide, a slider located in the slide, and a spring; one end of the spring is fixed to an inner end surface of the slide, and the other end of the spring is fixed to the slider; a yarn guide eye is installed on the slider; the visual sensor is used to detect the movement of the yarn guide eye. If the yarn guide eye moves in the same direction for a time exceeding a preset time threshold, the visual sensor will send a signal to accelerate or decelerate the motor speed; the tension sensor is used to detect yarn tension data; when the yarn tension exceeds the preset tension threshold, the tension sensor sends a signal to change the motor speed to achieve constant tension control; the buffer device is installed on the linear guide through a preset nut, and the linear guide is perpendicular to the platform where the motor and the yarn bobbin are located. After passing through the yarn guide eye, the yarn directly enters the tension sensor detection area vertically downward. The structure used for monitoring is relatively complex and requires a relatively large space, which is not conducive to the application and installation in the knitting machine corresponding to this application. Therefore, there is an urgent need for an abnormality detection system and method with a simple structure that can be applied to the knitting machine corresponding to this application. Summary of the Invention

[0004] One of the purposes of the present invention is to provide a braiding machine abnormality detection method and system, which can detect the raw materials of the braiding machine, thereby determining whether the raw materials are abnormal, and thus ensuring the quality of the product.

[0005] An embodiment of the present invention provides a method for detecting abnormalities in a knitting machine, comprising:

[0006] The first monitoring module is used to monitor the yarn status on the bobbin;

[0007] When the yarn state meets the preset first alarm condition, a first alarm message is output and the spindle is controlled to stop at a preset first position;

[0008] Wherein, the first monitoring module is arranged on one side of the bobbin.

[0009] Preferably, the first monitoring module includes: a color mark sensor.

[0010] Preferably, the braiding machine abnormality detection method further includes:

[0011] The second monitoring module is used to monitor the status of the threading guide wheel of the constant force device;

[0012] When the preset second alarm condition is detected, the second alarm information is output and the spindle is controlled to stop at the preset second position;

[0013] The second monitoring module is arranged on one side of the fixing seat of the spindle and is fixedly connected to the one side of the fixing seat of the spindle.

[0014] Preferably, the second monitoring module includes a shift fork.

[0015] Preferably, the braiding machine abnormality detection method further includes:

[0016] Using a first image acquisition device to perform image monitoring on the spindle;

[0017] Analyzing the first image obtained by image monitoring and extracting the regional image corresponding to each spindle;

[0018] Associating each area image with each spindle according to the spindle's operating data;

[0019] Grouping the associated regional images to obtain multiple analysis image groups,

[0020] Analyze each grouped image group according to the preset image analysis library;

[0021] Match the raw material data corresponding to each spindle determined by the analysis with the demand data of the corresponding spindle in the demand data of the current product;

[0022] When the matching result is inconsistent, the first abnormality reminder information is output.

[0023] The present invention also provides a braiding machine abnormality detection system, comprising: a first data acquisition module and a first alarm module; wherein, the first data acquisition module receives the yarn status of the yarn on the yarn tube sent by the first monitoring module; when the yarn status meets the preset first alarm condition, the first alarm module outputs a first alarm message and controls the spindle to stop to a preset first position.

[0024] Preferably, the first monitoring module includes: a color mark sensor.

[0025] Preferably, the braiding machine abnormality detection system also includes: a second data acquisition module and a second alarm module; wherein, the second data acquisition module receives the status of the constant force device threading guide wheel monitored by the second monitoring module; when the preset second alarm condition is met, the second alarm module outputs a second alarm message and controls the spindle to stop to a preset second position.

[0026] Preferably, the second monitoring module includes a shift fork.

[0027] Preferably, the braiding machine abnormality detection system also includes: a third data acquisition module and a third alarm module; wherein the third data acquisition module receives the first image obtained by the first image acquisition device through image monitoring of the spindle and analyzes it, and extracts the regional image corresponding to each spindle; associates each regional image with each spindle according to the operation data of the spindle, groups the associated regional images to obtain multiple analysis image groups, and analyzes each grouped image group according to a preset image analysis library; matches the raw material data corresponding to each spindle determined by the analysis with the demand data of the corresponding spindle in the demand data of the current product; when the matching result is inconsistent, the third alarm module outputs the first abnormality reminder information.

[0028] The present invention has the following advantages:

[0029] 1. It improves the degree of automation and production efficiency. It can automatically identify broken yarn or no yarn, stop the machine quickly, and reduce manual intervention.

[0030] 2. When the yarn is broken or there is no yarn, the spindle stays at the designated position, which is convenient for operation.

[0031] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0032] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0034] Figure 1 Schematic diagram of a braiding machine abnormality detection method according to an embodiment of the present invention;

[0035] Figure 2 A schematic diagram of the installation position of a monitoring module in an embodiment of the present invention;

[0036] Figure 3 This is a front view of a photographing device according to an embodiment of the present invention;

[0037] Figure 4 This is a left side view of a photographing device according to an embodiment of the present invention;

[0038] Figure 5 Schematic diagram of a braiding machine abnormality detection system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0040] Example 1:

[0041] The embodiment of the present invention provides a method for detecting abnormality of a knitting machine. Figure 1 Shown, including:

[0042] The first monitoring module is used to monitor the yarn status on the bobbin;

[0043] When the yarn state meets the preset first alarm condition, a first alarm message is output and the spindle is controlled to stop at a preset first position;

[0044] Wherein, the first monitoring module is arranged on one side of the bobbin.

[0045] Wherein, the first monitoring module includes: a color mark sensor.

[0046] The abnormality detection method for a knitting machine of this embodiment adopts a color code sensor for monitoring; wherein, the color code sensor is used to identify the color area, that is, the bobbin and the yarn are different colors, and when it is used, the yarn color and other colors are excluded. When the bobbin color is identified, it means that the yarn state has been used to the extent that the bobbin can be seen, that is, it meets the first alarm condition, and the first alarm information is output at this time; in order to facilitate the replacement of the staff, the spindle is controlled to continue to rotate and move for a distance and then stop and then reach the first position for the convenience of the staff to replace; for example: the bobbin color is white (white of nylon), the bobbin color code is set to the calibration color, the yarn color is white (white of glass fiber), the yarn color and other colors are excluded, the bobbin color is detected, and a signal is output. Figure 2 In the embodiment, the yarn 12 is wound on the bobbin 13, and the bobbin 13 is set on the spindle 17; the yarn 12 passes through the threading hole of the constant force device 14, passes around the threading guide wheel 15, and then passes through another threading hole at the top of the constant force device 14; in the specific setting, a sensor 11 is set at the upper and lower parts of the corresponding spindles respectively. During the rotation of the spindle, there will be a moment when a single spindle is detected, that is, no matter which direction the spindle moves, there will be a moment when it is detected by the sensor alone (that is, the corresponding Figure 2 If the yarn is used up to the last layer and the color of the yarn tube is exposed, a yarn-out signal will be output regardless of which side is detected first. After processing by the controller, the yarn-out spindle will stop at a specified range, which is convenient for manual operation.

[0047] Example 2:

[0048] An embodiment of the present invention provides a method for detecting abnormalities in a knitting machine, further comprising:

[0049] The second monitoring module is used to monitor the status of the threading guide wheel of the constant force device;

[0050] When the preset second alarm condition is detected, the second alarm information is output and the spindle is controlled to stop at the preset second position;

[0051] The second monitoring module is arranged on one side of the fixing seat of the spindle and is fixedly connected to the one side of the fixing seat of the spindle.

[0052] Among them, Figure 2 As shown, the second monitoring module includes a shift fork 16 .

[0053] The braiding machine anomaly detection method of this embodiment uses a shift fork for monitoring. The shift fork is a trigger-type sensor that outputs a signal when triggered. During normal operation, the constant force device guide wheel compensates (moves) back and forth with the tension of the yarn, and does not trigger the second monitoring module during movement. When the yarn breaks and there is no tension, the guide wheel returns to the bottom end and hits the second monitoring module during spindle rotation. At this time, the state of the guide wheel meets the second alarm condition, and a yarn break signal is generated. Furthermore, four sensors can be used simultaneously. Regardless of the running direction, as long as a yarn break occurs, the broken spindle will remain in a specified position range.

[0054] Example 3:

[0055] In one embodiment, the braiding machine abnormality detection method further includes:

[0056] Using a first image acquisition device to perform image monitoring on the spindle;

[0057] Analyzing the first image obtained by image monitoring and extracting the regional image corresponding to each spindle;

[0058] Associating each area image with each spindle according to the spindle's operating data;

[0059] Grouping the associated regional images to obtain multiple analysis image groups,

[0060] Analyze each grouped image group according to the preset image analysis library;

[0061] Match the raw material data corresponding to each spindle determined by the analysis with the demand data of the corresponding spindle in the demand data of the current product;

[0062] When the matching result is inconsistent, the first abnormality reminder information is output.

[0063] The braiding machine abnormality detection method of this embodiment adopts the image monitoring principle, and collects a first image containing each spindle through at least one first image acquisition device; based on the acquired operation data of each spindle, the first image is analyzed, and the regional image of each spindle is extracted, and the regional image is associated with each spindle; then the regional image of each operating moment within at least one operation cycle of each spindle is used as an analysis image group; based on the analysis image group and a preset image analysis library, the raw material data corresponding to each spindle is determined; when the raw material data is inconsistent with the demand data of the current product of the braiding machine, a first abnormality reminder message is output.

[0064] Determining the raw material data corresponding to each spindle based on the analysis image group and a preset image analysis library includes: performing feature extraction on each image in the analysis image group to obtain multiple image features; performing feature extraction on the multiple image features according to a preset feature extraction template to obtain multiple analysis feature values; constructing an analysis parameter set based on the multiple analysis feature values; invoking a corresponding preset image analysis library based on the positional relationship between the center of the spindle's trajectory and a first image acquisition device; retrieving the corresponding analysis results from the image analysis library based on the analysis parameter set; and determining the raw material data based on the analysis results. The yarn on the spindle is inspected by the first image acquisition device to determine whether the yarn meets production requirements. If it does not meet requirements, a first abnormality alert message is output to notify the operator to replace the yarn. The raw material data includes yarn type, color, and remaining quantity; the demand data includes yarn type, color, and total demand. Based on the raw material, the knitting machine's operation is monitored to ensure the correct use of the raw material. This ensures that the normal operation of the machine components ensures the normal production of the product.

[0065] Example 4:

[0066] In one embodiment, the braiding machine abnormality detection method further includes:

[0067] a second image captured by a second image capturing device on the periphery of each yarn;

[0068] Associating the second image with each raw material; for example, determining the raw material on the spindle based on the spindle associated with the second image acquisition device, and then performing the association;

[0069] Based on the raw material data corresponding to each raw material, the corresponding preset anomaly detection library is retrieved; different anomaly detection libraries are constructed in advance according to the different raw materials.

[0070] Matching the second image with each pre-stored image in the anomaly detection library;

[0071] Obtaining a detection result corresponding to a pre-stored image that matches the second image;

[0072] When the detection result is abnormal, a second abnormality reminder message is output.

[0073] Wherein, the second image acquisition device includes: a shooting device and a control device;

[0074] Among them, Figure 3 and 4As shown, the photographing device includes: a front bracket 21, a rotating mechanism 22, a front cover 23, a rear cover 24, a rear bracket 25, an arcuate guide groove body 26, a background plate 27, an upper end plate 28, and a camera 29. The background plate 27 and the upper end plate 28 are arranged in parallel, and their ends are connected to the front cover 23 and the rear cover 24, respectively. The front bracket 21 is connected to the fixed end of the rotating mechanism 22, and the rotating end of the rotating mechanism 22 is connected to the lower portion of the front cover 23. The rear bracket 25 is fixedly connected to the lower portion of the rear cover 24, and the lower end of the rear bracket 25 is slidably disposed within the guide groove of the arcuate guide groove body 26. Both the front cover 23 and the rear cover 24 have through holes in their centers. The yarn passes through the through holes, and the camera 29 located on the lower end surface of the upper end plate 28 captures a second image of the yarn.

[0075] Since the direction of the yarn being drawn out from the spindle is uncertain, in order to obtain an image that is more conducive to analysis, a control device is electrically connected to a rotating mechanism. The control device controls the rotation of the rotating mechanism according to the direction of the yarn. The rotation of the rotating mechanism causes the entire device to slide on the guide groove of the arc-shaped guide groove body, thereby adjusting the direction so that the yarn is located as close to the center of the image as possible. The specific control steps are as follows:

[0076] Determine the direction vector of the yarn in the outlet area of ​​the spindle and the direction vector of the yarn in the weaving area; query the preset control table corresponding to the spindle based on these two direction vectors, determine the control parameters of the rotating mechanism, and then control the rotating mechanism; the control parameters include rotation speed and rotation direction.

[0077] Among them, how to determine the direction vector of the yarn in the outlet area of ​​the spindle can be done by configuring a camera to shoot the outlet area, and then substituting the image taken by the camera into the coordinate system corresponding to the camera to determine the direction vector; the same method can be used to determine the yarn corresponding to the spindle in the weaving area, that is, by mapping the image of the camera configured to shoot the weaving area to the same coordinate system; the mapping process mainly involves matching the center of the image with the corresponding mapping point on the coordinate system.

[0078] In order to further improve the accuracy of control, when querying the preset control table corresponding to the spindle based on the direction vector of the yarn in the outlet area of ​​the spindle and the direction vector of the yarn in the weaving area, the motion parameters of the spindle (for example, speed and direction) can be substituted; that is, the control table is queried through the direction vector of the yarn in the outlet area of ​​the spindle, the direction vector of the yarn in the weaving area, and the motion parameters of the spindle; this requires that the direction vector of the yarn in the outlet area of ​​the spindle, the direction vector of the yarn in the weaving area, the motion parameters of the spindle and the control parameters be correspondingly associated in the pre-configured control table.

[0079] This embodiment uses a second image analysis of the images taken after each yarn is unfolded to determine whether there are any abnormalities in the yarn. The abnormalities include: uneven diameter distribution of the yarn and the presence of areas with large diameter fluctuations; when an abnormality occurs, a second abnormality reminder message is output to remind the staff to confirm and replace the corresponding raw materials.

[0080] In addition, although the yarn of each knitting machine is constantly moving from the constant force device to the mold side of the knitting machine, its movement has always been regular. Through a large amount of data, regular analysis is performed to summarize the pre-configured analysis library, which can be used for abnormality detection of the knitting machine. When analyzing the regularity, the representation parameters of each yarn are used as the analysis basis. The representation parameters can be the direction vector of the yarn in the knitting area; the analysis result (whether an abnormality occurs) is used as the result; the analysis library is constructed in the form of a one-to-one correspondence between the set of direction vectors of all the yarns of the knitting machine and the analysis result; therefore, the knitting machine abnormality detection method also includes: analyzing the yarn image in the knitting area taken to obtain the direction vector of each yarn; then arranging the direction vector to form a data set, matching the data set with the set corresponding to each analysis result in the analysis library, and extracting the corresponding analysis result; when the analysis result is abnormal, outputting an abnormality reminder. Among them, the direction vector of the yarn in the knitting area is determined by sampling the yarn at a point, and determining the direction vector by combining any two sampling points; then calculating the angle between each direction vector and the direction vector with the smallest sum of the angles as the direction vector of the yarn in the knitting area.

[0081] Example 5:

[0082] The present invention also provides a braiding machine abnormality detection system, such as Figure 5 As shown, the system comprises a first data acquisition module 1 and a first alarm module 2. The first data acquisition module 1 receives the yarn status of the yarn on the bobbin from the first monitoring module. When the yarn status meets a preset first alarm condition, the first alarm module 2 outputs a first alarm message and controls the spindle to stop at a preset first position. The first monitoring module includes a color mark sensor.

[0083] Example 6:

[0084] The present invention also provides a braiding machine abnormality detection system, further comprising: a second data acquisition module and a second alarm module; wherein the second data acquisition module receives the status of the threading guide wheel of the constant force device monitored by the second monitoring module; when a preset second alarm condition is met, the second alarm module outputs a second alarm message and controls the spindle to stop at a preset second position. The second monitoring module includes a shift fork.

[0085] Example 7:

[0086] The present invention also provides a braiding machine abnormality detection system, which also includes: a third data acquisition module and a third alarm module; wherein the third data acquisition module receives the first image obtained by the first image acquisition device for image monitoring of the spindle and analyzes it, and extracts the regional image corresponding to each spindle; associates each regional image with each spindle according to the operation data of the spindle, groups the associated regional images to obtain multiple analysis image groups, and analyzes each grouped image group according to a preset image analysis library; matches the raw material data corresponding to each spindle determined by the analysis with the demand data of the corresponding spindle in the demand data of the current product; when the matching result is inconsistent, the third alarm module outputs a first abnormality reminder message.

[0087] Example 8:

[0088] The present invention also provides a braiding machine abnormality detection system, which also includes: a fourth data acquisition module and a fourth alarm module, wherein the fourth data acquisition module collects a second image through a second image acquisition device on the periphery of each yarn; associates the second image with each raw material; based on the raw material data corresponding to each raw material, retrieves the corresponding preset abnormality detection library; matches the second image with each pre-stored image in the abnormality detection library; obtains the detection result corresponding to the pre-stored image matching the second image; and when the detection result is abnormal, outputs a second abnormality reminder message.

[0089] In addition, although the section of the wiring harness of each yarn of the knitting machine from the constant force device to the mold side of the knitting machine is always moving, its movement always has a regular pattern. A large amount of data is used to analyze the regularity and summarize it, and an analysis library is configured in advance, which can be used for abnormality detection of the knitting machine. When analyzing the regularity, the representation parameters of each yarn are used as the analysis basis. The representation parameters can be the direction vector of the yarn in the knitting area; the analysis result (whether an abnormality occurs) is used as the result; the analysis library is constructed in the form of a one-to-one correspondence between a set of direction vectors of all yarns of the knitting machine and the analysis result; therefore, the knitting machine abnormality detection system also includes: a yarn analysis module, the yarn analysis module performs the following operations: analyzes the captured yarn image in the knitting area to obtain the direction vector of each yarn; then arranges the direction vectors to form a data set, matches the data set with the set corresponding to each analysis result in the analysis library, and extracts the corresponding analysis result; when the analysis result is abnormal, an abnormality reminder is output.

[0090] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for detecting abnormality in a knitting machine, characterized in that: include: The first monitoring module is used to monitor the yarn status on the bobbin; When the yarn state meets the preset first alarm condition, a first alarm message is output and the spindle is controlled to stop at a preset first position; Wherein, the first monitoring module is arranged on one side of the bobbin.

2. The braiding machine abnormality detection method according to claim 1, wherein: The first monitoring module includes: a color mark sensor.

3. The braiding machine abnormality detection method according to claim 1, wherein: Also includes: The second monitoring module is used to monitor the status of the threading guide wheel of the constant force device; When the preset second alarm condition is detected, the second alarm information is output and the spindle is controlled to stop at the preset second position; Wherein, the second monitoring module is arranged on one side of the fixing base of the spindle and is fixedly connected to one side of the fixing base of the spindle.

4. The braiding machine abnormality detection method according to claim 3, wherein: The second monitoring module includes a shift fork.

5. The braiding machine abnormality detection method according to claim 1, wherein: Also includes: Using a first image acquisition device to perform image monitoring on the spindle; Analyzing the first image obtained by image monitoring and extracting the regional image corresponding to each spindle; Associating each area image with each spindle according to the spindle's operating data; Grouping the associated regional images to obtain multiple analysis image groups, Analyze each grouped image group according to the preset image analysis library; Match the raw material data corresponding to each spindle determined by the analysis with the demand data of the corresponding spindle in the demand data of the current product; When the matching result is inconsistent, the first abnormality reminder information is output.

6. A braiding machine abnormality detection system, characterized in that: include: A first data acquisition module and a first alarm module; wherein the first data acquisition module receives the yarn status of the yarn on the bobbin sent by the first monitoring module; when the yarn status meets the preset first alarm condition, the first alarm module outputs the first alarm information and controls the spindle to stop at the preset first position.

7. The braiding machine abnormality detection system according to claim 6, wherein: The first monitoring module includes: a color mark sensor.

8. The braiding machine abnormality detection system according to claim 6, wherein: Also includes: A second data acquisition module and a second alarm module; wherein, the second data acquisition module receives the status of the constant force device threading guide wheel monitored by the second monitoring module; when the preset second alarm condition is met, the second alarm module outputs a second alarm message and controls the spindle to stop at a preset second position.

9. The braiding machine abnormality detection system according to claim 8, wherein: The second monitoring module includes a shift fork.

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