Nonwoven fabric surface uniformity on-line detection method

Abstract

The present application relates to a kind of non-woven cloth surface uniformity online detection method, comprising the following steps:1), online image acquisition and pre-processing;2), cloth surface is divided into rectangular detection unit, the gray characteristic value in each detection unit is calculated;3), the unit is numbered respectively along X axis direction and Y axis direction after division;For the gray characteristic value of each X axis, Y axis detection unit, sequence is established along Y axis, X axis direction respectively;4), sequence adopts K-means clustering algorithm to analyze the abnormal cluster of gray characteristic value, and the unit of abnormal value is marked, and the uniformity rate index is calculated according to the total number of abnormal unit;Again, the result after all sequence processing is marked to cloth surface, and the analysis result is visualized;5), according to detection result, trigger multi-stage early warning, realize the online detection of non-woven cloth uniformity.The present application realizes the non-woven cloth from "instantaneous local detection" to "long time overall detection", effectively improves production quality.

Description

TECHNICAL FIELD

[0001] The present application relates to a cloth surface defect detection method, in particular to a non-woven cloth surface uniformity online detection method, and belongs to the technical field of machine vision. BACKGROUND

[0002] In modern industrial production, non-woven cloth as a new type of material is widely used in textile, medical, environmental protection and other fields. However, in the production process, due to uneven spraying of raw materials, environmental contamination, electrostatic adsorption and other reasons, non-woven cloth is prone to uneven appearance defects, which affects its use effect and product quality. The traditional non-woven cloth surface defect detection mainly relies on manual visual inspection, which is low in efficiency and easy to be affected by the fatigue, emotion and misjudgment of the detector, resulting in unstable detection results and difficult to guarantee the quality. At the same time, long-time cloth inspection work will also cause harm to the workers' bodies.

[0003] To solve this problem, some non-woven cloth surface defect detection systems based on machine vision have been put into use. For example, the SIMV non-woven cloth online detection system integrates advanced machine image vision acquisition, photoelectric identification technology, and computer vision image software and hardware specially designed for domestic non-woven cloth products, realizing accurate and rapid online non-woven cloth surface defect high-speed detection. However, these technologies still have limitations, and can only capture instantaneous local defects based on single frame image, and cannot effectively identify long-term and overall uneven problems that need to be judged by multiple frames of images.

[0004] Therefore, in order to solve the above problems, it is necessary to provide an innovative non-woven cloth surface uniformity online detection method to overcome the defects in the prior art. SUMMARY

[0005] The purpose of the present application is to provide a non-woven cloth surface uniformity online detection method which can accurately detect the overall uniformity of non-woven cloth in a period of time and improve the production quality of non-woven cloth.

[0006] To achieve the above purpose, the technical solution adopted by the present application is as follows: a non-woven cloth surface uniformity online detection method, which comprises the following process steps:

[0007] 1) online image acquisition and preprocessing;

[0008] 2) defining X axis in the width direction of the cloth surface and Y axis in the running direction, dividing rectangular detection units Cell(i,j) along the X axis and Y axis direction according to fixed width L, wherein i=1,2,...,M, j=1,2,...,N, calculating the gray value of each detection unit; , 3) calculating the gray value of each detection unit;

[0009] 3), the cells divided in step 2) are numbered along the X-axis direction and the Y-axis direction respectively; for the gray scale characteristic value of each X-axis detection cell, a sequence is established along the Y-axis direction; for the gray scale characteristic value of each Y-axis detection cell, a sequence is established along the X-axis direction;

[0010] 4), a feature vector is constructed for the detection cell Cell( , ) in step 3), the abnormal clusters of the gray scale characteristic values in the sequence are analyzed by using a K-means clustering algorithm, and the cells where the abnormal values are located are marked; the uniformity index is calculated according to the total number of abnormal cells; and the results after processing of all sequences are marked on the fabric surface, and the analysis results are visualized;

[0011] 5), according to the detection results, a warning is triggered to prompt the staff to adjust in time, so that the non-woven fabric uniformity online detection is realized.

[0012] The non-woven fabric surface uniformity online detection method further comprises that:

[0013] 1-1), real-time image capture of the non-woven fabric moving on the conveying belt is performed by means of an industrial camera;

[0014] 1-2), the collected color image is converted into a gray scale image;

[0015] 1-3), median filtering is used to eliminate noise in the image, and then edge detection technology is used to highlight the filament bundle profile, so as to form a pre-processed gray scale profile image.

[0016] The non-woven fabric surface uniformity online detection method further comprises that: the gray scale characteristic value in step 2) comprises a mean value , a standard deviation , and a maximum value Max i,j , and the specific calculation method is as follows:

[0017] , ,

[0018] In the formula, i and j are the numbers of the detection cells on the X-axis and the Y-axis respectively, the gray scale mean value of the jth cell; is the total number of pixels in a single detection cell, which is determined by the camera resolution; is the gray scale value of the kth pixel in the ith and jth detection cell.

[0019] The non-woven fabric surface uniformity online detection method further comprises that: the sequence construction method of step 3) is specifically:

[0020] for each X-axis position Xi The characteristic values of the corresponding detection units are collected in chronological order to form a three-dimensional time sequence:

[0021] wherein, represents the gray scale characteristic change sequence of the i-th detection unit along the Y axis from the starting end to the current real-time detection position range in the X axis; i represents the number of the detection unit in the X axis, ; is the N-th characteristic vector in the detection unit numbered i along the Y axis direction from the starting position to the detection place ; , are the gray scale mean value, standard deviation and maximum value respectively;

[0022] For each Y axis position , the characteristic values of the corresponding detection units are collected in position order to form a three-dimensional position sequence:

[0023] wherein, represents the gray scale characteristic change sequence of the j-th detection unit along the X axis from one end of the cloth surface to the other end in the Y axis; j represents the number of the detection unit in the Y axis, ; is the M-th characteristic vector in the detection unit numbered j along the X axis direction from one end of the cloth surface to the detection place .

[0024] The non-woven cloth surface uniformity online detection method of the application further comprises: in step 4), the feature vectors constructed by the detection units Cell( , ) are subjected to standardization processing, and the formula is as follows:

[0025]

[0026] : the normalized characteristic value; : the original value of the k-th characteristic of the i, j-th unit; : the mean value of the k-th characteristic of all units; : the standard deviation of the k-th characteristic of all units.

[0027] The non-woven fabric surface uniformity online detection method of the present application further comprises that in the step 4), the method for analyzing the abnormal jump of the gray scale characteristic value by the K-means clustering algorithm is: presetting a cluster number K=2, dividing the characteristic space into a normal cluster and an abnormal cluster, respectively corresponding to a standard fabric surface and a non-standard fabric surface, wherein the cluster label of the normal cluster is 0, and the normal cluster center is C0; the cluster label of the abnormal cluster is 1, and the abnormal cluster center is C1.

[0028] input the normalized gray scale characteristic vector sequence in the iteration process , the maximum iteration number , and the convergence threshold is: , and the cluster label 0 or 1 of each detection unit is output; the iteration process is specifically as follows:

[0029] the distribution stage: for each characteristic vector, the Euclidean distance from the current cluster center , is calculated, and is assigned to the cluster with a closer distance: the expression is as follows:

[0030]

[0031] in the formula, is the normalized gray scale characteristic vector, is the normal cluster center, is the abnormal cluster center;

[0032] the update stage: the mean value of all characteristic vectors in each cluster is recalculated as the new cluster center : the expression is as follows:

[0033]

[0034] wherein, is the normalized characteristic vector, is the total number of characteristic vectors in the kth cluster; is the set of all characteristic vectors contained in the Kth cluster.

[0035] if the cluster center has no change or reaches the maximum iteration number , the iteration is terminated; otherwise, the new cluster center is used to replace the old cluster center , and the distribution stage is returned to continue iteration.

[0036] The non-woven fabric surface uniformity online detection method of the present application further comprises that in the step 4), the specific method for marking the unit where the abnormal value is located is:

[0037] for the sequence T numbered i on the X axis iIf a certain feature vector is determined as an abnormal cluster, the area where the abnormal cluster is located is marked with yellow, and the number of abnormal clusters is recorded as i If 2 or more consecutive detection units are detected, the area is marked with dark yellow;

[0038] For the sequence numbered j on the Y axis If a certain feature vector is determined as an abnormal cluster, the area where the abnormal cluster is located is marked with yellow, and the number of abnormal clusters is recorded as j If 3 or more consecutive detection units are detected, the area is marked with dark yellow;

[0039] If the same detection unit is determined as abnormal in both the Y axis time sequence and the X axis position sequence, the number of such units is recorded as The area is marked with red.

[0040] The non-woven fabric surface uniformity online detection method of the present application further comprises: in step 4), the uniformity index U is the proportion of the number of normal units, and the calculation method is as follows:

[0041]

[0042] Wherein is the total number of detection units of the fabric surface;

[0043]

[0044] Wherein: i is the number of abnormal clusters of the sequence T i j is the number of abnormal clusters of the sequence P j is the number of the same detection unit that is determined as an abnormal cluster in the bidirectional sequence;

[0045] If U is greater than or equal to 0.95, the fabric surface is marked as high quality, if 0.85 is less than or equal to U and U is less than 0.95, the fabric surface is marked as good, and if U is less than 0.85, the fabric surface is marked as extremely uneven.

[0046] The non-woven fabric surface uniformity online detection method of the present application can also be: when the total number of abnormal detection units determined by K-means clustering in step 4) exceeds 5% of the total number of detection units of the fabric surface, or 5 or more consecutive detection units in the same sequence are determined as abnormal clusters, the system automatically triggers a voice warning to the worker; the warning feedback information should include the specific coordinate unit Cell , ​​), which is convenient for workers to quickly locate the abnormal area of the cloth surface; meanwhile, the system records the feature data of the current abnormality, i.e., the three-dimensional feature value of the abnormal unit, in the background.

[0047] Compared with the prior art, the present application has the following beneficial effects:

[0048] 1. The non-woven fabric surface uniformity online detection method of the present application can accurately detect the overall uniformity of the cloth by fusing the space-time features of multiple images and dynamic model training, overcoming the limitations of traditional single-frame image detection, and effectively identifying subtle unevenness accumulated over a long period of time.

[0049] 2. The non-woven fabric surface uniformity online detection method of the present application decomposes the cloth surface into standardized detection units and calculates multiple-dimensional features such as gray mean, standard deviation, and maximum value, avoiding the blurring of subtle unevenness defects and fully quantifying the uniformity state of the unit.

[0050] 3. The non-woven fabric surface uniformity online detection method of the present application triggers multi-level early warning based on the detection results, realizes in-process control of production parameters, and replaces manual detection to avoid fatigue misjudgment. Ultimately, it realizes accurate and efficient online detection of non-woven fabric uniformity, successfully upgrades the detection mode from "instantaneous local" to "long-term overall", and significantly improves production quality and control ability. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 is a flowchart of the non-woven fabric surface uniformity online detection method of the present application.

[0052] Figure 2 is a flowchart of K-means clustering analysis of abnormal units in step 4) of the present application.

[0053] Figure 3 is a uniformity rate visualization pattern finally generated in step 4) of the present application.

[0054] Figure 4 is a screenshot of actual detection of a certain non-woven fabric according to the present application. DETAILED DESCRIPTION

[0055] Please refer to the drawings in the specification Figure 1 The present application is a non-woven fabric surface uniformity online detection method, which comprises the following process steps:

[0056] 1), online image acquisition and preprocessing, i.e., real-time acquisition of dynamic cloth surface images on the non-woven fabric production line scene, preprocessing through gray scale conversion and median filtering to generate preprocessed images meeting the requirements of detection unit gray scale feature extraction. This step specifically includes:

[0057] 1-1), real-time capture of images of non-woven fabric moving on the conveyor belt with the help of an industrial camera.

[0058] 1-2), the collected color image is converted into a gray image.

[0059] 1-3), the noise in the image is eliminated using median filtering, and the filament profile is highlighted through edge detection technology to form a pre-processed gray profile image, providing a data basis for subsequent operations.

[0060] 2), define the width direction of the cloth as the X-axis and the running direction as the Y-axis. Along the X-axis and Y-axis directions, divide the rectangular detection unit Cell(i,j) according to the fixed width L , ), where i=1, 2,...,M, j=1, 2,...,N, M is the total number of units in the width direction, and N is the total number of units in the motion direction. Then calculate the gray feature value in each detection unit.

[0061] Among them, the gray feature value includes the mean value , the standard deviation , and the maximum value Max i,j . The mean value can reflect the average density of the fibers in the detection unit; the standard deviation reflects the dispersion degree of the pixel gray scale, which can reflect the density fluctuation of the fibers in the detection unit, and the specific calculation method is as follows:

[0062] , ,

[0063] In the formula, i and j are the gray mean value of the jth unit in the X-axis and Y-axis of the detection unit, respectively; is the total number of pixels in a single detection unit, which is determined by the camera resolution; is the gray value of the kth pixel in the ith and jth detection unit.

[0064] This step 2) avoids blurring of subtle uneven defects by dividing the cloth surface into standardized detection units and calculating the gray mean value, standard deviation, maximum value and other multi-dimensional features, and fully quantifies the uniformity of the unit.

[0065] 3), the units divided in step 2) are numbered along the X-axis and Y-axis directions, respectively; for the gray feature value of each X-axis detection unit, a sequence is established along the Y-axis direction; for the gray feature value of each Y-axis detection unit, a sequence is established along the X-axis direction, thereby forming a bidirectional feature sequence, breaking through the limitation of the prior art that can only detect locally and instantaneously, and realizing long-term overall monitoring in time and space dimensions.

[0066] Specifically, the sequence construction method is specifically:

[0067] For each X-axis position X i , the feature values of the corresponding detection cells are collected in time sequence to form a three-dimensional time sequence:

[0068] wherein, represents the gray scale feature variation sequence of the i-th detection cell along the Y-axis from the starting end to the current real-time detection position range; i represents the number of the detection cell on the X-axis, ; is the N-th feature vector in the detection cell numbered i along the Y-axis direction from the starting position to the detection place ; , are the gray scale mean, standard deviation, and maximum value, respectively;

[0069] For each Y-axis position , the feature values of the corresponding detection cells are collected in position sequence to form a three-dimensional position sequence:

[0070] wherein, represents the gray scale feature variation sequence of the j-th detection cell along the X-axis from one end of the cloth to the other end; j represents the number of the detection cell on the Y-axis, ; is the M-th feature vector in the detection cell numbered j along the X-axis direction from one end of the cloth to the detection place .

[0071] 4), the feature vectors of the detection cells Cell( , ) are constructed, the abnormal clusters of the gray scale feature values in the sequences described in step 3) are analyzed by using the K-means clustering algorithm, the cells with abnormal values are marked, the uniformity rate index is calculated according to the total number of abnormal cells, and the results of processing all the sequences are marked on the cloth surface to visualize the analysis results.

[0072] Specifically, to eliminate the influence of different feature dimensions on the clustering results, the feature vectors constructed by the detection cells Cell( , ) are first standardized, and the formula is as follows:

[0073]

[0074] wherein, : the standardized feature value; : the original value of the k-th feature of the i, j-th cell; : the mean value of the k-th feature of all cells; : Standard deviation of the k-th feature of all units.

[0075] The method for analyzing abnormal jumps of gray feature values by the K-means clustering algorithm is: presetting a cluster number K=2, dividing the feature space into a normal cluster and an abnormal cluster, respectively corresponding to a standard fabric and a non-standard fabric, wherein the cluster label of the normal cluster is 0, and the normal cluster center is C0; the cluster label of the abnormal cluster is 1, and the abnormal cluster center is C1.

[0076] The normal cluster center The mean of the gray feature vector of the 100 qualified non-woven fabric samples ; the abnormal cluster center : The mean of the gray feature vector of the historical abnormal sample, if there is no historical data, initialized as .

[0077] In the iteration process, input the normalized gray feature vector sequence , the maximum iteration number , and the convergence threshold is: , output the cluster label 0 or 1 of each detection unit; the iteration process is specifically:

[0078] The assignment stage: for each feature vector, calculate the Euclidean distance with the current cluster center , , and assign it to the cluster with a closer distance: the expression is as follows:

[0079]

[0080] In the formula, is the normalized gray feature vector, is the normal cluster center, is the abnormal cluster center.

[0081] The update stage: recalculate the mean of all feature vectors within each cluster as the new cluster center : the expression is as follows:

[0082]

[0083] Wherein, is the normalized feature vector, is the total number of feature vectors within the k-th cluster; is the set of all feature vectors contained in the K-th cluster.

[0084] If the cluster center does not change or reaches the maximum iteration number , terminate the iteration; otherwise, replace the old cluster center with the new cluster center , the assignment phase continues iteration.

[0085] The result of iteration is that the feature vectors of normal clusters are centralized around , and satisfy: where is the normal fluctuation threshold, which can be set as initial value according to historical data. The feature vectors of abnormal clusters are far away from , which can be manifested as significant deviation of mean value and too large standard deviation, and abnormal maximum value.

[0086] Further, to adapt to the subtle changes of raw materials, environment and other factors in the production process, the system updates the normal cluster center every hour based on the latest normal samples (feature vectors of nearly 100 qualified detection units), and adjusts using exponential smoothing method, the formula is as follows:

[0087]

[0088] is the normal cluster center of the tthhour; is the smoothing weight, fixed at 0.3; is the mean value of the feature vectors of the latest 100 normal units in the tthhour; is the normal cluster center of the t−1thhour.

[0089] After the cluster center is updated, the assignment phase continues iteration.

[0090] Further, the specific method for marking the unit where the abnormal value is located is:

[0091] For the sequence T i with X-axis number i, if a certain feature vector is determined to be an abnormal cluster, the area where the abnormal cluster is located is marked with yellow, and the number of abnormal clusters is recorded as i ; if 2 or more consecutive detection units are marked with dark yellow.

[0092] For the sequence with Y-axis number j, if a certain feature vector is determined to be an abnormal cluster, the area where the abnormal cluster is located is marked with yellow, and the number of abnormal clusters is recorded as j ; if 3 or more consecutive detection units are marked with dark yellow.

[0093] For the same detection unit in Y-axis time sequence and X-axis position sequence, if it is determined to be abnormal, the number of such units is recorded as , and the area is marked with red.

[0094] Further, the uniformity index is U, i.e. the proportion of normal unit number, and the calculation method is as follows:

[0095]

[0096] Wherein, is the total number of detection units of the cloth surface;

[0097]

[0098] Wherein: i is the number of abnormal clusters of sequence T i ; j is the number of abnormal clusters of sequence P j ; is the number of the same detection unit being determined as abnormal cluster in the bidirectional sequence.

[0099] If U is greater than or equal to 0.95, it is marked as high-quality cloth surface, if 0.85 is less than U and is less than 0.95, it is marked as good cloth surface, and if U is less than 0.85, it is marked as extremely uneven cloth surface.

[0100] In summary, the step 4) adopts the K-means clustering algorithm of dynamic updating normal cluster center, the single sequence + bidirectional cross classification abnormal marking and the quantitative uniformity rate calculation, and the cloth surface condition is visualized.

[0101] 5), according to the detection result, triggering early warning, prompting the staff to adjust in time, realizing the online detection of non-woven fabric uneven rate.

[0102] The step is specifically: when the total number of abnormal detection units determined by K-means clustering in step 4) accounts for more than 5% of the total number of detection units of the cloth surface or in the same sequence, 5 or more continuous detection units are determined as abnormal cluster, the system automatically triggers voice early warning to prompt the worker; the early warning feedback information needs to include the specific coordinate unit Cell , ) of the abnormal detection unit, so as to facilitate the worker to quickly locate the abnormal area of the cloth surface; at the same time, the system records the feature data of this abnormality in the background, i.e. the three-dimensional feature value of the abnormal unit. The worker can check the running parameters of the system combined with the feedback early warning information, and timely adjust the process parameters to correct the uneven problem of the cloth surface, realizing real-time online quality control.

[0103] The present application triggers multi-level early warning based on the detection result, realizes the in-process control of production parameters, replaces manual detection to avoid fatigue misjudgment, finally realizes the accurate and efficient online detection of non-woven fabric uniformity, successfully upgrades the detection mode from "instantaneous local" to "long time whole", and significantly improves the production quality and control ability.

[0104] The above detailed description is merely exemplary in nature and is not intended to limit the application or the application and uses of the application. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding description.

Claims

1. An online detection method for the surface uniformity of nonwoven fabrics, characterized in that: The process includes the following steps: 1) Online image acquisition and preprocessing; 2) Define the fabric width direction as the X-axis and the running direction as the Y-axis. Divide the fabric into rectangular detection cells (Cells) along the X and Y axes according to a fixed width L. , ), where i=1,2,...,M, j=1,2,...,N, calculate the grayscale feature value within each detection unit; 3) Number the units divided in step 2) along the X-axis and Y-axis respectively; establish a sequence of gray-scale feature values ​​of each detection unit along the Y-axis; establish a sequence of gray-scale feature values ​​of each detection unit along the X-axis. 4) For the detection unit Cell ( , ) Construct feature vectors, and use the K-means clustering algorithm to analyze the abnormal clusters of gray value features of the sequences described in step 3), mark the units where the abnormal values ​​are located, calculate the uniformity index U based on the total number of abnormal units, that is, the proportion of normal units; then mark the results of all processed sequences on the fabric surface to visualize the analysis results; Among them, the detection unit Cell ( , The constructed feature vectors are standardized using the following formula: Standardized eigenvalues; The original value of the k-th feature of the i-th,j-th unit; The mean of the k-th feature of all units; : The standard deviation of the k-th feature of all units; The K-means clustering algorithm analyzes abnormal jumps in gray-level feature values ​​as follows: The number of clusters is preset to K=2, and the feature space is divided into normal clusters and abnormal clusters, corresponding to standard and non-standard fabric surfaces, respectively. The cluster label of the normal cluster is 0, and the center of the normal cluster is C0; the cluster label of the abnormal cluster is 1, and the center of the abnormal cluster is C1. The standardized gray-scale feature vector sequence is input during the iteration process. Maximum number of iterations The convergence threshold is: Output the cluster label 0 or 1 for each detection unit; the iterative process is as follows: Allocation phase: For each feature vector, calculate the value relative to the current cluster center. , The Euclidean distance is used to assign individuals to clusters that are closer in distance: its expression is as follows: In the formula This is the standardized grayscale feature vector. As a normal cluster center, It is the center of an abnormal cluster; Update phase: Recalculate the mean of all eigenvectors within each cluster, and use this mean as the new cluster center. Its expression is as follows: in, The standardized feature vector, The total number of eigenvectors within the k-th cluster; The set of all feature vectors contained in the Kth cluster; If the cluster center remains unchanged or the maximum number of iterations has been reached... Terminate the iteration if necessary; otherwise, use the new cluster center. Replace the old cluster center Return to the allocation phase and continue iterating; 5) Trigger an early warning based on the test results to prompt staff to make timely adjustments, thereby achieving online detection of nonwoven fabric unevenness.

2. The online detection method for the surface uniformity of nonwoven fabrics as described in claim 1, characterized in that: Step 1) specifically refers to: 1-1), using an industrial camera to capture real-time images of the nonwoven fabric moving on the conveyor belt; 1-2), convert the acquired color images into grayscale images; (1-3) Median filtering is used to eliminate noise in the image, and edge detection technology is used to highlight the outline of the filament bundle, forming a preprocessed grayscale outline image.

3. The online detection method for the surface uniformity of nonwoven fabrics as described in claim 1, characterized in that: The grayscale feature values ​​in step 2) include the mean. Standard deviation Maximum value Max i,j The specific calculation method is as follows: ， ， In the formula, i and j are the gray average values ​​of the j-th unit, which is the number of the detection unit on the X-axis and Y-axis, respectively. It is the total number of pixels within a single detection unit, determined by the camera resolution; It is the grayscale value of the k-th pixel within the i-th and j-th detection units.

4. The online detection method for the surface uniformity of nonwoven fabrics as described in claim 3, characterized in that: The sequence construction method in step 3) is as follows: For each X-axis position X i The feature values ​​of the corresponding detection units are collected in chronological order to form a three-dimensional time series: in, This represents the sequence of grayscale feature changes along the Y-axis from the starting point to the current real-time detection position for the i-th detection unit on the X-axis; i represents the number of the detection unit on the X-axis. ; Let i be the Nth feature vector along the Y-axis from the starting position to the detection point in the detection unit numbered i. ; , These are the mean, standard deviation, and maximum grayscale values, respectively. For each Y-axis position The feature values ​​of the corresponding detection units are collected in order of position to form a three-dimensional position sequence: Where represents the sequence of grayscale feature changes of the j-th detection unit on the Y-axis from one end of the fabric to the other along the X-axis; j represents the number of the detection unit on the Y-axis. ; Let j be the Mth feature vector along the X-axis from one end of the fabric to the detection point in the detection unit. .

5. The online detection method for the surface uniformity of nonwoven fabrics as described in claim 1, characterized in that: In step 4), the specific method for marking the cell containing the outlier is as follows: For the sequence T with X-axis number i i If a certain feature vector is identified as an anomalous cluster, the region containing the anomalous cluster is marked in yellow, and the number of anomalous clusters is denoted as . i If two or more consecutive detection units are detected, the area will be marked in dark yellow. For the sequence numbered j on the Y-axis If a certain feature vector is identified as an anomalous cluster, the region containing the anomalous cluster is marked in yellow, and the number of anomalous clusters is denoted as . j If three or more consecutive detection units are detected, the area will be marked in dark yellow. If the same detection unit is judged as abnormal in both the Y-axis time series and the X-axis position series, the number of such units is denoted as . The area is marked in red.

6. The online detection method for the surface uniformity of nonwoven fabrics as described in claim 5, characterized in that: In step 4), the uniformity index U is calculated as follows: in This represents the total number of detection units on the fabric surface. in: i For sequence T i The number of anomalous clusters; j For sequence P j The number of anomalous clusters; It is the number of clusters in which the same detection unit is identified as an anomalous cluster in both bidirectional sequences; If U ≥ 0.95, it is marked as high-quality fabric; if 0.85 ≤ U < 0.95, it is marked as good fabric; if U < 0.85, it is marked as extremely uneven fabric.

7. The online detection method for the surface uniformity of nonwoven fabrics as described in claim 1, characterized in that: Step 5) specifically involves: when the total number of abnormal detection units determined by K-means clustering in step 4) exceeds a preset threshold of 5% or when 5 or more consecutive detection units in the same sequence are identified as abnormal clusters, the system automatically triggers a voice warning to alert the worker; the warning feedback information must include the specific coordinate unit (Cell) of the abnormal detection unit. , This allows staff to quickly locate abnormal areas on the fabric; at the same time, the system records the characteristic data of this abnormality in the background, namely the three-dimensional characteristic value of the abnormal unit.

Images