A method and system for locating damage in a belt

Abstract

The present application relates to a kind of damage positioning method and system for belt flaw detection, belong to image data processing and visual identification field.The present application is realized to the efficient, accurate identification of the damage of belt surface by deep neural network algorithm;The proposed edge detection method saves computing resources;Realize highly automated damage detection and positioning, reduce artificial intervention and human error;Combined with line array camera, speed sensor and specific marker, accurately position the damage location, so that damage location mapping is more reliable, it is convenient for maintenance personnel to repair in time, accurately;The present system can work stably under complex environmental conditions, overcome the problem of positioning failure caused by factors such as numbering missing, poor lighting, improve the reliability of detection;Compared with the damage positioning method based on numbering, the damage positioning method of the present application can provide specific damage location according to the reference around the belt, avoid maintenance personnel to find damage location after shutdown.

Description

Technical Field

[0001] The invention belongs to the field of image data processing and visual recognition, and relates to a damage location method and system for belt flaw detection. Background Art

[0002] Current methods for detecting damage to conveyor belts include visual inspection, ultrasonic testing, electromagnetic wave testing, infrared thermal imaging, vibration, and resistivity measurement. Visual inspection uses high-definition cameras and image processing technology to monitor belt surface damage, wear, and cracks in real time. Ultrasonic testing uses ultrasonic sensors to detect cracks and damage within the belt, and is suitable for inspecting the deep structure of the belt. Electromagnetic wave testing uses electromagnetic wave sensors to detect abnormalities on the surface and inside the belt, enabling non-contact inspection. Infrared thermal imaging uses thermal imaging technology to detect temperature changes during operation, and is used to detect abnormalities such as friction and overheating. Vibration monitoring uses vibration sensors to monitor the vibration of the belt during conveying, and can detect abnormal vibration and imbalance. Resistivity measurement measures the resistivity changes of the belt material to assess its health and is typically used to inspect older belts.

[0003] With the development of artificial intelligence (AI), the aforementioned visual inspection technology has gradually become the mainstream method for belt flaw detection. Simultaneously, due to the mature application of line scan camera technology, visual inspection technology based on line scan images has been widely used. Line scan cameras have high resolution and can capture subtle defects and details on the belt surface, such as cracks, wear, and scratches, making detection more accurate and reliable. Line scan cameras can scan continuously at high speed, making them suitable for inspecting high-speed belts. This is crucial for real-time monitoring and timely problem detection, especially in situations requiring continuous, large-area inspection. However, line scan cameras have stringent requirements for uniform lighting, necessitating the addition of strong fill light equipment to the inspection surface. Furthermore, the line scan camera's capture process must match the motion process, necessitating the addition of a position feedback link, namely a speed sensor. Therefore, this belt flaw detection system based on a line scan camera has higher practical value than traditional line scan cameras, is a highly recommended approach in the current belt flaw detection field, and is the fundamental system upon which the present invention relies.

[0004] A popular damage location method for belt flaw detection systems based on machine vision technology is also based on vision technology. The principle is as follows: There are a variety of joints available for conveyor belts on the market, including metal buckle joints, cold glue joints, and hot vulcanized joints. The purpose of these joints is to connect multiple conveyor belts into a ring to increase the length of the belt. Each joint is usually engraved with a joint number, such as "①, ②." Each number corresponds to a joint. Machine learning methods can then be used to detect the number and visually detect the position of the number on the conveyor belt. After detecting a damaged area on the conveyor belt, the nearby joint number location is used as a reference to determine whether the damage location is between two numbers on the belt or how far away it is from a certain number.

[0005] Vision-based positioning methods, while a relatively new and popular technology in recent years, use a principle similar to license plate recognition to identify belt joint numbers and are a relatively mature application. However, their application to detecting joint numbers on industrial conveyor belts faces numerous technical drawbacks. For example, the damage detection algorithm based on a deep learning network model suffers from a significant hardware resource impact due to its inference performance. This requires increased computing resources for field applications, significantly increasing system construction costs. Accurately identifying joint numbers requires that the belt joints be numbered. However, belt joints may not be engraved with numbers during production, rendering methods based on numbers ineffective for determining the location of belt damage. Similarly, if the numbers are on a non-load-bearing surface, the linear array camera cannot identify them, as it must be mounted on the load-bearing surface. Furthermore, conveyor belts often carry complex cargo, such as coal, gravel, and other dusty items. Number recognition can also fail due to obstruction of numbers or poor lighting. Defect 3: Since the distance between each joint number usually ranges from one hundred meters to several hundred meters, even if it is given which two numbers the damage is between or how many meters away from a certain number, more specific and effective location information cannot be given. Defect 4: If the belt is in operation, the measured defect location information needs to be cached in the system. At this time, in addition to continuing to identify the damage, the system must also identify different numbers, which increases the complexity of the system and reduces detection efficiency. Defect 5: When the belt is stopped, even if it is known which numbers the damage location is adjacent to, the belt maintenance personnel cannot know the specific location of the number on the belt, and thus cannot perform maintenance work on the damage. From the above defects, it can be concluded that the practicality of the method based on vision to locate belt damage is low. Summary of the Invention

[0006] In view of this, the technical problems to be solved by the present invention are: the damage detection model based on deep learning occupies a large amount of computing resources; the visual positioning method is greatly affected by environmental factors and has low detection efficiency; maintenance personnel are unable to quickly know the specific damage points, and its practicality is low.

[0007] To solve the above technical problems, the present invention proposes a damage location system and method for belt flaw detection, which detects belt damage such as scratches, potholes, and peeling through a deep neural network; in order to save computing resources, a belt edge gnawing detection method is proposed on this basis for selection in practical applications, which can achieve better detection effects while reducing computing resources; further, a damage location method is proposed, which calculates the relative distance between the damage and the installation position of the linear array camera when the belt is stopped, combined with specific reference objects around the belt, to obtain the specific location of the damage. This positioning method has high practicality and robustness, and greatly improves the efficiency and practicality of belt maintenance work.

[0008] In order to achieve the above object, the present invention provides the following technical solutions:

[0009] A damage location method for belt flaw detection comprises the following steps:

[0010] Assume the total length of the belt is L. Let the belt located above the horizontal line of the center of the roller be the upper belt, and its length is L / 2; let the belt located below the horizontal line of the center of the roller be the lower belt, and its length is L / 2; let the roller on the side closer to the linear array camera along the belt running direction be the head, and the distance between the linear array camera and the vertical tangent of the outer edge of the head be m; let the distance between the reference objects fixed and set continuously along the belt be n, and number the reference objects with the reference object at the head as the starting point;

[0011] When issuing a flaw detection task, the point on the belt corresponding to the linear array camera is set to A;

[0012] When the belt is running, the distance traveled by A at the current moment is s;

[0013] When the damage point x is identified, the distance A has traveled at the current moment is recorded as s x ;

[0014] When the belt is locked, the distance A runs at the current moment is s end , the position of the linear array camera corresponds to the point on the belt set as B; calculate the distance s between the damage point x and B along the belt running direction Bx , expressed as:

[0015] s Bx =(s end -s x )%L

[0016] Among them, send Indicates the distance the belt travels from the time the task is issued to the time the belt is locked; s x Indicates the distance the belt has traveled when the damage point is identified; % indicates the remainder operation, L indicates the total length of the belt; s end -s x It represents the distance that the damage point x moves when the belt is locked;

[0017] Use the distance s between the damage point x and B along the belt running direction Bx As well as the distance n between the reference objects and the reference object number, the position of the damage point x relative to the specific reference object is determined.

[0018] Furthermore, the distance s between the damage point x and B along the belt running direction is used. Bx As well as the distance n between the reference objects and the reference object number, the position of the damage point x relative to the specific reference object is determined, specifically:

[0019] If s Bx ≤(L / 2-m), then the damage point x is located in the lower belt, s away from the machine head Bx +m, the reference object number code_H near the damage point x is:

[0020]

[0021] If s Bx >(Lm), then the damage point x is located in the lower belt, s away from the nose Bx -(Lm), the reference object number code_H near the damage point x is:

[0022]

[0023] If (L / 2-m)<s Bx <(Lm), then the position of the damage point x is on the upper belt, and the distance from the head is Ls Bx -m, the reference object number code_H near the damage point x is:

[0024]

[0025] in, Indicates rounding down.

[0026] Furthermore, the method for identifying damage points is to detect and identify scratches, nibbling, potholes, and peeling damage on the belt using a convolutional neural network; and to detect and identify nibbling damage on the belt using a non-convolutional neural network, including the following steps:

[0027] The grayscale value difference between the belt and the environment in the image is obtained through image processing technology to extract the belt edge;

[0028] Perform dilation on the extracted edge image to fill the pixels near the edge to make the edge more obvious;

[0029] Perform a difference operation on the expanded edge image and the current edge image to obtain a difference image;

[0030] Perform connected domain analysis on the difference image, find all connected domains, and calculate the radius of the inscribed circle of each connected domain;

[0031] Customize the threshold value and compare the inscribed circle radius with the threshold value to determine the edge damage point.

[0032] Furthermore, the step of obtaining the grayscale value difference between the belt and the environment in the image by image processing technology to extract the belt edge is specifically as follows:

[0033] Assume that the input image is I(x,y) and its grayscale value is G(x,y). The edge information in the input image I(x,y) is extracted by edge detection algorithm, which is expressed as:

[0034] E(x,y)=ED(G(x,y))

[0035] Where x represents the horizontal position of the pixel; y represents the vertical position of the pixel; E(x,y) represents the binary image after edge detection, where the edge part of the binary image is marked as white and the non-edge part is marked as black; ED represents the edge detection algorithm;

[0036] The step of performing the dilation operation on the extracted edge image to fill the pixels near the edge to make the edge portion more obvious is specifically as follows:

[0037] The expansion operation is expressed as:

[0038] D(x,y)=D(E(x,y),k)

[0039] Where D represents the dilation operation, k represents the structural element of the dilation operation, which makes the white areas (foreground) in the image more connected by expanding them; D(x,y) represents the binary image after dilation;

[0040] The step of performing a difference operation on the expanded edge image and the current edge image to obtain a difference image is specifically as follows:

[0041] The difference image is expressed as:

[0042] Δ(x,y)=D(x,y)-E(x,y)

[0043] Among them, Δ(x,y) represents the difference image, which represents the difference between the expanded edge and the current edge;

[0044] The steps of performing connected domain analysis on the difference image, finding all connected domains, and calculating the radius of the inscribed circle of each connected domain are specifically as follows:

[0045] Assumption C i represents the i-th connected domain, then the connected domain C i The radius of the inscribed circle R(C i )for:

[0046] R(C i )=max (x,y)∈Ci (min(dist((x,y),boundary(C i ))))(4)

[0047] Among them, dist((x,y),boundary(C i )) represents the distance from the point (x, y) to the boundary of the connected domain;

[0048] The step of customizing the threshold and comparing the inscribed circle radius with the threshold to determine the edge damage point is as follows:

[0049] The threshold value is set to T. If the radius of the inscribed circle R(C i ) is greater than the threshold value T, then the inscribed circle radius R(C i ) is the edge damage point, if the radius of the inscribed circle R(C i ) is less than the threshold, then the inscribed circle radius R(C i ) is a non-edge-biting damage point.

[0050] Furthermore, the linear array camera has a ranging function, and the s, s end 、s x Acquired by the line array camera.

[0051] Furthermore, the method for realizing the ranging function is:

[0052] The linear array camera records a timestamp when capturing each frame of image;

[0053] The speed sensor is used to obtain the belt's speed, and the distance the belt travels between two adjacent frames of images is calculated by combining the timestamp and speed information.

[0054] The distance the belt moves between two adjacent frames of images is accumulated to obtain the total distance the belt moves from the start of shooting to any moment.

[0055] A damage location system for belt flaw detection, comprising:

[0056] Linear array camera, which collects images of the belt surface and transmits them to the server;

[0057] Fill light, which provides uniform lighting for the line scan camera under various lighting conditions;

[0058] Speed ​​sensor, which monitors the belt speed and provides feedback to the linear array camera, coordinating image acquisition with the belt operation;

[0059] Servers, including:

[0060] The image processing module pre-processes the images collected by the linear array camera to improve the image clarity; extracts the feature information in the image and transmits it to the damage location module;

[0061] The damage location module uses the feature information in the image to identify the damage points on the belt in real time and transmits them to the data storage and analysis module;

[0062] The data storage and analysis module uses the image data and the damage point to determine the damage point type and locate the damage point position, and stores the damage point type, damage point position and image data.

[0063] Furthermore, it also includes: an alarm display host computer, which is connected to the data storage and analysis module to display the image data, damage point type, damage point location and damage point generation time, and supports human-computer interaction.

[0064] Furthermore, the extracting feature information from the image specifically includes: extracting edge information from the image using an edge detection algorithm.

[0065] Furthermore, the data storage and analysis module only stores the types of damage points and their specific location information detected within the last two turns of the belt at the current moment.

[0066] The beneficial effects of the present invention are:

[0067] Efficient detection: Through the deep neural network algorithm, efficient and accurate identification of belt surface damage is achieved, which greatly improves the detection efficiency; at the same time, the proposed edge gnawing detection method is efficient and saves computing resources; the entire system realizes highly automated damage detection and positioning, reducing manual intervention and human errors. Accurate positioning: Combined with linear array cameras, speed sensors and specific markers, the location of the damage can be accurately located, making the damage location mapping more reliable, and facilitating maintenance personnel to carry out repairs in a timely and accurate manner. Strong robustness: The system can work stably under complex environmental conditions, overcomes the problem of positioning failure caused by factors such as missing numbers and poor lighting, and improves the reliability of detection. High practicality: Compared with the damage location method based on numbers, the method of the present invention can provide a specific damage location, avoiding the maintenance personnel from being unable to find the damage location after shutdown; at the same time, it is not limited by the belt material, width and speed, and has broad application prospects.

[0068] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0070] Figure 1 This is a schematic diagram of the installation of a damage location system for belt flaw detection according to an embodiment of the present invention;

[0071] Figure 2 Schematic diagram of the process of a belt edge damage detection method according to an embodiment of the present invention;

[0072] Figure 3 This is a schematic diagram of the belt structure of an embodiment of the present invention;

[0073] Figure 4 The figure is a flow chart of a damage location method for belt flaw detection according to an embodiment of the present invention. DETAILED DESCRIPTION

[0074] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0075] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0076] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0077] The damage location system for belt flaw detection proposed in an embodiment of the present invention mainly includes: a linear array camera, which collects images of the belt surface and transmits them to a server; a fill light, which provides uniform lighting for the linear array camera under various lighting conditions; a speed sensor, which monitors the running speed of the belt and feeds back to the linear array camera, and coordinates image acquisition with the synchronization of belt operation; the server includes: an image processing module, which preprocesses the images collected by the linear array camera, such as preprocessing such as filtering, denoising and enhancement to improve image clarity; extracts feature information from the image and transmits it to a damage location module; the damage location module uses the feature information in the image to identify damage points on the belt in real time and transmits it to a data storage and analysis module; the data storage and analysis module stores image data, and uses the image data and damage points to perform image analysis and location to obtain the position of the damage points.

[0078] See also Figure 1, which is a schematic diagram of the installation of a damage location system for belt flaw detection according to an embodiment of the present invention. A linear scan camera and fill light are installed above the belt's bearing surface. Because the linear scan camera has high lighting requirements, the fill light must remain on during operation. The optimal installation location for the linear scan camera and fill light is above one side of the belt roller. This avoids interference from cargo and ensures a relatively flat belt surface. It also minimizes interference with the detection of belt edge wear (a phenomenon caused by belt deviation, friction between the belt and rollers, or the frame). A speed sensor is installed above the non-bearing surface of the belt under the belt. Servers and other equipment can be installed in an equipment room or machine room. A network cable connects the linear scan camera, server, and speed sensor. Four-core cable is used for power and control of the linear scan camera. When the belt is running, the speed sensor rotates, generating pulse signals that are fed back to the linear scan camera. The linear scan camera then transmits the captured image to the server through the image processing module and damage location module for damage detection. The linear scan camera uses frame triggering mode and begins capturing images upon receiving an external signal. Users can customize the line height (pixel height) of the captured image, and the line scan camera will continue to shoot at a preset line rate (number of lines per second) until the specified line height is reached. This method enables the line scan camera to capture images of the continuously moving belt surface. Because the belt is in continuous motion, and the line scan camera continues to shoot at a set line rate after the frame is triggered, the distance the belt has moved can be determined by analyzing the captured image sequence, which means it has a distance measurement function. Specifically, distance measurement can be achieved through the following methods:

[0079] 1. Timestamp and speed calculation:

[0080] A line scan camera records a timestamp when capturing each frame.

[0081] The speed of the belt is obtained by the speed sensor. Combining the timestamp and speed information, the distance the belt moves between two adjacent frames of images can be calculated.

[0082] By adding up these distances, the total distance the belt has traveled since the start of the shot can be found.

[0083] 2. Image feature matching:

[0084] In a captured image sequence, look for frames with specific features, such as splice numbers, markings, or defects on the belt.

[0085] By matching the positions of these feature frames in the image sequence, the relative distance between the features can be calculated.

[0086] If the distance between the characteristic frames is known or can be measured by other means, the distance the entire belt moves can be calculated using a proportional relationship.

[0087] 3. Direct measurement:

[0088] If the parameters of the line scan camera such as the installation position, angle and focal length are known, and the texture or markings on the belt surface are clear enough, the actual belt travel distance can be directly inferred by measuring the pixel distance in the image.

[0089] This method requires high image resolution and accurate calibration of line scan camera parameters.

[0090] When the server identifies damage through the damage detection algorithm, it will record the current detected distance; when the belt stops, the current detected distance will also be recorded; the installation position of the integrated linear array camera can calculate the relative position of the damage when the belt stops, and then map the position to the position of specific markers around the belt, which can provide a reference position of the damage, facilitating timely inspection by maintenance personnel.

[0091] There are many common types of belt damage, such as scratches, edge dents, potholes, and peeling. Different types of damage require different detection methods. If all of them are detected and identified using convolutional neural networks, it will occupy a lot of computing resources and is not suitable for edge analysis and application. This embodiment proposes a non-convolutional neural network belt edge dent detection method, which can achieve better detection results while reducing computing resources. Figure 2 , which is a flow chart of a belt edge gnawing damage detection method according to an embodiment of the present invention; the belt edge gnawing damage detection method is specifically implemented as follows:

[0092] 1. Belt edge extraction:

[0093] First, image processing techniques are used to extract the belt's edges by deriving the grayscale difference between the belt and its surroundings. Assume the input image is I(x,y), with grayscale values ​​G(x,y). x represents the horizontal position of a pixel (often called a column), and y represents the vertical position of a pixel (often called a row). Edge detection algorithms, such as the Sobel operator and Canny edge detection, are used to extract edge information from the input image I(x,y):

[0094] E(x,y)=ED(G(x,y)) (1)

[0095] Where ED represents the edge detection algorithm, and E(x, y) is the binary image after edge detection. In this binary image, the edge part is marked as 1 (or white), and the non-edge part is marked as 0 (or black).

[0096] 2. Edge pixel filling:

[0097] Perform a dilation operation on the extracted edge image E(x,y) to fill the pixels near the edge and make the edge more obvious. The formula for the dilation operation algorithm is as follows:

[0098] D(x,y)=D(E(x,y),k) (2)

[0099] Where D represents the dilation operation, and k is the structural element of the dilation operation. By expanding the white area (foreground) in the image, the white area becomes more connected. Its specific implementation is to convolve a structural element with each pixel in the image. If the structural element covers the foreground pixel, the pixel is set to the foreground (i.e., white), otherwise it is set to the background (i.e., black). D(x, y) is the binary image after dilation, and the structural element is usually a small matrix of size n×n.

[0100] 3. Difference image calculation:

[0101] Perform a difference operation on the expanded edge image D(x,y) and the current edge image E(x,y) to obtain the difference image:

[0102] Δ(x,y)=D(x,y)-E(x,y) (3)

[0103] Among them, Δ(x,y) is the difference image, which represents the difference between the expanded edge and the current edge.

[0104] 4. Damage radius detection:

[0105] Perform connected domain analysis on the difference image, find all connected domains, and calculate the radius of the inscribed circle of each connected domain. Assume C i represents the i-th connected domain, then the connected domain C i The radius of the inscribed circle R(C i )for:

[0106] R(C i )=max (x,y)∈Ci (min(dist((x,y),boundary(C i ))))(4)

[0107] Among them, dist((x,y),boundary(C i )) represents the distance from the point (x,y) to the boundary of the connected domain.

[0108] 5. Judgment of gnawing edge:

[0109] Customize the threshold value to T and judge the radius of the inscribed circle R(C i) is greater than a threshold. If so, it is considered edge gnawing; if less than the threshold, it is considered non-edge gnawing. Customizable here is that after connected domain analysis, a gap in the belt will have multiple inscribed circles. Therefore, edge gnawing can be determined only when the radius of one or more of these inscribed circles exceeds the threshold.

[0110] This embodiment also proposes a belt damage location method: When the belt is running, the server identifies damage and records the current detection distance, recording each time a damage is identified; when the belt stops, the detection distance at that time is recorded. The relative position of the damage when the belt stops can then be calculated based on the installation position of the linear array camera. This position is then mapped to the positions of specific markers around the belt, providing a reference location for the damage, facilitating timely inspection by maintenance personnel. The specific implementation of this belt damage location method must meet the following prerequisites:

[0111] 1. Get the total length of the belt;

[0112] 2. Obtain the overall structure of the belt, such as the number of drive cylinders and steering cylinders and the distance between the drive cylinders and steering cylinders;

[0113] The data required for the above two steps are usually described in detail when the belt system is first built, so they are easy to obtain; they can also be measured again to verify or update the above data.

[0114] Since the construction of the belt system is a prerequisite for damage location, there are slight differences in the positioning calculation methods under different belt structures. Therefore, this embodiment describes the method using one of the belt structures. The belt is supported by continuously set H-frames along the belt line. Each H-frame has a different number. The upper and lower belts of the belt share the same H-frame number, and the distance between adjacent H-frames is constant. In industrial applications, due to the heavy weight of the goods transported by the belt, the H-frames required are relatively dense, and the interval between adjacent H-frames is generally about 1 meter. If the H-frame is used as a specific marker along the belt line, maintenance personnel can quickly find the damage point near the location of the H-frame by the H-frame number. Similarly, fixed reference objects other than the H-frame can also be determined as suitable markers based on on-site applications to help maintenance personnel find damage points nearby through the markers. Please refer to Figure 3 , is a schematic diagram of the belt structure of an embodiment of the present invention; assuming that the belt consists of a head roller and a tail roller, and the overall structure is relatively standard, with the upper belt and the lower belt approximately equidistant, the upper belt: the belt located above the roller; the lower belt: the belt located below the roller; assuming that the linear array camera is installed on the lower belt, the belt runs counterclockwise, that is, Figure 3 Shown is a frontal view with the nose on the left and the tail on the right.

[0115] Given a total belt length of L, a linear array camera is deployed at a distance m from the machine head below the belt. Taking a standard belt structure as an example, the upper and lower belts are equidistant and each is L / 2 long. After receiving the inspection task, the system begins belt flaw detection. The following analysis is performed on the key positions and distances during belt operation:

[0116] 1. When issuing a flaw detection task, the position of the linear array camera corresponds to point A on the belt.

[0117] 2. When the belt is running, record the distance s traveled by point A at the current moment; s is a variable; the value of s is the distance traveled by point A, which can be obtained through a linear array camera with a distance measurement function.

[0118] 3. When the belt is locked, the current running distance is recorded as s end , the position of the linear array camera corresponds to point B on the belt.

[0119] 4. When the damage point x is identified, the distance traveled by point A at the current moment is recorded as s x ;

[0120] 5. Assume the distance n between adjacent H frames, set H frame No. 0 at the head of the conveyor belt, and number the H frames according to their relative positions.

[0121] In this method, the calculation of damage location information strictly follows the conditions of belt locking. Specifically, this means that after the detection task is started, all potential damage location information detected by the system must be finalized and calculated at the moment the belt is locked. This process ensures the accuracy of damage location and emphasizes that belt locking is a key prerequisite for calculating damage location information. In other words, only when the belt stops running and is in a locked state will the system officially calculate the specific location information of the damage based on the collected detection data. Because only when the belt is in a locked state does the damage location information have practical significance and application value. Please refer to Figure 4 , is a flow chart of a damage location method for belt flaw detection according to an embodiment of the present invention. Therefore, after the inspection task is issued, when the belt is locked, the method steps for calculating the location of the damage point are as follows:

[0122] 1. Calculate the distance of the damage point x relative to the linear array camera in the positive direction (along the belt running direction):

[0123] When the belt is locked, the forward distance of the damage point x relative to the point where the linear scan camera is located (point B) is:

[0124] s Bx =(s end -s x )%L (5)

[0125] Among them, s endIndicates the distance the belt travels from the time the task is issued to the time the belt is locked; s x = represents the distance the belt has traveled when the damage point is identified; % represents the remainder operation, and L represents the total length of the belt. This formula describes the equivalent of point A having moved s when the damage x is identified. x m, and the damage point starts to move at this time. When the belt stops, the damage point x moves s end -s x The total length of the belt is L, which is the distance of one circle. Therefore, the remainder must be taken to calculate the relative distance between the damage point and the linear array camera.

[0126] 2. Determine the specific location of the damage point x:

[0127] Because the reference markers are numbered H-frames along the belt, the system's final calculation result is output in the form of the closest H-frame, and considering the upper and lower belts of the belt, the distance between the H-frames in this embodiment is 1.2 meters. The method for determining the specific location of the damage point x is as follows:

[0128] 1) If s Bx ≤(L / 2-m), then the position of the damage x is in the lower belt, s away from the machine head Bx +m meters, the nearby H frame number is Indicates rounding down.

[0129] The output positioning information is: "Near the H frame with code_H!"

[0130] 2) If s Bx >(Lm), then the position of the damage x is in the lower band, s away from the nose Bx -(Lm) meters, the nearby H frame number is Indicates rounding down.

[0131] The output positioning information is: "Near the H frame with code_H!"

[0132] 3) If (L / 2-m)<s Bx <(Lm), then the position of the damage x is on the upper belt, and the distance from the nose is Ls Bx -m meters, the nearby H frame number is Indicates rounding down.

[0133] The output positioning information is: "Near frame H with code_H!"

[0134] The parameters involved in the above steps are described as follows:

[0135] L is the total length of the belt, with a custom setting interface reserved;

[0136] m is the distance from the installation position of the linear array camera to the belt length of the machine head, and a custom setting interface is reserved;

[0137] s Bx is the positive distance between the damage point x and the linear array camera position when the belt is locked, and the remainder is always less than the belt length L;

[0138] code_H is the H frame number near the damaged point when the belt stops, and maintenance personnel can find the damage location based on it.

[0139] In summary, the process of the damage location method of the belt flaw detection system can be described as Figure 4 Under the condition of accurate preconditions, the accuracy of this method can be controlled within 1.5 meters, and it can be implemented only at the software level. It is not affected by environmental factors and is suitable for belt damage positioning detection in harsh environments such as coal flow transportation systems in coal mines.

[0140] Optionally, to conserve system cache resources, the following cache logic can be added: After a task is issued, the system caches the current detection distance each time a damage is detected. Continuous belt operation would waste resources, so a logic can be added to cache only the last two revolutions. This means that the system clears the information cache for each two revolutions, with the number of revolutions defined by distance. For example, 5,000 meters is considered one revolution. This means that before the belt stops, the system pushes the damage alarm information detected normally. After the belt stops, the system calculates the location information of the last two revolutions based on the cache and positioning method, and then pushes the alarm for the last two revolutions again.

[0141] In summary, to address related technical issues, the present invention provides a belt flaw detection and positioning system, comprising an analysis server, a linear array camera, a fill light, a speed sensor, and an alarm display host computer software. The host computer software uniformly displays the alarm event information and processing results of the entire system. The displayed information includes: damage alarm image, alarm type, alarm time, and location information. The linear array camera is installed above the belt's bearing surface near the roller, and the fill light is installed within the belt's line of sight and illuminates the belt surface. The speed sensor is installed between the upper and lower belts, specifically on the non-bearing surface of the lower belt. The server is installed in the electromechanical chamber or machine room.

[0142] The belt damage detection algorithm and damage location method described in this paper are both software logic, built into the analysis server. Once the entire system is implemented, when a flaw detection task is issued while the belt is running, the system will detect the damage type in real time and send an alarm image. When the belt stops, the system calculates its position using the proposed location method and sends an alarm message for a set time period. This allows maintenance personnel to easily identify the damage status and location, making timely repair decisions and preventing unexpected stops in conveying tasks that could lead to production interruptions or safety accidents.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A damage location method for belt flaw detection, characterized in that: The following steps are involved: Assume the total length of the belt is L. Let the belt located above the horizontal line of the center of the roller be the upper belt, and its length is L / 2; let the belt located below the horizontal line of the center of the roller be the lower belt, and its length is L / 2; let the roller on the side closer to the linear array camera along the belt running direction be the head, and the distance between the linear array camera and the vertical tangent of the outer edge of the head be m; let the distance between the reference objects fixed and set continuously along the belt be n, and number the reference objects with the reference object at the head as the starting point; When issuing a flaw detection task, the point on the belt corresponding to the linear array camera is set to A; When the belt is running, the distance traveled by A at the current moment is s; When the damage point x is identified, the distance A has traveled at the current moment is recorded as s x ; When the belt is locked, the distance A runs at the current moment is s end , the position of the linear array camera corresponds to the point on the belt set as B; calculate the distance s between the damage point x and B along the belt running direction Bx , expressed as: s Bx =(s end -s x )%L Among them, s end Indicates the distance the belt travels from the time the task is issued to the time the belt is locked; s x Indicates the distance the belt has traveled when the damage point is identified; % indicates the remainder operation, L indicates the total length of the belt; s end -s x It represents the distance that the damage point x moves when the belt is locked; Use the distance s between the damage point x and B along the belt running direction Bx As well as the distance n between the reference objects and the reference object number, the position of the damage point x relative to the specific reference object is determined, specifically: If s Bx ≤(L / 2-m), then the damage point x is located in the lower belt, s away from the machine head Bx +m, the reference object number code_H near the damage point x is: If s Bx >(Lm), then the position of the damage point x is in the lower belt, and the distance from the nose is s Bx -(Lm), the reference object number code_H near the damage point x is: If (L / 2-m) Bx <(Lm), then the damage point x is located on the upper belt, and the distance from the machine head is Ls Bx -m, the reference object number code_H near the damage point x is:​ in, Indicates rounding down.

2. The damage location method for belt flaw detection according to claim 1, characterized in that: The method for identifying damage points is to detect and identify scratches, nibbling, potholes, and peeling damage on the belt using a convolutional neural network; and to detect and identify nibbling damage on the belt using a non-convolutional neural network, including the following steps: The grayscale value difference between the belt and the environment in the image is obtained through image processing technology to extract the belt edge; Perform dilation on the extracted edge image to fill the pixels near the edge to make the edge more obvious; Perform a difference operation on the expanded edge image and the current edge image to obtain a difference image; Perform connected domain analysis on the difference image, find all connected domains, and calculate the radius of the inscribed circle of each connected domain; Customize the threshold value and compare the inscribed circle radius with the threshold value to determine the edge damage point.

3. The damage location method for belt flaw detection according to claim 2, characterized in that: The step of obtaining the grayscale value difference between the belt and the environment in the image by image processing technology to extract the belt edge is specifically as follows: Assume that the input image is I(x,y) and its grayscale value is G(x,y). The edge information in the input image I(x,y) is extracted by edge detection algorithm, which is expressed as: E(x,y)=ED(G(x,y)) Where x represents the horizontal position of the pixel; y represents the vertical position of the pixel; E(x,y) represents the binary image after edge detection, where the edge part of the binary image is marked as white and the non-edge part is marked as black; ED represents the edge detection algorithm; The step of performing the dilation operation on the extracted edge image to fill the pixels near the edge to make the edge portion more obvious is specifically as follows: The expansion operation is expressed as: D(x,y)=D(E(x,y),k) Where D represents the dilation operation, k represents the structural element of the dilation operation, which makes the white areas (foreground) in the image more connected by expanding them; D(x,y) represents the binary image after dilation; The step of performing a difference operation on the expanded edge image and the current edge image to obtain a difference image is specifically as follows: The difference image is expressed as: Δ(x,y)=D(x,y)-E(x,y) Among them, Δ(x,y) represents the difference image, which represents the difference between the expanded edge and the current edge; The steps of performing connected domain analysis on the difference image, finding all connected domains, and calculating the radius of the inscribed circle of each connected domain are specifically as follows: Assumption C i represents the i-th connected domain, then the connected domain C i The radius of the inscribed circle R(C i )for: Among them, dist((x,y),boundary(C i )) represents the distance from the point (x, y) to the boundary of the connected domain; The step of custom setting a threshold and comparing the inscribed circle radius with the threshold to determine the edge damage point is as follows: custom setting a threshold as T, if the inscribed circle radius R (C i ) is greater than the threshold value T, then the inscribed circle radius R(C i ) is the edge damage point, if the radius of the inscribed circle R(C i ) is less than the threshold, then the inscribed circle radius R(C i ) is a non-edge-biting damage point.

4. The damage location method for belt flaw detection according to claim 2, characterized in that: The linear array camera has a distance measurement function, and the s, s end 、s x Acquired by the line array camera.

5. The damage location method for belt flaw detection according to claim 4, characterized in that: The method for realizing the distance measurement function is: The linear array camera records a timestamp when capturing each frame of image; The speed sensor is used to obtain the belt's speed, and the distance the belt travels between two adjacent frames of images is calculated by combining the timestamp and speed information. The distance the belt moves between two adjacent frames of images is accumulated to obtain the total distance the belt moves from the start of shooting to any moment.

Images