CN114261713A - Belt deviation early warning system and method based on visual perception - Google Patents

Belt deviation early warning system and method based on visual perception Download PDF

Info

Publication number
CN114261713A
CN114261713A CN202210110541.4A CN202210110541A CN114261713A CN 114261713 A CN114261713 A CN 114261713A CN 202210110541 A CN202210110541 A CN 202210110541A CN 114261713 A CN114261713 A CN 114261713A
Authority
CN
China
Prior art keywords
belt
roller
exposed area
visual perception
area
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210110541.4A
Other languages
Chinese (zh)
Inventor
戴滨
徐德滨
祝斌
年福庆
李世强
夏雷
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Maanshan Iron and Steel Co Ltd
Original Assignee
Maanshan Iron and Steel Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Maanshan Iron and Steel Co Ltd filed Critical Maanshan Iron and Steel Co Ltd
Priority to CN202210110541.4A priority Critical patent/CN114261713A/en
Publication of CN114261713A publication Critical patent/CN114261713A/en
Pending legal-status Critical Current

Links

Images

Landscapes

  • Length Measuring Devices By Optical Means (AREA)

Abstract

本发明揭示了一种基于视觉感知的皮带偏移预警系统,驱动皮带转动的滚轮两端固定有摄像头,两个所述摄像头拍摄滚轮两端未覆盖皮带的裸露区域图像信息,两个所述摄像头均连接并输出图像信息至图像模块,所述图像模块连接并输出处理后的图像信息至工业控制计算机。本发明解决了人工点检盲区问题,实现皮带机所有点检内容的24小时不间断监测,形成设备状态预知预判、故障智能分析结果,指导运维人员有目的和计划的开展运维工作,从而降低劳动强度,提升工作效率。

Figure 202210110541

The invention discloses a belt excursion warning system based on visual perception. Cameras are fixed at both ends of a roller that drives the rotation of the belt. Both are connected and output image information to an image module, and the image module is connected and output processed image information to an industrial control computer. The invention solves the problem of manual spot inspection blind spots, realizes 24-hour uninterrupted monitoring of all spot inspection contents of the belt conveyor, forms the results of equipment status prediction and fault intelligent analysis, and guides operation and maintenance personnel to carry out operation and maintenance work with purpose and plan. Thereby reducing labor intensity and improving work efficiency.

Figure 202210110541

Description

Belt deviation early warning system and method based on visual perception
Technical Field
The invention relates to industrial conveyor belt deviation detection, in particular to a belt deviation early warning system and method based on visual perception.
Background
The raw fuel belt conveying system is complex and various in equipment, at present, the equipment mainly adopts an off-line monitoring mode, and equipment fault equipment is identified and diagnosed mainly through on-site manual point inspection operation and maintenance. In order to more conveniently, quickly and effectively master the operation condition of equipment, eliminate faults in a sprouting state, improve the maintenance level of the equipment and promote safe production, the remote monitoring and fault diagnosis of the equipment of a belt conveyor system are necessary to be innovated.
The belt can replace manpower to transport the material in all kinds of equipment, because its conveying bearing capacity is big, the distance is far away and advantage such as fast, has obtained extensive application in industry, agriculture scene. However, after a period of operation, the belt inevitably deviates due to uneven material stacking, deformation of the rollers and other reasons, and further causes unpredictable damage to industrial and agricultural fields. Therefore, the method has important significance in researching the real-time and accurate belt deviation early warning method.
In the prior art, chinese patent document (CN105197537A) discloses a belt off-tracking detection system and method based on color detection, however, the method of this patent that utilizes color bands to enhance the surface characteristics of the belt is not reliable, and the color bands are worn after long-time operation because the belt needs to continuously transport materials. In addition, part industrial field dust is great, leads to the typewriter ribbon colour to change colour, and then influences follow-up belt skew and detect the accuracy.
Chinese patent document (CN110902315A) discloses a method and a system for detecting belt deviation state, however, the method for extracting belt edge straight line features by using hough transform in the patent has poor real-time performance, and cannot generate belt deviation diagnosis information in time.
Disclosure of Invention
The invention solves the problem of real-time detection of the running state of the belt conveying equipment, eliminates the point inspection blind area, changes accident maintenance into state maintenance and improves the maintenance efficiency.
In order to achieve the purpose, the invention adopts the technical scheme that: the utility model provides a belt skew early warning system based on vision perception, drive belt pivoted gyro wheel both ends are fixed with the camera, two the camera shoots the exposed regional image information that the gyro wheel both ends do not cover the belt, two the camera all connects and output image information to image module, image module connects and outputs the image information after handling to industrial control computer.
The image module and the industrial control computer are internally provided with wireless communication modules, and are communicated with each other through the communication modules and transmit the processed signals to the industrial control computer.
The camera is provided with a light source irradiating the roller, and the output end of the industrial computer is connected with an alarm.
A belt deviation early warning method based on visual perception comprises the following steps:
step 1, preprocessing a video frame;
step 2, extracting an exposed area of the roller according to the set fixed size square frame;
step 3, calculating the outline area of the exposed area;
step 4, searching the outline with the largest area;
step 5, calculating the pixel width of the exposed area of the roller;
step 6, measuring the distance from the exposed area of the roller to the optical center;
step 7, calculating the actual width of the exposed area of the roller;
step 8, mutually verifying the actual widths of the exposed areas of the left and right rollers;
and 9, alarming if the actual width of the exposed area of the roller is larger than a set value.
In the step 2, the contour in the coarse positioning area is extracted by using a maximum inter-class variance method, and then the exposed area of the roller is extracted by using an optical flow method
In the step 4, sorting the area values by using a bubble sorting method, wherein the contour with the largest area value is regarded as the contour of the exposed area at the end part of the roller, and the rest contours are defined as interference filtering.
The steps areIn step 5, the minimum circumscribed rectangle of the convex set of the outline of the exposed area at the end of the roller is calculated, the width value of the rectangle is taken as the pixel width of the outline of the exposed area of the left wheel, and is set as a variable w1
In the step 6, the actual distance s between the lens surface of the camera and the exposed area of the side roller where the camera is located and the focal length f of the camera are known during system installation, the distance between the lens surface of the camera and the optical center is consulted and added with s to generate the object distance u, and the image distance v is calculated through a Gaussian imaging formula.
In the step 7, the width w of the exposed area of the roller2=w1*(u/v)。
The invention solves the problem of blind areas of manual point inspection, realizes 24-hour uninterrupted monitoring of all point inspection contents of the belt conveyor, forms an equipment state prediction prejudgment and fault intelligent analysis result, and guides operation and maintenance personnel to purposefully and planned develop operation and maintenance work, thereby reducing labor intensity and improving work efficiency.
Drawings
The following is a brief description of the contents of each figure in the description of the present invention:
FIG. 1 is a schematic diagram of a belt deviation warning system based on visual perception;
FIG. 2 is a flow chart of a belt deviation warning method based on visual perception.
Detailed Description
The following description of the embodiments with reference to the drawings is provided to describe the embodiments of the present invention, and the embodiments of the present invention, such as the shapes and configurations of the components, the mutual positions and connection relationships of the components, the functions and working principles of the components, the manufacturing processes and the operation and use methods, etc., will be further described in detail to help those skilled in the art to more completely, accurately and deeply understand the inventive concept and technical solutions of the present invention.
The belt deviation early warning system based on visual perception can monitor the running state of the belt on the industrial and agricultural fields in real time. When the belt shifts, alarm information can be sent out in time to remind workers to carry out maintenance work. The belt running state is guaranteed to be good, and meanwhile the deployment difficulty and the equipment cost are reduced.
The two ends of the roller of the driving belt are provided with exposed parts not covered by the belt, as shown in fig. 1, two cameras are arranged right ahead of the exposed area of the roller, the image information of the exposed area of the roller not covered by the belt is collected, the two cameras are connected with the image transmission module, the image transmission module is connected with the industrial control computer, the industrial control computer and the image transmission module are preferably provided with built-in wireless communication modules, wireless communication can be facilitated, and wiring troubles are avoided. The industrial control computer is arranged inside the central control room; the alarm is connected with industrial control and is calculated, and visual condition is installed inside the belt conveying field or the central control room, and in addition, the camera can be provided with a light source for irradiating the roller, so that the shooting definition of the camera is ensured.
The camera is used for collecting real-time images transmitted by the belt, and the number of the cameras can be increased or reduced according to field requirements. Two camera mounted position are fixed, are located the dead ahead in two exposed regions of gyro wheel respectively, and camera model and shooting mode also can be selected according to the on-the-spot demand.
The belt deviation early warning method based on visual perception comprises the following steps:
step 1, a camera collects a belt transmission video in real time, an image module preprocesses a video frame, and then the image transmission module sends the processed video to an internal storage of an industrial control computer
And 2, after the installation position of the camera is fixed, the positions of the left and right roller exposed areas in the image are also fixed, the roller exposed areas in the video frame are roughly positioned by setting a square frame with a fixed size, and the outline in the roughly positioned area is extracted by using a maximum inter-class variance method.
Step 3, calculating the area of each contour, and sequencing the area values by using a bubble sequencing method, wherein the contour with the largest area value is regarded as the contour of the exposed area at the end part of the roller, and the rest contours are regarded as interference filtering;
step 4, calculating the pixel width of the outline of the exposed area of the roller wheel, specifically, firstly calculating the minimum circumscribed rectangle of the convex set of the outline of the exposed area of the roller wheel, and regarding the width value of the rectangle as the pixel width of the outline of the exposed area of the roller wheelDegree is set as variable w1
Step 5, calculating the pixel width of the exposed area of the roller;
step 6, measuring the distance from the exposed area of the roller to the optical center; constructor measures the actual distance of camera mirror surface and the exposed regional of gyro wheel, consults the camera mirror surface to the distance between the optical center, and both add and generate object distance u, because focus f is known, and image distance v is calculated to accessible gaussian imaging formula, and the formula is as follows:
Figure BDA0003494952390000051
step 7, calculating the actual width of the exposed area of the roller; the upper left corner of the image plane and the lower right corner of the object plane are on the same straight line, the lower left corner of the image plane and the upper right corner of the object plane are on the same straight line, and the four corner points form two similar triangles. The width w of the exposed area of the roller can be calculated according to the similar triangle principle2As follows:
Figure BDA0003494952390000052
step 8, mutually verifying the actual widths of the exposed areas of the left and right rollers; if the belt deviates rightward, the width value of the left roller exposed area is increased, and conversely, the width value of the right roller exposed area is decreased, so that the accuracy of calculating the width value of the left roller exposed area can be verified by using the decrease of the width value of the right roller exposed area, and vice versa.
Step 9, alarming if the actual width of the exposed area of the roller is larger than a set value, regarding the width of the exposed area of the left roller as the offset distance of the belt, comparing the distance value with a historical experience value, and generating alarm information and recording the current time if the distance value is larger than the historical experience value; otherwise, no alarm information is generated.
The current offset can be stored in a background database, and a new historical experience value is generated by combining the service life of the belt and the material conveying amount and is used for the next-stage alarm judgment. Compared with the prior art, the belt deviation early warning system and method based on visual perception are designed by taking visual perception and measurement as technical schemes. Compared with the method for directly detecting the belt area in the prior art, the method for detecting the exposed area of the roller is easier to implement and more reliable, namely: the more exposed areas of the rollers indicate greater belt deflection. Whether the belt deviates in the transmission process is inferred through the exposed area of the roller, and an alarm is given according to the situation.
The invention has been described above with reference to the accompanying drawings, it is obvious that the invention is not limited to the specific implementation in the above-described manner, and it is within the scope of the invention to apply the inventive concept and solution to other applications without substantial modification.

Claims (9)

1.一种基于视觉感知的皮带偏移预警系统,其特征在于:驱动皮带转动的滚轮两端固定有摄像头,两个所述摄像头拍摄滚轮两端未覆盖皮带的裸露区域图像信息,两个所述摄像头均连接并输出图像信息至图像模块,所述图像模块连接并输出处理后的图像信息至工业控制计算机。1. A belt excursion warning system based on visual perception is characterized in that: cameras are fixed at both ends of the rollers that drive the belt to rotate, and two described cameras capture the image information of the exposed areas of the rollers that are not covered by the belt at both ends, and the two cameras are The cameras are all connected and output image information to an image module, and the image module is connected and output processed image information to an industrial control computer. 2.根据权利要求1所述的基于视觉感知的皮带偏移预警系统,其特征在于:所述图像模块和工业控制计算机内均设有无线通信模块,所述图像模块和工业控制计算机通过通信模块相互通信,并将图像模块将所处理的信号输送至工业控制计算机。2. The belt excursion warning system based on visual perception according to claim 1, wherein the image module and the industrial control computer are both provided with a wireless communication module, and the image module and the industrial control computer pass through the communication module. communicate with each other, and the image module sends the processed signal to the industrial control computer. 3.根据权利要求1或2所述的基于视觉感知的皮带偏移预警系统,其特征在于:所述摄像头旁配有照射向滚轮的光源,所述工业计算机的输出端连接有报警器。3 . The belt excursion warning system based on visual perception according to claim 1 or 2 , wherein the camera is equipped with a light source illuminating the roller, and an alarm is connected to the output end of the industrial computer. 4 . 4.一种基于视觉感知的皮带偏移预警方法,其特征在于,包括以下步骤:4. a belt excursion warning method based on visual perception, is characterized in that, comprises the following steps: 步骤1、视频帧预处理;Step 1, video frame preprocessing; 步骤2、根据设置的固定大小方框,提取滚轮裸露区域;Step 2. According to the fixed size box set, extract the exposed area of the roller; 步骤3、计算裸露区域轮廓面积;Step 3. Calculate the contour area of the exposed area; 步骤4、寻找面积最大轮廓;Step 4. Find the contour with the largest area; 步骤5、计算滚轮裸露区域像素宽度;Step 5. Calculate the pixel width of the exposed area of the scroll wheel; 步骤6、测量滚轮裸露区域到光心的距离;Step 6. Measure the distance from the exposed area of the roller to the optical center; 步骤7、计算滚轮裸露区域实际宽度;Step 7. Calculate the actual width of the exposed area of the roller; 步骤8、左右滚轮裸露区域实际宽度相互验证;Step 8. Mutual verification of the actual width of the exposed area of the left and right rollers; 步骤9、若滚轮裸露区域实际宽度大于设定值则报警。Step 9. If the actual width of the exposed area of the roller is greater than the set value, it will alarm. 5.根据权利要求4所述基于视觉感知的皮带偏移预警方法,其特征在于:所述步骤2中,使用最大类间方差法提取粗定位区域内的轮廓,再采用光流法提取滚轮裸露区域。5. The belt excursion warning method based on visual perception according to claim 4, it is characterized in that: in the described step 2, use the maximum inter-class variance method to extract the outline in the rough positioning area, and then use the optical flow method to extract the exposed roller area. 6.根据权利要求5所述基于视觉感知的皮带偏移预警方法,其特征在于:所述步骤4中,使用冒泡排序法对各面积值进行排序,其中,面积值最大的轮廓视为为滚轮端部裸露区域轮廓,其余轮廓定义为干扰滤除。6. The belt excursion warning method based on visual perception according to claim 5, characterized in that: in the step 4, each area value is sorted using a bubble sort method, wherein the contour with the largest area value is regarded as The outline of the exposed area at the end of the roller, and the rest of the outline is defined as interference filtering. 7.根据权利要求4、5或6所述基于视觉感知的皮带偏移预警方法,其特征在于:所述步骤5中,首先计算滚轮端部裸露区域轮廓凸集的最小外接矩形,将该矩形的宽度值视为左轮裸露区域轮廓的像素宽度,设为变量w17. The belt excursion warning method based on visual perception according to claim 4, 5 or 6, characterized in that: in the step 5, first calculate the minimum circumscribed rectangle of the convex set of the outline of the exposed area at the end of the roller, and the rectangle The width value of is regarded as the pixel width of the outline of the exposed area of the left wheel, and is set to the variable w 1 . 8.根据权利要求7所述基于视觉感知的皮带偏移预警方法,其特征在于:所述步骤6中,系统安装时已知摄像头镜面与所在侧滚轮裸露区域的实际距离s,以及摄像头焦距f,查阅摄像头镜面到光心之间的距离并与s相加生成物距u,通过高斯成像公式计算出像距v。8. The belt excursion warning method based on visual perception according to claim 7, it is characterized in that: in described step 6, when the system is installed, the actual distance s of the known camera mirror surface and the exposed area of the side roller, and the camera focal length f , check the distance between the camera mirror and the optical center and add it to s to generate the object distance u, and calculate the image distance v by the Gaussian imaging formula. 9.根据权利要求8所述基于视觉感知的皮带偏移预警方法,其特征在于:所述步骤7中,滚轮裸露区域的宽度w2=w1*(u/v)。9 . The method for early warning of belt excursion based on visual perception according to claim 8 , wherein in the step 7, the width of the exposed area of the roller w 2 =w 1 *(u/v). 10 .
CN202210110541.4A 2022-01-29 2022-01-29 Belt deviation early warning system and method based on visual perception Pending CN114261713A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210110541.4A CN114261713A (en) 2022-01-29 2022-01-29 Belt deviation early warning system and method based on visual perception

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210110541.4A CN114261713A (en) 2022-01-29 2022-01-29 Belt deviation early warning system and method based on visual perception

Publications (1)

Publication Number Publication Date
CN114261713A true CN114261713A (en) 2022-04-01

Family

ID=80833490

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210110541.4A Pending CN114261713A (en) 2022-01-29 2022-01-29 Belt deviation early warning system and method based on visual perception

Country Status (1)

Country Link
CN (1) CN114261713A (en)

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0912172A (en) * 1995-06-29 1997-01-14 Canon Inc Endless belt deviation preventing mechanism
CN105627939A (en) * 2015-12-17 2016-06-01 广东正业科技股份有限公司 A micro-gap detection device, method and system based on industrial equipment
CN107816943A (en) * 2017-10-23 2018-03-20 广东工业大学 A kind of box for material circulation volume weight measuring system and its implementation
CN108615321A (en) * 2018-06-07 2018-10-02 湖南安隆软件有限公司 Security pre-warning system and method based on radar detecting and video image behavioural analysis
CN109035293A (en) * 2018-05-22 2018-12-18 安徽大学 Method suitable for segmenting remarkable human body example in video image
CN110838142A (en) * 2019-11-05 2020-02-25 沈阳民航东北凯亚有限公司 Luggage size identification method and device based on depth image
CN111104913A (en) * 2019-12-23 2020-05-05 福州大学 A PPT method for video extraction based on structure and similarity
CN112419250A (en) * 2020-11-13 2021-02-26 湖北工业大学 Pavement crack digital image extraction, crack repair and crack parameter calculation method
CN113744267A (en) * 2021-11-04 2021-12-03 智洋创新科技股份有限公司 Method for detecting icing and estimating thickness of transmission conductor based on deep learning
CN113928824A (en) * 2021-10-25 2022-01-14 三一汽车制造有限公司 Belt deviation detection method and device and mixing station

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0912172A (en) * 1995-06-29 1997-01-14 Canon Inc Endless belt deviation preventing mechanism
CN105627939A (en) * 2015-12-17 2016-06-01 广东正业科技股份有限公司 A micro-gap detection device, method and system based on industrial equipment
CN107816943A (en) * 2017-10-23 2018-03-20 广东工业大学 A kind of box for material circulation volume weight measuring system and its implementation
CN109035293A (en) * 2018-05-22 2018-12-18 安徽大学 Method suitable for segmenting remarkable human body example in video image
CN108615321A (en) * 2018-06-07 2018-10-02 湖南安隆软件有限公司 Security pre-warning system and method based on radar detecting and video image behavioural analysis
CN110838142A (en) * 2019-11-05 2020-02-25 沈阳民航东北凯亚有限公司 Luggage size identification method and device based on depth image
CN111104913A (en) * 2019-12-23 2020-05-05 福州大学 A PPT method for video extraction based on structure and similarity
CN112419250A (en) * 2020-11-13 2021-02-26 湖北工业大学 Pavement crack digital image extraction, crack repair and crack parameter calculation method
CN113928824A (en) * 2021-10-25 2022-01-14 三一汽车制造有限公司 Belt deviation detection method and device and mixing station
CN113744267A (en) * 2021-11-04 2021-12-03 智洋创新科技股份有限公司 Method for detecting icing and estimating thickness of transmission conductor based on deep learning

Similar Documents

Publication Publication Date Title
CN109969736B (en) An intelligent detection method for deviation fault of large carrying belt
US20240386539A1 (en) Pavement technical condition detection method and device based on three-dimensional contour
CN100535647C (en) On-line detection device of defects in float glass based on machine vision
CN107052086A (en) Stamping parts surface defect detection apparatus and detection method based on 3D vision
CN111681241A (en) Quality control method and system based on machine vision detection and measurement depth integration
CN104668738B (en) Cross type double-line laser vision sensing welding gun height real-time identification system and method
CN203124215U (en) Frame sealant coating machine
CN209657151U (en) A kind of intelligent track crusing robot
CN105548185A (en) Automobile wheel hub screw hole recognition method based on machine vision and shielding method and system
WO2015055060A1 (en) Online detecting method for continuous casting slab surface quality
CN114758322B (en) Road quality detection system based on machine identification
CN107909575A (en) For the binocular vision on-line measuring device and detection method of vibrating screen operating status
CN115655119B (en) A method for detecting the range and size of road line laser rutting subsidence
CN112651612A (en) Modern urban road running condition real-time online monitoring and early warning management cloud platform based on big data and cloud computing
CN112025727A (en) Novel patrol and examine track robot device
CN111352412B (en) Intelligent track inspection robot
CN114445636A (en) Train bottom item mapping method
CN117191794A (en) Part vision detection system
CN117406666A (en) An automated CNC machine tool control system
CN114261713A (en) Belt deviation early warning system and method based on visual perception
CN107271446B (en) Visual detection method for rubber wire of timing gear chamber of engine
CN114670061A (en) Machine vision-based composite tool service life monitoring system and method
CN118915721A (en) Vehicle-mounted mobile detection system and method based on embedded industrial personal computer
CN112241949A (en) Concrete placement mould intelligent monitoring device that fuses computer vision technique
CN204730813U (en) A kind of medium plate Shap feature detection system controlled based on symmetric double line laser angle

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20220401