CN109969736B - Intelligent detection method for deviation fault of large carrying belt - Google Patents
Intelligent detection method for deviation fault of large carrying belt Download PDFInfo
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- CN109969736B CN109969736B CN201910041877.8A CN201910041877A CN109969736B CN 109969736 B CN109969736 B CN 109969736B CN 201910041877 A CN201910041877 A CN 201910041877A CN 109969736 B CN109969736 B CN 109969736B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G43/00—Control devices, e.g. for safety, warning or fault-correcting
- B65G43/02—Control devices, e.g. for safety, warning or fault-correcting detecting dangerous physical condition of load carriers, e.g. for interrupting the drive in the event of overheating
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G2203/00—Indexing code relating to control or detection of the articles or the load carriers during conveying
- B65G2203/02—Control or detection
- B65G2203/0266—Control or detection relating to the load carrier(s)
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G2203/00—Indexing code relating to control or detection of the articles or the load carriers during conveying
- B65G2203/04—Detection means
- B65G2203/041—Camera
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G2207/00—Indexing codes relating to constructional details, configuration and additional features of a handling device, e.g. Conveyors
- B65G2207/40—Safety features of loads, equipment or persons
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Abstract
The invention relates to a dynamic image-based intelligent detection method for deviation faults of a large carrier belt, which comprises the steps of firstly determining coordinate values away from two edges when the carrier belt normally runs aiming at a large carrier belt transportation system; then determining the installation position of an intelligent camera at the edge of a certain belt; transporting the belt normallyEffective distance value d from edge of belt rack during traveling1And d2Converted into pixel values in the image, and the pixel values on the abscissa are calibrated to be f respectively1And f2(ii) a Determining the horizontal coordinate value s of the straight line of the two edges of the belt by image processing1And s2. If p is1＜|s1‑f1|≤p2Or p1＜|s2‑f2|≤p2The actual deviation distance of the belt is c1～c2Judging as a secondary fault; if s1‑f1|＞p2Or | s2‑f2|＞p2The actual deviation distance of the belt is more than c3And judging as a primary fault. Compared with the prior art, the method has the advantages of intelligently identifying the deviation fault and not using manual intervention, and can accurately and automatically judge whether the large carrying belt with the length of L (more than 700) and the width of W (more than 1) is subjected to the deviation fault by using three intelligent cameras.
Description
Technical Field
The invention relates to the field of intelligent detection of large-scale carrier belt equipment of an industrial automatic production line, in particular to an intelligent detection method for deviation faults of a large-scale carrier belt.
Background
The carrying belt is widely used in the industrial fields of coal mine production, metallurgy and the like, and the materials transported above the belt are not uniformly distributed, so that the carrying belt deviates after long-term operation, belt abrasion is increased, the service life of the belt is seriously influenced, severe deviation of the belt even can cause severe faults such as belt tearing and the like, and the normal production of a coal mine is influenced.
At present, the deviation detection of the belt is mostly based on a contact type detection method of a mechanical sensor, on one hand, the installation position of the detection physical equipment is fixed, a physical device for deviation detection can be damaged due to collision and abrasion after working for a period of time, the detection precision is reduced, and the stability is poor. On the other hand, only when the belt off tracking is bigger, the device can be acted, and the off tracking of the belt can not be quantitatively detected in real time. Therefore, the research on the belt deviation fault detection method is valuable and needs to be solved urgently.
The machine vision technology has the advantages of non-contact, high detection speed, high detection precision and objective and reliable detection results, and whether the carrying belt is off-tracking fault or not can be detected quickly and accurately by matching with a proper intelligent detection algorithm. Machine vision has been applied in many detection fields, and also has a precedent application in the field of belt deviation detection, but most of the application is to design a mounting device of an industrial camera, and whether deviation faults occur or not is manually identified according to collected images (for example, patent CN 207703158U). Because manual intervention is needed, the base slides to realize coarse adjustment of the focal length of the industrial camera, and then the focal length is adjusted through the precision adjusting and rotating button of the industrial camera. Dynamic images of the belt on the industrial site are collected without being matched with a detection algorithm to process the images, and the images are checked manually to judge whether the belt deviates or not. The industrial camera only plays a role in image acquisition and monitoring, and the acquired images are not processed in real time, so that the belt deviation fault is detected. In general, the complexity of processing a dynamic image and detecting and calculating faults of a moving object is high, the time consumption is long, the rapid detection and rapid elimination of industrial production faults are difficult to meet, particularly, in a coal mine production line, a carrying belt is important for the transportation of a coal mine, and how to realize rapid and accurate deviation detection on the carrying belt without manual intervention is a desire of production technicians for many years.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides the intelligent detection method for the deviation fault of the large carrying belt.
In order to achieve the purpose, the invention has the following conception:
the method comprises the steps of installing a high-speed industrial intelligent camera on a station to be detected on a coal mine material transportation production line, illuminating the lower belt surface of the station to be detected by using a special light source, collecting the running image information of a carrying belt, and carrying out online real-time processing on the collected image information. The key of the invention is a rapid algorithm for detecting the belt deviation fault, and the intelligent detection algorithm for processing the belt surface image comprises rapid positioning of the belt edge from the belt frame coordinate position, extraction of the belt surface image characteristic parameters, detection of the belt edge straight line and the like. And the intelligent camera online image processing system processes the surface image of the belt and then outputs fault information, and the fault information is transmitted to an upper computer interface for dynamic display. The industrial camera is a monochrome or color area array scanning high-speed industrial camera, can be attached to the existing coal mine material transportation production line or a special production line for belt fault detection, and is arranged at a working section position which can conveniently photograph the belt edge and belt frame images. The special light source is an annular LED light source and provides illumination for the industrial camera. The industrial camera is positioned right above the special light source, and a lens of the industrial camera is framed through the annular middle of the special light source. The upper computer interface comprises an industrial computer and belt deviation fault monitoring software.
According to the conception, the invention adopts the following technical scheme:
an intelligent detection method for deviation faults of a large carrier belt is used for obtaining deviation and fault levels of the belt in a carrier belt motion control system, and comprises the following steps:
step 1, determining the distance between the belt edge and a belt frame when a carrier belt normally runs aiming at a large carrier belt running system: based on the characteristic that a large carrying belt has left and right parallel deviation, only the effective value d of the transverse distance between one edge of the belt and the belt frame when the carrying belt normally runs needs to be determined1And d2；
2, based on the obtained distance between the belt edge and the belt frame when the belt normally runs, selecting a proper position to install an intelligent camera at a certain edge of the tail of the belt conveyor so as to better acquire a dynamic image of the running of the belt in real time; the image samples are collected for a plurality of times, the coordinate of the image is transformed, and the effective value d of the distance from the edge of the left or right belt frame when the belt normally runs1And d2Converting into corresponding pixel values in the image, and calibrating the abscissa pixel values to be f1And f2；
Step 3, based on the video image collected by the intelligent camera, processing the image in real time by adopting a Hough transform method in image processing, and determining linear abscissa values s of two edges of the belt1And s2(ii) a If p is1＜|s1-f1|≤p2Or p1＜|s2-f2|≤p2Corresponding to the actual running deviation distance of the carrier belt being c1～c2Judging a secondary fault, namely moderate deviation; if s1-f1|＞p1Or | s2-f2|＞p2Corresponding to the actual deviation distance of the carrier belt being greater than c3Judging that the first-level fault is serious deviation; thereby determining the deviation amount and the deviation fault grade of the belt. Wherein p is1,p2Respectively are coordinate pixel threshold values of the belt deviation determined in advance.
The step 1 specifically comprises the following steps:
step 1.1, tracking the running state of the large carrying belt, recording related data, and analyzing to find that the large carrying belt has the characteristic of left-right parallel deviation; whether the belt deviates can be judged as long as whether the horizontal coordinate of the straight line of the belt edge exceeds the distance from the edge of the left or right belt frame when the belt normally runs is determined;
step 1.2, when the carrier belt is in a running state, determining the distance l between the center of the belt and the edge of the left or right belt frame1And l2The length of the large carrying belt is L, the width is W, then d1＝l1-W/2，d2＝l2-W/2。
The step 2 specifically comprises the following steps:
2.1, determining the installation positions of the intelligent cameras at three stations of the tail, the heavy hammer and the head of the large-scale carrier belt in the whole process of L meters based on the obtained normal operation interval of the belt edge abscissa;
step 2.2, based on the installation position of the intelligent camera, the intelligent camera collects a target imageEstablishing a coordinate system in the processed image, and calibrating the origin of coordinates to be the upper left position of the image; the position of the belt edge is marked in the image and the output distance is in units of pixel values s3Determining the actual transverse linear distance d from the edge of the belt in the running state to the installed intelligent camera3(ii) a By measuring multiple sets of data, the model is trained, and the corresponding relation between the pixel value of the distance in the image and the actual distance is obtained to be 25px at 1 cm.
The step 3 specifically comprises the following steps:
step 3.1, processing the collected carrier belt running image according to a Hough transform method in image processing to obtain a straight line of the belt edge, and calibrating the abscissa of the straight line of the belt edge in the image:
a straight line may be defined by two points a ═ x in a cartesian coordinate system1,y1) And B ═ x2,y2) Determining; let the linear equation be y ═ kx + q, which is converted into a functional expression for (k, q) under hough space
A straight line under the Cartesian coordinate system corresponds to a point in Hough space, if the points of the Cartesian coordinate system are collinear, the points intersect at one point on the straight line corresponding to the Hough space, and when the points where a plurality of straight lines intersect are also a plurality of points, a common processing mode after Hough transformation is adopted, namely, the points where as many straight lines as possible converge are selected; however, there are limitations in converting cartesian coordinates into hough space, which is not well described when k ∞, and there are infinite cases in the value of q; therefore, consider the conversion of a cartesian coordinate system to a polar coordinate system:
and (3) solving a straight line: the method comprises the following steps of refining into a coordinate form, accumulating coordinates corresponding to intersection points after rounding, finding a point with the maximum value, namely (rho, theta) to be solved finally, and solving a straight line; where ρ is the diameter of the straight line and θ is the polar angle.
Step 3.2, writing an image processing program according to the basic principle of Hough transform to obtain the linear abscissa of the belt edge, when p is1＜|s1-f1|≤p2Or p1＜|s2-f2|≤p2Corresponding to the actual running deviation distance of the carrier belt being c1～c2Judging as a secondary fault, namely moderate deviation; | s1-f1|＞p3Or | s2-f2|＞p3Corresponding to the actual deviation distance of the carrier belt being greater than c3Judging that the fault is a primary fault, namely serious deviation; and determining the deviation amount and the deviation fault grade of the belt.
Compared with the prior art, the invention has the following advantages:
1. the method is simple, easy to implement, free of manual intervention and capable of automatically detecting faults in real time.
2. The belt deviation detection speed is high and the precision is high.
3. The online real-time diagnosis of the belt deviation fault can be realized.
4. The method can be used for carrying out deviation fault diagnosis on a large-scale carrying belt operation system, finding out faults in time and providing reference for adjusting the deviation of the belt.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a Cartesian coordinate and Hough transform spatial polar coordinate transformation diagram;
FIG. 3 is a schematic view of a smart camera mounting location and a large carrier strip in accordance with an embodiment of the present invention;
FIG. 4 is a graph of the result of accumulated probability Hough transform image processing on a carrier belt according to an embodiment of the present invention;
fig. 5 is a deviation fault diagnosis interface of a large carrier belt according to an embodiment of the invention.
Detailed Description
The technical solution in the embodiments of the present invention is clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, an intelligent detection method for deviation fault of a large carrier belt is used for obtaining deviation amount and fault grade of a belt in a carrier belt motion control system, and the method comprises the following steps:
step 1, determining the distance between the belt edge and a belt frame when a carrier belt normally runs aiming at a large carrier belt running system: based on the characteristic that a large carrying belt has left and right parallel deviation, only the effective value d of the transverse distance between one edge of the belt and the belt frame when the carrying belt normally runs needs to be determined1And d2(ii) a The method comprises the following specific steps:
step 1.1, tracking the running state of the large carrying belt, recording related data, and analyzing to find that the large carrying belt has the characteristic of left-right parallel deviation; whether the belt deviates can be judged as long as whether the horizontal coordinate of the straight line of the belt edge exceeds the distance from the edge of the left or right belt frame when the belt normally runs is determined;
step 1.2, when the carrier belt is in a running state, determining the distance l between the center of the belt and the edge of the left or right belt frame1And l2The length of the large carrying belt is L, the width is W, then d1＝l1-W/2，d2＝l2-W/2。
2, based on the obtained distance between the belt edge and the belt frame when the belt normally runs, selecting a proper position to install an intelligent camera at a certain edge of the tail of the belt conveyor so as to better acquire a dynamic image of the running of the belt in real time; the image samples are collected for a plurality of times, the coordinate of the image is transformed, and the effective value d of the distance from the edge of the left or right belt frame when the belt normally runs1And d2Converting into corresponding pixel values in the image, and calibrating the abscissa pixel values to be f1And f2(ii) a The method comprises the following specific steps:
step 2.1, as shown in fig. 3, determining the installation positions of the intelligent cameras at the tail, the heavy hammer and the head of the large-scale carrier belt in the whole process of L meters based on the obtained normal operation interval of the belt edge abscissa;
step 2.2, based on the installation position of the intelligent camera, the intelligent camera collects a target imageEstablishing a coordinate system in the processed image, and calibrating the origin of coordinates to be the upper left position of the image; the position of the belt edge is marked in the image and the output distance is in units of pixel values s3Determining the actual transverse linear distance d from the edge of the belt in the running state to the installed intelligent camera3(ii) a By measuring multiple sets of data, the model is trained, and the corresponding relation between the pixel value of the distance in the image and the actual distance is obtained to be 25px at 1 cm.
Step 3, based on the video image collected by the intelligent camera, processing the image in real time by adopting a Hough transform method in image processing, and determining linear abscissa values s of two edges of the belt1And s2(by writing the relevant program code of image processing, burning in the intelligent camera, processing the collected video image in real time to obtain the distance between the carrier belt and two edges, the unit is pixel); if p is1＜|s1-f1|≤p2Or p1＜|s2-f2|≤p2Corresponding to the actual running deviation distance of the carrier belt being c1～c2Judging a secondary fault, namely moderate deviation; if s1-f1|＞p1Or | s2-f2|＞p2Corresponding to the actual deviation distance of the carrier belt being greater than c3Judging that the first-level fault is serious deviation; thereby determining the deviation amount and the deviation fault grade of the belt. The method comprises the following specific steps:
step 3.1, processing the collected carrier belt running image according to a Hough transform method in image processing to obtain a straight line of the belt edge, and calibrating the abscissa of the straight line of the belt edge in the image:
a straight line may be defined by two points a ═ x in a cartesian coordinate system1,y1) And B ═ x2,y2) Determining; let the linear equation be y ═ kx + q, which is converted into a functional expression for (k, q) under hough space
A straight line under the Cartesian coordinate system corresponds to a point in Hough space, if the points of the Cartesian coordinate system are collinear, the points intersect at one point on the straight line corresponding to the Hough space, and when the points where a plurality of straight lines intersect are also a plurality of points, a common processing mode after Hough transformation is adopted, namely, the points where as many straight lines as possible converge are selected; however, there are limitations in converting cartesian coordinates into hough space, which is not well described when k ∞, and there are infinite cases in the value of q; therefore, consider the conversion of a cartesian coordinate system to a polar coordinate system:
and (3) solving a straight line: the method comprises the following steps of refining into a coordinate form, accumulating coordinates corresponding to intersection points after rounding, finding a point with the maximum value, namely (rho, theta) to be solved finally, and solving a straight line; a cartesian coordinate and hough transform spatial polar coordinate transformation diagram in the present embodiment is shown in fig. 2.
Step 3.2, as shown in fig. 4, writing an image processing program according to the basic principle of Hough transform to obtain the linear abscissa of the belt edge, when p is1＜|s1-f1|≤p2Or p1＜|s2-f2|≤p2Corresponding to the actual running deviation distance of the carrier belt being c1～c2Judging as a secondary fault, namely moderate deviation; | s1-f1|＞p3Or | s2-f2|＞p3Corresponding to the actual deviation distance of the carrier belt being greater than c3Judging that the fault is a primary fault, namely serious deviation; and determining the deviation amount and the deviation fault grade of the belt. The off-tracking fault diagnosis interface of the large carrier belt in the embodiment is shown in fig. 5.
So far, the fault diagnosis of the deviation amount and the deviation grade of the large carrier belt is completed from step 1 to step 3.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (4)
1. An intelligent detection method for deviation fault of a large carrier belt is used for obtaining deviation and fault grade of the belt in a carrier belt motion control system, and is characterized by comprising the following steps:
step 1, determining the distance between the belt edge and a belt frame when a carrier belt normally runs aiming at a large carrier belt running system: based on the characteristic that a large carrying belt has left and right parallel deviation, only the effective value d of the transverse distance between one edge of the belt and the belt frame when the carrying belt normally runs needs to be determined1And d2；
2, based on the obtained distance between the belt edge and the belt frame when the belt normally runs, selecting a proper position to install an intelligent camera at a certain edge of the tail of the belt conveyor so as to better acquire a dynamic image of the running of the belt in real time; the image samples are collected for a plurality of times, the coordinate of the image is transformed, and the effective value d of the distance from the edge of the left or right belt frame when the belt normally runs1And d2Converting into corresponding pixel values in the image, and calibrating the abscissa pixel values to be f1And f2；
Step 3, based on the video image collected by the intelligent camera, processing the image in real time by adopting a Hough transform method in image processing, and determining linear abscissa values s of two edges of the belt1And s2(ii) a If p is1＜|s1-f1|≤p2Or p1＜|s2-f2|≤p2Corresponding to the actual running deviation distance of the carrier belt being c1～c2Judging a secondary fault, namely moderate deviation; if s1-f1|＞p1Or | s2-f2|＞p2Corresponding to the actual deviation distance of the carrier belt being greater than c3Judging that the first-level fault is serious deviation; thereby determining the deviation amount and the deviation fault grade of the belt; wherein p is1,p2Respectively are coordinate pixel threshold values of the belt deviation determined in advance.
2. The intelligent detection method for the deviation fault of the large carrier belt according to claim 1, wherein the step 1 specifically comprises the following steps:
step 1.1, tracking the running state of the large carrying belt, recording related data, and analyzing to find that the large carrying belt has the characteristic of left-right parallel deviation; whether the belt deviates can be judged as long as whether the horizontal coordinate of the straight line of the belt edge exceeds the distance from the edge of the left or right belt frame when the belt normally runs is determined;
step 1.2, when the carrier belt is in a running state, determining the distance l between the center of the belt and the edge of the left or right belt frame1And l2The length of the large carrying belt is L, the width is W, then d1＝l1-W/2，d2＝l2-W/2。
3. The intelligent detection method for the deviation fault of the large carrier belt according to claim 1, wherein the step 2 specifically comprises the following steps:
2.1, determining the installation positions of the intelligent cameras at three stations of the tail, the heavy hammer and the head of the large-scale carrier belt in the whole process of L meters based on the obtained normal operation interval of the belt edge abscissa;
2.2, based on the installation position of the intelligent camera, the intelligent camera collects a target image, a coordinate system is established in the processed image, and the origin of coordinates is calibrated to be the upper left position of the image; the position of the belt edge is marked in the image and the output distance is in units of pixel values s3Determining the actual transverse linear distance d from the edge of the belt in the running state to the installed intelligent camera3(ii) a By measuring multiple sets of data, the model is trained, and the corresponding relation between the pixel value of the distance in the image and the actual distance is obtained to be 25px at 1 cm.
4. The intelligent detection method for the deviation fault of the large carrier belt according to claim 1, wherein the step 3 specifically comprises the following steps:
step 3.1, processing the collected carrier belt running image according to a Hough transform method in image processing to obtain a straight line of the belt edge, and calibrating the abscissa of the straight line of the belt edge in the image:
a straight line may be defined by two points a ═ x in a cartesian coordinate system1,y1) And B ═ x2,y2) Determining; let the linear equation be y ═ kx + q, which is converted into a functional expression for (k, q) under hough space
A straight line under the Cartesian coordinate system corresponds to a point in Hough space, if the points of the Cartesian coordinate system are collinear, the points intersect at one point on the straight line corresponding to the Hough space, and when the points where a plurality of straight lines intersect are also a plurality of points, a common processing mode after Hough transformation is adopted, namely, the points where as many straight lines as possible converge are selected; however, there are limitations in converting cartesian coordinates into hough space, which are not well described when the slope k of a straight line is ∞, and there are infinite situations in the value of q; therefore, consider the conversion of a cartesian coordinate system to a polar coordinate system:
and (3) solving a straight line: the method comprises the following steps of refining into a coordinate form, accumulating coordinates corresponding to intersection points after rounding, finding a point with the maximum value, namely (rho, theta) to be solved finally, and solving a straight line; wherein rho is the polar diameter of a straight line, and theta is a polar angle;
step 3.2, writing an image processing program according to the basic principle of Hough transform to obtain the linear abscissa of the belt edge, when p is1＜|s1-f1|≤p2Or p1＜|s2-f2|≤p2Corresponding to the actual running deviation distance of the carrier belt being c1～c2Judging as a secondary fault, namely moderate deviation; | s1-f1|＞p3Or | s2-f2|＞p3Corresponding to the actual deviation distance of the carrier belt being greater than c3Judging that the fault is a primary fault, namely serious deviation; and determining the deviation amount and the deviation fault grade of the belt.
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Families Citing this family (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110490995B (en) * | 2019-08-26 | 2021-08-17 | 精英数智科技股份有限公司 | Method, system, equipment and storage medium for monitoring abnormal running state of belt |
| CN110902315B (en) * | 2019-12-10 | 2022-04-01 | 浙江蓝卓工业互联网信息技术有限公司 | Belt deviation state detection method and system |
| CN111325787A (en) * | 2020-02-09 | 2020-06-23 | 天津博宜特科技有限公司 | Mobile belt deviation and transportation amount detection method based on image processing |
| CN112027566B (en) * | 2020-09-30 | 2021-12-24 | 武汉科技大学 | Conveying belt deviation type judging and deviation measuring and calculating system based on laser scanning |
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| CN113112485A (en) * | 2021-04-20 | 2021-07-13 | 中冶赛迪重庆信息技术有限公司 | Belt conveyor deviation detection method, system, equipment and medium based on image processing |
| CN113378952A (en) * | 2021-06-22 | 2021-09-10 | 中冶赛迪重庆信息技术有限公司 | Method, system, medium and terminal for detecting deviation of belt conveyor |
| CN113401615A (en) * | 2021-06-29 | 2021-09-17 | 攀钢集团西昌钢钒有限公司 | Method and device for diagnosing belt fault of belt conveyor, electronic equipment and medium |
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| CN115082456A (en) * | 2022-07-27 | 2022-09-20 | 煤炭科学研究总院有限公司 | Coal mine belt conveyor fault diagnosis method and device |
| CN115557197A (en) * | 2022-09-28 | 2023-01-03 | 苏州中材建设有限公司 | Device and method for monitoring running track of long rubber belt conveyor |
| CN117800039A (en) * | 2024-02-23 | 2024-04-02 | 太原理工大学 | Belt deviation detecting system of belt conveyor |
| CN117830416A (en) * | 2024-03-05 | 2024-04-05 | 山西戴德测控技术股份有限公司 | Method, device, equipment and medium for positioning abnormal position of conveying belt |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0793535A (en) * | 1993-09-22 | 1995-04-07 | Fanuc Ltd | Picture correction processing method |
| CN102602681B (en) * | 2012-01-13 | 2014-01-08 | 天津工业大学 | Machine vision based online deviation fault detecting method for conveying belts |
| CN102673979B (en) * | 2012-06-12 | 2014-06-11 | 青岛科技大学 | Method and device for judging deviation of conveying belt |
| KR101500541B1 (en) * | 2013-08-14 | 2015-03-09 | 주식회사 포스코 | Appratus for controlling operation of belt conveyer and method thereof |
| CN104828517B (en) * | 2015-05-05 | 2017-03-29 | 中国矿业大学(北京) | The belt deviation detection method of view-based access control model |
| CN105083912B (en) * | 2015-07-07 | 2017-07-11 | 青岛科技大学 | A kind of belt deflection detection method based on image recognition |
| WO2017058557A1 (en) * | 2015-09-30 | 2017-04-06 | Contitech Transportbandsysteme Gmbh | Conveyor belt edge detection system |
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