CN109969736A - A kind of large size carrier strip deviation fault intelligent detecting method - Google Patents
A kind of large size carrier strip deviation fault intelligent detecting method Download PDFInfo
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- CN109969736A CN109969736A CN201910041877.8A CN201910041877A CN109969736A CN 109969736 A CN109969736 A CN 109969736A CN 201910041877 A CN201910041877 A CN 201910041877A CN 109969736 A CN109969736 A CN 109969736A
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- straight line
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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
-
- 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)
-
- 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
-
- 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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- Image Analysis (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The present invention relates to a kind of large-scale carrier strip deviation fault intelligent detecting method based on dynamic image, including, step 1, for large-scale carrier strip transportation system determines coordinate value when carrier strip operates normally from two edges；Step 2, the installation site for determining a certain belt edge smart camera.Virtual value and it is converted into pixel value in the picture with a distance from belt holder edge when belt is operated normally, calibration abscissa pixel value is respectivelyWith；Step 3 passes through image procossing, determines the abscissa value of belt two edges straight lineWith.IfOr, the practical sideslip distance of belt is c1~c2, it is determined as secondary failure；IfOr, the practical sideslip distance of belt is greater than c3, it is determined as level fault.Compared with prior art, the present invention has the advantages that intelligent recognition deviation fault, hands off, and accurately can be greater than 700) that rice is long, W(is greater than whether 1) the wide large-scale carrier strip of rice occurs deviation fault by automatic discrimination L(with three smart cameras.
Description
Technical field
The present invention relates to automatic industrial manufacturing line large size carrier strip device intelligence detection fields, more particularly, to one kind
Large-scale carrier strip deviation fault intelligent detecting method.
Background technique
Carrier strip is widely used in industrial circles such as coal production, metallurgy, by institute's transported material distribution above belt
Unevenly, longtime running causes carrier strip sideslip, increases belt abrasion, seriously affects the service life of belt, belt weight
Degree sideslip even will cause the pernicious failure such as belt tearing, affect the normal production of coal mine.
It is all based on the contaction measurement method of mechanical sensor greatly to the sideslip detection of belt at present, on the one hand detects
The position of physical equipment installation is fixed, and the physical unit of sideslip detection can damage after a period of operation because of collision, abrasion,
The accuracy decline of detection, stability are poor.On the other hand, only when belt deviation is bigger, device can just work,
Quantitative detection cannot be carried out by the running deviation value to belt in real time.Therefore, the research of belt deviation fault detection method problem is that have very much
It is worth and urgently to be resolved.
Machine vision technique has non-contact, and detection speed is fast, and detection accuracy is high, the objective reliable advantage of testing result,
Matching suitable intelligent measurement algorithm can rapidly and accurately detect whether carrier strip occurs deviation fault.Machine vision is very
More detection fields have application, also have in belt deviation detection field using precedent but are largely the installation to industrial camera
Device is designed, and deviation fault (such as patent CN207703158U) whether occurs according to acquisition image manual identified.Because
It requires manual intervention, carries out the sliding of pedestal to realize the coarse adjustment of industrial camera focal length, then pass through the precision of industrial camera itself
Rotation button is adjusted to focus.It acquires industry spot belt dynamic image and mismatches detection algorithm and image is handled, only
Be manually check image so that judge belt whether sideslip.This mode industrial camera has only played an acquisition image and monitoring
Effect, acquisition image is not handled in real time, and then detects belt deviation failure.Under normal circumstances, to dynamic image
The detection computation complexity for carrying out processing and moving object failure is higher, and time-consuming, is difficult to meet the news speed inspection of industrial production failure
It surveys, the target quickly excluded, especially in coal production line, carrier strip is most important to the transport of coal mine how
Realize fast and accurately to be production technician to whether carrier strip occurs sideslip detection in the case where being not necessarily to manual intervention
The expectation of many years.
Summary of the invention
The object of the invention is to provide a kind of large-scale carrier strip to overcome the problems of the above-mentioned prior art
Deviation fault intelligent detecting method, it is fast that this method detects speed than existing methods, and detection accuracy is high, hands off etc. excellent
Point.
In order to achieve the above objectives, insight of the invention is that
This method is installed to the station to be detected on coal mine material transportation production line using high-speed industrial smart camera, utilizes
Special light source illuminates the hypodermis zone face of belt station to be measured, carrier strip operation image information is acquired, to the image information of acquisition
Carry out online processing in real time.Key of the invention is the fast algorithm detected to belt deviation failure, the belt surface
The intelligent measurement algorithm that image is handled includes belt edge from the quick positioning of belt holder coordinate position, belt surface image
Extraction, detection of belt edge straight line of characteristic parameter etc..The smart camera is in ray image processing system to the Watch combined with leather belt
Fault message is exported after the image procossing of face, host computer interface is transferred to and carries out Dynamically Announce.The industrial camera be it is monochromatic or
Colored planar array scanning high-speed industrial camera, the industrial camera can be attached to existing coal mine material transportation production line or additional
It is detected on dedicated assembly line to belt failure, installation site is can be to the workshop section position that belt edge and belt holder image are conveniently taken pictures
It sets.The special light source is annular LED light source, provides illumination for the industrial camera.The industrial camera is located at described
The camera lens of the surface of special light source, the industrial camera is found a view by the annular centre of the special light source.It is described upper
Machine interface includes industrial computer and belt deviation malfunction monitoring software.
According to above-mentioned design, the present invention adopts the following technical scheme:
A kind of large size carrier strip deviation fault intelligent detecting method, for obtaining skin in carrier strip kinetic control system
The running deviation value and fault level of band, the method the following steps are included:
Step 1 is directed to large-scale carrier strip operating system, determines that belt edge is from belt holder when carrier strip operates normally
Distance: the parallel sideslip in left and right is had the characteristics that based on large-scale carrier strip, belt when need to only determine carrier strip normal operation
Lateral distance virtual value d of a certain edge far from belt holder1And d2；
Step 2, based on belt obtained operate normally when belt edge with a distance from belt holder, at belt machine end certain
One edge selects appropriate position to install smart camera, so as to the dynamic image that preferably acquisition belt is run in real time；By more
Secondary acquisition image pattern, is coordinately transformed image, has with a distance from left or right belt holder edge when belt is operated normally
Valid value d1And d2It is converted into corresponding pixel value in the picture, demarcating its abscissa pixel value is respectively f1And f2；
Step 3, based on smart camera acquisition video image, using the Hough transformation method in image procossing to image into
Row processing in real time, determines the straight line abscissa value s of belt two edges1And s2；If p1< | s1-f1|≤p2Or p1< | s2-f2|≤
p2, the corresponding practical sideslip distance of carrier strip is c1~c2 is determined as secondary failure, i.e. moderate sideslip；If | s1-f1| > p1Or |
s2-f2| > p2, the corresponding practical sideslip distance of carrier strip is greater than c3, it is determined as level fault, i.e., serious sideslip；So that it is determined that
The running deviation value and deviation fault grade of belt.Wherein p1,p2The coordinate pixel threshold of respectively predetermined belt deviation.
The step 1 specifically includes the following steps:
Step 1.1 tracks large-scale carrier strip operating status, records related data, and large-scale fortune is found after analysis
Carry the feature that belt has the parallel sideslip in left and right；As long as determine whether the abscissa of belt side straight line exceeds belt normal operation
It can judge whether belt occurs sideslip with a distance from left or right belt holder edge；
Step 1.2, when carrier strip is in operating status, determine Belt Centre with a distance from left or right belt holder edge
l1And l2, large-scale carrier strip length is L, width W, then d1=l1- W/2, d2=l2-W/2。
The step 2 specifically includes the following steps:
Step 2.1 operates normally section based on belt side obtained abscissa, determines the large size delivery at whole L meters
The installation site of belt machine end, three weight, head station smart cameras；
Step 2.2, the installation site based on camera, camera acquire target image, establish coordinate in handled image
System, calibration coordinate origin are the upper left side position of image；The position of belt edge is demarcated in the picture, and output parasang is picture
Plain value s3, the belt edge of operating status is measured to the practical lateral linear distance d of installed camera3；By measuring multiple groups number
According to training pattern, the corresponding relationship for obtaining the pixel value and actual range of distance in image is 1cm=25px.
The step 3 specifically includes the following steps:
Step 3.1, according to the Hough transformation method in image procossing, the carrier strip operation image of acquisition is handled
The straight line of belt edge is obtained, the abscissa of belt side straight line in uncalibrated image:
Straight line in cartesian coordinate system can be by two point A=(x1,y1) and B=(x2,y2) determine；If straight line side
Journey is y=kx+q, is converted the function expression under hough space about (k, q)
Straight line under cartesian coordinate system corresponds to a point in hough space, if the point of cartesian coordinate system
Collinearly, these points are met at a bit in the corresponding straight line of hough space, when the point of a plurality of straight line intersection is also multiple, using Hough
Common processing mode after transformation selects the point that multi straight converges into as far as possible；But cartesian coordinate is converted into hough space and deposits
It is bad description as k=∞ in limitation, and the value of q has unlimited a variety of situations；Accordingly, it is considered to which Descartes is sat
Mark system is converted to polar coordinate system:
The solution of straight line: being refined into coordinate form, and the corresponding coordinate of intersection point adds up after rounding, finds numerical value maximum
Point be exactly (ρ, the θ) finally to be solved, and then solved straight line；Wherein ρ is the polar diameter of straight line, and θ is polar angle.
Step 3.2 writes image processing program according to the basic principle of Hough transformation, obtains the horizontal seat of straight line on belt side
Mark, works as p1< | s1-f1|≤p2Or p1< | s2-f2|≤p2, the corresponding practical sideslip distance of carrier strip is c1~c2, is determined as two
Grade failure, i.e. moderate sideslip；|s1-f1| > p3Or | s2-f2| > p3, the corresponding practical sideslip distance of carrier strip is greater than c3, differentiation
For level fault, i.e., serious sideslip；Determine the running deviation value and deviation fault grade of belt.
Compared with prior art, the invention has the following advantages that
1, method is simple, it is easy to accomplish, do not need manual intervention, real time automatic detection failure.
2, belt deviation detection speed is fast and precision is high.
3, the real-time diagnosis of belt deviation On-line Fault can be achieved.
4, deviation fault diagnosis can be carried out to large-scale carrier strip operating system, finds failure in time, run for adjustment belt
Deviator provides reference
Detailed description of the invention
Fig. 1 is the flow chart of the method for the present invention；
Fig. 2 is cartesian coordinate and Hough transformation space polar coordinate transition diagram；
Fig. 3 is the smart camera installation site and large size carrier strip schematic diagram of the embodiment of the present invention；
Fig. 4 is the embodiment of the present invention to carrier strip progress accumulated probability Hough transformation processing result image figure；
Fig. 5 is the large-scale carrier strip deviation fault Diagnostics Interfaces of the embodiment of the present invention.
Specific embodiment
Technical scheme in the embodiment of the invention is clearly and completely described with reference to the accompanying drawing.
As shown in Figure 1, a kind of large size carrier strip deviation fault intelligent detecting method, for obtaining carrier strip movement control
The running deviation value and fault level of belt in system processed, the method the following steps are included:
Step 1 is directed to large-scale carrier strip operating system, determines that belt edge is from belt holder when carrier strip operates normally
Distance: the parallel sideslip in left and right is had the characteristics that based on large-scale carrier strip, belt when need to only determine carrier strip normal operation
Lateral distance virtual value d of a certain edge far from belt holder1And d2；Specific steps are as follows:
Step 1.1 tracks large-scale carrier strip operating status, records related data, and large-scale fortune is found after analysis
Carry the feature that belt has the parallel sideslip in left and right；As long as determine whether the abscissa of belt side straight line exceeds belt normal operation
It can judge whether belt occurs sideslip with a distance from left or right belt holder edge；
Step 1.2, when carrier strip is in operating status, determine Belt Centre with a distance from left or right belt holder edge
l1And l2, large-scale carrier strip length is L, width W, then d1=l1- W/2, d2=l2-W/2。
Step 2, based on belt obtained operate normally when belt edge with a distance from belt holder, at belt machine end certain
One edge selects appropriate position to install smart camera, so as to the dynamic image that preferably acquisition belt is run in real time；By more
Secondary acquisition image pattern, is coordinately transformed image, has with a distance from left or right belt holder edge when belt is operated normally
Valid value d1And d2It is converted into corresponding pixel value in the picture, demarcating its abscissa pixel value is respectively f1And f2；Specific steps
Are as follows:
Step 2.1, as shown in figure 3, based on belt side obtained abscissa operate normally section, determine L meters whole
Large-scale carrier strip tail, three weight, head station smart cameras installation site；
Step 2.2, the installation site based on camera, camera acquire target image, establish coordinate in handled image
System, calibration coordinate origin are the upper left side position of image；The position of belt edge is demarcated in the picture, and output parasang is picture
Plain value s3, the belt edge of operating status is measured to the practical lateral linear distance d of installed camera3；By measuring multiple groups number
According to training pattern, the corresponding relationship for obtaining the pixel value and actual range of distance in image is 1cm=25px.
Step 3, based on smart camera acquisition video image, using the Hough transformation method in image procossing to image into
Row processing in real time, determines the straight line abscissa value s of belt two edges1And s2(by writing the related program code of image procossing,
Burned smart camera obtains carrier strip at a distance from two edges to acquisition real-time video processing, and unit is pixel)；If
p1< | s1-f1|≤p2Or p1< | s2-f2|≤p2, the corresponding practical sideslip distance of carrier strip is c1~c2, is determined as second level event
Barrier, i.e. moderate sideslip；If | s1-f1| > p1Or | s2-f2| > p2, correspond to the practical sideslip distance of carrier strip and be greater than c3, be determined as
Level fault, i.e., serious sideslip；So that it is determined that the running deviation value and deviation fault grade of belt.Specific step is as follows:
Step 3.1, according to the Hough transformation method in image procossing, the carrier strip operation image of acquisition is handled
The straight line of belt edge is obtained, the abscissa of belt side straight line in uncalibrated image:
Straight line in cartesian coordinate system can be by two point A=(x1,y1) and B=(x2,y2) determine；If straight line side
Journey is y=kx+q, is converted the function expression under hough space about (k, q)
Straight line under cartesian coordinate system corresponds to a point in hough space, if the point of cartesian coordinate system
Collinearly, these points are met at a bit in the corresponding straight line of hough space, when the point of a plurality of straight line intersection is also multiple, using Hough
Common processing mode after transformation selects the point that multi straight converges into as far as possible；But cartesian coordinate is converted into hough space and deposits
It is bad description as k=∞ in limitation, and the value of q has unlimited a variety of situations；Accordingly, it is considered to which Descartes is sat
Mark system is converted to polar coordinate system:
The solution of straight line: being refined into coordinate form, and the corresponding coordinate of intersection point adds up after rounding, finds numerical value maximum
Point be exactly (ρ, the θ) finally to be solved, and then solved straight line；Cartesian coordinate and Hough transformation space in the present embodiment
Polar coordinates transition diagram is as shown in Figure 2.
Step 3.2, as shown in figure 4, writing image processing program according to the basic principle of Hough transformation, obtain belt side
Straight line abscissa, works as p1< | s1-f1|≤p2Or p1< | s2-f2|≤p2, the corresponding practical sideslip distance of carrier strip is c1~c2,
It is determined as secondary failure, i.e. moderate sideslip；|s1-f1| > p3Or | s2-f2| > p3, correspond to the practical sideslip distance of carrier strip and be greater than
C3 is determined as level fault, i.e., serious sideslip；Determine the running deviation value and deviation fault grade of belt.The medium-and-large-sized fortune of the present embodiment
It is as shown in Figure 5 to carry belt deviation fault diagnosis interface.
So far, the fault diagnosis for large-scale carrier strip running deviation value and sideslip grade is completed from step 1 to step 3.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can readily occur in various equivalent modifications or replace
It changes, these modifications or substitutions should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with right
It is required that protection scope subject to.
Claims (4)
1. a kind of large size carrier strip deviation fault intelligent detecting method, for obtaining belt in carrier strip kinetic control system
Running deviation value and fault level, which is characterized in that the method the following steps are included:
Step 1, for large-scale carrier strip operating system, determine belt edge when carrier strip operates normally from belt holder away from
From: the parallel sideslip in left and right is had the characteristics that based on large-scale carrier strip, need to only determine that belt is a certain when carrier strip operates normally
Lateral distance virtual value d of the edge far from belt holder1And d2；
Step 2, based on belt obtained operate normally when belt edge with a distance from belt holder, at belt machine end certain on one side
Edge selects appropriate position to install smart camera, so as to the dynamic image that preferably acquisition belt is run in real time；By repeatedly adopting
Collect image pattern, image is coordinately transformed, when belt is operated normally with a distance from left or right belt holder edge virtual value
d1And d2It is converted into corresponding pixel value in the picture, demarcating its abscissa pixel value is respectively f1And f2；
Step 3, the video image based on smart camera acquisition carry out image using the Hough transformation method in image procossing real
When handle, determine the straight line abscissa value s of belt two edges1And s2；If p1< | s1-f1|≤p2Or p1< | s2-f2|≤p2, right
Answering the practical sideslip distance of carrier strip is c1~c2, it is determined as secondary failure, i.e. moderate sideslip；If | s1-f1| > p1Or | s2-f2|
> p2, the corresponding practical sideslip distance of carrier strip is greater than c3, it is determined as level fault, i.e., serious sideslip；So that it is determined that belt
Running deviation value and deviation fault grade；Wherein p1,p2The coordinate pixel threshold of respectively predetermined belt deviation.
2. large size carrier strip deviation fault intelligent detecting method according to claim 1, which is characterized in that the step
1 specifically includes the following steps:
Step 1.1 tracks large-scale carrier strip operating status, records related data, and large-scale delivery skin is found after analysis
Band has the feature of the parallel sideslip in left and right；As long as determining whether the abscissa of belt side straight line exceeds when belt operates normally from a left side
Or the distance at right leather belt frame edge can judge whether belt occurs sideslip；
Step 1.2, when carrier strip is in operating status, determine Belt Centre from left or right belt holder edge distance l1With
l2, large-scale carrier strip length is L, width W, then d1=l1- W/2, d2=l2-W/2。
3. large size carrier strip deviation fault intelligent detecting method according to claim 1, which is characterized in that the step
2 specifically includes the following steps:
Step 2.1 operates normally section based on belt side obtained abscissa, determines the large-scale carrier strip at whole L meters
The installation site of tail, three weight, head station smart cameras；
Step 2.2, the installation site based on camera, camera acquire target image, coordinate system are established in handled image, mark
Position fixing origin is the upper left side position of image；The position of belt edge is demarcated in the picture, and output parasang is pixel value
s3, the belt edge of operating status is measured to the practical lateral linear distance d of installed camera3；By measuring multi-group data, instruction
Practice model, the corresponding relationship for obtaining the pixel value and actual range of distance in image is 1cm=25px.
4. large size carrier strip deviation fault intelligent detecting method according to claim 1, which is characterized in that the step
3 specifically includes the following steps:
Step 3.1, according to the Hough transformation method in image procossing, processing acquisition is carried out to the carrier strip operation image of acquisition
The straight line of belt edge, the abscissa of belt side straight line in uncalibrated image:
Straight line in cartesian coordinate system can be by two point A=(x1,y1) and B=(x2,y2) determine；If linear equation is y
=kx+q is converted the function expression under hough space about (k, q)
Straight line under cartesian coordinate system corresponds to a point in hough space, if the point of cartesian coordinate system is total
Line, these points are met at a bit in the corresponding straight line of hough space, when the point of a plurality of straight line intersection is also multiple, are become using Hough
Rear common processing mode is changed, that is, selects the point that multi straight converges into as far as possible；But cartesian coordinate is converted into hough space presence
Limitation is bad description as straight slope k=∞, and the value of q has unlimited a variety of situations；Accordingly, it is considered to by flute
Karr coordinate system is converted to polar coordinate system:
The solution of straight line: being refined into coordinate form, and the corresponding coordinate of intersection point adds up after rounding, finds the maximum point of numerical value
It is exactly (ρ, the θ) finally to be solved, and then has solved straight line；Wherein ρ is the polar diameter of straight line, and θ is polar angle；
Step 3.2 writes image processing program according to the basic principle of Hough transformation, obtains the straight line abscissa on belt side, works as p1
< | s1-f1|≤p2Or p1< | s2-f2|≤p2, the corresponding practical sideslip distance of carrier strip is c1~c2, it is determined as secondary failure,
That is moderate sideslip；|s1-f1| > p3Or | s2-f2| > p3, correspond to the practical sideslip distance of carrier strip and be greater than c3, be determined as level-one event
Barrier, i.e., serious sideslip；Determine the running deviation value and deviation fault grade of belt.
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| CN110490995A (en) * | 2019-08-26 | 2019-11-22 | 精英数智科技股份有限公司 | A kind of belt operating status method for monitoring abnormality, system, equipment and storage medium |
| CN110902315A (en) * | 2019-12-10 | 2020-03-24 | 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 |
| CN112027566A (en) * | 2020-09-30 | 2020-12-04 | 武汉科技大学 | Conveying belt deviation type judging and deviation measuring and calculating system based on laser scanning |
| CN112919050A (en) * | 2021-02-04 | 2021-06-08 | 华润电力技术研究院有限公司 | Conveyor belt monitoring method, device, equipment and computer readable storage medium |
| 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 |
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| CN113674302A (en) * | 2021-08-26 | 2021-11-19 | 中冶赛迪重庆信息技术有限公司 | Belt conveyor charge level deviation identification method and system, electronic equipment and medium |
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| CN114742864A (en) * | 2022-03-18 | 2022-07-12 | 国能网信科技(北京)有限公司 | Belt deviation detection method and device |
| 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 |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0793535A (en) * | 1993-09-22 | 1995-04-07 | Fanuc Ltd | Picture correction processing method |
| CN102602681A (en) * | 2012-01-13 | 2012-07-25 | 天津工业大学 | Machine vision based online deviation fault detecting method for conveying belts |
| CN102673979A (en) * | 2012-06-12 | 2012-09-19 | 青岛科技大学 | Method and device for judging deviation of conveying belt |
| KR20150019495A (en) * | 2013-08-14 | 2015-02-25 | 주식회사 포스코 | Appratus for controlling operation of belt conveyer and method thereof |
| CN104828517A (en) * | 2015-05-05 | 2015-08-12 | 中国矿业大学(北京) | Belt deviation detecting method based on visual sense |
| CN105083912A (en) * | 2015-07-07 | 2015-11-25 | 青岛科技大学 | Conveyer belt deviation detecting method based on image identification |
| WO2017058557A1 (en) * | 2015-09-30 | 2017-04-06 | Contitech Transportbandsysteme Gmbh | Conveyor belt edge detection system |
-
2019
- 2019-01-17 CN CN201910041877.8A patent/CN109969736B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0793535A (en) * | 1993-09-22 | 1995-04-07 | Fanuc Ltd | Picture correction processing method |
| CN102602681A (en) * | 2012-01-13 | 2012-07-25 | 天津工业大学 | Machine vision based online deviation fault detecting method for conveying belts |
| CN102673979A (en) * | 2012-06-12 | 2012-09-19 | 青岛科技大学 | Method and device for judging deviation of conveying belt |
| KR20150019495A (en) * | 2013-08-14 | 2015-02-25 | 주식회사 포스코 | Appratus for controlling operation of belt conveyer and method thereof |
| CN104828517A (en) * | 2015-05-05 | 2015-08-12 | 中国矿业大学(北京) | Belt deviation detecting method based on visual sense |
| CN105083912A (en) * | 2015-07-07 | 2015-11-25 | 青岛科技大学 | Conveyer belt deviation detecting method based on image identification |
| WO2017058557A1 (en) * | 2015-09-30 | 2017-04-06 | Contitech Transportbandsysteme Gmbh | Conveyor belt edge detection system |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110490995A (en) * | 2019-08-26 | 2019-11-22 | 精英数智科技股份有限公司 | A kind of belt operating status method for monitoring abnormality, system, equipment and storage medium |
| 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 |
| CN110902315A (en) * | 2019-12-10 | 2020-03-24 | 浙江蓝卓工业互联网信息技术有限公司 | Belt deviation state detection method and system |
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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 |
| CN113674302B (en) * | 2021-08-26 | 2024-03-05 | 中冶赛迪信息技术(重庆)有限公司 | Belt conveyor material level deviation identification method, system, electronic equipment and medium |
| CN113674302A (en) * | 2021-08-26 | 2021-11-19 | 中冶赛迪重庆信息技术有限公司 | Belt conveyor charge level deviation identification method and system, electronic equipment and medium |
| CN114419852A (en) * | 2021-12-27 | 2022-04-29 | 天地科技股份有限公司 | Offset judgment and grading early warning method and device for scraper conveyor |
| CN114419852B (en) * | 2021-12-27 | 2024-02-23 | 天地科技股份有限公司 | Scraper conveyor deviation judging and grading early warning method and device |
| CN114742864A (en) * | 2022-03-18 | 2022-07-12 | 国能网信科技(北京)有限公司 | Belt deviation detection method and device |
| 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 |
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