CN116424802B - Scraper conveyor chain working condition monitoring system and monitoring method - Google Patents
Scraper conveyor chain working condition monitoring system and monitoring method Download PDFInfo
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- CN116424802B CN116424802B CN202310060184.XA CN202310060184A CN116424802B CN 116424802 B CN116424802 B CN 116424802B CN 202310060184 A CN202310060184 A CN 202310060184A CN 116424802 B CN116424802 B CN 116424802B
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 96
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000012423 maintenance Methods 0.000 claims description 48
- 230000007547 defect Effects 0.000 claims description 37
- 238000007790 scraping Methods 0.000 claims description 34
- 238000001514 detection method Methods 0.000 claims description 16
- 238000012545 processing Methods 0.000 claims description 16
- 238000005299 abrasion Methods 0.000 claims description 14
- 230000005477 standard model Effects 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 238000011418 maintenance treatment Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 10
- 238000004458 analytical method Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 238000000605 extraction Methods 0.000 description 4
- 238000012806 monitoring device Methods 0.000 description 4
- 238000005452 bending Methods 0.000 description 3
- 230000000295 complement effect Effects 0.000 description 3
- 230000004927 fusion Effects 0.000 description 3
- 238000009434 installation Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000000926 separation method Methods 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 239000003245 coal Substances 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 241001391944 Commicarpus scandens Species 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000003638 chemical reducing agent Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000005336 cracking Methods 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
- 230000026676 system process Effects 0.000 description 1
- 238000013024 troubleshooting Methods 0.000 description 1
Classifications
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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
- B65G19/00—Conveyors comprising an impeller or a series of impellers carried by an endless traction element and arranged to move articles or materials over a supporting surface or underlying material, e.g. endless scraper conveyors
- B65G19/18—Details
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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
- B65G19/00—Conveyors comprising an impeller or a series of impellers carried by an endless traction element and arranged to move articles or materials over a supporting surface or underlying material, e.g. endless scraper conveyors
- B65G19/18—Details
- B65G19/20—Traction chains, ropes, or cables
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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
- B65G19/00—Conveyors comprising an impeller or a series of impellers carried by an endless traction element and arranged to move articles or materials over a supporting surface or underlying material, e.g. endless scraper conveyors
- B65G19/18—Details
- B65G19/22—Impellers, e.g. push-plates, scrapers; Guiding means therefor
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention discloses a scraper conveyor chain working condition monitoring system which comprises an explosion-proof computer, wherein the explosion-proof computer is respectively connected with a 2D camera and a 3D laser camera, the 2D camera and the 3D laser camera are arranged at the tail of a scraper conveyor and irradiate downwards, and a speed measuring device is arranged on the scraper conveyor. The invention also discloses a monitoring method of the monitoring system, and the invention can be used for carrying out real-time online monitoring on the chain working condition of the scraper conveyor and reducing the shutdown loss.
Description
Technical Field
The invention belongs to the technical field of automatic monitoring and detection of coal mine mechanical equipment, relates to a scraper conveyor chain working condition monitoring system, and further relates to a scraper conveyor chain working condition monitoring method.
Background
The scraper conveyor is used as one of important production and transportation equipment of a fully-mechanized mining face, and is mainly characterized in that a motor drives a speed reducer to reduce speed and then drives a chain wheel shaft group, the chain wheel shaft group drives a chain, and the chain drives a scraper to push coal in a middle groove for conveying type circulating operation. The scraper conveyor chain is often under high load and severe working condition, and the chain links are easy to break, crack, stretch pitch, wear and the like; the scraping plate is easy to generate the phenomena of horizontal inclination, fracture, abrasion and other faults; the scraper bolt is easy to loose, break, lose and the like. The faults and problems seriously affect the safe and reliable operation and production efficiency of the scraper conveyor, so that the current requirements on chain working condition monitoring are stronger and stronger, the chain working condition can be monitored in real time, the equipment faults or shutdown losses caused by untimely maintenance and detection are avoided, and meanwhile, the reliable operation of the equipment can be ensured by utilizing an intelligent monitoring means.
Although scraper conveyor broken chain monitoring devices exist in the market, most of the broken chain monitoring devices can be monitored after faults occur, the broken chain monitoring devices belong to post-shutdown protection, early warning of faults cannot be achieved, and the broken chain monitoring devices are maintained in advance.
Disclosure of Invention
The invention aims to provide a scraper conveyor chain working condition monitoring system which can monitor the scraper conveyor chain working condition on line in real time and reduce the shutdown loss.
The invention further provides a scraper conveyor chain working condition monitoring method.
The first technical scheme adopted by the invention is that the scraper conveyor chain working condition monitoring system comprises an explosion-proof computer, wherein the explosion-proof computer is respectively connected with a 2D camera and a 3D laser camera, the 2D camera and the 3D laser camera are arranged at the tail of the scraper conveyor and irradiate downwards, and a speed measuring device is arranged on the scraper conveyor.
The first technical scheme of the invention is characterized in that:
and a light supplementing lamp is arranged on the mounting rack of the 2D camera.
The number of 3D laser cameras is at least 1.
The speed measuring equipment is an encoder or a laser speed measuring instrument; the encoder is arranged on a driving shaft of the scraper conveyor, and the laser velocimeter is arranged at the tail of the scraper conveyor;
when the speed measuring equipment is an encoder, the tail of the scraper conveyor is also provided with a proximity switch.
The tail of the scraper conveyor is provided with an alarm lamp.
The second technical scheme adopted by the invention is that the scraper conveyor chain working condition monitoring method specifically comprises the following steps:
step 1, when a chain working condition monitoring system of a scraper conveyor is started for the first time, carrying out one-circle acquisition on standard characteristics of chains, scrapers and scraper screws in the scraper conveyor to obtain standard initial model data of related tested sample pieces, and constructing a standard model library in an autonomous learning system;
step 2, after 2D and 3D vector image information of a sample to be measured are acquired through a 2D camera and a 3D laser camera respectively, comparing the image information acquired in real time with related information in a standard model library in the autonomous learning system established in the step 1, and identifying defect characteristics of a chain, a scraping plate and a scraping plate screw; performing hierarchical control on the identified defect characteristics, storing the identified defect characteristics into a defect model library of an autonomous learning system, photographing, leaving a certificate, and storing all information into an explosion-proof computer; the method comprises the steps of performing conversion and processing in an explosion-proof computer, dividing images according to monitoring characteristics of chains, scrapers and scraper screws, displaying the images on a display screen of the explosion-proof computer, and displaying different grading early warning information;
and 3, performing corresponding maintenance treatment on the scraper conveyor by an operator according to the early warning level prompted on the explosion-proof computer.
The second technical scheme of the invention is characterized in that:
the defect characteristics of the chain, the scraping plate and the scraping plate screw identified in the step 2 are specifically as follows:
the pitch change of the chain, the diameter change of the chain links, the crack of the links, the fracture of the links, the abrasion loss of the links and the foreign matters are monitored;
the transverse inclination, fracture and abrasion loss of the scraping plate;
and (5) missing and loosening the scraper screw.
The process of performing the machine separation control and the grading early warning on each defect characteristic of the chain is as follows:
pitch change monitoring: measuring the diameter D1 of the chain flat ring, the diameter D2 of the vertical ring, the outline length L2 of the vertical ring and the spacing L3 of the chain flat ring by adopting a 3D laser camera, comparing the pitch value between the real leveling ring and the vertical ring with the standard pitch L by calculating through an explosion-proof computer, and comparing the pitch elongation delta L of the chain flat ring and the vertical ring with delta L0;
the grading early warning condition is as follows:
when ΔL < ΔL0, the system does not prompt, only providing a numerical reference;
when DeltaL is more than or equal to DeltaL 0, the system prompts that the alarm lamp turns red, and the machine can be stopped, but maintenance is carried out in the overhaul time;
diameter change monitoring of chain links: measuring the diameters D1 and D2 of the flat and vertical rings of the chain by using a 3D laser camera, comparing the diameters with the standards D1 and D2 to calculate the actual deformation delta D,
the grading early warning condition is as follows:
when ΔD < ΔD0, the system does not prompt, only providing a numerical reference;
when the delta D is more than or equal to delta D0, the system prompts that the alarm lamp turns red, and the machine can be stopped, but maintenance is carried out in the overhauling time;
crack monitoring of chain links: the 3D laser camera is used for measuring the width of the crack, the 2D camera is used for measuring the color difference of the crack and the chain ring to identify and judge, and the actually measured crack width b1mm is compared with the allowable crack width bmm:
the grading early warning condition is as follows:
b1 When the color is more than or equal to b, the system prompts that the alarm lamp turns red and stops for maintenance;
when b1 is larger than 3b, the system prompts that the alarm lamp turns red and the machine has to be stopped for maintenance;
fracture monitoring of chain links: measuring the size of the fracture break by a 3D laser camera, and measuring the color difference of the fracture break by a 2D camera to identify and judge;
the grading early warning condition is as follows:
when detecting the broken opening of the chain ring, the system prompts that the alarm lamp turns red and the machine has to be stopped for maintenance;
monitoring the abrasion loss of chain links: measuring the diameters D1 and D2 of the chain flat and vertical rings by a 3D laser camera, wherein the appearance length L2 of the vertical rings, the distance L3 of the chain flat and vertical rings, the appearance outline and the height information, obtaining the actual deformation quantity X of the chain ring, and comparing the actual deformation quantity X of the chain ring with the error allowable deformation quantity X0:
the grading early warning condition is as follows:
when X < X0, the system does not prompt, only provides a numerical reference;
when X is more than or equal to X0, the system prompts that the alarm lamp turns red, and the machine can be stopped, but maintenance is carried out in the overhauling time;
foreign matter monitoring on the chain: the foreign matter appearance outline and the color information are acquired by a 2D camera, and the actually measured foreign matter area S is compared with the foreign matter area S0 allowed by errors:
the grading early warning condition is as follows:
when S is less than S0, the system does not prompt and only provides numerical references;
when S is more than or equal to 10S0, the system prompts that the alarm lamp turns red, and the machine can be stopped, but maintenance is carried out in the overhauling time.
The process of performing the machine separation control and the grading early warning on each defect characteristic of the scraping plate is as follows:
horizontal inclination of the scraper blade: the 3D laser camera monitors the profile information of the scraping plates, the 2D camera recognizes and judges whether the distances between two adjacent scraping plates are parallel, the parallelism R of the scraping plates is obtained, and meanwhile, the two scraping plates are compared with a defect library set value R0 in real time:
the grading early warning condition is as follows:
when R < R0, the system does not prompt, and only provides numerical references;
when R is more than or equal to R0, the system prompts that the alarm lamp turns red, and the machine can be stopped, but maintenance is carried out in the overhauling time; when the power is more than 2 times, the machine must be stopped for maintenance;
fracture of the blade: detecting the outline and crack width information of the scraper by a 3D laser camera, and identifying the outline, missing parts and color information of the scraper by a 2D camera;
the early warning condition is as follows: when detecting that the scraping plate has a fracture part, the system prompts that the alarm lamp turns red and the scraping plate must be stopped for maintenance to check faults;
and (3) monitoring the abrasion loss of the scraping plate: measuring the information of the width B1 of the scraper head, the width B2 of the middle part of the scraper, the width B3 of the scraper chain nest and the height H1 by a 3D laser camera, processing by a system to generate a 3D vector color map, obtaining the actual deformation delta M of the profile of the scraper, and comparing the delta M with the error allowable deformation;
the grading early warning condition is as follows:
when ΔM < ΔM0, the system does not prompt, only providing a numerical reference;
when the delta M is more than or equal to delta M0, the system prompts that the alarm lamp turns red and maintenance is carried out in the overhaul time;
the width B3 and the height H1 of the scraper chain nest need to be alarmed as long as any value is smaller than the corresponding specified value B3 and the height H1, the system prompts that the alarm lamp turns red, and maintenance is carried out in the overhaul time.
The process of performing the machine separation control and the grading early warning on each defect characteristic of the scraper screw is as follows:
absence of scraper screw: the 3D laser camera detects the three-dimensional information of the appearance outline and the height of the scraper screw, and the 2D camera recognizes and judges the missing of the scraper screw by identifying the appearance, the missing part and the color information of the scraper screw.
The grading early warning condition is as follows:
when the number of the missing detection scraper screws is less than 35% of the number of the single scraper screws, the alarm lamp turns yellow, and maintenance is carried out in the overhauling time;
when the number of the missing detection scraper screws is greater than or equal to 40% of the number of the single scraper screws, the alarm lamp turns red, the machine can be stopped, and maintenance can be carried out in the overhaul time;
loosening of scraper screws: measuring the distance H2 between the outer plane of the scraper nut and the nut mounting surface by a 3D laser camera, and comparing the distance H2 with the standard specified tight distance H2, wherein the height difference delta H between the two is smaller than delta H0;
the grading early warning condition is as follows:
when Δh < Δh0, the system does not prompt, only provides a numerical reference;
when Δh is more than or equal to Δh0, the system prompts that the alarm lamp turns red and maintenance is carried out in the overhaul time.
The invention has the advantages that the combination mode of the 2D camera and the 3D laser camera is utilized, the recognition precision, the accuracy and the autonomous learning function of the system are improved, the defect model library construction is perfected, the adaptability is strong, the large slot width application can be realized, the diversified detection of multiple parameters, multiple targets, multiple dimensions and the like of the chain working condition is solved, the comprehensive and reasonable operation is realized, the predictability maintenance is realized, the equipment operation is effectively ensured, the safety accidents are reduced, and the automatic monitoring is realized.
Drawings
FIG. 1 is a schematic diagram of a scraper conveyor chain condition monitoring system of the present invention;
FIG. 2 is a schematic diagram of a 3D laser camera in the scraper conveyor chain condition monitoring system of the present invention;
FIG. 3 is a schematic diagram of a 2D camera in the scraper conveyor chain condition monitoring system of the present invention;
FIG. 4 is a schematic view of a chain, flight and flight screw monitoring defect in the method for monitoring chain conditions in a flight conveyor according to the present invention;
FIG. 5 is a schematic cross-sectional view of a scraper screw monitoring process in the method for monitoring the link conditions of the scraper conveyor of the present invention;
fig. 6 (a) to 6 (b) are schematic diagrams showing the state of foreign matters and scraper screws on a chain when the working condition of the scraper conveyor is monitored by a 2D camera according to the method for monitoring the working condition of the chain of the scraper conveyor;
fig. 7 (a) to 7 (c) are schematic diagrams of states of scraper screws when the scraper conveyor chain working condition monitoring method of the invention adopts a 3D laser camera to monitor the scraper conveyor working condition;
fig. 8 (a) to 8 (b) are schematic diagrams showing the state of cracks in the chain when the working condition of the scraper conveyor is monitored by using a 3D laser camera according to the method for monitoring the working condition of the scraper conveyor.
In the figure, 1. An explosion-proof computer;
2.3D laser camera, 201.3D laser camera post;
3.2D camera, 301.2D camera terminal, 302. Light supplement lamp;
4. the device comprises a proximity switch, a scraper screw, a chain, a scraper, an encoder and a scraper conveyor.
Detailed Description
The invention will be described in detail below with reference to the drawings and the detailed description.
According to the chain working condition monitoring system of the scraper conveyor, as shown in fig. 1, the 2D camera 3 and the 3D laser camera 2 are installed at the tail of the scraper conveyor 9 and irradiate downwards, and the outlines of the scraper chain 6, the scraper 7 and the scraper screw 5 are photographed and scanned; an encoder 8 is arranged on a driving shaft of the scraper conveyor 9, and the encoder 8 is used for collecting movement speed information of the scraper conveyor 9; a proximity switch 4 is also arranged at the tail of the scraper conveyor 9, and the proximity switch 4 is used for collecting position signals of the scraper 7.
As shown in fig. 2 and 3, the 2D camera 3 is connected with the terminal of the explosion-proof computer 1 through the 2D camera terminal 301 and the 3D laser camera 2 through the 3D laser camera terminal 201 by using a data line, the acquired data information is transmitted to the explosion-proof computer 1 through the data line, meanwhile, the encoder 8 and the proximity switch 4 which are arranged in the scraper conveyor also start to work, related information is also transmitted to the explosion-proof computer 1, and the light compensating lamp 302 in the 2D camera 3 is automatically turned on when the light is insufficient, so that the requirement of the camera for the light is ensured.
Then, chain working condition monitoring system software is installed in the explosion-proof computer 1, the chain working condition monitoring system software comprises a point cloud data processing system and an autonomous learning system, and the point cloud data processing system and the autonomous learning system process the information acquired by the received 2D camera 3 and the 3D laser camera 2 in a processing mode: and (3) carrying out algorithm fusion technologies such as edge extraction, surface fitting, gray level binarization, visual recognition and the like, so as to remove interference and noise data.
The autonomous learning system and the model library in the chain working condition monitoring system software acquire 2D and 3D vector image information of a tested sample, then learn and analyze the information, identify the defect characteristics of a chain, a scraper and a scraper screw, conduct hierarchical control on the defect characteristics (the hierarchical control is distinguished according to the severity degree of the defect, namely the grade of the defect is distinguished according to whether the defect can influence the normal operation of a scraper conveyor or not), and store the defect characteristics into the autonomous learning system defect model library for later fault diagnosis and monitoring. All information records and explosion-proof computer storage devices.
And then the images are divided into different functional display pictures according to the monitoring characteristics of the chains, the scraping plates and the scraping plate screws through conversion and processing, and different grading early warning information (the grading early warning is consistent with the grading management and control standard) is displayed. When a fault occurs, the installed encoder data can accurately report the space position of the fault point according to the position of the camera after being processed, so that the worker can conveniently find and maintain the fault point. Finally, the display module of the explosion-proof computer 1 is used for displaying, and the function of the communication module is utilized, so that related information can be transmitted to a ground monitoring center or remote monitoring and checking can be realized.
Step 1, when the system is used for the first time or matched with new equipment, the system is required to perform one-circle acquisition on the information characteristics of the whole scraper conveying chain 6, the scraper 7 and the scraper screw 5, obtain the standard initial model data of related tested sample pieces, and store the standard model data in the autonomous learning system for later comparison and defect model database data establishment.
Step 2, after 2D and 3D vector image information of a tested sample piece is acquired through a 2D camera 3 and a 3D laser camera 2 respectively, learning and analysis processing are carried out, and defect characteristics of a chain 6, a scraper 7 and a scraper screw 5 are identified; the 2D camera 3 collects a plane image (RGB color image), and the 3D laser camera 2 collects a height information image, and the 3D laser camera 2 does not distinguish the color of the sample to be measured; the system eliminates interference and noise data according to the data shot by different cameras and the initial calibration data of the proximity switch 4 (the initial calibration data is determined according to the installation positions of the 2D camera 3 and the 3D laser camera 2, namely, the initial position of each scraper 7 in an image) (which is convenient for dividing and capturing picture acquisition information), and utilizes the phase relation, edge extraction, curved surface fitting, gray level binarization and vision discrimination algorithm fusion technology to remove interference and noise data so as to form geometric data (geometric data, namely, length, width and height) and characteristic information of a measured sample, and then divides, calibrates and fits the data to generate a 2D gray level map and a 3D color point cloud map of corresponding vector information.
After the autonomous learning system and the model library in the chain working condition monitoring system software acquire the 2D and 3D vector image information of the tested sample, learning and analysis processing are carried out, and the defect characteristics of the chain, the scraping plate and the scraping plate screw are identified as follows:
(1) Pitch change, elongation, cracks, wear marks, breaks, wear on the chain 6;
(2) The transverse inclination and bending angle, fracture and partial missing and abrasion loss of the scraping plate 7;
(3) The scraper screw 5 is not lost and loosened.
Step 3, carrying out hierarchical control on the defect characteristics identified in the step 2, storing the defect characteristics into a defect model library of an autonomous learning system for photographing, leaving evidence and storing the defect characteristics into the defect model library for later fault diagnosis and monitoring, and recording all information into an explosion-proof computer 1; and then through conversion and processing, each characteristic of image segmentation monitoring is displayed on a main picture of the explosion-proof computer 1 according to the monitoring characteristics of the chain, the scraping plate and the scraping plate screw, and different grading early warning information is displayed.
The fault information is divided into three steps:
(1) In the fault information prompting stage, the prompting content of the system is checked and attended;
(2) Fault information early warning, preventive maintenance and inspection of system prompt information and the like;
(3) And (3) alarming faults, and carrying out even processing or direct shutdown maintenance and investigation on system prompt information.
And finally, the related information can be transmitted to a ground monitoring center or remote monitoring and checking can be realized through the communication module of the explosion-proof computer 1.
The specific judgment and early warning conditions of the defect monitoring process are as follows:
(1) Monitoring individual links of the chain 6 (relating to height, length, diameter information of the links, in mm); flat or vertical rings (links are generally vertical rings)
Monitoring items:
pitch change monitoring: the pitch elongation from standard chain 6 cannot exceed 8% (typically between 108-217 mm);
the method mainly comprises the steps of completing measurement by a 3D laser camera 2, measuring the flat ring diameter D1, the vertical ring diameter D2 and the L2-vertical ring outline length of a chain 6 and the L3-chain flat and vertical ring spacing as shown in fig. 4, and comparing a pitch value between the flat ring and the vertical ring with a standard pitch L through an explosion-proof computer 1 to obtain that the pitch elongation delta L of the flat ring and the vertical ring is smaller than the standard elongation delta L0 (generally between 6% and 8%);
early warning:
when ΔL < ΔL0, the system does not prompt, only providing a numerical reference;
when DeltaL approaches DeltaL 0, the system prompts that the alarm lamp turns yellow;
when DeltaL is more than or equal to DeltaL 0, the system prompts that the alarm lamp turns red, and the machine can be stopped, but maintenance is carried out in the overhaul time;
diameter change monitoring of the links of the chain 6: the diameter deformation amount is possible to be broken or not (the general diameter specification is between 32 and 60 mm);
the detection is mainly finished by a 3D laser camera 2, the diameters D1 and D2 of the flat and vertical rings of the chain are measured as shown in fig. 4, and the actual deformation delta D is calculated by comparing the diameters with the standards D1 and D2, wherein the diameter deformation change is smaller than delta D0 (generally defined as 1 mm);
early warning:
when ΔD < ΔD0, the system does not prompt, only providing a numerical reference;
when the delta D is close to delta D0, the system prompts that the alarm lamp turns yellow;
when the delta D is more than or equal to delta D0, the system prompts that the alarm lamp turns red, and the machine can be stopped, but maintenance is carried out in the overhauling time;
crack monitoring of the chain 6 links: monitoring cracks on the surface of the chain ring, identifying and prompting for alarm, wherein the width of the cracks is more than or equal to 0.3mm;
the detection is mainly completed by combining the 3D laser camera 2 and the 2D camera 3, the 3D laser camera 2 measures the width of the crack, the 2D camera 3 can measure the color difference between the crack and the chain ring to perform identification judgment, the crack of the chain flat and vertical rings is measured as shown in fig. 4, the two cameras play a complementary role according to the crack size, the error of single monitoring is avoided, the width of the actually measured crack b1 is smaller than bmm (the precision of 0.1mm is generally influenced by the actual working condition);
early warning:
b1 When the color is more than or equal to b, the system prompts that the alarm lamp turns red and stops for maintenance;
b1 is larger than 3b (3 times), the system prompts that the alarm lamp turns red and the machine has to be stopped for maintenance;
fracture monitoring of the links of the chain 6: monitoring the broken condition of the chain ring, and alarming (chain ring breaking);
the detection is mainly completed by combining a 3D camera and a 2D camera, the size of a fracture crack is measured by the 3D laser camera 2, the color difference of the fracture crack can be measured by the 2D camera 3 for identification and judgment, the size of the fracture crack of a flat and vertical ring of a chain is measured as shown in fig. 4, the complementary effect is exerted according to the size and the form of the fracture crack, and the error of single monitoring is avoided
Early warning: when detecting the broken opening of the chain ring, the system prompts that the alarm lamp turns red and the machine has to be stopped for maintenance;
wear monitoring of the links of the chain 6: monitoring the abrasion loss of the chain ring, and prompting replacement (monitoring the abrasion deformation of the chain ring);
the measurement is mainly completed by a 3D laser camera 2, as shown in fig. 4, the information such as the diameters D1 and D2 of a chain flat and a vertical ring, the appearance length of an L2-vertical ring, the L3-chain flat, the space between vertical rings, the appearance outline, the height and the like is measured, then the information is processed and restored into a 2D vector diagram and a 3D vector diagram by a system, and the actual deformation quantity X of the chain ring is obtained through comprehensive analysis, wherein the X is smaller than X0 (generally defined as 20-30 percent and can be independently formulated);
early warning:
when X < X0, the system does not prompt, only provides a numerical reference;
when X is close to X0, the system prompts that the alarm lamp turns yellow;
when X is more than or equal to X0, the system prompts that the alarm lamp turns red, and the machine can be stopped, but maintenance is carried out in the overhauling time;
and (3) positioning and judging the fault points of the chain rings: finding out the position of a fault pre-judging fault point and stopping (positioning the fault point, positioning accuracy, 0.5 meter level);
the system software is mainly used for receiving signals of the speed measuring encoder 8 arranged on the driving shaft of the scraper conveyor 9, and the encoder 8 and the driving shaft of the scraper conveyor 9 run synchronously, so that when a fault point is found out through calculation and analysis, the relative distance between the encoder 8 and the camera is real-time or in a stop state, and the fault is convenient to check.
Early warning: the system displays the relative distance between the fault point and the camera when the fault point is found, and the positioning accuracy of 0.5M can be realized.
Foreign matter monitoring on chain 6: monitoring whether the chain 6 is clamped by a foreign object (recognition of a special-shaped structure object);
the detection is mainly completed by the 2D camera 3, and by collecting the information of the outline, the color and the like of the foreign matters as shown in figure 4,
because the appearance of the foreign matter is greatly different from that of the chain 6 and the color of the object is clearly different, the system is used for processing to generate a gray image for identification and judgment, and the area S of the foreign matter obtained by comprehensive analysis is smaller than S0 (generally defined as 10 square centimeters and can be independently formulated);
early warning:
when S is less than S0, the system does not prompt and only provides numerical references;
when S is close to S0, the system prompts that the alarm lamp turns yellow;
when S is more than or equal to 10S0 (10 times, can be set), the system prompts that the alarm lamp turns red, and the maintenance is carried out in the overhaul time without stopping;
(2) Monitoring of the blade 7
And (3) length monitoring of the scraping plate 7: according to the different length of the matched equipment, the length is generally 730mm-1450 mm;
horizontal tilting of the scraper 7: the transverse inclination and the bending of the scraping plate 7 exceed 5 degrees to give an alarm;
the detection is mainly completed by combining the 3D laser camera 2 and the 2D camera 3, the 3D laser camera 2 monitors the information such as the outline of the scraping plate, the 2D camera 3 recognizes and judges whether the distance between two adjacent scraping plates is parallel, and the parallelism R of the scraping plate 7 is obtained by combining and analyzing the information by system software, and meanwhile, the information is compared with a defect library set value R0 (the bending degree exceeds 5-10 and can be self-determined) in real time.
Early warning:
when R < R0, the system does not prompt, and only provides numerical references;
when R is close to R0, the system prompts that the alarm lamp turns yellow;
when R is more than or equal to R0, the system prompts that the alarm lamp turns red, and the machine can be stopped, but maintenance is carried out in the overhauling time; when it is more than 2 times, maintenance must be stopped.
Cracking and breaking of the blade 7: blade fracture alarm (unilateral fracture);
the detection is mainly completed by combining the 3D laser camera 2 and the 2D camera 3, as shown in fig. 4, the 3D laser camera 2 detects the information such as the outline of the scraper 7, the width of the crack and the like, and the 2D camera 3 respectively identifies and judges the fracture, the crack and the like of the scraper by identifying the appearance, the missing part and the color information of the scraper, and plays a complementary role according to the size and the shape of the fracture crack, so that the error of single monitoring is avoided.
Early warning: as long as the scratch board 7 is detected to have a crack or a fracture break or a missing part, the system prompts that an alarm lamp on a display in the explosion-proof computer 1 turns red and the explosion-proof computer must be stopped for maintenance and troubleshooting;
wear amount monitoring of the blade 7: monitoring the abrasion loss of the scraper, and carrying out early warning (the scraper head and the middle plane) when the abrasion loss exceeds a specified value;
the method mainly comprises the steps of completing measurement by a 3D laser camera 2, measuring the information such as the width B1 of the scraper head, the width B2 of the middle part of the scraper, the width B3 of the scraper chain pit, the height H1 and the like as shown in fig. 4 and 5, processing by a system to generate a 3D vector color map, and comprehensively analyzing to obtain the actual deformation delta M of the scraper, wherein the actual deformation delta M is smaller than delta M0 (generally defined as 15-25% and can be independently formulated); sometimes, the width B3 and the height H1 of the scraping plate chain nest are directly judged, and when the width is smaller than the standard value, the alarm is directly given, and the time to be overhauled is maintained.
Early warning:
when ΔM < ΔM0, the system does not prompt, only providing a numerical reference;
when the delta M is more than or equal to delta M0, the system prompts that the alarm lamp turns red and maintenance is carried out in the overhaul time;
the width B3 and the height H1 of the scraper chain nest need to be alarmed as long as any value is smaller than the corresponding specified value B3 and the height H1, the system prompts that the alarm lamp turns yellow, and maintenance is carried out in the overhaul time;
(3) Monitoring of the scraper screw 5:
scraper screw 5: selecting M20-M36;
absence of the blade screw 5: monitoring whether the scraper screw 5 is missing or not (generally 3 groups of bolts and nuts);
the detection is mainly completed by combining the 3D laser camera 2 and the 2D camera 3, as shown in fig. 5, the 3D laser camera 2 detects three-dimensional information such as the outline, the height and the like of the scraper screw, and the 2D camera 3 judges the absence of the scraper screw by identifying the information such as the outline, the absence part, the color and the like of the scraper screw.
Early warning: when the number of the missing detection scraper screws 5 is less than 35% of the number of the single scraper screws, the alarm lamp turns yellow, and maintenance is carried out in the overhaul time;
when the number of the missing detection scraper screws is greater than or equal to 40% of the number of the single scraper screws, the alarm lamp turns red, the machine can be stopped, and maintenance can be carried out in the overhaul time;
loosening of the scraper screw 5: monitoring loosening of the scraper screw;
the detection is mainly completed by the 3D laser camera 2, as shown in FIG. 5, the distance H2 between the outer plane of the scraper nut and the nut mounting surface is measured, and compared with the standard specified close distance H2, the height difference delta H is smaller than delta H0 (generally 0.5mm and can be self-determined).
Early warning:
when Δh < Δh0, the system does not prompt, only provides a numerical reference;
when Δh is more than or equal to Δh0, the system prompts that the alarm lamp turns yellow and maintenance is carried out in the overhaul time;
the alarm mode is changed to a certain extent according to different loosening quantity of screws on the same scraper, and the on-site setting can be carried out;
note that: the scraper is subjected to omnibearing monitoring and 3D laser camera data acquisition, a three-dimensional picture is synthesized, real-time monitoring is performed, and once a problem is found, early warning and fault position pre-judging are performed, and healthy operation of equipment is maintained in real time.
In the figure, d 1-chain flat ring diameter; d 2-diameter of the chain standing ring; l1-chain flat ring pitch; l2-the overall length of the standing ring; l3-chain flat and vertical ring spacing; b1-the width of the blade head; b2-width of the middle part of the scraping plate; b3-scraper chain nest width; h1-height of the scraper; h2-scraper nut tightness height.
Fig. 6 (a) is a photograph of a missing foreign matter and a scraper screw on the chain 6 taken by the 2D camera 3 during the actual monitoring process; fig. 6 (b) is a picture displayed on the display of the explosion-proof computer 1 after converting the photograph taken in fig. 6 (a);
fig. 7 (a) is a schematic diagram showing a normal installation state of the blade screw 5 photographed by the 3D laser camera 2 displayed on the explosion-proof computer 1; fig. 7 (b) is a schematic diagram showing a missing state of the blade screw 5 photographed by the 3D laser camera 2 displayed on the explosion-proof computer 1; fig. 7 (c) is a view showing the effect of the missing scraper screw 5 shot by the 3D laser camera 2 displayed on the explosion-proof computer 1.
Fig. 8 (a) is a graph showing the effect of processing the crack of the chain 6 photographed by the 3D laser camera 2 displayed on the explosion-proof computer 1; fig. 8 (a) is an original photograph of a crack of the chain 6 taken by the 3D laser camera 2 displayed on the explosion-proof computer 1.
And 4, when a fault occurs, the number of pulses of the installed encoder 8 is processed, and then the space position of the fault point can be accurately reported according to the position of a camera and the like, so that the worker can conveniently find and maintain the fault point.
According to the scraper conveyor chain working condition monitoring system, a 2D camera and a 3D laser box are combined, a visual extraction and level space sensing technology is utilized, the profile and the size of a scraper chain, a scraper and a scraper screw are photographed, laser scanning is conducted, characteristic data and positioning information are extracted, and the obtained data information is transmitted to an explosion-proof computer through a data line. Then, the explosion-proof computer is loaded with chain working condition monitoring system software, and the point cloud data processing and autonomous learning system in the software processes the transmitted information. Secondly, the system eliminates interference and noise point data by utilizing algorithm fusion technologies such as phase relation, edge extraction, curved surface fitting, gray level binarization, visual discrimination and the like to form geometric data and characteristic information of a measured sample, and then segments, calibrates and fits the data to generate a 2D gray level map and a 3D color point cloud map of corresponding vector information.
In the scraper conveyor chain working condition monitoring system, the installation quantity of the 3D laser cameras 2 is changed due to the groove width of the scraper conveyor 9, and the groove width is 764nm and less than 1; the groove width is 800nm and above, and the number is 2.
Claims (3)
1. The monitoring method of the scraper conveyor chain working condition monitoring system is characterized by comprising the following steps of: the method specifically comprises the following steps:
step 1, when a chain working condition monitoring system of a scraper conveyor is started for the first time, carrying out one-circle running and acquisition on standard characteristics of a chain (6), a scraper (7) and a scraper screw (5) in the scraper conveyor (9), obtaining standard initial model data of related tested sample pieces, and constructing a standard model library in an autonomous learning system;
step 2, after 2D and 3D vector image information of a sample to be measured is acquired through a 2D camera (3) and a 3D laser camera (2), comparing the image information acquired in real time with relevant information in a standard model library in the autonomous learning system established in the step 1, and identifying defect characteristics of a chain (6), a scraper (7) and a scraper screw (5); classifying and controlling the identified defect characteristics, storing the identified defect characteristics into a defect model library of an autonomous learning system, photographing, reserving a certificate, and storing all information into an explosion-proof computer (1); the method comprises the steps of performing conversion and processing in an explosion-proof computer (1), dividing images according to monitoring characteristics of a chain (6), a scraper (7) and a scraper screw (5), displaying the images on a display screen of the explosion-proof computer (1), and displaying different grading early warning information;
the defect characteristics of the chain (6), the scraper (7) and the scraper screw (5) identified in the step 2 are specifically as follows:
the pitch change, the diameter change, the chain ring crack, the chain ring fracture, the chain ring abrasion loss and the foreign matters of the chain (6) are monitored;
monitoring the transverse inclination, fracture and abrasion loss of the scraping plate (7);
monitoring the missing and loosening of the scraper screw (5);
the process for carrying out hierarchical control and hierarchical early warning on each defect characteristic of the chain (6) comprises the following steps:
pitch change monitoring: measuring the diameter D1 of a flat ring, the diameter D2 of a vertical ring, the outline length L2 of the vertical ring and the spacing L3 of the vertical ring of the chain (6) by adopting a 3D laser camera (2), calculating a pitch value between the real leveling and the vertical ring by an explosion-proof computer (1) and comparing the pitch value with a standard pitch L, and comparing the pitch elongation delta L of the horizontal and vertical rings of the chain with the standard elongation delta L0;
the grading early warning condition is as follows:
when ΔL < ΔL0, the system does not prompt, only providing a numerical reference;
when DeltaL is more than or equal to DeltaL 0, the system prompts that the alarm lamp turns red, and the machine can be stopped, but maintenance is carried out in the overhaul time;
diameter change monitoring of chain (6) links: measuring the diameters D1 and D2 of the chain flat and vertical rings by using a 3D laser camera (2), and comparing the diameters with the standards D1 and D2 to calculate the actual deformation delta D;
the grading early warning condition is as follows:
when ΔD < ΔD0, the system does not prompt, only providing a numerical reference;
when the delta D is more than or equal to delta D0, the system prompts that the alarm lamp turns red, and the machine can be stopped, but maintenance is carried out in the overhauling time;
crack monitoring of chain (6) links: the 3D laser camera (2) is used for measuring the width of the crack, the 2D camera (3) is used for measuring the color difference of the crack and the chain ring for identification and judgment, and the actually measured crack width b1 is compared with the allowable crack width b:
the grading early warning condition is as follows:
b1 When the color is more than or equal to b, the system prompts that the alarm lamp turns red and stops for maintenance;
when b1 is larger than 3b, the system prompts that the alarm lamp turns red and the machine has to be stopped for maintenance;
fracture monitoring of chain (6) links: measuring the size of the fracture crack by a 3D laser camera (2), and measuring the color difference of the fracture crack by a 2D camera (3) to perform identification judgment;
the grading early warning condition is as follows:
when detecting the broken opening of the chain ring, the system prompts that the alarm lamp turns red and the machine has to be stopped for maintenance;
monitoring the abrasion loss of a chain (6) and a link of a chain: the 3D laser camera (2) is used for measuring the diameters D1 and D2 of the flat and vertical rings of the chain (6), the appearance length L2 of the vertical rings, the appearance outline and the height information of the chain, obtaining the actual deformation quantity X of the chain ring, and comparing the actual deformation quantity X of the chain ring with the error allowable deformation quantity X0:
the grading early warning condition is as follows:
when X < X0, the system does not prompt, only provides a numerical reference;
when X is more than or equal to X0, the system prompts that the alarm lamp turns red, and the machine can be stopped, but maintenance is carried out in the overhauling time;
foreign matter monitoring on chain (6): the foreign matter outline and the color information are acquired by a 2D camera (3), and the actually measured foreign matter area S is compared with the foreign matter area S0 allowed by the error:
the grading early warning condition is as follows:
when S is less than S0, the system does not prompt and only provides numerical references;
when S is more than or equal to 10S0, the system prompts that the alarm lamp turns red, and the system can not stop the machine, but maintains the machine in the overhaul time;
and 3, performing corresponding maintenance treatment on the scraper conveyor (9) by an operator according to the early warning level prompted on the explosion-proof computer (1).
2. The method for monitoring the link condition monitoring system of the scraper conveyor according to claim 1, wherein: the process of carrying out hierarchical control and hierarchical early warning on each defect characteristic of the scraping plate (7) comprises the following steps:
transverse inclination of the scraping plate (7): the profile information of the scrapers is monitored by a 3D laser camera (2), whether the distances between two adjacent scrapers are parallel or not is judged by the 2D camera (3), the parallelism R of the scrapers (7) is obtained, and meanwhile, the two scrapers are compared with a defect library set value R0 in real time:
the grading early warning condition is as follows:
when R < R0, the system does not prompt, and only provides numerical references;
when R is more than or equal to R0, the system prompts that the alarm lamp turns red, and the machine can be stopped, but maintenance is carried out in the overhauling time; when the power is more than 2 times, the machine must be stopped for maintenance;
fracture of the scraper (7): detecting the outline and crack width information of the scraping plate (7) by a 3D laser camera (2), and identifying the shape, missing parts and color information of the scraping plate by the 2D camera (3);
the early warning condition is as follows: as long as the broken part of the scraper (7) is detected, the system prompts that the alarm lamp turns red and the machine must be stopped for maintenance to check faults;
and (3) monitoring the abrasion loss of the scraping plate (7): measuring the information of the head width B1, the middle width B2 and the width B3 and the height H1 of the scraper (7) and the scraper chain nest by a 3D laser camera (2), processing by a system to generate a 3D vector color map, obtaining the actual deformation delta M of the profile of the scraper, and comparing the delta M with the error allowable deformation delta M0;
the grading early warning condition is as follows:
when ΔM < ΔM0, the system does not prompt, only providing a numerical reference;
when the delta M is more than or equal to delta M0, the system prompts that the alarm lamp turns red and maintenance is carried out in the overhaul time;
the width B3 and the height H1 of the scraper chain nest need to be alarmed as long as any value is smaller than the corresponding specified value B3 and the height H1, the system prompts that the alarm lamp turns red, and maintenance is carried out in the overhaul time.
3. The method for monitoring the link condition monitoring system of the scraper conveyor according to claim 1, wherein: the process of carrying out hierarchical control and hierarchical early warning on each defect characteristic of the scraper screw (5) comprises the following steps:
absence of the scraper screw (5): detecting three-dimensional information of the appearance outline and the height of the scraper screw by a 3D laser camera (2), and identifying and judging the absence of the scraper screw by the 2D camera (3) according to the appearance, the absence part and the color information of the scraper screw;
the grading early warning condition is as follows:
when the number of missing scraper screws (5) is detected to be less than 35% of the number of single scraper screws, the alarm lamp turns yellow, and maintenance is carried out in the overhaul time;
when the number of the missing detection scraper screws is greater than or equal to 40% of the number of the single scraper screws, the alarm lamp turns red, the machine can be stopped, and maintenance can be carried out in the overhaul time;
loosening of the scraper screw (5): measuring the distance H2 between the outer plane of the scraper nut and the nut mounting surface by a 3D laser camera (2), and comparing the distance H2 with a standard specified tight distance, wherein the height difference delta H between the outer plane of the scraper nut and the nut mounting surface is smaller than delta H0;
the grading early warning condition is as follows:
when Δh < Δh0, the system does not prompt, only provides a numerical reference;
when Δh is more than or equal to Δh0, the system prompts that the alarm lamp turns red and maintenance is carried out in the overhaul time.
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