CN104535356A - Method and system for monitoring rope arrangement faults of drum steel wire rope on basis of machine vision - Google Patents

Method and system for monitoring rope arrangement faults of drum steel wire rope on basis of machine vision Download PDF

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Publication number
CN104535356A
CN104535356A CN201510025651.0A CN201510025651A CN104535356A CN 104535356 A CN104535356 A CN 104535356A CN 201510025651 A CN201510025651 A CN 201510025651A CN 104535356 A CN104535356 A CN 104535356A
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rope
row
image
steel wire
industrial camera
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CN104535356B (en
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谭建平
吴志鹏
刘溯奇
薛少华
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Central South University
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Central South University
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Abstract

The invention discloses a method and system for monitoring rope arrangement faults of a drum steel wire rope on the basis of machine vision. An industrial camera is arranged on the back surface of a drum, the automatic calibration of internal parameters of a vision system is finished by sampling static images of a designated marker, the industrial camera is used for performing continuous image sampling on the steel wire rope arranged on the drum, and the horizontal coordinates of the center point of the steel wire rope just wound on the drum in an image coordinate system are acquired by virtue of a corresponding image processing algorithm; according to the parameters obtained by the automatic calibration, the horizontal coordinates are converted into corresponding position coordinates in an actual coordinate system, so that the center distance between the steel wire rope just wound on the drum and an adjacent steel wire rope is calculated; and whether a fault happens is judged according to the center distance, and a fault signal is transmitted to a monitoring center to give an alarm. Whether the drum steel wire rope has rope arrangement faults is quantitatively judged by virtue of the industrial camera, the data processing and error analysis are simple and convenient, the reliability is high, and the practicability is strong.

Description

A kind of row of the reel steel wire ropes based on machine vision rope fault monitoring method and system
Technical field
The present invention establishes and relates to industry spot reel steel wire ropes row rope On-line Fault real time monitoring, the specifically a kind of row of the reel steel wire ropes based on machine vision rope fault monitoring method and system.
Background technology
Mine hoisting equipment connects ground and underground " throat equipment ", is responsible for the task of lifting coal, ore, lower putting material, lifts personnel and equipment.What require along with scientific and technological progress and shaft production modernization improves constantly, and hoisting device also progressively introduces new technology, particularly contributes to the technology of lifting means security of operation and raising mine hoisting equipment informatization level, is progressively applied.
Except mine hoist, Large Towers machine, winch, rig etc. are all utilize driven by motor spool turns and promote or transfer weight by wire rope, in the middle of lifting process, along with lifting the increase of the degree of depth and the increase of pulling speed or multiple wraps time, reel steel wire ropes occurs that the probability of row's rope fault increases, as got rid of not in time, disorder cable can make mutually to squeeze folder major injury wire rope between wire rope, rope service-life is shortened, need frequent more change steel rope, bring larger waste, affect project progress, also there is great potential safety hazard simultaneously.So detect in real time reel row rope, fix a breakdown in time and can bring larger economic benefit.
Occur that the solution of row's rope fault is substantially by manual observation for this wire rope at present, lack objectivity, intelligence degree is inadequate.
Machine vision be research computing machine to simulate the science and technology of biological vision, the primary goal of Vision Builder for Automated Inspection is by image creation or recovers Real-world model, is then familiar with real world.Machine vision is a quite new and development research field very rapidly, and one of important research field becoming computer science.In recent decades, along with computing machine, industrial camera, and the lifting of hardware performance that the vision system such as light source is correlated with, and image processing algorithm is constantly perfect, makes the measuring technique precision based on machine vision higher, antijamming capability is stronger, better reliability.And the advantage that utilize the non-contact detecting of vision system in manufacturing industry, the speed that shows is fast, precision is suitable, on-the-spot antijamming capability is strong etc. is given prominence to, makes machine vision technique be widely used, achieves huge economic and social profit.
Summary of the invention
The present invention need solve problem be, in conjunction with the application of current machine vision in industrial detection, the technical deficiency part of fault of restricting for original monitoring mine lifting and reel steel wire ropes row, propose a kind of reel steel wire ropes based on machine vision row rope fault monitoring method and system, according to whether producing to make to fault to the quantitative detection of wire rope center judge qualitatively, to provide fault ground Real-Time Monitoring.
Based on a reel steel wire ropes row rope fault monitoring method for machine vision, comprise the following steps:
Step 1: industrial camera is fixed on the reel back side, makes the imaging region of industrial camera cover the row of reel steel wire ropes and to restrict region;
Step 2: demarcate industrial camera, obtains the actual range calibrating parameters representated by industrial camera unit picture element;
Step 3: rotating drum, utilizes industrial camera according to the time interval of setting, and continuous acquisition is in the reel steel wire ropes row rope image of row's rope form state;
Reel steel wire ropes often discharges a circle, and industrial camera then obtains a two field picture;
With the true origin that the lower left corner of image is image;
Step 4: successively pre-service is carried out to each frame reel steel wire ropes row rope image, and edge segmentation is carried out to pretreated image, extract the extreme coordinates of two parallel edge straight-line segments outside wire rope movable end, and solve the coordinate of wire rope central pixel point corresponding to described two parallel edge straight-line segments;
Step 5: if current frame image is first, then return step 4, otherwise, enter step 6;
Step 6: if the slope of two parallel edge straight-line segments outside the wire rope movable end obtained in the slope of two parallel edge straight-line segments in current frame image outside wire rope movable end and previous frame image differs by more than the slope threshold value of setting, then enter step 9; Otherwise, enter step 7;
Step 7: the distance between the coordinate of the wire rope central pixel point utilizing Euclidean distance equations present frame to obtain and the coordinate of former frame wire rope central pixel point, and the calibrating parameters utilizing step 2 to obtain, try to achieve the distance s between two adjacent wire rope centers;
Namely the wire rope center pixel point coordinate of wire rope center pixel point coordinate and the previous frame image being just entangled to reel in every two field picture is obtained;
Step 8: utilize the distance s between two adjacent wire rope centers, judges whether row's rope fault occurs;
If s meets formula 0<s-d< σ, then there is not row's rope fault; Otherwise, then think and row's rope fault occur;
D represents wirerope diameter, and σ is the safe wire cable threshold value of setting;
Step 9: if there is row's rope fault, then trigger alarm signals.
Described each frame reel steel wire ropes row rope image is carried out pre-service and comprised, first to the smoothing denoising of image, then, then to the Image Segmentation Using after smoothing denoising, generate bianry image.
The demarcation of described industrial camera, gather by adopting the image of industrial camera to the chessboard mark of stationary state, according to the ratio of the distance between known chessboard angle point with the distance of corresponding chessboard angle point on the image gathered, obtain the actual range representated by industrial camera unit picture element, complete the automatic Calibration of industrial camera parameter.
Actual range representated by described industrial camera unit picture element is less than or equal to 0.01m.
Probability Hough transform method is adopted to extract two, edge parallel edge straight-line segment in described step 4.
A kind of row of the reel steel wire ropes based on machine vision rope fault monitoring system, based on the described a kind of row of the reel steel wire ropes based on machine vision rope fault monitoring method, comprise the industrial camera, data interface unit, image pre-processing unit, image characteristics extraction unit, breakdown judge unit, Surveillance center's unit and the alarm unit that are connected successively;
The row of described industrial camera collection restricts after area image is sent to image pre-processing unit by data interface unit, characteristic is extracted through image characteristics extraction unit, fault-signal is exported to Surveillance center after characteristic being carried out analyzing and processing by breakdown judge unit, Surveillance center's record and display fault-signal, carry alarm unit, trigger alarm simultaneously.
Beneficial effect
The invention provides a kind of lifting drum wire rope winding fault monitoring method based on machine vision and system, merged machine vision and Precision Inspection, relative to prior art, the present invention has following useful technology and economic effect:
1) judgement to row's rope fault can be completed by single industrial camera collection wire rope picture, decrease the link that may produce error in test process;
2) detection mode is that non-contact vision is measured, and reduces the requirement of test macro to mechanical references precision;
3) this method image acquisition element is general industry camera, requires low, relative inexpensiveness to environment for use;
4) this method is simple to the mathematical model of fault verification, good reliability;
5) this method is detected row's rope fault in real time by machine vision, eliminates the potential safety hazard brought due to row's rope fault in mine hoisting process.
Accompanying drawing explanation
Fig. 1 is the structural representation of the invention process reel steel wire ropes row rope fault monitoring system;
Fig. 2 is the processing flow chart of the method for the invention;
Fig. 3 is the transfer process schematic diagram of image coordinate coordinate system of the present invention and actual coordinates;
Fig. 4 is wire rope centre distance Computing Principle schematic diagram of the present invention;
Fig. 5 is row's rope fault schematic diagram, and wherein, (a) is rope skipping, and (b) is folder rope;
Fig. 6 is industrial camera scheme of installation;
Fig. 7 is the chessboard mark schematic diagram that the present invention uses.
Embodiment
Below in conjunction with drawings and Examples, the present invention is described further.
As shown in Figure 2, a kind of row of the reel steel wire ropes based on machine vision rope fault monitoring method, comprises the following steps:
Step 1: industrial camera 8 is fixed on reel 9 back side, reel is fixed in mounting seat, as shown in Figure 6, makes the imaging region of industrial camera cover the row of reel steel wire ropes and to restrict region;
Step 2: demarcate industrial camera, obtains the actual range calibrating parameters representated by industrial camera unit picture element;
System, from the pixel coordinate extracted to calculating actual centre distance, need be set up plane of delineation coordinate system and actual coordinates, as shown in Figure 3, solving geometrical-restriction relation corresponding to two coordinates by the automatic Calibration of camera parameter.
Step 3: rotating drum, utilizes industrial camera according to the time interval of setting, and continuous acquisition is in the reel steel wire ropes row rope image of row's rope form state;
Reel steel wire ropes often discharges a circle, and industrial camera then obtains a two field picture;
With the true origin that the lower left corner of image is image;
Step 4: successively pre-service is carried out to each frame reel steel wire ropes row rope image, and edge segmentation is carried out to pretreated image, extract the extreme coordinates of two parallel edge straight-line segments outside wire rope movable end, and solve the coordinate of wire rope 7 central pixel point corresponding to described two parallel edge straight-line segments;
Step 5: if current frame image is first, then return step 4, otherwise, enter step 6;
Step 6: if the slope of two parallel edge straight-line segments outside the wire rope movable end obtained in the slope of two parallel edge straight-line segments in current frame image outside wire rope movable end and previous frame image differs by more than the slope threshold value of setting, then enter step 9; Otherwise, enter step 7;
Step 7: the distance between the coordinate of the wire rope central pixel point utilizing Euclidean distance equations present frame to obtain and the coordinate of former frame wire rope central pixel point, and the calibrating parameters utilizing step 2 to obtain, try to achieve the distance s between two adjacent wire rope centers;
Namely the wire rope center pixel point coordinate of wire rope center pixel point coordinate and the previous frame image being just entangled to reel in every two field picture is obtained;
Step 8: utilize the distance s between two adjacent wire rope centers, judges whether row's rope fault occurs;
If s meets formula 0<s-d< σ, then there is not row's rope fault; Otherwise, then think and row's rope fault occur;
D represents wirerope diameter, and σ is the safe wire cable threshold value of setting;
Step 9: if there is row's rope fault, then trigger alarm signals.
If there is rope skipping fault, then figure (a) as shown in Figure 5, if there is folder rope fault, then figure (b) as shown in Figure 5.
As shown in Figure 4, extract the center position coordinates of the wire rope be just entangled to, and store, be designated as B, suppose that its coordinate is (x i+1, y i+1), the wire rope central point be adjacent is designated as A, and its position coordinates is (x i, y i), in fault verification module, the Euclidean distance solved between pixel is: [(x i+1-x i) 2+ (y i+1-y t) 2] 1/2, be converted into the distance in actual coordinates according to calibrating parameters, when gained centre distance is beyond set threshold range, produces fault-signal, and be delivered to supervisory system and report to the police.
Described each frame reel steel wire ropes row rope image is carried out pre-service and comprised, first to the smoothing denoising of image, then, then to the Image Segmentation Using after smoothing denoising, generate bianry image.
The demarcation of described industrial camera, gather by adopting the image of industrial camera to the chessboard mark of stationary state, as shown in Figure 7, according to the ratio of the distance between known chessboard angle point with the distance of corresponding chessboard angle point on the image gathered, obtain the actual range representated by industrial camera unit picture element, complete the automatic Calibration of industrial camera parameter.
Actual range representated by described industrial camera unit picture element is less than or equal to 0.01m.
Probability Hough transform method is adopted to extract two, edge parallel edge straight-line segment in described step 4.
As shown in Figure 1, a kind of row of the reel steel wire ropes based on machine vision rope fault monitoring system, based on the described a kind of row of the reel steel wire ropes based on machine vision rope fault monitoring method, comprise the industrial camera, data interface unit, image pre-processing unit, image characteristics extraction unit, breakdown judge unit, Surveillance center's unit and the alarm unit that are connected successively;
The row of described industrial camera collection restricts after area image is sent to image pre-processing unit by data interface unit, characteristic is extracted through image characteristics extraction unit, fault-signal is exported to Surveillance center after characteristic being carried out analyzing and processing by breakdown judge unit, Surveillance center's record and display fault-signal, carry alarm unit, trigger alarm simultaneously.

Claims (6)

1., based on a reel steel wire ropes row rope fault monitoring method for machine vision, it is characterized in that, comprise the following steps:
Step 1: industrial camera is fixed on the reel back side, makes the imaging region of industrial camera cover the row of reel steel wire ropes and to restrict region;
Step 2: demarcate industrial camera, obtains the actual range calibrating parameters representated by industrial camera unit picture element;
Step 3: rotating drum, utilizes industrial camera according to the time interval of setting, and continuous acquisition is in the reel steel wire ropes row rope image of row's rope form state;
Step 4: successively pre-service is carried out to each frame reel steel wire ropes row rope image, and edge segmentation is carried out to pretreated image, extract the extreme coordinates of two parallel edge straight-line segments outside wire rope movable end, and solve the coordinate of wire rope central pixel point corresponding to described two parallel edge straight-line segments;
Step 5: if current frame image is first, then return step 4, otherwise, enter step 6;
Step 6: if the slope of two parallel edge straight-line segments outside the wire rope movable end obtained in the slope of two parallel edge straight-line segments in current frame image outside wire rope movable end and previous frame image differs by more than the slope threshold value of setting, then enter step 9; Otherwise, enter step 7;
Step 7: the distance between the coordinate of the wire rope central pixel point utilizing Euclidean distance equations present frame to obtain and the coordinate of former frame wire rope central pixel point, and the calibrating parameters utilizing step 2 to obtain, try to achieve the distance s between two adjacent wire rope centers;
Step 8: utilize the distance s between two adjacent wire rope centers, judges whether row's rope fault occurs;
If s meets formula 0<s-d< σ, then there is not row's rope fault; Otherwise, then think and row's rope fault occur;
D represents wirerope diameter, and σ is the safe wire cable threshold value of setting;
Step 9: if there is row's rope fault, then trigger alarm signals.
2. a kind of row of the reel steel wire ropes based on machine vision rope fault monitoring method according to claim 1, it is characterized in that, described each frame reel steel wire ropes row rope image is carried out pre-service and comprised, first to the smoothing denoising of image, then, then to the Image Segmentation Using after smoothing denoising, bianry image is generated.
3. a kind of row of the reel steel wire ropes based on machine vision rope fault monitoring method according to claim 2, it is characterized in that, the demarcation of described industrial camera, gather by adopting the image of industrial camera to the chessboard mark of stationary state, according to the ratio of the distance between known chessboard angle point with the distance of corresponding chessboard angle point on the image gathered, obtain the actual range representated by industrial camera unit picture element, complete the automatic Calibration of industrial camera parameter.
4. a kind of row of the reel steel wire ropes based on machine vision rope fault monitoring method according to claim 3, it is characterized in that, the actual range representated by described industrial camera unit picture element is less than or equal to 0.01m.
5. a kind of row of the reel steel wire ropes based on the machine vision rope fault monitoring method according to any one of claim 1-4, is characterized in that, adopt probability Hough transform method to extract two, edge parallel edge straight-line segment in described step 4.
6. the row of the reel steel wire ropes based on a machine vision rope fault monitoring system, it is characterized in that, based on a kind of row of the reel steel wire ropes based on the machine vision rope fault monitoring method described in any one of claim 1-5, comprise the industrial camera, data interface unit, image pre-processing unit, image characteristics extraction unit, breakdown judge unit, Surveillance center's unit and the alarm unit that are connected successively;
The row of described industrial camera collection restricts after area image is sent to image pre-processing unit by data interface unit, characteristic is extracted through image characteristics extraction unit, fault-signal is exported to Surveillance center after characteristic being carried out analyzing and processing by breakdown judge unit, Surveillance center's record and display fault-signal, carry alarm unit, trigger alarm simultaneously.
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Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105174109A (en) * 2015-07-15 2015-12-23 中南大学 Experiment table for winding type mine hoist
CN106379784A (en) * 2016-10-28 2017-02-08 齐鲁工业大学 Visual detection method and system for mine reinforcing steel bar inhaul cable
CN106970091A (en) * 2017-05-08 2017-07-21 深圳市立昌机电设备有限公司 The quality detecting method and system of coil winding machine
CN107369177A (en) * 2017-07-03 2017-11-21 东南大学 A kind of roadside assistance equipment capstan winch rope based on figure identification is anti-to cross drawing method for early warning
CN107403443A (en) * 2017-07-28 2017-11-28 中南大学 A kind of more rope multi-lay windings row's rope form state online test method and device based on machine vision
CN107764839A (en) * 2017-10-12 2018-03-06 中南大学 A kind of steel wire rope surface defect online test method and device based on machine vision
CN108062768A (en) * 2017-12-12 2018-05-22 中国矿业大学 It is a kind of based on the wirerope axis of surface texture feature to mobile status recognition methods
EP3323765A1 (en) 2016-11-22 2018-05-23 Manitowoc Crane Companies, LLC Optical detection and analysis of crane hoist and rope
CN108946534A (en) * 2018-08-10 2018-12-07 河北环航科技股份有限公司 A kind of aeroengine winches
CN109085791A (en) * 2018-07-25 2018-12-25 嘉兴锐川电气有限公司 Punching machine visual monitor system and its monitoring method
WO2019075919A1 (en) * 2017-10-20 2019-04-25 中国矿业大学 Multi-state health monitoring device and monitoring method for critical components of hoisting system
US10544012B2 (en) 2016-01-29 2020-01-28 Manitowoc Crane Companies, Llc Visual outrigger monitoring system
CN110940372A (en) * 2019-12-19 2020-03-31 江西太平洋电缆集团有限公司 Cable arrangement detection system based on machine vision
CN111392626A (en) * 2020-04-30 2020-07-10 中国水利水电夹江水工机械有限公司 Rope disorder detection method and detection device
CN112394682A (en) * 2020-11-24 2021-02-23 河南中中中环保设备有限公司 Thermal cracking steam boiler linkage control system
CN113086876A (en) * 2021-04-06 2021-07-09 武汉港迪电气传动技术有限公司 Method for detecting fault of winding drum steel wire rope in multi-layer winding drum steel wire rope transmission mode
CN113326824A (en) * 2021-08-03 2021-08-31 山东中都机器有限公司 Car puller abnormity detection method based on image processing

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4734176B2 (en) * 2006-05-22 2011-07-27 財団法人電力中央研究所 Wire abnormality detection method, wire abnormality detection device, and wire abnormality detection program
CN102519990A (en) * 2011-12-05 2012-06-27 天津工业大学 Fault on-line detection method of steel wire rope conveyer belt based on texture regularity analysis
CN102661952A (en) * 2012-05-02 2012-09-12 慈溪思达电子科技有限公司 Image-based steel wire rope breakage detection device
CN203317113U (en) * 2013-06-25 2013-12-04 浙江海洋学院 Screw support frame
CN103482513A (en) * 2012-09-27 2014-01-01 中联重科股份有限公司 Method and system for rope disorder prevention control of winding drum and engineering machinery

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4734176B2 (en) * 2006-05-22 2011-07-27 財団法人電力中央研究所 Wire abnormality detection method, wire abnormality detection device, and wire abnormality detection program
CN102519990A (en) * 2011-12-05 2012-06-27 天津工业大学 Fault on-line detection method of steel wire rope conveyer belt based on texture regularity analysis
CN102661952A (en) * 2012-05-02 2012-09-12 慈溪思达电子科技有限公司 Image-based steel wire rope breakage detection device
CN103482513A (en) * 2012-09-27 2014-01-01 中联重科股份有限公司 Method and system for rope disorder prevention control of winding drum and engineering machinery
CN203317113U (en) * 2013-06-25 2013-12-04 浙江海洋学院 Screw support frame

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
杨彦利, 苗长云, 亢 伉, 李现国: "输送带跑偏故障的机器视觉检测技术", 《中北大学学报(自然科学版)》 *

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US10544012B2 (en) 2016-01-29 2020-01-28 Manitowoc Crane Companies, Llc Visual outrigger monitoring system
CN106379784A (en) * 2016-10-28 2017-02-08 齐鲁工业大学 Visual detection method and system for mine reinforcing steel bar inhaul cable
US11124392B2 (en) 2016-11-22 2021-09-21 Manitowoc Crane Companies, Llc Optical detection and analysis for boom angles on a crane
US10829347B2 (en) 2016-11-22 2020-11-10 Manitowoc Crane Companies, Llc Optical detection system for lift crane
US10717631B2 (en) 2016-11-22 2020-07-21 Manitowoc Crane Companies, Llc Optical detection and analysis of crane hoist and rope
EP3323765A1 (en) 2016-11-22 2018-05-23 Manitowoc Crane Companies, LLC Optical detection and analysis of crane hoist and rope
US11130658B2 (en) 2016-11-22 2021-09-28 Manitowoc Crane Companies, Llc Optical detection and analysis of a counterweight assembly on a crane
CN106970091A (en) * 2017-05-08 2017-07-21 深圳市立昌机电设备有限公司 The quality detecting method and system of coil winding machine
CN107369177B (en) * 2017-07-03 2019-10-11 东南大学 A kind of roadside assistance equipment capstan winch rope based on figure identification is anti-to cross drawing method for early warning
CN107369177A (en) * 2017-07-03 2017-11-21 东南大学 A kind of roadside assistance equipment capstan winch rope based on figure identification is anti-to cross drawing method for early warning
CN107403443A (en) * 2017-07-28 2017-11-28 中南大学 A kind of more rope multi-lay windings row's rope form state online test method and device based on machine vision
CN107403443B (en) * 2017-07-28 2019-10-25 中南大学 A kind of more rope multi-lay windings row's rope form state online test method and device based on machine vision
CN107764839A (en) * 2017-10-12 2018-03-06 中南大学 A kind of steel wire rope surface defect online test method and device based on machine vision
CN107764839B (en) * 2017-10-12 2020-05-05 中南大学 Machine vision-based steel wire rope surface defect online detection method and device
US10815098B2 (en) 2017-10-20 2020-10-27 China University Of Mining And Technology Multiple-state health monitoring apparatus and monitoring method for critical components in hoisting system
WO2019075919A1 (en) * 2017-10-20 2019-04-25 中国矿业大学 Multi-state health monitoring device and monitoring method for critical components of hoisting system
CN108062768A (en) * 2017-12-12 2018-05-22 中国矿业大学 It is a kind of based on the wirerope axis of surface texture feature to mobile status recognition methods
CN109085791A (en) * 2018-07-25 2018-12-25 嘉兴锐川电气有限公司 Punching machine visual monitor system and its monitoring method
CN108946534A (en) * 2018-08-10 2018-12-07 河北环航科技股份有限公司 A kind of aeroengine winches
CN108946534B (en) * 2018-08-10 2019-11-29 河北环航科技股份有限公司 A kind of aeroengine winches
CN110940372B (en) * 2019-12-19 2020-07-21 江西太平洋电缆集团有限公司 Cable arrangement detection system based on machine vision
CN110940372A (en) * 2019-12-19 2020-03-31 江西太平洋电缆集团有限公司 Cable arrangement detection system based on machine vision
CN111392626A (en) * 2020-04-30 2020-07-10 中国水利水电夹江水工机械有限公司 Rope disorder detection method and detection device
CN112394682A (en) * 2020-11-24 2021-02-23 河南中中中环保设备有限公司 Thermal cracking steam boiler linkage control system
CN113086876A (en) * 2021-04-06 2021-07-09 武汉港迪电气传动技术有限公司 Method for detecting fault of winding drum steel wire rope in multi-layer winding drum steel wire rope transmission mode
CN113326824A (en) * 2021-08-03 2021-08-31 山东中都机器有限公司 Car puller abnormity detection method based on image processing

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