CN114740086A - Monorail crane track defect detection method - Google Patents
Monorail crane track defect detection method Download PDFInfo
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- CN114740086A CN114740086A CN202210203920.8A CN202210203920A CN114740086A CN 114740086 A CN114740086 A CN 114740086A CN 202210203920 A CN202210203920 A CN 202210203920A CN 114740086 A CN114740086 A CN 114740086A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/04—Analysing solids
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/16—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B17/00—Measuring arrangements characterised by the use of infrasonic, sonic or ultrasonic vibrations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B17/00—Measuring arrangements characterised by the use of infrasonic, sonic or ultrasonic vibrations
- G01B17/04—Measuring arrangements characterised by the use of infrasonic, sonic or ultrasonic vibrations for measuring the deformation in a solid, e.g. by vibrating string
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
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Abstract
The invention discloses a method for detecting the defect of a monorail crane track, which adopts hardware mainly comprising an ultrasonic sensor, an RFID module, a Wi-Fi signal transmission module and an image acquisition module; the method for inspecting the defects of the monorail crane track comprises data statistics, periodic inspection, sensor data processing and analysis, accumulation comparison and defect positioning. According to the scheme, a monorail crane is used as power to move on the rail, the monorail crane is used for carrying the ultrasonic sensor, the RFID module, the Wi-Fi signal transmission module, the image acquisition module and the like, a rail parameter identification detection system is developed, rail parameters (Y-direction dislocation, Z-direction dislocation and straightness) can be detected, defects are located, an upper computer interaction interface capable of displaying rail video information and detection results in real time is designed, and autonomous detection of rail dislocation defects and straightness is achieved.
Description
Technical Field
The invention relates to the technical field of rail defect detection, and particularly discloses a method for detecting a defect of a monorail crane rail.
Background
The underground electromechanical transportation of a coal mine is an important component of a mine production link, the underground electromechanical transportation of the coal mine runs through each production link of the mine, the existing roadway transportation mostly uses a rail lifting transportation mode, along with the continuous development of coal mine roadway auxiliary transportation, a monorail crane can be widely used due to the advantages of high utilization rate, low maintenance convenience and cost, the monorail crane can run in various vertical curves, horizontal curves and complex curves, and can directly reach a mining working surface, but the safety and the reliability of the monorail crane are greatly reduced because the rail is fixed in a suspension mode, the machine body is easy to swing in the operation of a heavy-load monorail crane, the rail is impacted frequently and greatly, the defects of joint dislocation, rail deformation and the like are easily caused, the health condition of the monorail crane directly determines the life and property safety of each production worker, however, the safety of the inspection personnel is not guaranteed by the existing manual inspection, the subjective judgment and low efficiency of inspection personnel are also a big problem, and due to the factors of short development history of the monorail crane, bad and inferior conditions of mine roadways and the like, no efficient and reliable defect detection method exists at present, and the method for detecting the natural state parameters of the monorail crane, which can replace manual inspection to perform efficient and automatic inspection, is designed urgently and has high practical application value.
Disclosure of Invention
In view of this, the monorail crane rail defect detection method is provided for the problems of joint dislocation, rail deformation and the like of the monorail crane rail in the coal mine underground electromechanical transportation system.
In order to achieve the purpose, the invention adopts the following technical scheme:
a single-rail hanging rail defect inspection method mainly adopts hardware comprising an ultrasonic sensor, an RFID module, a Wi-Fi signal transmission module and an image acquisition module;
the ultrasonic sensor is arranged at the front end of the designed inspection device, the longitudinal ultrasonic sensor of the ultrasonic sensor faces the bottom of the monorail crane rail groove to detect longitudinal dislocation of the monorail crane, and the transverse ultrasonic sensor faces the side face of the monorail crane rail groove to detect transverse dislocation of a monorail crane rail joint;
the image acquisition module mainly comprises an explosion-proof infrared camera, a camera base is fixed below the shell of the inspection device through a bolt, and a camera is hinged with the camera base and can rotate in a certain angle to be opposite to the bottom of the monorail crane rail so as to acquire the trend information of the bottom of the rail;
the card reader of the RFID module is fixed on the front side of the shell of the inspection device through a bolt, and the label of the RFID module is strategically arranged at the bottom of the monorail hanging rail;
the antenna of the Wi-Fi transmission module is fixed on the side edge of the shell of the inspection device and is connected with the sensor through the signal converter;
the monorail crane track defect inspection method generally comprises the following steps:
step one, data statistics and periodic inspection: processing image information acquired by the explosion-proof infrared camera, data acquired by the ultrasonic sensor, the acceleration sensor and the RFID card reader in the operation process as original data, and performing accurate defect position positioning and image feedback according to a processing result;
and secondly, processing and analyzing sensor data: the data acquired by each sensor is transmitted to an upper computer through a Wi-Fi module, and the original data is processed and analyzed through the data processing method, so that a defect abnormal value is solved;
thirdly, accumulating and comparing, and positioning defects: and (3) processing the data detected by each sensor, and then describing the data on the upper computer by combining the defect position to finish visual detection.
Particularly, in the step of sensor data processing and analysis, a monorail crane rail joint dislocation detection method is adopted to perform initialization operation on the mounted ultrasonic sensor, so that the data obtained by the sensor is rail deviation data, the data is subjected to drawing processing, analysis is performed according to a test result, a smooth part is data change caused by the follow-up of the sensor along with the inspection device, an image excitation point is data transaction caused by the dislocation of the rail joint, position data are matched after the data are selected according to a detection function, and finally dislocation data are obtained.
Particularly, in the step of sensor data processing and analysis, the single-rail hanging rail straightness detection image processing is adopted, and the method specifically comprises the following steps:
graying and median filtering are carried out on the track image, a top view with mutually parallel track lines is obtained by utilizing inverse perspective transformation, an interested area is set to reduce interference, histogram equalization is used for image enhancement, and the influence caused by brightness change is reduced;
selecting a Canny operator to carry out edge detection, detecting straight lines in an edge image by using Hough transformation, and reducing the calculation amount of the Hough transformation by constraining polar angles according to the characteristics of a track image;
adopting an active voting method-based track line detection algorithm, wherein the active expression is that each straight line is assumed to be a track line, the characteristic attribute of the algorithm is taken as a track line standard to vote for other straight lines, and the track line is screened out according to the total vote number;
and after the track line is stably fitted, performing track line tracking by using Kalman filtering, performing fitting calculation on the extracted track edge straight line and the ideal straight line, and solving out the straightness accuracy to obtain the deformation condition of the monorail crane track.
Particularly, in the step of 'accumulation comparison and defect positioning', a defect positioning method is adopted, and the method is characterized in that firstly, a positioning system of an underground roadway is combined, and section identification is carried out according to the actual working condition of the monorail crane rail, so that the position of the defect can be quickly positioned according to an identification rule.
Particularly, in the step of 'accumulation comparison and defect positioning', a defect positioning method is adopted, the RFID tag containing position information is strategically arranged at the bottom of the track, and when the inspection robot reaches a corresponding position, the card reader module senses and reads the tag and then sends the position information to the upper computer through the Wi-Fi module, so that the defect positioning and displaying functions are realized.
The invention has the following technical effects:
the monorail crane is used as power to move on the track, and the monorail crane carries an ultrasonic sensor, an RFID module, a Wi-Fi signal transmission module, an image acquisition module and the like; developing a track parameter identification detection system, detecting track parameters (Y-direction, Z-direction dislocation and straightness) and positioning defects; and designing an upper computer interactive interface capable of displaying track video information and detection results in real time. The automatic detection of the rail dislocation defects and the straightness is realized, the workers can conveniently master the safety state of the transportation system, and the automatic detection device has important research value for improving the intelligent operation capability of mining enterprises and reducing the loss of personnel and property.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof.
Drawings
FIG. 1 is a general schematic diagram of a monorail crane defect detection method of the invention;
FIG. 2 is a schematic diagram of positioning marks of the monorail crane defect detection method;
FIG. 3 is a flow chart of a data processing method of the monorail crane defect detection method of the invention;
FIG. 4 is a schematic diagram of a straightness extraction algorithm of the monorail crane defect detection method;
FIG. 5 is a schematic diagram of an interface of an upper computer of the monorail crane defect detection method.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
As shown in FIGS. 1 to 5, the method for inspecting the defects of the monorail crane rail provided by the invention specifically comprises the following steps:
step one, data statistics and periodic inspection: and processing data images acquired by the camera, the ultrasonic sensor, the acceleration sensor and the RFID card reader in the whole operation process of the inspection device as original data, and performing accurate defect position positioning and image feedback according to a processing result.
And secondly, processing and analyzing sensor data: and processing the counted data, wherein the specific processing process is as follows:
the method for detecting the dislocation of the monorail crane rail joint comprises the steps of initializing an installed ultrasonic sensor to enable the sensor to obtain data which are rail deviation data, drawing the data, analyzing according to a test result, enabling a smooth part to be data change caused by follow-up of the sensor along with an inspection device, enabling an image change point to be data transaction caused by dislocation of the rail joint, matching position data after selection according to a detection function, and finally obtaining dislocation data.
The image processing part for detecting the straightness of the monorail crane rail comprises the steps of firstly carrying out graying and median filtering processing on a rail image, obtaining a top view with mutually parallel rail lines by utilizing inverse perspective transformation, setting an interested area to reduce interference, carrying out image enhancement by utilizing histogram equalization and reducing the influence caused by brightness change. And analyzing different edge detection operators, and respectively comparing the principle with the actual application effect to determine that Canny operators are selected for edge detection. Straight lines in the edge graph are detected by using Hough transformation, and calculation amount of Hough transformation is greatly reduced by constraining polar angles according to the characteristics of track images. The track line detection algorithm based on the active voting method actively shows that each straight line is assumed to be a track line, the characteristic attribute of the algorithm is used as a track line standard to vote for other straight lines, and the track line is screened out according to the total number of votes. After the track line is stably fitted, the Kalman filtering is used for track line tracking, the region of interest is further reduced, the calculated amount is reduced, the real-time performance of the algorithm is improved, the fitted track line is more accurate, the extracted track edge straight line and the ideal straight line are further subjected to fitting calculation, the straightness is solved, and therefore the deformation condition of the monorail crane track is obtained.
Thirdly, accumulating and comparing, and positioning defects: the positions of the target detection points are obtained through the RFID module, and are uploaded to an upper computer through the Wi-Fi module for description, and visual detection is completed by combining data of other sensors.
The method is characterized in that firstly, a roadway positioning system is combined, sectional identification is carried out according to the actual working condition of the monorail crane track, as shown in fig. 2, the track is divided into N sections, each section comprises 4 tracks, 12-4 is represented as a 12 th section of the 4 th track, workers can conveniently and quickly locate the position of the defect according to identification rules, and further, according to a specific operation method, RFID tags containing position information are strategically arranged at the bottom of the track, when the inspection robot reaches the corresponding position, a card reader module senses and reads the tags and then sends the position information to an upper computer through a Wi-Fi module, and therefore the functions of defect location and display are achieved.
The foregoing is merely an example of the present invention and common general knowledge of known specific structures and features of the embodiments is not described herein in any greater detail. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the present invention.
Claims (5)
1. A monorail crane track defect detection method is characterized in that adopted hardware mainly comprises an ultrasonic sensor, an RFID module, a Wi-Fi signal transmission module and an image acquisition module;
the ultrasonic sensor is arranged at the front end of the designed inspection device, the longitudinal ultrasonic sensor of the ultrasonic sensor faces the bottom of the monorail crane rail groove to detect longitudinal dislocation of the monorail crane, and the transverse ultrasonic sensor faces the side face of the monorail crane rail groove to detect transverse dislocation of a monorail crane rail joint;
the image acquisition module mainly comprises an explosion-proof infrared camera, a camera base is fixed below the shell of the inspection device through a bolt, and a camera is hinged with the camera base and can rotate in a certain angle to be opposite to the bottom of the monorail crane rail so as to acquire the trend information of the bottom of the rail;
the card reader of the RFID module is fixed on the front side of the shell of the inspection device through a bolt, and the label of the RFID module is strategically arranged at the bottom of the monorail crane rail;
the antenna of the Wi-Fi transmission module is fixed on the side edge of the shell of the inspection device and is connected with the sensor through the signal converter;
the monorail crane rail defect inspection method comprises the following steps:
data statistics and periodic inspection: processing image information acquired by the explosion-proof infrared camera, data acquired by the ultrasonic sensor, the acceleration sensor and the RFID card reader in the operation process as original data, and performing accurate defect position positioning and image feedback according to a processing result;
sensor data processing and analysis: the data acquired by each sensor is transmitted to an upper computer through a Wi-Fi module, and the original data is processed and analyzed through the data processing method, so that a defect abnormal value is solved;
accumulation and comparison, defect positioning: and (3) processing the data detected by each sensor, and then describing the data on the upper computer by combining the defect position to finish visual detection.
2. The method for detecting the defect of the monorail crane rail as claimed in claim 1, wherein in the step of sensor data processing and analysis, a monorail crane rail joint dislocation detection method is adopted, initialization operation is carried out on the installed ultrasonic sensor, so that the sensor obtains data as rail deviation data, drawing processing is carried out on the data, according to test result analysis, a smooth part is data change caused by the fact that the sensor follows a routing inspection device, an image excitation point is data transaction caused by dislocation of the rail joint, and position data are matched after selection according to a detection function, and finally dislocation data are obtained.
3. The method for detecting the defects of the monorail crane rail as claimed in claim 1 or 2, wherein in the step of sensor data processing and analysis, image processing is detected by using the straightness of the monorail crane rail, and the method specifically comprises the following steps:
graying and median filtering are carried out on the track image, a top view with mutually parallel track lines is obtained by utilizing inverse perspective transformation, an interested area is set to reduce interference, histogram equalization is used for image enhancement, and the influence caused by brightness change is reduced;
selecting a Canny operator to carry out edge detection, detecting straight lines in an edge image by using Hough transformation, and reducing the calculation amount of the Hough transformation by restricting polar angles according to the characteristics of a track image;
adopting an active voting method-based track line detection algorithm, which actively shows that each straight line is assumed to be a track line, voting other straight lines by taking the characteristic attribute of the straight line as a track line standard, and screening out the track line according to the total number of votes;
and after the track line is stably fitted, performing track line tracking by using Kalman filtering, performing fitting calculation on the extracted track edge straight line and the ideal straight line, and solving out the straightness accuracy to obtain the deformation condition of the monorail crane track.
4. The method for detecting the defects of the monorail crane rail as claimed in claim 3, wherein in the step of "cumulative comparison and defect location", a defect location method is adopted, and is characterized in that firstly, a downhole roadway location system is combined, and section identification is carried out according to actual working conditions of the monorail crane rail, so that the positions of the defects can be located quickly according to identification rules.
5. The method for detecting the defect of the monorail crane track according to claim 4, wherein in the step of accumulating comparison and defect positioning, a defect positioning method is adopted, and the method further comprises the step of strategically arranging an RFID tag containing position information at the bottom of the track, and when the inspection robot reaches a corresponding position, a card reader module senses and reads the tag and then sends the position information to an upper computer through a Wi-Fi module, so that the functions of defect positioning and displaying are realized.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202210203920.8A CN114740086A (en) | 2022-03-03 | 2022-03-03 | Monorail crane track defect detection method |
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| CN202210203920.8A CN114740086A (en) | 2022-03-03 | 2022-03-03 | Monorail crane track defect detection method |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115060665A (en) * | 2022-08-16 | 2022-09-16 | 君华高科集团有限公司 | Automatic inspection system for food safety |
| CN117002544A (en) * | 2023-08-17 | 2023-11-07 | 中关村科学城城市大脑股份有限公司 | Folding track inspection equipment and track fault information sending method |
| CN117078687A (en) * | 2023-10-17 | 2023-11-17 | 常州海图信息科技股份有限公司 | Track inspection system and method based on machine vision |
-
2022
- 2022-03-03 CN CN202210203920.8A patent/CN114740086A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115060665A (en) * | 2022-08-16 | 2022-09-16 | 君华高科集团有限公司 | Automatic inspection system for food safety |
| CN117002544A (en) * | 2023-08-17 | 2023-11-07 | 中关村科学城城市大脑股份有限公司 | Folding track inspection equipment and track fault information sending method |
| CN117002544B (en) * | 2023-08-17 | 2024-04-12 | 中关村科学城城市大脑股份有限公司 | Folding track inspection equipment and track fault information sending method |
| CN117078687A (en) * | 2023-10-17 | 2023-11-17 | 常州海图信息科技股份有限公司 | Track inspection system and method based on machine vision |
| CN117078687B (en) * | 2023-10-17 | 2023-12-15 | 常州海图信息科技股份有限公司 | Track inspection system and method based on machine vision |
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