CN113222935A - Method for detecting looseness and pretightening force loss of steel bridge bolt - Google Patents
Method for detecting looseness and pretightening force loss of steel bridge bolt Download PDFInfo
- Publication number
- CN113222935A CN113222935A CN202110522703.0A CN202110522703A CN113222935A CN 113222935 A CN113222935 A CN 113222935A CN 202110522703 A CN202110522703 A CN 202110522703A CN 113222935 A CN113222935 A CN 113222935A
- Authority
- CN
- China
- Prior art keywords
- nut
- bolt
- image
- loosening
- version
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 37
- 239000010959 steel Substances 0.000 title claims abstract description 37
- 238000001514 detection method Methods 0.000 claims abstract description 24
- 230000008569 process Effects 0.000 claims abstract description 17
- 230000009467 reduction Effects 0.000 claims abstract description 9
- 230000000877 morphologic effect Effects 0.000 claims abstract description 5
- 238000007781 pre-processing Methods 0.000 claims description 8
- 230000009466 transformation Effects 0.000 claims description 8
- 230000005540 biological transmission Effects 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 238000005260 corrosion Methods 0.000 claims description 4
- 230000007797 corrosion Effects 0.000 claims description 4
- 230000008878 coupling Effects 0.000 claims description 4
- 238000010168 coupling process Methods 0.000 claims description 4
- 238000005859 coupling reaction Methods 0.000 claims description 4
- 230000036316 preload Effects 0.000 claims 1
- 238000001914 filtration Methods 0.000 abstract 1
- 230000011218 segmentation Effects 0.000 abstract 1
- 238000011426 transformation method Methods 0.000 abstract 1
- 238000010276 construction Methods 0.000 description 3
- 238000007689 inspection Methods 0.000 description 2
- 238000011179 visual inspection Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009440 infrastructure construction Methods 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 238000003466 welding Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration using local operators
- G06T5/30—Erosion or dilatation, e.g. thinning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/66—Analysis of geometric attributes of image moments or centre of gravity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
- G06T2207/20061—Hough transform
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20172—Image enhancement details
- G06T2207/20192—Edge enhancement; Edge preservation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30164—Workpiece; Machine component
-
- 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
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Quality & Reliability (AREA)
- Geometry (AREA)
- Length Measuring Devices By Optical Means (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a method for detecting looseness and pretightening force loss of a steel bridge bolt. Belongs to the technical field of steel bridge detection, and comprises the following steps: marking selected characteristic points of a nut corresponding to a bolt to be tested, and using the selected characteristic points as pixel positioning areas of algorithm salient recognition in the bolt loosening process; acquiring nut images, and uploading the batch nut images to a computer; carrying out image noise reduction through Gaussian filtering, converting an RGB color space into an HSV color space, and completing the pretreatment of an image by combining morphological opening operation, image binarization and threshold segmentation to position nut feature mark points; and positioning the geometric centroid of the nut by adopting a hough transformation method for distinguishing internal and external value boundaries, identifying the coordinates of the characteristic mark points of the nut by adopting a harris corner point detection method, deducing the position change of the coordinates of the characteristic mark points of the nut between adjacent sample pictures, and calculating the loosening angle and the pretightening force loss value of the bolt. The invention has low detection cost and high precision; the bolts in the steel structure bridge can be detected in batches.
Description
Technical Field
The invention belongs to the technical field of steel bridge detection, and relates to a method for detecting the looseness and the pretightening force loss of a steel bridge bolt; and more particularly, to a method for detecting looseness and pretightening force loss of a steel bridge bolt based on image recognition.
Background
With the development of economy, bridges have become the key point of infrastructure construction in China. In recent years, with the increasing of steel yield in China, the steel capacity is excessive, and the steel structure is popularized and widely applied to construction and traffic systems by the national institute of government and the ministry of housing construction, transportation department and other parts of committee to export documents. The proportion of the steel structure bridges is increased year by year, and by 2019, the number of roads and railway bridges in China is about more than 100 ten thousand, and the bridges are ascended in the world of the first bridge of the great country.
The steel structure bridge gradually becomes a preferred bridge form of a large-span bridge, a viaduct and a overpass bridge due to the outstanding advantages of high bearing capacity, strong spanning capability, wide application range and the like.
The high-strength bolt connection gradually replaces the traditional riveting and welding connection mode due to the advantages of simple and convenient construction, replaceable, high bearing capacity and the like, and becomes the main connection mode of steel bridge node connection. In the operation process of the bridge structure, the bridge structure is continuously subjected to the action of dynamic load fatigue vibration, and potential safety hazards are brought to bolt connection of a bridge. The bolt looseness can not be detected in time, and the safety of the whole bridge structure can be endangered. Therefore, a set of efficient detection method is needed to perform batch detection on the steel bridge bolts. The traditional bolt detection method mainly comprises manual visual inspection and sensor-based detection. The error of the manual visual inspection method is large, mainly depends on the experience of inspection personnel, and the detection efficiency is low; the detection method based on the sensor can only detect one bolt each time, the equipment is high in price, the bolt needs to be installed on the bolt to be detected, and the bolt is not easy to replace due to the fact that the equipment is expensive.
The application numbers are: CN202011141777.1, name: although the invention discloses a method for identifying loosening images of fastening bolts of a railway wagon, the method is not suitable for batch quick inspection of bolts used for internal connection of a steel structure bridge, does not clearly determine the relationship among bolt types, shooting distances and shooting machine types, and is not suitable for detection of bolts of various types. Secondly, there is no report on the study of detecting bolt pretension loss.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a method for detecting the loosening of the steel bridge bolt and the loss of the pretightening force, which has the advantages of low detection cost, high precision, high efficiency, capability of detecting the loosening condition of the bolt and the loss condition of the pretightening force in a steel bridge in batch and the like.
The technical scheme is as follows: the invention discloses a method for detecting the looseness and the pretightening force loss of a steel bridge bolt, which comprises the following specific operation steps of:
(1.1) classifying samples of the steel structure bridge bolts according to specification models and strength grades, sequentially determining the intersection angle points of two adjacent sides of the upper surface of the nut corresponding to each class of bolts, marking by using a marking pen to form characteristic marking points, and taking the characteristic marking points as pixel positioning areas highlighted and identified by an algorithm;
(1.2) arranging an industrial camera on the front surface of the nut marked with the characteristic mark points, namely, enabling the industrial camera lens to be opposite to the nut;
(1.3) setting the shooting frequency of the industrial camera, and adopting the set industrial camera to acquire images in the nut loosening process;
(1.4) transmitting the acquired nut image to a computer through a data transmission device, and carrying out image preprocessing to obtain a final version of the nut image;
(1.5) positioning the geometric centroid of the nut surface in the final version of the nut image by hough transformation;
(1.6) identifying the coordinates (x) of the nut characteristic mark points on a circle which takes the geometric centroid of the nut surface in the final version of the nut image as the center of the circle and the distance between the angle point of the measured nut with the characteristic mark points and the opposite angle point as the diameter by adopting a harris angle point detection method1,y1)，(x2,y2),(x3,y3)L(xm,ym)；
(1.7) substituting the coordinates of the characteristic mark points of any two nuts identified by the harris corner point detection method into the following formula to carry out angle estimation, wherein the estimation formula is as follows:
thereby obtaining the angle difference of the characteristic mark points between the two images, namely the loosening angle of the bolt where the nut is positioned;
in the formula, xccdRepresenting the industrial camera pel size; r represents the distance between the angular point of the characteristic mark point of the nut and the relative angular point; dfRepresenting the industrial camera focal length size; d0Representing the distance from the nut to the industrial camera lens; (x)m,ym)，(xn,yn) Representing the position coordinates of the nut feature mark points of any two images;
(1.8) substituting the measured bolt loosening angle into a relation between the bolt loosening angle and the pretightening force loss, wherein the specific formula is as follows:
thereby forming a set of method for deducing the loosening angle of the bolt and judging the loss of the pretightening force of the bolt in real time;
wherein θ represents the bolt loosening angle; ffIndicating the loss of the bolt loosening pretightening force; p represents the bolt pitch dimension; ktRepresenting a spring constant of the bolted connection; kcRepresenting a spring constant on the coupling on which the bolt is located;
and (1.9) repeating the steps (1.1) to (1.8), thereby detecting all shooting bolt loosening conditions and pretightening force loss conditions.
Further, in the step (1.1), the shape of the feature mark point is a circle, and the radius of the feature mark point is less than or equal to 0.5 mm.
Further, in the step (1.2), the axial direction of the lens of the industrial camera is perpendicular to the plane of the nut, and the distance between the end of the industrial camera close to the nut sample plane and the nut sample plane is 0.5 m-1.5 m.
Further, in step (1.3), the industrial camera takes nut images at 2 frames per second.
Further, in step (1.4), the specific operation steps of the image preprocessing are as follows:
(1.4.1) carrying out noise reduction on the collected nut image by using Gaussian filter transformation, and eliminating interference points in the nut image to obtain a nut image version;
(1.4.2) converting the RGB color space of the nut image subjected to the noise reduction treatment into HSV color space, and enabling the characteristic mark points on the upper surface of the nut and the color of the upper surface of the nut to form contrast to obtain a nut image second edition after the color space conversion;
(1.4.3) carrying out digital corrosion process on the two-version structural elements of the nut image by utilizing morphological open operation to eliminate the connected part of the two-version structural elements of the nut image to obtain a three-version nut image, then carrying out digital expansion process to amplify the element details of the three-version nut image to obtain a four-version nut image, and carrying out denoising process on the edge of the four-version nut image to obtain a five-version nut image;
and (1.4.4) carrying out binarization processing on the five versions of the nut image to enable the edge contour of the nut image to be clearer so as to obtain six versions of the nut image, and dividing the six versions of the nut image subjected to binarization processing into nut characteristic mark points and nut non-characteristic mark points by combining a global threshold value so as to obtain a final version of the nut image.
Further, in step (1.6), the nut feature mark points identified by the harris corner point detection method are on a circle with the geometric centroid of the nut surface in the final version of the nut image as the center of the circle and the distance between the corner point of the measured nut feature mark point and the opposite corner point as the diameter.
Furthermore, the bolt where the nut is located is a large hexagon high-strength bolt connected and used inside the steel structure bridge.
Has the advantages that: compared with the prior art, the method has the advantages of low detection cost, high precision, high efficiency, capability of detecting the bolt loosening condition and the pretightening force loss condition in the steel structure bridge in batch and the like.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
the point 1 in fig. 2 is the selected position of the nut feature mark point in the invention;
the point shown at 2 in fig. 3 is the geometric centroid of the nut surface in the final version of the nut image of the present invention.
Detailed Description
The following describes the practice of the present invention in detail with reference to the accompanying drawings.
The invention relates to a method for detecting the looseness and the pretightening force loss of a steel bridge bolt, which comprises the following specific operation steps of:
(1.1) classifying samples of the steel structure bridge bolts according to specification models and strength grades, sequentially determining the intersection angle points of two adjacent sides of the upper surface of the nut corresponding to each class of bolts, marking by using a marking pen to form characteristic marking points, and taking the characteristic marking points as pixel positioning areas highlighted and identified by an algorithm;
(1.2) arranging an industrial camera on the front surface of the nut marked with the characteristic mark points, namely, enabling the industrial camera lens to be opposite to the nut;
(1.3) setting the shooting frequency of the industrial camera, and acquiring images by using the set industrial camera in the loosening process of the nut;
(1.4) transmitting the acquired nut image to a computer through a data transmission device, and carrying out image preprocessing to obtain a final version of the nut image;
(1.5) positioning the geometric centroid of the nut surface in the final version of the nut image by hough transformation;
(1.6) use of harrisThe angular point detection method identifies the coordinates (x) of the nut characteristic mark points on a circle which takes the geometric centroid of the nut surface in the final version of the nut image as the center of the circle and the distance between the angular point of the measured nut with the characteristic mark points and the relative angular point as the diameter1,y1)，(x2,y2),(x3,y3)L(xm,ym)；
(1.7) substituting the coordinates of the characteristic mark points of any two nuts identified by the harris corner point detection method into the following formula to carry out angle estimation, wherein the estimation formula is as follows:
thereby obtaining the angle difference of the characteristic mark points between the two images, namely the loosening angle of the bolt where the nut is positioned;
in the formula, xccdRepresenting the industrial camera pel size; r represents the distance between the angular point of the characteristic mark point of the nut and the relative angular point; dfRepresenting the industrial camera focal length size; d0Representing the distance from the nut to the industrial camera lens; (x)m,ym)，(xn,yn) Representing the position coordinates of the nut feature mark points of any two images;
(1.8) substituting the measured bolt loosening angle into a relation between the bolt loosening angle and the pretightening force loss, wherein the specific formula is as follows:
thereby forming a set of method for deducing the loosening angle of the bolt and judging the loss of the pretightening force of the bolt in real time;
wherein θ represents the bolt loosening angle; ffIndicating the loss of the bolt loosening pretightening force; p represents the bolt pitch dimension; ktRepresenting a spring constant of the bolted connection; kcRepresenting a spring constant on the coupling on which the bolt is located;
and (1.9) repeating the steps (1.1) to (1.8), thereby detecting all shooting bolt loosening conditions and pretightening force loss conditions.
Further, in the step (1.1), the shape of the feature mark point is a circle, and the radius of the feature mark point is less than or equal to 0.5 mm.
Further, in the step (1.2), the axial direction of the lens of the industrial camera is perpendicular to the plane of the nut, and the distance between the end of the industrial camera close to the nut sample plane and the nut sample plane is 0.5 m-1.5 m.
Further, in step (1.3), the industrial camera takes nut images at 2 frames per second.
Further, in step (1.4), the specific operation steps of the image preprocessing are as follows:
(1.4.1) carrying out noise reduction on the collected nut image by using Gaussian filter transformation, and eliminating interference points in the nut image to obtain a nut image version;
(1.4.2) converting the RGB color space of the nut image subjected to the noise reduction treatment into HSV color space, and enabling the characteristic mark points on the upper surface of the nut and the color of the upper surface of the nut to form contrast to obtain a nut image second edition after the color space conversion;
(1.4.3) carrying out digital corrosion process on the two-version structural elements of the nut image by utilizing morphological open operation to eliminate the connected part of the two-version structural elements of the nut image to obtain a three-version nut image, then carrying out digital expansion process to amplify the element details of the three-version nut image to obtain a four-version nut image, and carrying out denoising process on the edge of the four-version nut image to obtain a five-version nut image;
and (1.4.4) carrying out binarization processing on the five versions of the nut image to enable the edge contour of the nut image to be clearer so as to obtain six versions of the nut image, and dividing the six versions of the nut image subjected to binarization processing into nut characteristic mark points and nut non-characteristic mark points by combining a global threshold value so as to obtain a final version of the nut image.
Further, in step (1.6), the nut feature mark points identified by the harris corner point detection method are on a circle with the geometric centroid of the nut surface in the final version of the nut image as the center of the circle and the distance between the corner point of the measured nut feature mark point and the opposite corner point as the diameter.
Furthermore, the bolt where the nut is located is a large hexagon head high-strength bolt of M20, M22, M24, M27 or M30 type which is connected and used in the steel structure bridge.
Specifically, as shown in fig. 1, the novel method for detecting the looseness and the pretightening force loss of the steel bridge bolt provided by the invention comprises the following specific steps:
firstly, classifying samples of steel structure bridge bolts according to specification models and strength grades, selecting M30 and 10.9S large hexagon head type bolts in the steel structure bridge as collected samples, sequentially determining intersecting angular points of two adjacent edges of the upper surface of the nut corresponding to the class bolts, marking the intersecting angular points of the two adjacent edges of the upper surface of the nut by using a red marker pen to form feature marking points, and taking the feature marking points as pixel positioning areas highlighted and identified by an algorithm;
secondly, erecting a Haokawav MV-CA020-10GC industrial camera, and arranging the industrial camera on the front surface of the nut with a characteristic mark point, namely arranging an industrial camera lens opposite to the nut, wherein the axial direction of the industrial camera lens is vertical to the plane of the nut, and the distance between the end of the industrial camera close to the nut sample plane and the plane of the nut female parent is 0.5 m;
thirdly, setting the shooting frequency of the industrial camera to be 2 frames per second, sequentially loosening the nut with the characteristic mark points by 10 degrees, 20 degrees and 30 degrees by taking the initial position of the nut with the characteristic mark points as a reference, and acquiring images by adopting the set industrial camera;
fourthly, transmitting the acquired nut image to a computer through a data transmission device, and performing image preprocessing;
preferably, the specific content and steps of the image preprocessing include:
step 1, carrying out noise reduction treatment on the collected nut image by using Gaussian filter transformation, and eliminating interference points in the nut image to obtain a first version of the nut image;
step 3, performing a digital corrosion process on the structural elements of the second version of the nut image by using morphological opening operation to eliminate the connected part of the structural elements of the second version of the nut image to obtain a third version of the nut image, performing a digital expansion process to amplify the element details of the third version of the nut image to obtain a fourth version of the nut image, and performing a denoising process on the edge of the fourth version of the nut image to obtain a fifth version of the nut image;
step 4, performing binarization processing on the five versions of the nut image to enable the edge contour of the nut image to be clearer to obtain six versions of the nut image, and dividing the six versions of the nut image subjected to binarization processing into nut characteristic mark points and nut non-characteristic mark points by combining a global threshold value to obtain a final version of the nut image;
fifthly, positioning the geometric centroid of the nut surface in the final version of the nut image by hough transformation;
sixthly, recognizing coordinates of the nut characteristic mark points on a circle which takes the geometric centroid of the surface of the nut in the final version of the nut image as the center of the circle and the distance between the angle point of the nut with the characteristic mark point and the opposite angle point as the diameter by adopting a harris angle point detection method as (x)1＝925，y1＝772)；(x2＝943，y2＝771)；
(x3＝960，y3＝769)；(x4＝976，y4＝768)；
And seventhly, substituting the coordinates of the centers of any two nut mark areas identified by the harris corner point detection method into the following formula to calculate the angle:
wherein: the pixel size x of the industrial cameraccdIs 4.5 multiplied by 10-6mm; the distance R between the corner point of the nut with the characteristic mark point and the opposite corner point is 56.63 mm; the size of the focal length D of the industrial camerafIs 12 mm; the distance from the nut to the industrial camera lens is D0Is 5 x 102mm。
The bolt loosening angle and accuracy are counted in the table 1;
table 1: m30 bolt 5X 102Identification result of mm horizontal distance sample algorithm
And step eight, substituting the angle identified by the algorithm into the relation between the bolt loosening angle and the bolt pretightening force loss:
wherein: the bolt loosening angle theta identified by the algorithm is shown in table 1; the loss of the pretightening force of the bolt is FfSee table 2 for details; the pitch size P of the M30 bolt is 3.5 mm; spring constant K of the bolted connectiontIs 8.8 multiplied by 104kg/mm; spring constant K on the coupling piece on which the bolt is locatedcIs 126X 104kg/mm；
Table 2: m30 bolt 5X 102Identification result of mm horizontal distance sample algorithm
The ninth step: and repeating the steps, and detecting the loosening condition and the pretightening force loss condition of all the shooting bolts.
Claims (7)
1. A method for detecting the looseness and the pretightening force loss of a steel bridge bolt is characterized by comprising the following steps of: the specific operation steps are as follows:
(1.1) classifying samples of the steel structure bridge bolts according to specification models and strength grades, sequentially determining the intersection angle points of two adjacent sides of the upper surface of the nut corresponding to each class of bolts, marking by using a marking pen to form characteristic marking points, and taking the characteristic marking points as pixel positioning areas highlighted and identified by an algorithm;
(1.2) arranging an industrial camera on the front surface of the nut marked with the characteristic mark points, namely, enabling the industrial camera lens to be opposite to the nut;
(1.3) setting the shooting frequency of the industrial camera, and adopting the set industrial camera to acquire images in the nut loosening process;
(1.4) transmitting the acquired nut image to a computer through a data transmission device, and carrying out image preprocessing to obtain a final version of the nut image;
(1.5) positioning the geometric centroid of the nut surface in the final version of the nut image by hough transformation;
(1.6) identifying the coordinates (x) of the nut characteristic mark points on a circle which takes the geometric centroid of the nut surface in the final version of the nut image as the center of the circle and the distance between the angle point of the measured nut with the characteristic mark points and the opposite angle point as the diameter by adopting a harris angle point detection method1,y1)，(x2,y2),(x3,y3)L(xm,ym)；
(1.7) substituting the coordinates of the characteristic mark points of any two nuts identified by the harris corner point detection method into the following formula to carry out angle estimation, wherein the estimation formula is as follows:
thereby obtaining the angle difference of the characteristic mark points between the two images, namely the loosening angle of the bolt where the nut is positioned;
in the formula, xccdRepresenting the industrial camera pel size; r represents the distance between the angular point of the characteristic mark point of the nut and the relative angular point; dfRepresenting the industrial camera focal length size; d0Representing the distance from the nut to the industrial camera lens; (x)m,ym)，(xn,yn) Representing the position coordinates of the nut feature mark points of any two images;
(1.8) substituting the measured bolt loosening angle into a relation between the bolt loosening angle and the pretightening force loss, wherein the specific formula is as follows:
thereby forming a set of method for deducing the loosening angle of the bolt and judging the loss of the pretightening force of the bolt in real time;
wherein θ represents the bolt loosening angle; ffIndicating the loss of the bolt loosening pretightening force; p represents the bolt pitch dimension; ktRepresenting a spring constant of the bolted connection; kcRepresenting a spring constant on the coupling on which the bolt is located;
and (1.9) repeating the steps (1.1) to (1.8), thereby detecting all shooting bolt loosening conditions and pretightening force loss conditions.
2. The method for detecting the loosening and the pretightening force loss of the steel bridge bolt as claimed in claim 1, wherein in the step (1.1), the characteristic mark points are circular and have a radius of less than or equal to 0.5 mm.
3. The method for detecting the loosening and the pretightening force loss of the steel bridge bolt according to claim 1, wherein in the step (1.2), the axial direction of the lens of the industrial camera is perpendicular to the plane of the nut, and the distance between the end of the lens of the industrial camera close to the nut sample plane and the nut sample plane is 0.5-1.5 m.
4. The method for detecting loosening and loss of preload of steel bridge bolt of claim 1, wherein in step (1.3), said industrial camera takes nut images at 2 frames per second.
5. The method for detecting the loosening and the pretightening force loss of the steel bridge bolt according to claim 1, wherein in the step (1.4), the image preprocessing comprises the following specific operation steps:
(1.4.1) carrying out noise reduction on the collected nut image by using Gaussian filter transformation, and eliminating interference points in the nut image to obtain a nut image version;
(1.4.2) converting the RGB color space of the nut image subjected to the noise reduction treatment into HSV color space, and enabling the characteristic mark points on the upper surface of the nut and the color of the upper surface of the nut to form contrast to obtain a nut image second edition after the color space conversion;
(1.4.3) carrying out digital corrosion process on the two-version structural elements of the nut image by utilizing morphological open operation to eliminate the connected part of the two-version structural elements of the nut image to obtain a three-version nut image, then carrying out digital expansion process to amplify the element details of the three-version nut image to obtain a four-version nut image, and carrying out denoising process on the edge of the four-version nut image to obtain a five-version nut image;
and (1.4.4) carrying out binarization processing on the five versions of the nut image to enable the edge contour of the nut image to be clearer so as to obtain six versions of the nut image, and dividing the six versions of the nut image subjected to binarization processing into nut characteristic mark points and nut non-characteristic mark points by combining a global threshold value so as to obtain a final version of the nut image.
6. The method for detecting the loosening and the pretightening force loss of the steel bridge bolt according to claim 1, wherein in the step (1.6), the characteristic mark points of the nut identified by the harris angular point detection method are on a circle which takes the geometric centroid of the surface of the nut in the final version of the nut image as the center of the circle and the distance between the angular point of the measured nut with the characteristic mark points and the opposite angular point as the diameter.
7. The method for detecting the loosening and the pretightening force loss of the steel bridge bolt according to the claims 1-6, wherein the bolt where the nut is located is a large hexagon head high-strength bolt connected and used in the steel bridge.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110522703.0A CN113222935B (en) | 2021-05-13 | 2021-05-13 | Method for detecting looseness and pretightening force loss of steel bridge bolt |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110522703.0A CN113222935B (en) | 2021-05-13 | 2021-05-13 | Method for detecting looseness and pretightening force loss of steel bridge bolt |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113222935A true CN113222935A (en) | 2021-08-06 |
| CN113222935B CN113222935B (en) | 2024-04-23 |
Family
ID=77095336
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110522703.0A Active CN113222935B (en) | 2021-05-13 | 2021-05-13 | Method for detecting looseness and pretightening force loss of steel bridge bolt |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113222935B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114820620A (en) * | 2022-06-29 | 2022-07-29 | 中冶建筑研究总院（深圳）有限公司 | Bolt loosening defect detection method, system and device |
| CN117853420A (en) * | 2023-12-18 | 2024-04-09 | 天津农学院 | Steel structure bolt loosening detection method based on machine vision |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104680517A (en) * | 2015-01-22 | 2015-06-03 | 清华大学 | Looseness detection method of bolt |
| US9127998B1 (en) * | 2012-09-04 | 2015-09-08 | University Of South Florida | Active ultrasonic method of quantifying bolt tightening and loosening |
| KR20160136905A (en) * | 2015-05-21 | 2016-11-30 | 부경대학교 산학협력단 | Bolt-loosening Detection Method and Computer Program Thereof |
| CN107145905A (en) * | 2017-05-02 | 2017-09-08 | 重庆大学 | The image recognizing and detecting method that elevator fastening nut loosens |
| KR101819711B1 (en) * | 2016-07-22 | 2018-01-29 | 충북대학교 산학협력단 | Apparatus and method for detecting nut locking using machine vision |
| CN111002259A (en) * | 2019-10-23 | 2020-04-14 | 武汉理工大学 | Rail nut loosening detection and maintenance device and method based on CCD camera |
| US20200278240A1 (en) * | 2018-02-07 | 2020-09-03 | Dalian University Of Technology | Piezoelectric patch-based real-time and high-precision bolt preload detection method and system |
| CN112545790A (en) * | 2020-11-20 | 2021-03-26 | 江苏科技大学 | Combined bed-chair integrated equipment and control method thereof |
| CN112767359A (en) * | 2021-01-21 | 2021-05-07 | 中南大学 | Steel plate corner detection method and system under complex background |
-
2021
- 2021-05-13 CN CN202110522703.0A patent/CN113222935B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9127998B1 (en) * | 2012-09-04 | 2015-09-08 | University Of South Florida | Active ultrasonic method of quantifying bolt tightening and loosening |
| CN104680517A (en) * | 2015-01-22 | 2015-06-03 | 清华大学 | Looseness detection method of bolt |
| KR20160136905A (en) * | 2015-05-21 | 2016-11-30 | 부경대학교 산학협력단 | Bolt-loosening Detection Method and Computer Program Thereof |
| KR101819711B1 (en) * | 2016-07-22 | 2018-01-29 | 충북대학교 산학협력단 | Apparatus and method for detecting nut locking using machine vision |
| CN107145905A (en) * | 2017-05-02 | 2017-09-08 | 重庆大学 | The image recognizing and detecting method that elevator fastening nut loosens |
| US20200278240A1 (en) * | 2018-02-07 | 2020-09-03 | Dalian University Of Technology | Piezoelectric patch-based real-time and high-precision bolt preload detection method and system |
| CN111002259A (en) * | 2019-10-23 | 2020-04-14 | 武汉理工大学 | Rail nut loosening detection and maintenance device and method based on CCD camera |
| CN112545790A (en) * | 2020-11-20 | 2021-03-26 | 江苏科技大学 | Combined bed-chair integrated equipment and control method thereof |
| CN112767359A (en) * | 2021-01-21 | 2021-05-07 | 中南大学 | Steel plate corner detection method and system under complex background |
Non-Patent Citations (3)
| Title |
|---|
| 刘景良;陈飞宇;郑文婷;盛叶;骆勇鹏: "基于归一化频响函数曲率差的钢-木组合梁螺栓松动定位方法", 铁道科学与工程学报, no. 002 * |
| 张建辉，宋平岗: "基于图像识别技术的电机换向片自动检测***", 电工电能新技术, no. 04 * |
| 郭珍珠1;赵伟1, 2: "公路钢桥高强度螺栓松动检测技术研究进展", 中国钢结构协会结构稳定与疲劳分会第17届(ISSF-2021)学术交流会暨教学研讨会 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114820620A (en) * | 2022-06-29 | 2022-07-29 | 中冶建筑研究总院（深圳）有限公司 | Bolt loosening defect detection method, system and device |
| CN114820620B (en) * | 2022-06-29 | 2022-09-13 | 中冶建筑研究总院（深圳）有限公司 | Bolt loosening defect detection method, system and device |
| CN117853420A (en) * | 2023-12-18 | 2024-04-09 | 天津农学院 | Steel structure bolt loosening detection method based on machine vision |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113222935B (en) | 2024-04-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Prasanna et al. | Automated crack detection on concrete bridges | |
| Kim et al. | Unmanned aerial vehicle (UAV)-powered concrete crack detection based on digital image processing | |
| Zhu et al. | Detection of large-scale concrete columns for automated bridge inspection | |
| CN113222935A (en) | Method for detecting looseness and pretightening force loss of steel bridge bolt | |
| German et al. | Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments | |
| Ahmed et al. | Rebar detection and localization for bridge deck inspection and evaluation using deep residual networks | |
| WO2019183475A1 (en) | Vision-based fastener loosening detection | |
| CN113379712B (en) | Steel bridge bolt disease detection method and system based on computer vision | |
| CN106225702A (en) | Fracture width detection apparatus and method | |
| CN110610516B (en) | Railway fastener nut center positioning method | |
| CN113011283B (en) | Non-contact type rail sleeper relative displacement real-time measurement method based on video | |
| CN113538503B (en) | Solar panel defect detection method based on infrared image | |
| CN112198170B (en) | Detection method for identifying water drops in three-dimensional detection of outer surface of seamless steel tube | |
| CN113516629A (en) | Intelligent detection system for TFDS passing operation | |
| Feng et al. | Computer vision for structural dynamics and health monitoring | |
| CN111563896A (en) | Image processing method for catenary anomaly detection | |
| CN115294541A (en) | Local feature enhanced Transformer road crack detection method | |
| CN114926415A (en) | Steel rail surface detection method based on PCNN and deep learning | |
| CN108645865A (en) | A kind of measurement method of the submerged-arc welding steel pipe weld seam amount of the being partially welded parameter based on CCD | |
| CN115078393A (en) | Method for detecting damage of hinge joint of simply supported hollow slab bridge based on computer vision | |
| CN114581385A (en) | Welding seam defect area mapping algorithm based on circle positioning | |
| Liu et al. | Automatic detection on the bolt loose based on digital image processing | |
| Pansare et al. | Crack detection using image processing | |
| CN107654848A (en) | A kind of pipeline location and direction detection method | |
| Huynh et al. | Vision-based inspection of bolted joints: Field evaluation on a historical truss bridge in Vietnam |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |