Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a bolt structure with a loosening detection function and a bolt loosening detection method.
The purpose of the invention is realized by the following technical scheme:
the utility model provides a bolt structure with not hard up detection function, includes nut and packing ring, the nut bottom is provided with shielding piece, be provided with boss and warning area on the packing ring, boss and warning area set up on same circumference, shielding piece includes first state and second state, and under the first state, the nut is in the stable connection state, and shielding piece shields the warning area, and under the second state, the nut is in not hard up state, and shielding piece and warning area dislocation, warning area are in part or whole exposure state.
Further, an annular connecting piece is arranged at the bottom of the nut, and the shielding piece is arranged on the side wall of the annular connecting piece. In the actual use process, when the bolt structure is installed, the nut and the washer are sleeved on the bolt, the nut is rotated, the distance between the washer and the head of the bolt is adjusted, the track mechanical unit is fixedly connected by utilizing the matching of the nut, the washer and the bolt, when the nut is screwed down and is stably connected with the bolt, the shielding piece completely shields the warning area on the washer and is abutted against the side wall of the boss, and at the moment, the bolt structure is shown to be in a stable connection state; and when the not hard up condition appeared in bolt structure, the nut after becoming flexible will drive the shielding piece rotation, lets dislocation between shielding piece and the warning district, and is different according to the not hard up degree of nut, and the warning district will be partly or whole in the state of exposing, and at this moment, whether warning district in the direct observation bolt structure exposes, just can directly judge whether not hard up of bolt structure, effectively improves the portability to the track maintenance detection.
Further, the outer diameter of the annular connecting piece is the same as the diameter of the circumcircle of the nut.
Further, the shield is of an arch-shaped structure.
Furthermore, boss and warning area are domes respectively.
Furthermore, the shielding piece is of a 180-degree arch structure, and the boss and the warning area are of 90-degree arch structures respectively.
Further, the boss and the warning area are arranged adjacently. The shielding piece is arranged to be in a 180-degree arch structure, the boss and the warning area are respectively arranged to be in a 90-degree arch structure, the boss and the warning area are arranged adjacently, the nut is in a stable connection state when the shielding piece is in a first state, the shielding piece shields the warning area, meanwhile, the shielding piece is abutted against one side wall of the boss, at the moment, the boss is located on the circumference of the gasket, 1/4 is the boss, 1/2 is shielded by the shielding piece, and 1/4 is in an exposed state; and when the shielding piece was in and the second state, shielding piece and warning area dislocation, at this moment, utilized the opposite side lateral wall of boss, can carry on spacingly to the shielding piece to a certain extent, restraint nut's not hard up degree realizes exposing warning area, shows not hard up warning information's basis, prevents effectively that bolt structure from further becoming flexible, improves bolt structure's safety in utilization.
Furthermore, the outer diameter of the shielding piece is not smaller than the outer diameter of the warning area, and the inner diameter of the shielding piece is not larger than the inner diameter of the warning area.
Further, the friction coefficient of the upper surface of the gasket where the warning area is located is smaller than that of the lower surface of the gasket. Through being less than the lower surface friction system of packing ring with the coefficient of friction of warning district place gasket upper surface, let the packing ring be difficult for taking place to slide with mechanical unit, let the difficult nut that is become flexible after the packing ring drive the rotation simultaneously, guarantee that the warning district on the packing ring is changeed after the nut is not hard up and is revealed out.
Further, the surface in warning district is provided with the dope layer. In the installation use of bolt structure, according to the difference of installation requirement, can select the control boss to be in the low point, let bolt structure can not cause the unexpected condition that shows in warning district to take place because of receiving the boss dead weight influence, also can select the control boss to be in the high point, and possess and receive the dead weight and have the trend to low point pivoted, after bolt structure is not hard up, the packing ring is because the extrusion force that receives reduces when unable to overcome the influence of boss dead weight, the packing ring just can receive the gravity influence of boss to take place the rotation, through the rotation of packing ring, and the rotation of the nut that becomes flexible, expose warning district maximum range, show the not hard up warning information of this bolt structure.
Further, the shielding piece is abutted against the side wall of the boss in the first state.
Further, boss and warning area set up on same circumference, warning area and boss adjacent setting, and the one end that the boss was kept away from in the warning area is provided with elasticity locating part. Under the first state, elasticity locating part receives the shielding piece oppression to be in the shrink state, when bolt assembly is not hard up, nut after not hard up will drive the shielding piece rotatory, let the dislocation between shielding piece and the warning area, treat that the not hard up degree of shielding piece exceeds the predetermined value, shielding piece can rotate 90 promptly, let warning area and elasticity locating part expose completely, at this moment, elasticity locating part loses the oppression of shielding piece, extend at once, the lateral wall in warning area is kept away from to the cooperation boss, spacing to the shielding piece, let shielding piece and packing ring joint, make the warning area keep exposing the state always, let the information in warning area can not receive bolt assembly further not hard up and shielded once more, improve equipment's use convenience.
Further, the diameter of the inner arc of the boss is not smaller than the outer diameter of the annular connecting piece.
A rail bolt looseness detection method is based on the bolt structure and is used for detecting through a method comprising the following steps: the method comprises the steps of obtaining picture information of a bolt on a track, judging whether a warning area on a bolt structure is in an exposed state or not according to the picture information, if part or all of the warning area is in the exposed state, indicating that the bolt is loosened, and if the warning area is not exposed, indicating that the bolt is not loosened.
Furthermore, in the process of judging the picture information, intelligent analysis and judgment are carried out on the picture information by adopting a deep learning method, the deep learning method is realized on the basis of a Tensorflow deep learning framework, and a data source uses an MNIST data set and carries out model training by respectively adopting a softmax regression algorithm and CNN deep learning.
Further, the deep network structure for CNN deep learning comprises a convolutional layer, an excitation layer, a pooling layer and a full-link layer
The convolutional layer is: the operation of performing inner product on the image and the filter matrix is convolution, the convolution layer can be used for acquiring the characteristics of the image through the convolution operation, the modules are stacked, different convolution kernels are used for acquiring higher-order characteristics, and in the stacked depth structure, the weight parameters of the convolution kernels are shared, so that the depth structure can acquire higher-dimensional characteristics, but the parameter quantity is not greatly increased;
the excitation layer is as follows: the excitation layer is not explicitly indicated in the figure, and is usually used for selecting an appropriate excitation function after the convolution operation to process the result of the convolution operation; the function of the activation function is to reserve and map the characteristics of the activated neurons through the function, so as to solve the nonlinear problem of the network structure;
the pooling layer is as follows: and the data is downsampled, so that overfitting can be avoided, and the operations such as data characteristics and data calculation amount are reduced.
The full connection layer: the connection of the layer is the connection of the neurons in the neural network structure between each layer; the fully connected layer plays a role of a classifier in the whole convolutional neural network; mapping original data to a hidden layer feature space through operations of a convolutional layer, a pooling layer, an activation function layer and the like, wherein a full connection layer is used for mapping learned distributed feature representation to a sample mark space; a layer of structure which can be customized after passing through the layer is used for classification or regression; the full connection layer is realized by acquiring more characteristics of high-dimensional images through a depth network structure to process the images, and controlling parameters through convolution operation.
The invention has the beneficial effects that: according to the bolt structure with the loosening detection function and the bolt loosening detection method, when the bolt structure is loosened, the loosened nut drives the shielding piece to rotate, the shielding piece and the warning area are staggered, part or all of the warning area is in an exposed state according to different loosening degrees of the nut, and at the moment, whether the warning area on the bolt structure is exposed or not is directly observed, so that whether the bolt structure is loosened or not can be directly judged, and the portability of rail maintenance detection is effectively improved.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
As shown in fig. 1-3, a bolt structure with become flexible and detect function, includes nut and 1 packing ring 2, 1 bottom of nut is provided with shielding piece 3, be provided with boss 4 and warning area 5 on the packing ring 2, boss 4 and warning area 5 set up on same circumference, shielding piece 3 includes first state and second state, and under the first state, nut 1 is in stable connection state, and shielding piece 3 shields warning area 5, and under the second state, nut 1 is in not hard up state, and shielding piece 3 misplaces with warning area 5, and warning area 5 is in partial or whole exposure state.
Specifically, the bottom of the nut 1 is provided with an annular connecting piece, and the shielding piece 3 is arranged on the side wall of the annular connecting piece. In the actual use process, when the bolt structure is installed, the nut 1 and the washer 2 are sleeved on the bolt, the nut 1 is rotated, the distance between the washer 2 and the head of the bolt is adjusted, the track mechanical unit is fixedly connected by utilizing the matching of the nut 1, the washer 2 and the bolt, when the nut 1 is screwed down and is stably connected with the bolt, the shielding piece 3 completely shields the warning area 5 on the washer 2, and the shielding piece 3 is abutted against the side wall of the boss 1, at the moment, the bolt structure is shown to be in a stable connection state; and when the not hard up condition appeared in the bolt structure, nut 1 after not hard up will drive shielding piece 3 rotatory, lets dislocation between shielding piece 3 and the warning district 5, and according to the not hard up degree difference of nut 1, warning district 5 will be partly or all in the state of exposing, and at this moment, whether warning district 5 in the direct observation bolt structure exposes, just can directly judge whether not hard up of bolt structure, effectively improves the portability to the track maintenance detection.
Specifically, the outer diameter of the annular connector is the same as the diameter of the circumcircle of the nut 1.
In particular, the shutter 3 is of arched configuration.
Specifically, the boss 4 and the warning area 5 are respectively of an arch structure.
Specifically, the shielding member 3 is of an arch structure of 180 degrees, and the boss 4 and the warning area 5 are of arch structures of 90 degrees respectively.
Specifically, the boss 4 and the warning area 5 are adjacently arranged. By arranging the shielding piece 3 into a 180-degree arch structure, arranging the boss 4 and the warning area 5 into 90-degree arch structures respectively, and arranging the boss 4 and the warning area 5 adjacently, the nut 1 is in a stable connection state when the shielding piece 3 is in a first state, the shielding piece 3 shields the warning area 5, meanwhile, the shielding piece 3 is abutted against one side wall of the boss 4, at the moment, the boss 4 is arranged on the circumference of the gasket 2, 1/4 is the boss 4, 1/2 is shielded by the shielding piece 3, and 1/4 is in an exposed state; and when shielding piece 3 was in and the second state, shielding piece 3 misplaced with warning area 5, at this moment, utilized boss 4's opposite side lateral wall, can carry on spacingly to shielding piece 3 to a certain extent, and restraint nut 1's not hard up degree realizes exposing warning area 5, shows not hard up warning information's basis, prevents effectively that bolt structure is further not hard up, improves bolt structure's safety in utilization.
Specifically, the outer diameter of the shielding piece 3 is not smaller than the outer diameter of the warning area 5, and the inner diameter of the shielding piece 3 is not larger than the inner diameter of the warning area 5.
In particular, the friction coefficient of the upper surface of the washer 2 on which said warning zone 5 is located is lower than the friction system of the lower surface of the washer 2. Through being less than the lower surface friction system of packing ring 2 with the coefficient of friction of 5 surperficial washers in warning area 2, let packing ring 2 be difficult for taking place to slide with mechanical unit, let packing ring 2 be difficult for being driven by the nut after becoming flexible simultaneously and rotate, guarantee that warning area 5 on packing ring 2 is more easily revealed out after nut 1 is become flexible.
Specifically, the surface of warning district 5 is provided with the dope layer. In the installation use process of bolt structure, according to the difference of installation requirement, can select control boss 4 to be in the low point, let bolt structure can not cause the unexpected condition that shows in warning district to take place because of receiving the influence of boss dead weight, also can select control boss 4 to be in the high point, and possess and receive the dead weight and have the trend that rotates to the low point, after bolt structure is not hard up, when the packing ring reduces to unable the influence of overcoming boss 4 dead weight because the extrusion force that receives, packing ring 2 just can be influenced by the gravity of boss 4 and take place the rotation, through the rotation of packing ring 2, and the rotation of the nut 1 that becomes flexible, will expose warning district 5 maximum ranges, show the not hard up warning information of this bolt structure.
Specifically, in the first state of the shield 3, the shield 3 abuts against the side wall of the boss 1.
Specifically, boss 3 and warning area 5 set up on same circumference, warning area 5 sets up with boss 4 is adjacent, and warning area 5 keeps away from the one end of boss 4 and is provided with elasticity locating part. Under the first state, elasticity locating part receives shielding piece 3 oppression to be in the shrink state, when bolt assembly is not hard up, nut 1 after not hard up will drive shielding piece 3 rotatory, let dislocation between shielding piece 3 and the warning area 5, treat that shielding piece 3 not hard up degree exceeds the predetermined value, shielding piece 3 rotates 90 promptly, let warning area 5 and elasticity locating part expose completely, at this moment, elasticity locating part loses shielding piece 3's oppression, the extension at once, cooperation boss 4 keeps away from the lateral wall of warning area 5, it is spacing to carry out shielding piece 3, let shielding piece 3 and packing ring 2 joint, make warning area 5 keep exposing the state always, let the information of warning area 5 can not receive bolt assembly further not hard up and be shielded once more, improve equipment's use convenience.
Specifically, as shown in fig. 4, a limiting plate 7 is arranged at the bottom of the shielding piece 3, the limiting plate 7 is of an arch structure, and the limiting plate 7 is connected with the shielding piece 3 through a clamping elastic piece 9.
Specifically, the limiting plate 7 is in an arch structure of 150-175 degrees. Preferably, the limiting plate 7 is in an arch structure of 170 degrees.
Specifically, the bottom of shielding member 3 is provided with recess 8, recess 8 and limiting plate 7 phase-match, joint elastic component 9 evenly sets up in recess 8. In the first state, the nut 1 is stably connected, the elastic limiting piece is pressed by the shielding piece 3 to be in a contraction state, and the shielding piece 3 simultaneously compresses and clamps the elastic piece 9 and is positioned in the groove 8; at the not hard up degree of shielding member 3 and surpassing the predetermined value, shielding member 3 rotates to let warning district 5 and elasticity locating part expose completely promptly, at this moment, elasticity locating part loses shielding member 3's oppression, extend at once, the lateral wall of warning district 5 is kept away from to cooperation boss 4, constitute a spacing region, simultaneously, joint elastic component 9 releases recess 8 with limiting plate 7, push limiting plate 7 into in the spacing region, spacing to limiting plate 7, let shielding member 3 and packing ring 2 joint, make warning district 5 keep exposing the state always, let the information in warning district 5 can not receive bolt assembly further not hard up and shielded once more, improve equipment's use convenience.
Preferably, the clamping elastic piece 9 is partially embedded on the inner wall of the groove 8.
Specifically, the diameter of the inner arc of the boss 4 is not less than the outer diameter of the annular connecting piece.
A rail bolt looseness detection method is based on the bolt structure and is used for detecting through a method comprising the following steps: the method comprises the steps of obtaining picture information of a bolt on a track, judging whether a warning area on a bolt structure is in an exposed state or not according to the picture information, if part or all of the warning area is in the exposed state, indicating that the bolt is loosened, and if the warning area is not exposed, indicating that the bolt is not loosened.
Specifically, in the process of judging the picture information, intelligent analysis and judgment are carried out on the picture information by adopting a deep learning method, the deep learning method is realized on the basis of a Tensorflow deep learning framework, and a data source uses an MNIST data set and carries out model training by respectively adopting a softmax regression algorithm and CNN deep learning.
Specifically, the deep network structure for CNN deep learning comprises a convolutional layer, an excitation layer, a pooling layer and a full-connection layer
The convolutional layer is: the operation of performing inner product on the image and the filter matrix is convolution, the convolution layer can be used for acquiring the characteristics of the image through the convolution operation, the modules are stacked, different convolution kernels are used for acquiring higher-order characteristics, and in the stacked depth structure, the weight parameters of the convolution kernels are shared, so that the depth structure can acquire higher-dimensional characteristics, but the parameter quantity is not greatly increased;
the excitation layer is as follows: the excitation layer is not explicitly indicated in the figure, and is usually used for selecting an appropriate excitation function after the convolution operation to process the result of the convolution operation; the function of the activation function is to reserve and map the characteristics of the activated neurons through the function, so as to solve the nonlinear problem of the network structure;
the pooling layer is as follows: and the data is downsampled, so that overfitting can be avoided, and the operations such as data characteristics and data calculation amount are reduced.
The full connection layer: the connection of the layer is the connection of the neurons in the neural network structure between each layer; the fully connected layer plays a role of a classifier in the whole convolutional neural network; mapping original data to a hidden layer feature space through operations of a convolutional layer, a pooling layer, an activation function layer and the like, wherein a full connection layer is used for mapping learned distributed feature representation to a sample mark space; a layer of structure which can be customized after passing through the layer is used for classification or regression; the full connection layer is realized by acquiring more characteristics of high-dimensional images through a depth network structure to process the images, and controlling parameters through convolution operation.
Test examples
When the detection method is adopted to detect the loosening condition of the bolt structure on the track, the unmanned aerial vehicle is used for fixed-point back-and-forth aerial photography monitoring to acquire real-time images; the camera drivers of labview, NI Vision Development, Module Vision Development kit and NI Vision acquisition Software are selected, and the specific modules and processes comprise,
1. camera acquisition module
A VI is newly built, a front panel diagram is organized, and a program block diagram is opened; then, selecting an IMAQ Create data cache region and creating a character string constant for the IMAQ Create data cache region;
IMAQdx Open Camera: opening a camera, loading a camera configuration file, and creating a session handle of IMAQdx for the camera;
3. call IMAQdx Configure Grab: finishing the initialization of the Grab, executing the capture images continuously circulating on the buffer zone ring by the Grab, and acquiring the high-speed images by using the Grab VI;
4. calling IMAQdx Grab to acquire the latest image output of the current frame and creating an image display for the latest image output; this VI is only invoked after the IMAQdx configuration Grab VI is invoked; if the image type does not match the video format of the camera, this VI will change the image type to the appropriate format;
5. displaying pictures
Before displaying the picture, converting the collected picture into a gray picture by using an IMAQ ExtractSingleColorplane, thereby facilitating the subsequent image processing operation;
6. convolving acquired images
The convolution is carried out in a conditional structure, so that the function of convolution on the image can be realized by pressing a convolution button; if this condition is false, no action is done within the structure;
(1) firstly, a character string is taken to store a convolution kernel; then, calling IMAQ building Kernel, and constructing a convolution matrix through converting character strings;
(2) secondly, calling IMAQ volume, and filtering the image by using a linear filter; the output of the function is a convoluted picture; one of the two input pictures is the picture obtained in the acquisition step, the other picture is the picture obtained in the acquisition step, the picture is copied and stored in the other memory after being acquired, namely, a picture buffer zone is also established for storing the copy, and the other input is the convolution matrix obtained in the step (1); finally, a normalization factor is added, elements in the matrix are summed, and then the sum is divided by the normalization factor;
(3) copying and displaying pictures
Establishing a picture buffer area again, storing the convolved picture, copying the obtained convolved picture, otherwise replacing the convolved picture in a later while loop, and storing the picture which is not the convolved picture; the picture display calls a picture display window.
7. Saving the convolved pictures
Saving the convolved picture is in another conditional structure because the saved button function is to be implemented; calling a file dialog box for path selection during storage; then, calling the IMAQ Write BMP File, and storing the picture in a BMP format; saving to create a picture buffer a picture is copied to a buffer in a convolutional conditional structure and then saved by pulling a line in the buffer to an IMAQ Write BMP File.
8. Realize the real-time collection of pictures and the continuous operation of programs
Using a while loop to display the IMAQdx Grab and the subsequent pictures in the two condition structures and the picture acquisition, and then creating an input control under the loop condition to realize the exit button function of the program;
the back end carries out intelligent analysis and judgment on the acquired image information by adopting a deep learning method, the deep learning method is realized based on a Tensorflow deep learning framework, and a data source uses an MNIST data set and carries out model training by respectively adopting a softmax regression algorithm and CNN deep learning; whether the warning area on the bolt structure is in an exposed state or not is judged through a deep learning method, if part or all of the warning area is in the exposed state, the bolt is loosened, and if the warning area is not exposed, the bolt is not loosened, and the result is shown in fig. 5 and 6.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.