CN112132824A - Automatic detection method for failure of freight car axle box spring - Google Patents
Automatic detection method for failure of freight car axle box spring Download PDFInfo
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Abstract
A method for automatically detecting a failure of a truck axle box spring belongs to the technical field of truck operation. The invention aims to solve the problems that the accuracy and the stability of fault detection of a truck axle box spring in a manual image detection mode are not high, and the driving safety of a truck cannot be ensured. The method comprises the following steps: 3D high-definition imaging equipment is built around the track, and after the truck passes through the equipment, a height image and a gray image are obtained; roughly positioning a shaft box spring component in the image by combining hardware wheel base information with priori knowledge; after the height image correction is carried out, the axle box spring is accurately positioned after the axle box spring vehicle type is identified by using an image processing method; carrying out fault analysis on the journal box spring by using an advanced image processing algorithm and a mode identification method, and judging whether the journal box spring has a fleeing-out fault or a breaking fault; and uploading an alarm to the axle box spring component with the fault, and carrying out corresponding processing by the staff according to the identification result. The invention is used for detecting the failure of the axle box spring of the truck.
Description
Technical Field
The invention relates to a method for automatically detecting a failure of a spring of a freight car axle box. Belongs to the technical field of truck operation.
Background
The truck axle box spring plays roles of buffering and fixing, is used for avoiding the vehicle from meandering instability within the running speed range, ensures that a curve has good guiding performance when passing through, reduces the abrasion and noise between the wheel rim and the steel rail, and ensures safe and stable running. The breakage or play of the pedestal spring endangers the driving safety, and thus, the detection of the pedestal spring failure is very important for railway-related parts. In axle box spring failure detection, failure detection is generally performed by manually checking an image. However, the conditions of fatigue, inattention and the like are easily caused by vehicle inspection personnel in the working process, and the conditions of missed inspection and wrong inspection are possibly caused by personal reasons, so that the driving safety of the truck cannot be ensured.
The image processing and pattern recognition technology is mature continuously, and the detection efficiency and stability can be improved by adopting an automatic image recognition mode. Therefore, the axle box spring fault recognition is automatically carried out by adopting image processing and mode recognition, and the detection accuracy and stability can be effectively improved.
Disclosure of Invention
The invention aims to solve the problem that the existing truck axle box spring is not high in fault detection accuracy and stability in a mode of manually detecting images, so that the driving safety of a truck cannot be ensured. A method for automatically detecting the failure of a wagon axle box spring is provided.
A method for automatically detecting a failure of a freight car axle box spring comprises the following steps:
the method comprises the following steps that firstly, after a truck passes through imaging equipment, a large image of the axle box spring is obtained, wherein the large image of the axle box spring comprises a height image and a gray level image;
step two, correcting a height image in a large image of the shaft box spring;
respectively acquiring an integral height sub-image and a gray sub-image of the axle box spring;
judging the fault type of the journal box spring according to the obtained gray level image;
and step five, after the fault is identified, calculating the position of the fault in the original image through the mapping relation between the sub-image and the axle box spring large image and between the axle box spring large image and the original image, and displaying the fault through a fault display platform.
Advantageous effects
1. According to the invention, the manual detection is replaced by an automatic image identification mode, so that the detection efficiency and accuracy are improved, and the driving safety of the truck is ensured.
2. The image shot by the 3D hardware equipment can be used for directly positioning the component according to the distance between the component and the camera, namely the depth information of the component, and the subsequent identification is simple and efficient.
3. The system identifies faults on the height and gray level images acquired by the 3D equipment, and the 3D height images are depth information of the detected object, so that fault detection is not affected by rainwater, chalk, white paint and stains, and the stability of the system is enhanced.
4. The 3D height image is corrected and then identified, so that the complexity of detecting faults later is reduced, and the identification accuracy is high.
5. And image fusion is carried out on different detection stations and different vehicle types to obtain a normal fault-free template image of the spring, so that the system universality is improved.
6. The head ring fault is firstly identified, the tail ring fault is then identified, and finally the flow of left and right fleeing and middle layer breaking is identified, so that the system detection efficiency is improved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of the specific fault identification of the present invention;
fig. 3 is a diagram of depth information y versus line position x.
Detailed Description
The first embodiment is as follows: referring to fig. 1, the present embodiment will be described, which is a method for automatically detecting a failure of a journal spring of a freight car, including:
step one, building imaging equipment around a track, wherein the imaging equipment and the side face of a truck body have a certain elevation angle, and acquiring a large image (namely a coarse positioning image, an image which contains an axle box spring and is wider than the axle box spring in range) of the axle box spring after a truck passes through the imaging equipment according to axle distance information and the prior knowledge of the position of the axle box spring in a bogie, wherein the large image comprises a height image and a gray image;
step two, correcting a height image in a large image of the shaft box spring;
respectively acquiring an integral height sub-image and a gray sub-image of the axle box spring;
judging the fault type of the journal box spring according to the obtained gray level image;
and step five, after the fault is identified, calculating the position of the fault in the original image through the mapping relation between the sub-image and the axle box spring large image and between the axle box spring large image and the original image, and displaying the fault through a fault display platform.
The second embodiment is as follows: the first step is that imaging equipment is built around a track, and after a truck passes through the equipment, a large image of the axle box spring is obtained, wherein the large image of the axle box spring comprises a height image and a gray image; the specific process is as follows:
the imaging device comprises a camera acquisition unit, a magnetic steel unit, a 3D image acquisition industrial personal computer unit, a control industrial personal computer unit and an image recognition unit, wherein the camera acquisition unit comprises a camera and a compensation optical module;
the camera acquisition unit shoots and acquires the images of the passing truck, and the 3D image acquisition industrial personal computer unit stores the acquired images; the magnetic steel unit transmits signals of the near-end magnetic steel and the far-end magnetic steel to the control industrial personal computer unit, the control industrial personal computer unit calculates vehicle speed and wheel base information through the acquired signal information of the near-end magnetic steel and the far-end magnetic steel, and transmits the vehicle speed and the wheel base information of the magnetic steel to the image recognition unit, and the image recognition unit realizes automatic recognition algorithm by utilizing the acquired wheel base information, the image information and the like;
the magnetic steel wheel base information obtained by controlling the industrial personal computer unit is combined with the original image of the truck, the position of the axle box spring component can be roughly estimated through priori knowledge, and a large image of the axle box spring is obtained, wherein the large image of the axle box spring comprises a height image and a gray level image which correspond to each other one by one.
Other steps and parameters are the same as those in the first embodiment.
The third concrete implementation mode: the difference between the first embodiment and the second embodiment is that the step corrects the height image in the axle box spring large image; the specific process is as follows:
the side images of the truck are shot through a camera with an elevation angle, so that the depth information of the same plane of the initially obtained 3D image is different, for example, the 3D image of the bearing is not an ideal cylinder, the depth information value of the part far away from the ground is smaller, and the depth information value of the part near the ground is larger;
for objects in the same plane, the ideal depth information value should be the same, but initially in the depth image there is a difference in the images of different rows; the depth information has certain disturbance, but the relation between the difference value of the actual depth information and the initial depth information and the line number in the image is equivalent to two right-angle sides of a right-angle triangle; the depth information of the current x-row position is y, the actual depth information is yr, the corresponding relationship is shown in fig. 3, and the formula is as follows:
(yr-y) ＝ k(x-x0) (1)
wherein k represents a dip tangent value; x0 represents the number of lines in the image of the same plane object when the depth value in the height image takes the maximum value; the k and x0 values are obtained by calculation and fitting of a large amount of collected data; selecting a place where actual objects of the side frames of the bogie are in the same plane and the size of the part is large to acquire data;
since the trucks move in the driving fault, the distance between each truck and the 3D high-definition imaging equipment is changed, so that depth images of different truck types, different stations and passing trucks at different time are collected, and sufficient data are provided for depth image correction;
for the axle box spring large image, yr is obtained after calculation of a formula (1), and then correction of the height image is completed; the objects in the same plane are substantially the same value at this point, and the bearing 3D image is substantially cylindrical.
Other steps and parameters are the same as those in the first or second embodiment.
The fourth concrete implementation mode: the third embodiment is different from the first to third embodiment in that the third step is to acquire a whole height sub-image and a gray sub-image of the axle box spring respectively; the specific process is as follows:
for the corrected height image, because the distance between the bearing and the camera is close to the distance between the axle box spring and the camera, the height information value also uses similar prior knowledge to judge whether the axle box spring component exists in the current image;
if the parts in the height image within the range near the bearing height are only bearings, the current image does not contain the axle box spring parts and is not in the identification range; if the parts in the height image within the range near the bearing height have the bearing and the journal box spring, subsequent identification is carried out; firstly, carrying out global binarization on a shaft box spring by using a threshold th 1; then searching a connected region with the area larger than a threshold th2 in the binary image; if more than 1 communication area is found, the vehicle type containing the axle box spring is identified, and subsequent identification is carried out; if only one communication area is detected, the vehicle type does not contain the axle box spring, and subsequent identification is not carried out;
removing the communication area of the middle bearing in the global binary image to obtain the positions of the two journal box springs; respectively intercepting an axle box spring integral height sub-image and a gray level sub-image through the position of the communication area; intercepting a height sub-image and a gray sub-image of the journal box spring head ring and the upper adjacent component according to the proportion; and (4) intercepting a height sub-image and a gray level sub-image of the tail coil of the journal box spring and the lower adjacent component according to the proportion.
Other steps and parameters are the same as those in one of the first to third embodiments.
The fifth concrete implementation mode: the present embodiment is different from the first to the fourth embodiments in that the fourth step determines and identifies the failure type of the journal box spring; the specific process is as follows:
the shaft box spring play is divided into three conditions of head ring play, tail ring play and left-right inclined play; the axle box spring is broken into head ring breaking, middle layer spring breaking and crack breaking; the shaft box spring head ring play and the tail ring play belong to end spring play, and the same identification mode is adopted; the left and right inclined fleeing is identified by other modes; the axle box spring is broken by adopting two identification modes of head ring breaking and middle layer breaking; if the head ring is identified to be broken or broken, the subsequent identification is not carried out; if the tail ring is identified to be shifted out, subsequent identification is not carried out; further identifying and identifying the left and right play-out and middle break of the shaft box spring;
step four, establishing a template database:
collecting gray level images of axle box springs and adjacent parts at the end parts of different detection stations; carrying out multi-scale image fusion on the acquired images to obtain normal fault-free images, wherein the fused images have more and more valuable information;
step four and step two, end fleeing and breaking fault identification
Under normal conditions, the head ring of the axle box spring is closely attached to the upper adjacent part, the head ring of the axle box spring is broken off or is shifted out, the head ring spring is not closely attached to the upper adjacent part, the edge (the bright-dark joint in the image) is increased compared with a normal fault-free image, the texture is richer, and whether a fault occurs or not can be identified by comparing the gray level subimage of the head ring of the axle box spring and the upper adjacent part with the normal fault-free image; the normal position relation between the tail ring of the journal box spring and the lower adjacent part is that the tail ring is clamped in a groove of the lower adjacent part, the tail ring of the journal box spring rides on the lower adjacent part after jumping out, and whether a fault occurs can be identified by comparing a gray level sub-image of the tail ring of the spring and the lower adjacent part with a normal fault-free image;
and matching the gray level subgraphs of the axle box spring at the front end part and the adjacent parts with the normal fault-free image in the previous step, and performing fault alarm when the detected image similarity is lower than a certain threshold value.
Step four and step three, identifying the middle layer spring fault
Identifying whether the spring has a fault of left-right inclined jumping or not by the integral inclination degree of the spring, and identifying the break fault of the middle layer of the axle box spring if the fault is not detected;
identifying the left and right inclined fleeing faults of the axle box spring by calculating the gradient of an external rectangle through a height image, carrying out OTSU binaryzation processing on the whole height subimage of the axle box spring, then eliminating the spring gap between adjacent springs through morphological opening operation processing, and finally detecting the largest communication area in the image; when the inclination angle of the minimum circumscribed rectangle of the maximum communication area is larger than k, the journal box spring is judged to have a fault of jumping out in a left-right inclined mode;
for fractures of the axle box spring intermediate layer, identified by further analysis for each layer of springs; each layer of spring is firstly segmented, each layer of spring in a normal image can be segmented into a plurality of small areas with the height smaller than the diameter of the normal spring by the segmentation method, if a connected area with the height larger than the height of a circle of spring appears, the occurrence of a vertically staggered breaking fault is proved, and fault alarm is carried out; if the fault is not detected yet, but the width of the communication area is detected to be less than 80% of the normal width, the existence of crack type fracture of the spring is proved, and fault alarm is carried out.
Sixth embodiment, which is different from the first to fifth embodiments, is the dividing method including:
acquiring a spring mask image:
performing global binarization on the original height image of the springs in the current row (the journal box spring has two rows of springs, and after positioning each row (strip) of subgraph, performing subsequent fault detection on the journal box spring in each row respectively), wherein the gray value is 255 when the gray value is larger than th1 and 0 when the gray value is smaller than th1, and thus a mask image can be obtained;
acquiring a spring filtering image:
assigning all pixel values of the filtered image to be 0, wherein the pixel values are the same as those of the original image; if the (i, j) position is not 0 in the mask image, the following operations are performed: taking (i, j) as a center, calculating a brightness mean value in a rectangular region with the original image length of W and the width of H (only non-0 pixel position in the mask image is considered) as a pixel value in the filtering image;
acquiring a spring segmentation image:
setting all the pixels of the segmented image to be 0, wherein the size of the pixels is the same as that of the original image; for the (i, j) position, when the pixel is not 0 in the mask image, the following operations are performed: the pixel values at the (i-h1, j) and (i + h1, j) positions in the filtered image are all subtracted from (i, j), and when the sum of the absolute values of the differences is larger than th, the pixel value of the segmented image is 255.
Claims (8)
1. A method for automatically detecting the failure of a freight car axle box spring is characterized by comprising the following steps:
the method comprises the following steps that firstly, after a truck passes through imaging equipment, a large image of the axle box spring is obtained, wherein the large image of the axle box spring comprises a height image and a gray level image;
step two, correcting a height image in a large image of the shaft box spring;
respectively acquiring an integral height sub-image and a gray sub-image of the axle box spring;
judging the fault type of the journal box spring according to the obtained gray level image;
and step five, after the fault is identified, calculating the position of the fault in the original image through the mapping relation between the sub-image and the axle box spring large image and between the axle box spring large image and the original image, and displaying the fault through a fault display platform.
2. The method for automatically detecting the axle box spring fault of the freight car according to claim 1, wherein in the first step, imaging equipment is built around a track, and after the freight car passes through the equipment, a large image of the axle box spring is obtained and comprises a height image and a gray level image; the specific process is as follows:
combining magnetic steel wheel base information obtained by processing of imaging equipment with an original image of a truck, estimating the position of an axle box spring component by priori knowledge to obtain a large image of the axle box spring, wherein the large image of the axle box spring comprises a height image and a gray image which are in one-to-one correspondence.
3. The method according to claim 2, wherein the step of correcting the height image in the large journal spring image; the specific process is as follows:
shooting a truck side image through a camera to obtain depth information of a 3D image, taking a relation between actual depth information and an initial depth information difference value and a line number in the image as a relation between two right-angle sides of a right-angle triangle, taking the depth information of a current x-line position as y, taking the actual depth information as yr, and taking a corresponding relation formula as follows:
(yr-y)＝k(x-x0) (1)
wherein k represents a dip tangent value; x0 represents the number of lines in the image of the same plane object when the depth value in the height image takes the maximum value; after the calculation of the formula (1), yr is obtained, and the correction of the height image is completed.
4. The automatic failure detection method for the axle box spring of the freight car according to claim 3, wherein the step three of obtaining the whole height sub-image and the gray sub-image of the axle box spring respectively; the specific process is as follows:
if only the bearing is arranged in the bearing height range in the height image, the image does not contain the axle box spring component and is not in the identification range; if the parts in the height image within the range near the bearing height are provided with the bearing and the axle box spring, the axle box spring is globally binarized by a threshold th 1; then searching a connected region with the area larger than a threshold th2 in the binary image; if more than 1 communication area is found, the vehicle type containing the axle box spring is identified, and subsequent identification is carried out; if only one connected region is detected, subsequent identification is not carried out;
removing the communication area of the middle bearing in the global binary image to obtain the positions of the two journal box springs; respectively intercepting an axle box spring integral height sub-image and a gray level sub-image through the position of the communication area; intercepting a height sub-image and a gray sub-image of the journal box spring head ring and the upper adjacent component according to the proportion; and (4) intercepting a height sub-image and a gray level sub-image of the tail coil of the journal box spring and the lower adjacent component according to the proportion.
5. The method according to claim 4, wherein the fourth step is to determine and identify the type of axle box spring failure; the specific process is as follows:
the shaft box spring play is divided into head ring play, tail ring play and left and right inclined play; the axle box spring is broken into head ring breaking, middle layer spring breaking and crack breaking;
when the head ring is identified to be broken or broken, the subsequent fault identification is not carried out; if the tail ring is identified to be shifted out, subsequent identification is not carried out; further identifying the left and right fleeing of the shaft box spring and the middle breaking of the shaft box spring;
fourthly, acquiring grayscale images of the axle box springs at the end parts of different detection stations and adjacent parts, and carrying out image fusion on the acquired images to obtain normal fault-free images;
step four, identifying the end fleeing and breaking faults:
the position relation of the head ring of the axle box spring and the upper adjacent part is in a normal condition of close attachment, when the head ring of the axle box spring is broken off or is shifted out, the head ring spring is not closely attached to the upper adjacent part any more, the edge is more than normal and fault-free images, and whether a fault occurs or not is judged by comparing the grey level sub-images of the head ring of the axle box spring and the upper adjacent part with the normal and fault-free images; the position relation of the tail ring of the axle box spring and the lower adjacent part is that the tail ring is normally clamped in a groove of the lower adjacent part, the tail ring of the axle box spring can ride on the lower adjacent part after coming out, and whether a fault occurs or not is judged by comparing gray level sub-images of the tail ring of the axle box spring and the lower adjacent part with normal fault-free images;
matching the similarity of the gray level subgraphs of the current end part axle box spring and the adjacent parts with a normal fault-free image, and performing fault alarm when the detected image similarity is lower than a certain threshold value;
step four and step three, identifying faults of the middle layer spring:
identifying whether the spring has a fault of left-right inclined jumping or not by the integral inclination degree of the spring, and identifying the break fault of the middle layer of the axle box spring if the fault is not detected;
identifying the left and right inclined fleeing faults of the axle box spring by calculating the gradient of an external rectangle through a height image, carrying out OTSU binaryzation processing on the whole height subimage of the axle box spring, eliminating the spring gap between adjacent springs through morphological opening operation processing, and finally detecting the largest communication area in the image; when the inclination angle of the minimum circumscribed rectangle of the maximum communication area is larger than k, the journal box spring is judged to have a fault of jumping out in a left-right inclined mode;
for the breakage of the middle layer of the axle box spring, each layer of spring in a normal image is divided into a plurality of small areas with the height smaller than the diameter of the normal spring by dividing each layer of spring, if a communicated area with the height larger than the height of one circle of spring appears, the breakage fault of up-down dislocation is proved to occur, and fault alarm is carried out; if the fault is not detected yet, but the width of the communication area is detected to be smaller than the normal width by a certain proportion, the existence of crack type fracture of the spring is proved, and fault alarm is carried out.
6. The automatic failure detection method for the axle box spring of the freight car according to claim 5, wherein the dividing method comprises:
(1) acquiring a spring mask image:
globally binarizing the spring original height image of the current column, wherein the gray value is 255 when the gray value is larger than th1, and 0 when the gray value is smaller than or equal to th1 to obtain a mask image;
(2) acquiring a spring filtering image:
assigning all pixel values of the filtered image to be 0, wherein the pixel values are the same as those of the original image; if the (i, j) position is not 0 in the mask image, the following operations are performed: taking (i, j) as a center, calculating a brightness mean value in a rectangular region with the original image length of W and the width of H (only non-0 pixel position in the mask image is considered) as a pixel value in the filtering image;
(3) acquiring a spring segmentation image:
setting all the pixels of the segmented image to be 0, wherein the size of the pixels is the same as that of the original image; for the (i, j) position, when the pixel is not 0 in the mask image, the following operations are performed: the pixel values at the (i-h1, j) and (i + h1, j) positions in the filtered image are all subtracted from (i, j), and when the sum of the absolute values of the differences is larger than th, the pixel value of the segmented image is 255.
7. The method for automatically detecting the axle box spring fault of the freight car according to claim 1, wherein an imaging device is built around the track, and the imaging device is a high-definition 3D imaging device.
8. The method according to claim 5, wherein the width of the communicating region is smaller than the normal width by a ratio of 80% of the normal width.
Priority Applications (1)
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