Defect detection system and method for monorail crane track

Abstract

The invention discloses a monorail crane track defect detection system and method, and relates to the technical field of track detection, the monorail crane track defect detection system comprises a vehicle selection module, a data acquisition module, a data judgment module, a secondary acquisition module and a defect division module, comparing the pilot operation data with a standard threshold interval to judge whether the vehicle meets requirements or not; the technical key points are as follows: a vehicle is detected before detection, and the reference of the detection is taken as the reference for subsequently judging whether a defect exists on a monorail crane track or not, so that the subsequent judgment on the defect is more accurate, smaller defects can be identified, and then the defect position is further acquired and analyzed; defect content and defect levels are analyzed, a corresponding defect repair plan is conveniently formulated according to the defect levels subsequently, the amount of data processed in the whole process is small, defect positions can be analyzed in real time, and the method has a good use prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of track detection, and in particular to a defect detection system and method for a monorail hanging track. Background Art

[0002] The monorail crane uses a special I-beam suspended above the tunnel as a track, is connected by hanging vehicles with various functions, and is towed by traction equipment to run along the track.

[0003] The monorail suspended track is made of a special I-beam suspended above the tunnel.

[0004] The monorail locomotive is a new type of mine auxiliary transportation equipment. It makes up for the shortcomings of overhead line electric locomotives and battery locomotives that do not have the ability to climb slopes. Therefore, it can realize long-distance continuous transportation and reduce transportation links. The monorail locomotive also has the advantages of fast running speed and less staff input, which not only improves transportation efficiency but also reduces labor costs. Therefore, it is widely used underground.

[0005] Monorail crane underground transportation environment is complex, space is narrow, and lighting is insufficient. Traditional manually driven monorail cranes face many uncertainties and become one of the main sources of mine transportation accidents. Monorail crane locomotive accidents have a wide range of hazards and take a long time to repair. Therefore, in order to reduce the accident rate of monorail cranes and reduce property losses, it is necessary to reduce and curb monorail crane accidents through technical measures. The most direct way is to ensure that the monorail crane track is in normal condition.

[0006] The existing application publication number is CN114740086A, and the invention patent named A Monorail Crane Track Defect Detection Method records that the hardware used mainly includes ultrasonic sensors, RFID modules, Wi-Fi signal transmission modules, and image acquisition modules; the monorail crane track defect inspection method includes data statistics and periodic inspections, sensor data processing and analysis, cumulative comparison, and defect location. This solution uses a monorail crane as a power source to move on the track, carrying ultrasonic sensors, RFID modules, Wi-Fi signal transmission modules, image acquisition modules, etc., to develop a track parameter identification and detection system that can detect track parameters (Y-axis, Z-axis misalignment, straightness) and locate defects, and design a host computer interactive interface that can display track video information and detection results in real time, to achieve autonomous detection of track misalignment defects and straightness.

[0007] However, according to the above content combined with the prior art, the central scheme of the scheme recorded in the above patent is mainly used to detect the disconnection of the joint position of the monorail crane track and the deformation of the track. The detection process mainly relies on the large-scale collection of detailed track data, and then the collected data is analyzed in detail to determine whether the entire monorail crane track is deformed and the joint position is disconnected.

[0008] However, the above scheme has great defects in actual use. First of all, the analysis is mainly based on the outer contour to determine whether the joint position of the monorail crane track is disconnected and the track is deformed. It is suitable for situations where large deformation occurs and the joint position is obviously disconnected. It is difficult to monitor when the monorail crane track is partially deformed (track wear and local bulges) (when the crane is running, the position where the wheels contact the side of the I-beam is blocked by the structure of the I-beam itself). Therefore, when it detects that there are defects in the monorail crane track, the situation of the monorail crane track is often more serious, which requires a lot of time to deal with and involves certain risks, which does not meet people's use requirements. For this reason, we have developed a defect detection system and method for monorail crane tracks. Summary of the invention

[0009] 1. Technical issues to be resolved

[0010] In view of the deficiencies in the prior art, the present invention provides a defect detection system and method for a monorail crane track, which inspects the vehicle before inspection, and uses the benchmark of the inspection as a benchmark for subsequent judgment on whether there are defects on the monorail crane track, so that the subsequent judgment on the existence of defects is more accurate and can identify smaller defects. The defect locations are then further collected and analyzed, and the content and level of the defects are analyzed, so as to facilitate the subsequent formulation of corresponding defect repair plans according to the defect level. During the whole process, there is no need to conduct a comprehensive analysis of the entire monorail crane track, the amount of data processed is small, and the defect location can be analyzed in real time, which effectively solves the problem that it takes a while for the existing monorail crane track data to produce results after data collection, causing the monorail crane track to be unsuitable for a period of time and having a low effect. The overall use effect is good and has a good prospect for use.

[0011] (II) Technical solution

[0012] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0013] A defect detection system for a monorail hanging track includes a vehicle selection module, a data acquisition module, a data judgment module, a secondary acquisition module and a defect classification module:

[0014] Vehicle selection module: used to obtain vehicle test run data, and compare the test run data with the standard threshold range to determine whether the vehicle meets the requirements. If it meets the requirements, the test vehicle is determined and the average parameters of the vehicle are calculated;

[0015] Data acquisition module: used to obtain the parameters of the vehicle running on the monorail in real time and the track data captured by the explosion-proof camera on the vehicle;

[0016] Data judgment module: used to receive the data collected by the data acquisition module, and analyze the collected data to determine whether there are defects in the monorail crane track;

[0017] Secondary acquisition module: used to receive a defect signal, and start the defect data acquisition device to collect data of the defect position when receiving the defect signal;

[0018] Defect classification module: used to analyze the data of the defect location, determine the defect type, and calculate the defect level according to the defect type, defect data and detection vehicle operating parameters, obtain the defect location, mark the defect level at the defect location, generate defect drawings, and activate the warning device at the defect location according to the defect level.

[0019] Further, the vehicle test run data includes a stability index of the vehicle running at a constant speed for 50 meters on a horizontal monorail hanging track. The vehicle test run data is collected by a vibration sensor installed on the vehicle. The vibration sensor collection time period is set to 0.05 seconds. The vehicle average parameter is the vehicle stability index parameter;

[0020] The parameters of the vehicle running on the monorail are the stability indicators generated by the uniform motion of the vehicle on the monorail, and the track data captured by the on-board explosion-proof camera are the picture data captured when the vehicle is running;

[0021] The defect location is sent via a positioning device on the inspection vehicle.

[0022] Further, the steps to determine whether the vehicle meets the requirements are as follows:

[0023] Remove the stability index of the first 10 meters and the last 10 meters of the vehicle running on the horizontal monorail. The first 10 meters and the last 10 meters involve the acceleration and deceleration process of the vehicle. The acceleration and deceleration process will cause the vibration data to change, so it needs to be removed. After removal, the overall data is more accurate;

[0024] Get the maximum value of the stability index when the vehicle runs at a constant speed for 30 meters on the horizontal monorail hanging track;

[0025] Determine whether the maximum value is within the standard threshold interval. If the maximum value is within the standard threshold interval, the vehicle meets the detection requirements. If the maximum value is outside the standard threshold interval, the vehicle does not meet the detection requirements.

[0026] The formula for calculating the vehicle stability index parameter is as follows:

[0027]

[0028] In the formula, is the vehicle stability index parameter, n is the number of stability indicators collected during the 30-meter uniform motion, Zdcsi is the i-th stability indicator.

[0029] Furthermore, the steps for analyzing the track data captured by the explosion-proof camera on the detection vehicle and determining whether there are defects in the monorail crane track are as follows:

[0030] S1 obtains the picture taken by the explosion-proof camera on the inspection vehicle, and then crops the picture;

[0031] S2 grayscales the cropped image, and then performs Gaussian filtering on the grayscale image;

[0032] S3 calculates the average difference between the grayscale values ​​of the pixel point in the image after Gaussian filtering and the four adjacent pixels above, below, left and right;

[0033] S4 marks the pixel points whose average difference in grayscale values ​​of adjacent pixel points is located in the set extraction interval, and sets the grayscale values ​​of other pixel points to the maximum value;

[0034] S5 selects the lower left corner of the cropped image as the coordinate origin, linearly fits a linear function, and determines the number of linear functions formed by the marked pixel points;

[0035] If the number of linear functions is 2 and there is no interruption in the linear function, it is determined that the monorail track of the corresponding section in the photo has no defects;

[0036] If the linear function is any other number, or the number of linear functions is 2, but the linear function is interrupted, it is determined that there is a defect in the monorail track. The defect position is the intersection of the interruption and the linear function. The defect type is a broken monorail track. The detection vehicle stops running and the warning device at the corresponding position is activated to issue a warning.

[0037] Furthermore, the calculation formula for graying the cropped image is as follows:

[0038] Hdz=0.3×R+0.59×G+0.11×B

[0039] Hdz is the grayscale value of the image after grayscale, and R, G, and B are the red, green, and blue values ​​of the pixels in the image, respectively.

[0040] Furthermore, the formula for calculating the average difference between the grayscale values ​​of the pixel point and the four adjacent pixels above, below, left and right in the image after Gaussian filtering is as follows:

[0041]

[0042] Where Pjcz is the average difference between the grayscale values ​​of the pixel and the four adjacent pixels above, below, left and right, Hdz is the grayscale value of the pixel, and Hdz 上 、Hdz 下、Hdz 左 and Hdz 右 They are the grayscale values ​​of the four adjacent pixels above, below, left and right.

[0043] Furthermore, the parameters of the test vehicle running on the monorail are analyzed to determine whether there are defects in the monorail and obtain the stability index of the uniform speed running on the monorail, and compare the obtained data with the set standard interval. Compare and if the stability index is within the standard range, the monorail crane track is judged to be normal; if the stability index is outside the standard range, the monorail crane track has defects, and the defective position is the position corresponding to the stability index that exceeds the standard range;

[0044] The calculation formula for the fluctuation value δ is as follows:

[0045]

[0046] Among them, α is a constant coefficient, 0.10<α<0.35, e is a natural constant, Pd is the slope value, Pdxs 上 and Pdxs 下 are the uphill slope coefficient and the downhill slope coefficient, Pdxs 下 <Pdxs 上 <1.

[0047] Furthermore, the defect data collection device collects the data of the defect location as a photo of the side of the monorail crane track; the steps of analyzing the data of the defect location and determining the defect type are as follows:

[0048] Grayscale the image of the defect location, and then perform Gaussian filtering on the grayscale image;

[0049] The calculation formula for graying the image is as follows:

[0050]

[0051] Compare the image processed by Gaussian filtering with the standard image, calculate the absolute value of the gray value difference, and mark the pixels whose absolute value of the gray value difference is greater than the set value β. The defect area S = m × γ is calculated by the number of marked pixels, where m is the number of marked pixels and γ is the ratio of the difference between the pixel and the actual point; record the minimum gray value difference Hdz′ min , maximum gray value difference Hdz′ max and the average gray value difference Pjcz′, cz i is the difference between the grayscale value of the i-th marked pixel and the grayscale value of the pixel at the corresponding position on the standard image;

[0052] Gray value minimum difference Hdz′min , maximum gray value difference Hdz′ max The calculation method is to subtract the grayscale value of the pixel in the defect photo from the grayscale value of the pixel in the standard photo;

[0053] Compare the minimum grayscale value difference and the maximum grayscale value difference with the set classification interval to determine the defect type;

[0054] The classification intervals include the deformation concave interval (-255, -μβ), the wear interval (-μβ, -β), and the deformation convex interval (β, 255), 1 < μ < 3;

[0055] If the minimum gray value difference Hdz′ min If it is located in the deformation depression interval, the defect type is that there is a depression in the track;

[0056] If the minimum gray value difference Hdz′ min If it is in the wear range, the defect type is track wear;

[0057] If the maximum gray value difference Hdz′ max If it is located in the deformation convex area, the defect type is that there is a convexity in the track.

[0058] Furthermore, the defect level is calculated as follows:

[0059]

[0060] Where Qxdj is the defect level, Zdcs is the stability index of the defect position, A and C are the weight ratios corresponding to the stability index and the photo data, respectively, A+C=1, C>A.

[0061] Furthermore, a method for detecting defects in a monorail crane track comprises the following steps:

[0062] Obtain vehicle test run data, and compare the test run data with the standard threshold range to determine whether the vehicle meets the requirements. If it meets the requirements, determine to test the vehicle and calculate the average parameters of the vehicle;

[0063] Real-time acquisition of the parameters of the test vehicle running on the monorail and the track data captured by the on-board explosion-proof camera of the test vehicle;

[0064] Receive data collected by the data acquisition module, and analyze the collected data to determine whether there are defects in the monorail crane track;

[0065] receiving a defect signal, and starting a defect data collection device to collect data of the defect position when receiving the defect signal;

[0066] Analyze the data of the defect location, determine the defect type, calculate the defect level based on the defect type, defect data and test vehicle operating parameters, obtain the defect location, mark the defect level at the defect location, generate a defect drawing, and activate the warning device at the defect location based on the defect level.

[0067] (III) Beneficial effects

[0068] The present invention provides a defect detection system and method for a monorail crane track, which has the following beneficial effects:

[0069] 1. The present invention records a defect detection system for a monorail track, which detects the vehicle before detection, and uses the benchmark of this detection as a benchmark for subsequent judgment on whether there are defects on the monorail track, so that the subsequent judgment on the existence of defects is more accurate, and smaller defects can be identified. Then, the defect location is further collected and analyzed, and the content and level of the defect are analyzed, so as to facilitate the subsequent formulation of corresponding defect repair plans according to the defect level. During the whole process, there is no need to conduct a comprehensive analysis of the entire monorail track, the amount of data processed is small, and the defect location can be analyzed in real time, which effectively solves the problem that it takes a while for the existing monorail track data to produce results after data collection, causing the monorail track to be unsuitable for a period of time and the effect is not high. The overall use effect is good and has a good prospect for use.

[0070] 2. The present invention records a defect detection system for a monorail crane track, which performs multiple defect judgments during operation, and uses the track data photographed by the explosion-proof camera on the detection vehicle to make a preliminary judgment to determine whether the monorail crane track has a more serious defect due to an accident. When the defect exists, the detection is stopped to ensure safety, and then the parameters of the detection vehicle running on the monorail crane track are used to determine whether there is a defect. When a defect exists, defect data is further collected, and the defect type and defect level are determined by combining the defect data and the parameters of the detection vehicle running on the monorail crane track. The entire defect analysis is relatively accurate, the use effect is good, and it has a good prospect for use.

[0071] 3. The present invention records a defect detection system for a monorail crane track, which further collects defect data when a defect occurs and processes the defect data. It can conduct a specific analysis of the defect position on the monorail crane track where the detection vehicle passes, and can analyze whether there is wear, protrusion or depression on the monorail crane track. The overall defect detection of the monorail crane track is more detailed, and can effectively avoid the situation where too many defects on the monorail crane track lead to a sharp decline in the subsequent life of the monorail crane track and greater difficulty in subsequent defect repair. The defect detection effect is better and has good prospects for use. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1It is a flow chart of a defect detection system for a monorail hanging track of the present invention;

[0073] Figure 2 A diagram showing the use status of a detection vehicle used in a defect detection system for a monorail hanging track according to the present invention;

[0074] Figure 3 A collection position diagram of a defect data collection device in a defect detection system of a monorail hanging track of the present invention;

[0075] Figure 4 A schematic diagram of track interruption and marking pixel points in a defect detection system for a monorail hanging track of the present invention;

[0076] Figure 5 The present invention is a flow chart of a method for detecting defects of a monorail hanging track. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] Research reasons

[0079] The existing intelligent detection of monorail crane tracks usually focuses on the disconnection of monorail crane track joints and track deformation. The detection process mainly relies on the large-scale collection of detailed track data, and then a detailed analysis of the collected data is performed to determine whether the entire monorail crane track is deformed and the joints are disconnected.

[0080] When using intelligent monitoring, the first thing to do is to analyze whether the monorail crane track joint is disconnected and the track is deformed, mainly through the outer contour. It is suitable for situations where large deformation occurs and the joint position is obviously disconnected. It is difficult to monitor when the monorail crane track is partially deformed (track wear and local bulges of the track) (when the crane is running, the position where the wheels contact the side of the I-beam is blocked by the structure of the I-beam itself). Therefore, when it detects that there are defects in the monorail crane track, the situation of the monorail crane track is often more serious, which requires a lot of time to deal with, and there are certain risks.

[0081] As for the subtle inspection on the monorail track, it usually relies on regular human observation. The more obvious places on the monorail track can be found, but for the slightly deformed or slightly worn areas, since the entire monorail track is long and it relies on naked eye observation, it is easy to miss the inspection by manpower, and the defect detection is not detailed enough. In the long run, the number of defects on the monorail track will increase, thereby affecting the service life of the monorail track and making it more difficult to repair the defects later. Part of the monorail track needs to be replaced, which is costly and does not meet people's usage requirements.

[0082] Design ideas

[0083] Most of the existing systems collect pictures of the monorail track comprehensively (from multiple angles), then detect the collected pictures of the monorail track, analyze the outline of the monorail track, determine whether the outline of the monorail track is deformed, and determine whether there is an abnormal track joint interruption by whether the outline of the monorail track is broken. However, the amount of data analyzed is large, and the entire analysis time is relatively long. It usually takes some time to analyze whether the track is defective. When no result appears, there is a certain risk in continuing to operate the inspection vehicle.

[0084] Therefore, during the research and development process, the initial idea was to reduce the analysis time. Therefore, we carefully analyzed the interruptions and other situations, simplified the existing detection scheme, and used the explosion-proof camera on the detection vehicle to collect data. At the same time, the detection scheme was simplified, which not only eliminated the need to add multiple separate camera components, reducing costs, but also greatly shortened the amount of data processing, and enabled real-time detection of tracks (the explosion-proof camera captures sections of the monorail track where the detection vehicle is not running, and conducts advance detection to analyze whether there are defects before the detection vehicle reaches the operating position). Whether to continue detection is determined based on the defects, and the overall safety is relatively high.

[0085] However, in actual use, it was found that this method can only be used for detection when the monorail crane track is greatly deformed. The reality is that when the monorail crane track is greatly deformed, except for sudden situations, it is basically caused by small defects slowly leading to deformation. When the monorail crane track is deformed, it is relatively difficult to repair, and during the period when small defects turn into large defects, it will have a certain impact on the operation of the monorail crane, causing the monorail crane to swing during movement, reducing the overall safety, and failing to meet people's usage requirements.

[0086] Therefore, in the middle of the research and development, we changed our thinking and studied new detection schemes. After research, we adopted a step-by-step detection method, first detecting whether there are defects and then analyzing the degree of defects. This detection method does not require a comprehensive analysis of the entire monorail crane track, so the amount of data analyzed is not large.

[0087] However, in actual use, it was found that there would be differences in the operation of the inspection vehicles themselves. The operating data of each inspection vehicle was different, and as the inspection vehicles were used, their subsequent operating data would change significantly from the previous data, which would affect the results of confirmation identification and cause defect identification errors.

[0088] Therefore, in the later stage of research and development, the previous plan is fully combined to conduct a trial run of the test vehicle, and the trial run data of the test vehicle is used as the basic data to analyze and judge defects, thereby greatly improving the accuracy of defect analysis and achieving good results.

[0089] Study plan

[0090] Example 1

[0091] A defect detection system for a monorail hanging track includes a vehicle selection module, a data acquisition module, a data judgment module, a secondary acquisition module and a defect classification module:

[0092] Detection vehicle selection

[0093] Since the operating data of the inspection vehicles are different, the operating data will cause certain interference to the subsequent inspections. Therefore, it is necessary to select inspection vehicles that meet the requirements. The selection of inspection vehicles is based on the vehicle selection module.

[0094] like Figure 2 As shown, this is a diagram of the detection vehicle running on a monorail track.

[0095] The vehicle selection module is used to obtain vehicle test run data and compare the test run data with the standard threshold range to determine whether the vehicle meets the requirements. If it meets the requirements, the vehicle is determined to be tested and the average parameters of the vehicle are calculated;

[0096] The parameters for testing the running of a vehicle on a monorail are the stability indicators generated by the uniform motion of the vehicle on the monorail;

[0097] The vehicle trial run data includes the stability index of the vehicle running at a constant speed for 50 meters on a horizontal monorail hanging track. The vehicle trial run data is collected by a vibration sensor installed on the vehicle, and the average parameters of the vehicle are the vehicle stability index parameters.

[0098] The steps to determine whether a vehicle meets the requirements are as follows:

[0099] Remove the stability index of the first 10 meters and the last 10 meters of the vehicle running on the horizontal monorail (the first 10 meters and the last 10 meters are mainly acceleration or deceleration, and the vibration is large in the process, so the data needs to be removed);

[0100] Get the maximum value of the stability index when the vehicle runs at a constant speed of 30 meters on the horizontal monorail (the longer the distance, the more accurate it is, but if the distance is too long, more data will be processed, and if the distance is short, the data collected may be insufficient, so 30 meters is selected);

[0101] Determine whether the maximum value is within the standard threshold interval. If the maximum value is within the standard threshold interval, the vehicle meets the detection requirements. If the maximum value is outside the standard threshold interval, the vehicle does not meet the detection requirements.

[0102] The calculation formula of the stability index is as follows:

[0103]

[0104] Where Zdcs is the stability index, j is the vibration acceleration, and f is the vibration frequency.

[0105] The standard threshold range is 0-2.5. When the maximum value of the calculated stability index is within the standard threshold range, the vehicle meets the detection requirements.

[0106] The formula for calculating the vehicle stability index parameter is as follows:

[0107]

[0108] In the formula, is the vehicle stability index parameter, n is the number of stability indicators collected during the 30-meter uniform motion, Zdcs i is the i-th stability indicator.

[0109] The present invention records a defect detection system for a monorail crane track, which detects vehicles before defect detection, and uses the benchmark of this detection as a benchmark for subsequent judgment on whether there are defects on the monorail crane track, so that subsequent judgment on the existence of defects is more accurate and can identify smaller defects, and then further collect and analyze the defect locations, analyze the content of the defects and the levels of the defects, so as to facilitate the subsequent formulation of corresponding defect repair plans according to the defect levels. During the whole process, there is no need to conduct a comprehensive analysis of the entire monorail crane track, the amount of data processed is small, and the defect location can be analyzed in real time, which effectively solves the problem that it takes a while for the existing monorail crane track data to produce results after data collection, causing the monorail crane track to be unsuitable for a period of time and the effect to be low. The overall use effect is good and has a good prospect for use.

[0110] Data Acquisition

[0111] After the inspection vehicle is determined, all data collected by the inspection vehicle on the monorail crane track are obtained. This process is based on the data acquisition module.

[0112] The data acquisition module is used to obtain the parameters of the vehicle running on the monorail in real time and the track data captured by the explosion-proof camera on the vehicle;

[0113] The data collected by the data acquisition module is transmitted through the wireless device on the detection vehicle.

[0114] Defect Analysis

[0115] After obtaining the data from the inspection vehicle, it is necessary to analyze and judge the data to determine whether there are defects on the monorail track. The specific data analysis is based on the data judgment module.

[0116] The data judgment module is used to receive the data collected by the data acquisition module, and analyze the collected data to determine whether there are defects in the monorail crane track;

[0117] Judging whether there are defects in the monorail crane track includes judging whether there are major defects in the monorail crane track (track interruption and serious deformation, in which case, for safety reasons, detection is abandoned) and whether there are defects on the monorail crane track (local deformation and wear).

[0118] The track data captured by the on-board explosion-proof camera is the image data captured when the detection vehicle is running;

[0119] The steps for analyzing the track data captured by the explosion-proof camera on the inspection vehicle and determining whether the monorail track has defects (large defects) are as follows:

[0120] S1 obtains the picture taken by the explosion-proof camera on the inspection vehicle, and then crops the picture;

[0121] The explosion-proof camera shoots with fixed focus, so you only need to crop the photo to retain the upper middle part of the shot.

[0122] S2 grayscales the cropped image, and then performs Gaussian filtering on the grayscale image;

[0123] The calculation formula for graying the cropped image is as follows:

[0124] Hdz=0.3×R+0.59×G+0.11×B

[0125] Hdz is the grayscale value of the image after grayscale, and R, G, and B are the red, green, and blue values ​​of the pixels in the image, respectively.

[0126] This step is a simple grayscale method for the image. This step is mainly used to determine whether there are major defects. Minor interruptions do not affect normal defect detection (subsequent tests will detect them). By using a simple processing method, this step is unable to detect minor interruptions, allowing the inspection vehicle to operate normally when there are small defects in the monorail track.

[0127] S3 calculates the average difference between the grayscale values ​​of the pixel point in the image after Gaussian filtering and the four adjacent pixels above, below, left and right;

[0128] The formula for calculating the average difference between the grayscale values ​​of a pixel in the image after Gaussian filtering and the four adjacent pixels above, below, left and right is as follows:

[0129]

[0130] Where Pjcz is the average difference between the grayscale values ​​of the pixel and the four adjacent pixels above, below, left and right, Hdz is the grayscale value of the pixel, and Hdz 上 、Hdz 下 、Hdz 左 and Hdz 右 They are the grayscale values ​​of the four adjacent pixels above, below, left and right.

[0131] This step obtains the outline of the track edge. The track is suspended in the air, and there are no real objects on both sides of the track in the photographed photos, so there will be obvious grayscale differences.

[0132] During normal shooting, the grayscale values ​​of the left and upper pixels of the edge pixels of the track (left side) are both above 230, and the grayscale values ​​of the right and lower pixels are close to the edge pixels. Through the above calculation method, the position of the edge pixels can be clearly calculated.

[0133] Through the above steps, the pixel points at the edge of the track can be calculated.

[0134] S4 marks the pixel points whose average difference in grayscale values ​​of adjacent pixel points is located in the set extraction interval, and sets the grayscale values ​​of other pixel points to the maximum value;

[0135] The extraction range is 10-255.

[0136] S5 selects the lower left corner of the cropped image as the coordinate origin, linearly fits a linear function, and determines the number of linear functions formed by the marked pixel points;

[0137] If the number of linear functions is 2 and there is no interruption in the linear function, it is determined that the monorail track of the corresponding section in the photo has no defects;

[0138] If the linear function is any other number, or the number of linear functions is 2, but the linear function is interrupted, it is determined that there is a defect in the monorail track. The defect position is the intersection of the interruption and the linear function. The defect type is a broken monorail track. The detection vehicle stops running and the warning device at the corresponding position is activated to issue a warning.

[0139] The number of linear functions displayed by track misalignment has increased significantly and is also serious, which also belongs to the fracture of the monorail crane track.

[0140] The bottom surface of the track is tested. The crane does not contact the bottom surface of the track. When the bottom surface of the track is obviously deformed, the overall deformation of the track is greater. Although it does not belong to track fracture, it is also extremely dangerous (this situation is rare and will not happen except by accident, so it is not separately subdivided and classified). In addition, the number of linear functions is not 2 (new functions are generated at the deformation point), so a fracture warning is also issued.

[0141] The defect location is sent via a positioning device on the inspection vehicle.

[0142] like Figure 3 As shown, there are three common situations of monorail suspension track interruption, and the lines on the right are marked pixel points.

[0143] This system can detect common defects in the monorail crane track and repair the monorail crane track in time, which can effectively avoid the interruption of the monorail crane track.

[0144] When judging whether there are defects on the monorail track, the parameters of the inspection vehicle running on the monorail track are analyzed. When judging whether there are defects on the monorail track, the stability index of uniform speed running on the monorail track is obtained, and the obtained data is compared with the set standard interval (0, Zdcs+δ). If the stability index is within the standard interval, the monorail track is judged to be normal. If the stability index is outside the standard interval, the monorail track has defects, and the defect position is the position corresponding to the stability index that exceeds the standard interval.

[0145] The defect location is sent via a positioning device on the inspection vehicle.

[0146] The calculation formula for the fluctuation value δ is as follows:

[0147]

[0148] Among them, α is a constant coefficient, 0.10<α<0.35, e is a natural constant, Pd is the slope value, Pdxs 上 and Pdxs 下 are the uphill slope coefficient and the downhill slope coefficient, Pdxs 下 <Pdxs 上 <1.

[0149] The lower α is, the higher the accuracy is and the higher the requirement for detecting vehicles is.

[0150] During the defect detection process, the vehicle maintains the same speed. When encountering a downhill slope, it will brake and the vibration data will increase. When encountering an uphill slope, the power of the electric mechanism will increase and the vibration will increase, so a corresponding standard range is required.

[0151] Defect Detection

[0152] When a major defect is detected, the machine will stop immediately and end the measurement. When a common defect exists, further testing is required. When no defect exists, the machine will operate normally.

[0153] Further testing requires more detailed test data and further data collection, which is based on the secondary collection module.

[0154] The secondary acquisition module receives a defect signal and starts the defect data acquisition device to collect data on the defect location when receiving the defect signal. The defect data acquisition device collects data on the defect location as a photo of the side of the monorail crane track (in contact with the crane wheel).

[0155] like Figure 4 As shown, the defect data collection device is installed on one side of the crane wheel. When in use, the crane wheel first contacts the defect position, and after analysis and determination that there is a defect, the defect data collection device is started. At this time, the inspection vehicle just drives the defect data collection device to move to the vicinity of the defect position for shooting, thereby realizing the collection of the defect position.

[0156] Analyze defects

[0157] When the detailed data of the defect is obtained, in order to further understand the defect, it is necessary to further analyze the obtained data. The specific analysis is based on the defect classification module.

[0158] The defect classification module analyzes the data of the defect location, determines the defect type, and calculates the defect level based on the defect type, defect data and detection vehicle operating parameters, obtains the defect location, marks the defect level at the defect location, generates a defect drawing, and activates the warning device at the defect location according to the defect level.

[0159] The steps to analyze the data of the defect location and determine the defect type are as follows:

[0160] Grayscale the image of the defect location, and then perform Gaussian filtering on the grayscale image;

[0161] The calculation formula for graying the image is as follows:

[0162]

[0163] Compare the image processed by Gaussian filtering with the standard image, calculate the absolute value of the grayscale value difference, and mark the pixels whose absolute value of the grayscale value difference is greater than the set value β. β is usually set to 10, which is set according to the defect detection accuracy. The smaller β is, the higher the detection accuracy is. The area of ​​the defect S = m × γ is calculated by the number of marked pixels, where m is the number of marked pixels and γ is the ratio of the difference between the pixel and the actual point. Record the minimum grayscale value difference Hdz′ min , maximum gray value difference Hdz′ max and the average gray value difference Pjcz′, cz i is the difference between the grayscale value of the i-th marked pixel and the grayscale value of the pixel at the corresponding position on the standard image;

[0164] Gray value minimum difference Hdz′ min , maximum gray value difference Hdz′ max The calculation method is to subtract the grayscale value of the pixel in the defect photo from the grayscale value of the pixel in the standard photo;

[0165] At regular intervals, select a defect-free position on the monorail track and shoot with fixed focus and equidistant distance. The shot pictures are converted into standard pictures after grayscale conversion and Gaussian filtering.

[0166] The picture taken is the side of the monorail crane track. Existing monorail cranes are all equipped with cleaning structures. Therefore, the grayscale value of the picture taken can directly reflect the actual situation of the monorail crane track.

[0167] Compare the minimum grayscale value difference and the maximum grayscale value difference with the set classification interval to determine the defect type;

[0168] The classification intervals include the deformation concave interval (-255, -μβ), the wear interval (-μβ, -β), and the deformation convex interval (β, 255), 1 < μ < 3;

[0169] If the minimum gray value difference Hdz′ min If it is located in the deformation depression interval, the defect type is that there is a depression in the track;

[0170] If the minimum gray value difference Hdz′ min If it is in the wear range, the defect type is track wear;

[0171] If the maximum gray value difference Hdz′ max If it is located in the deformation convex area, the defect type is that there is a convexity in the track.

[0172] Since defects are repaired promptly after being detected, it is normal that there will not be both bulges and depressions on the track at the same time. In fact, bulges and depressions on the track are the same situation, with one end bulging and the other end depressed. However, this scheme uses single-sided data collection, so the results are divided into two categories.

[0173] The present invention records a defect detection system for a monorail crane track, which further collects defect data when a defect occurs and processes the defect data. It can conduct a specific analysis of the defect position on the monorail crane track where a detection vehicle passes by, and can analyze whether there is wear, protrusion or depression on the monorail crane track. The overall defect detection of the monorail crane track is more detailed, and can effectively avoid the situation where too many defects on the monorail crane track lead to a sharp decline in the subsequent service life of the monorail crane track and greater difficulty in subsequent defect repair. The defect detection effect is better and has a good prospect for use.

[0174] The defect level calculated for track wear is lower than that for track protrusions and track depressions. Therefore, when track wear, track protrusions and track depressions occur at the same time, the defect level calculated is larger according to the track protrusions and track depressions (which does not affect the repair).

[0175] The calculation formula for calculating the defect level is as follows:

[0176]

[0177] Where Qxdj is the defect level, Zdcs is the stability index of the defect position, A and C are the weight ratios corresponding to the stability index and the photo data, respectively, A+C=1, C>A.

[0178] The defect level can help maintenance personnel understand the defect situation. At this time, repair personnel carry corresponding tools to the defect location for maintenance according to the defect type and defect level.

[0179] Moreover, the defect level can help maintenance personnel understand the extent to which the section of monorail has been repaired (this location is prone to defects after major repairs), making it easier to formulate a separate maintenance and overhaul plan for this location.

[0180] The defect location is obtained and sent through the positioning device on the detection vehicle. The defect level is marked at the defect location, a defect drawing is generated, and the warning device at the defect location is activated according to the defect level.

[0181] Activating the warning device at the defect location can help maintenance personnel quickly determine when they have reached the vicinity of the defect location.

[0182] The weight coefficient is determined by the coefficient of variation method, which is a method of weighting each indicator according to the degree of variation between the current value of each evaluation indicator and the target value. If the numerical difference of an indicator is large, it can clearly distinguish the evaluated objects, indicating that the indicator has rich discrimination information, and thus the indicator should be given a larger weight. On the contrary, if the numerical difference of each evaluated object on a certain indicator is small, then the ability of this indicator to distinguish the evaluation objects is weak, and thus the indicator should be given a smaller weight. This method directly uses the information contained in each indicator and obtains the weight of the indicator through calculation, so it is objective.

[0183] The present invention records a defect detection system for a monorail crane track, which performs multiple defect judgments during operation, and uses track data photographed by an explosion-proof camera on a detection vehicle to make a preliminary judgment to determine whether the monorail crane track has a more serious defect due to an accident. When the defect exists, the detection is stopped to ensure safety, and then the parameters of the detection vehicle running on the monorail crane track are used to determine whether there is a defect. When a defect exists, defect data is further collected, and the defect type and defect level are determined by combining the defect data and the parameters of the detection vehicle running on the monorail crane track. The entire defect analysis is relatively accurate, the use effect is good, and it has a good use prospect.

[0184] Example 2

[0185] like Figure 5 As shown, based on Example 1, this embodiment also provides a method for detecting defects of a monorail hanging track, comprising the following steps:

[0186] Obtain vehicle test run data, and compare the test run data with the standard threshold range to determine whether the vehicle meets the requirements. If it meets the requirements, determine to test the vehicle and calculate the average parameters of the vehicle;

[0187] Real-time acquisition of the parameters of the test vehicle running on the monorail and the track data captured by the on-board explosion-proof camera of the test vehicle;

[0188] Receive data collected by the data acquisition module, and analyze the collected data to determine whether there are defects in the monorail crane track;

[0189] If there is a large defect, stop the inspection, obtain the defect location, and activate the warning device at the defect location;

[0190] receiving a defect signal when a common defect exists, and starting a defect data collection device to collect data of the defect position when receiving the defect signal;

[0191] Analyze the data of the defect location, determine the defect type, calculate the defect level based on the defect type, defect data and test vehicle operating parameters, obtain the defect location, mark the defect level at the defect location, generate a defect drawing, and activate the warning device at the defect location based on the defect level.

[0192] The above formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0193] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0194] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0195] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A defect detection system for a monorail hanging track, characterized in that: include: Vehicle selection module: used to obtain vehicle test run data, and compare the test run data with the standard threshold range to determine whether the vehicle meets the requirements. If it meets the requirements, the test vehicle is determined and the average parameters of the vehicle are calculated; Data acquisition module: used to obtain the parameters of the vehicle running on the monorail in real time and the track data captured by the explosion-proof camera on the vehicle; Data judgment module: used to receive the data collected by the data acquisition module, and analyze the collected data to determine whether there are defects in the monorail crane track; Secondary acquisition module: used to receive a defect signal, and start the defect data acquisition device to collect data of the defect position when receiving the defect signal; Defect classification module: used to analyze the data of the defect location, determine the defect type, and calculate the defect level according to the defect type, defect data and detection vehicle operating parameters, obtain the defect location, mark the defect level at the defect location, generate defect drawings, and activate the warning device at the defect location according to the defect level.

2. A defect detection system for a monorail crane track according to claim 1, characterized in that: The vehicle test run data includes the stability index of the vehicle running at a constant speed for 50 meters on a horizontal monorail. The vehicle test run data is collected by a vibration sensor installed on the vehicle, and the average parameters of the vehicle are the vehicle stability index parameters; The parameters of the detection vehicle running on the monorail are the stability indicators generated by the uniform motion of the detection vehicle on the monorail, and the track data captured by the on-board explosion-proof camera are the picture data captured when the detection vehicle is running; The defect location is sent via a positioning device on the inspection vehicle.

3. A defect detection system for a monorail hanging track according to claim 2, characterized in that: The steps to determine whether a vehicle meets the requirements are as follows: Remove the stability indicators of the first 10 meters and the last 10 meters of the vehicle running on the horizontal monorail hanging track; Get the maximum value of the stability index when the vehicle runs at a constant speed for 30 meters on the horizontal monorail hanging track; Determine whether the maximum value is within the standard threshold interval. If the maximum value is within the standard threshold interval, the vehicle meets the detection requirements. If the maximum value is outside the standard threshold interval, the vehicle does not meet the detection requirements. The formula for calculating the vehicle stability index parameter is as follows: In the formula, is the vehicle stability index parameter, n is the number of stability indicators collected during the 30-meter uniform motion, Zdcs i is the i-th stability indicator.

4. A defect detection system for a monorail crane track according to claim 3, characterized in that: The steps to analyze the track data captured by the explosion-proof camera on the inspection vehicle and determine whether there are defects in the monorail crane track are as follows: S1 obtains the picture taken by the explosion-proof camera on the inspection vehicle, and then crops the picture; S2 grayscales the cropped image, and then performs Gaussian filtering on the grayscale image; S3 calculates the average difference between the grayscale values ​​of the pixel point in the image after Gaussian filtering and the four adjacent pixels above, below, left and right; S4 marks the pixel points whose average difference in grayscale values ​​of adjacent pixel points is located in the set extraction interval, and sets the grayscale values ​​of other pixel points to the maximum value; S5 selects the lower left corner of the cropped image as the coordinate origin, linearly fits a linear function, and determines the number of linear functions formed by the marked pixel points; If the number of linear functions is 2 and there is no interruption in the linear function, it is determined that the monorail track of the corresponding section in the photo has no defects; If the linear function is any other number, or the number of linear functions is 2, but the linear function is interrupted, it is determined that there is a defect in the monorail track. The defect position is the intersection of the interruption and the linear function. The defect type is a broken monorail track. The detection vehicle stops running and the warning device at the corresponding position is activated to issue a warning.

5. A defect detection system for a monorail hanging track according to claim 4, characterized in that: The calculation formula for graying the cropped image is as follows: Hdz=0.3×R+0.59×G+0.11×B Hdz is the grayscale value of the image after grayscale, and R, G, and B are the red, green, and blue values ​​of the pixels in the image, respectively.

6. A defect detection system for a monorail hanging track according to claim 5, characterized in that: The formula for calculating the average difference between the grayscale values ​​of a pixel in the image after Gaussian filtering and the four adjacent pixels above, below, left and right is as follows: Where Pjcz is the average difference between the grayscale values ​​of the pixel and the four adjacent pixels above, below, left and right, Hdz is the grayscale value of the pixel, and Hdz 上 、Hdz 下 、Hdz 左 and Hdz 右 They are the grayscale values ​​of the four adjacent pixels above, below, left and right.

7. A defect detection system for a monorail crane track according to claim 6, characterized in that: Analyze the parameters of the test vehicle running on the monorail track, determine whether there are defects in the monorail track, obtain the stability index of the uniform speed running on the monorail track, and compare the obtained data with the set standard interval Compare and if the stability index is within the standard range, the monorail crane track is judged to be normal; if the stability index is outside the standard range, the monorail crane track has defects, and the defective position is the position corresponding to the stability index that exceeds the standard range; The calculation formula for the fluctuation value δ is as follows: Among them, α is a constant coefficient, 0.10<α<0.35, e is a natural constant, Pd is the slope value, Pdxs 上 and Pdxs 下 are the uphill slope coefficient and the downhill slope coefficient, Pdxs 下 <Pdxs 上 <1.

8. A monorail crane track defect detection system according to claim 7, characterized in that: The defect data collection device collects the data of the defect location as a photo of the side of the monorail crane track; the steps of analyzing the data of the defect location and determining the defect type are as follows: Grayscale the image of the defect location, and then perform Gaussian filtering on the grayscale image; The calculation formula for graying the image is as follows: Compare the image processed by Gaussian filtering with the standard image, calculate the absolute value of the gray value difference, and mark the pixels whose absolute value of the gray value difference is greater than the set value β. The defect area S = m × γ is calculated by the number of marked pixels, where m is the number of marked pixels and γ is the ratio of the difference between the pixel and the actual point; record the minimum gray value difference Hdz′ min , maximum gray value difference Hdz′ max and the average gray value difference Pjcz′, cz i is the difference between the grayscale value of the i-th marked pixel and the grayscale value of the pixel at the corresponding position on the standard image; Gray value minimum difference Hdz′ min , maximum gray value difference Hdz′ max The calculation method is to subtract the grayscale value of the pixel in the defect photo from the grayscale value of the pixel in the standard photo; Compare the minimum grayscale value difference and the maximum grayscale value difference with the set classification interval to determine the defect type; The classification intervals include the deformation concave interval (-255, -μβ), the wear interval (-μβ, -β), and the deformation convex interval (β, 255), 1 < μ < 3; If the minimum gray value difference Hdz′ min If it is located in the deformation depression interval, the defect type is that there is a depression in the track; If the minimum gray value difference Hdz′ min If it is in the wear range, the defect type is track wear; If the maximum gray value difference Hdz′ max If it is located in the deformation convex area, the defect type is that there is a convexity in the track.

9. A monorail crane track defect detection system according to claim 8, characterized in that: The calculation formula for calculating the defect level is as follows: Where Qxdj is the defect level, Zdcs is the stability index of the defect position, A and C are the weight ratios corresponding to the stability index and the photo data, respectively, A+C=1, C>A.

10. A method for detecting defects in a monorail crane track, using the system described in any one of claims 1 to 9, characterized in that: The steps include: Obtain vehicle test run data, and compare the test run data with the standard threshold range to determine whether the vehicle meets the requirements. If it meets the requirements, determine to test the vehicle and calculate the average parameters of the vehicle; Real-time acquisition of the parameters of the test vehicle running on the monorail and the track data captured by the on-board explosion-proof camera of the test vehicle; Receive data collected by the data acquisition module, and analyze the collected data to determine whether there are defects in the monorail crane track; receiving a defect signal, and starting a defect data collection device to collect data of the defect position when receiving the defect signal; Analyze the data of the defect location, determine the defect type, calculate the defect level based on the defect type, defect data and test vehicle operating parameters, obtain the defect location, mark the defect level at the defect location, generate a defect drawing, and activate the warning device at the defect location based on the defect level.

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