Magnetic levitation maintenance base steel beam connecting bolt health monitoring system

Abstract

The invention relates to the technical field of magnetic levitation track intelligent operation and maintenance, and particularly discloses a magnetic levitation maintenance base steel beam connecting bolt health monitoring system which specifically comprises a data acquisition module, a data processing module, a failure diagnosis module and a cloud service platform. The strain gauges are arranged on the bolt heads for monitoring, the bolts do not need to be disassembled or damaged, stress monitoring can be conducted on the bolts in the service state under the condition that the structural integrity is not affected, the risk and cost in the monitoring process are reduced, in addition, the vibration characteristics of the steel beam are monitored, and the monitoring accuracy is improved. According to the method, information about whether bolt connection is normal or not is indirectly acquired, finally, bolt looseness is monitored in real time by adopting an image recognition technology, and comprehensive structure health monitoring information can be provided by fusing and superposing three technologies of direct stress, indirect representation and image monitoring on operation condition data of the magnetic levitation vehicle. And the overall performance and safety of the connecting bolt can be comprehensively evaluated, and real-time early warning can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent operation and maintenance of maglev tracks, and in particular to a health monitoring system for steel beam connecting bolts in a maglev maintenance base. Background Art

[0002] High-speed maglev transportation is a new type of transportation system that has attracted much attention worldwide. It is a contactless ground rail transportation system with an operating speed of 500-600 km / h, filling the speed gap between aviation and high-speed rail. Since the beginning of the 21st century, high-speed maglev transportation technology and its engineering research have received strong support at the national level, with huge development potential and broad prospects.

[0003] The maglev maintenance base is a crucial location for the inspection and maintenance of maglev vehicles. The upper structure of the maintenance platform within the base is a frame-type steel beam, which meets the practical requirements of positioning, installation, and operating space during train maintenance. However, this structure has low stiffness and damping. As the starting and ending points of train operations, the maintenance platform often operates under conditions where the vehicle is statically suspended and moving at low speeds. Under these conditions, electromagnets are continuously loaded with dynamic excitation. However, the frame-type steel beam has low damping and slow energy dissipation, which can easily cause resonance. Furthermore, its low stiffness leads to large vibration amplitudes during resonance, posing a potential threat to structural stability and safety.

[0004] However, there is a lack of regular maintenance strategies and methods for the base's steel beams. Manual inspections of the apparent quality of the connecting bolts are not only time-consuming and labor-intensive, but also inefficient and require long inspection cycles, failing to meet real-time requirements. Furthermore, the accuracy and reliability of inspection results are often limited by the inspectors' skills and experience, making it impossible to accurately determine the degree of bolt failure, thus increasing safety risks.

[0005] In view of this, we proposed a health monitoring system for steel beam connection bolts in the maglev maintenance base. Summary of the Invention

[0006] The purpose of the present invention is to provide a health monitoring system for steel beam connection bolts in a maglev maintenance base to solve the technical problems in the above background.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] The present invention provides a health monitoring system for steel beam connection bolts in a maglev maintenance base, specifically comprising: a data acquisition module, a data processing module, a failure diagnosis module and a cloud service platform;

[0009] Data acquisition module: including bolt status data acquisition unit, steel beam status data acquisition unit and vehicle working condition data acquisition unit;

[0010] Among them, the bolt status acquisition unit is used to monitor and obtain bolt status data, including two bolt monitoring sub-units, and the two bolt monitoring sub-units are located in different positions;

[0011] Steel beam status acquisition unit: used to obtain steel beam status data;

[0012] Bolt status data is obtained through two bolt monitoring sub-units, which are located in different positions;

[0013] Vehicle operating condition data acquisition unit: used to acquire the operating condition data of the maglev vehicle, wherein the operating condition data includes the state change time and signal state change;

[0014] Data processing module: including bolt status data processing unit, steel beam vibration data processing unit and steel beam modal data processing module;

[0015] Output the bolt state representation value SL, acceleration index representation value JS and steel beam modal representation value MT through the data processing module;

[0016] Failure diagnosis module: processes the bolt state characterization value, acceleration index characterization value and steel beam modal characterization value, outputs the failure judgment value, and judges the failure degree.

[0017] As a further solution of the present invention: the data processing unit specifically includes:

[0018] Bolt status data processing unit: analyzes the bolt status data within the sampling period and outputs the bolt status representation value;

[0019] Steel beam vibration parameter data processing unit: collects steel beam vibration parameter data within the sampling period and outputs acceleration index representation values;

[0020] Steel beam modal parameter data processing module: Analyzes the steel beam modal parameter data within the sampling period and outputs the modal characterization value of the steel beam. The modal parameter data of the steel beam includes vibration mode, frequency and damping ratio.

[0021] As a further solution of the present invention: the acquisition process of the two bolt monitoring subunits is:

[0022] The manufacturing steps for one of the bolt monitoring subunits are as follows: The bolt head of the in-service bolt is polished and four strain gauges are attached. The strain gauges are arranged along the radial direction of the bolt head. The strain gauges are of the same specifications and are evenly spaced in a circular array.

[0023] Another manufacturing step of the bolt monitoring subunit is: grinding the bolt head of the service bolt and attaching a strain gauge, with the strain gauge's strain grid direction arranged along the radial direction of the bolt head.

[0024] As a further solution of the present invention, the bolt status data can also be recognized by image recognition, and the specific process is as follows:

[0025] Select a camera to capture images, pre-process the captured images, and use image processing algorithms to extract key features of the bolts;

[0026] A template image of the bolt tightening state is created in advance, and the captured image is matched with the template image using a pixel-based matching algorithm. The specific process is as follows:

[0027] For the collected image and the template image, the grayscale value or color value of the two is compared pixel by pixel, and the difference value of each pixel is calculated. The difference values ​​of all pixels are combined into a difference map, where the value of each pixel represents the difference between the collected image and the template image at that position. The difference map is traversed, and the sum of the difference values ​​that exceed the threshold is counted. Based on the sum value, the degree of difference between the collected image and the template image is determined;

[0028] If the sum is greater than the threshold, the result of bolt loosening is output;

[0029] If the sum is less than or equal to the threshold, the bolt tightening judgment result is output.

[0030] As a further solution of the present invention: the process of obtaining the bolt state characterization value is as follows:

[0031] Analyze the abnormal stress data points and bolt stress values ​​to obtain the abnormal data point ratio, the average value of the bolt stress ratio, and the bolt stress ratio;

[0032] Substitute the formula SL=a1*ZB+a2*JZ+a3*ZD to calculate the bolt state representation value SL, where ZB represents the proportion of abnormal data points, JZ represents the average value of the bolt stress ratio, ZD represents the maximum value of the bolt stress ratio, and a1, a2, and a3 are weight coefficients

[0033] As a further solution of the present invention: the process of obtaining the abnormal data point ratio, the bolt stress ratio mean value and the bolt stress ratio is as follows:

[0034] Preset the sampling frequency and sampling period, and obtain the bolt status data within the sampling period, wherein the bolt status data includes the bolt force value;

[0035] Compare the bolt force value with the bolt force threshold. If the bolt force value is less than or equal to the bolt force threshold, it is marked as a normal force data point. If the bolt force value is greater than the bolt force threshold, it is marked as an abnormal force data point.

[0036] During the sampling period, the number of abnormal force data points is obtained, and the ratio of the number of abnormal data points to the total number of data points is processed to obtain the proportion of abnormal data points;

[0037] The bolt force value of the abnormal force data point is calculated by difference with the bolt force threshold to obtain the bolt force deviation value, the bolt force deviation value is processed by ratio with the bolt force threshold to obtain the bolt force degree ratio, and the bolt force degree ratios of all abnormal force data points are summed and averaged to obtain the average bolt force degree ratio;

[0038] Extract the maximum value of the bolt stress ratio of all abnormal stress data points.

[0039] As a further solution of the present invention: the process of obtaining the acceleration index characterization value is:

[0040] Preprocessing the collected vibration displacement amplitude;

[0041] Acquire acceleration data through an accelerometer, extract the maximum value in the acceleration data to obtain the acceleration peak value, calculate the difference between the acceleration peak value and the acceleration standard value, take the absolute value of the difference to obtain the acceleration deviation value, perform ratio processing on the acceleration deviation value and the acceleration standard value to obtain the acceleration peak deviation ratio;

[0042] Substitute the acceleration data into the RMS formula to calculate the RMS value of acceleration;

[0043] Substitute into the formula JS=s1*FZ+s2*JF to calculate the acceleration index representation value JS, where FZ represents the acceleration peak deviation ratio, JF represents the acceleration root mean square value, s1 and s2 are weight coefficients, and s1+s2=1.

[0044] As a further solution of the present invention: the process of obtaining the modal characterization value of the steel beam is:

[0045] Calculate the difference between the modal frequency and the natural frequency, take the absolute value of the difference, and compare it with the natural frequency to obtain the modal frequency ratio;

[0046] Substitute into the formula The modal characterization value MT of the steel beam is calculated, where MP represents the modal frequency ratio, MZ represents the damping ratio, d1 and d2 are weight coefficients, and d1+d2=1.

[0047] As a further solution of the present invention: the process of obtaining the failure judgment value is:

[0048] Obtain the bolt state representation value SL, acceleration index representation value JS and steel beam modal representation value MT, and substitute them into the formula The failure judgment value SX is calculated, where p1, p2, and p3 are preset proportional coefficients.

[0049] As a further solution of the present invention: the process of determining the degree of failure is:

[0050] Obtaining a failure judgment value, and comparing the failure judgment value with a failure judgment threshold;

[0051] If the failure judgment value is less than or equal to the failure judgment threshold, a warning signal is generated;

[0052] If the failure judgment value is greater than the failure judgment threshold, an abnormal signal is generated.

[0053] Beneficial effects of the present invention:

[0054] (1) The present invention monitors the force of bolts in service by providing strain gauges on the bolt heads, without disassembling or destroying the bolts. This allows monitoring without affecting the structural integrity, thus reducing the risk and cost of the monitoring process.

[0055] (2) The present invention can perceive the operating status of the vehicle in real time through the vehicle operating condition data acquisition unit, including key information such as the vehicle's entry and exit time and the absolute suspension time, etc., providing an important time node reference for accurately extracting valid data segments related to bolt force and steel beam dynamic response from a large amount of monitoring data, thereby improving the efficiency and accuracy of data extraction;

[0056] (3) The present invention not only monitors the status of the bolts, but also monitors the vibration characteristics of the steel beams, indirectly obtaining information on whether the bolt connection is normal. Finally, image recognition technology is used to monitor the loosening of the bolts in real time. The integration of the three technologies of direct force, indirect characterization, and image monitoring, combined with the operating condition data of the maglev vehicle, can provide comprehensive structural health monitoring information, which helps to comprehensively evaluate the overall performance and safety of the connection bolts and provide real-time warnings.

[0057] (4) The present invention analyzes the bolt state, steel beam vibration and modal parameters through the data processing module, and outputs the corresponding bolt state characterization value, acceleration index characterization value and steel beam modal characterization value. Then, the failure diagnosis module calculates the failure judgment value based on the bolt state characterization value, acceleration index characterization value and steel beam modal characterization value, so as to judge the failure degree of the bolt. The failure degree of the bolt is diagnosed through the multi-source fusion bolt failure diagnosis scheme, and comprehensive monitoring of the bolt state, steel beam vibration and modal parameters is achieved, thereby improving the accuracy and reliability of monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The present invention will be further described below with reference to the accompanying drawings.

[0059] Figure 1 This is a flowchart of the health monitoring system for steel beam connection bolts in a maglev maintenance base according to the present invention;

[0060] Figure 2 This is a flowchart of the process of obtaining the failure judgment value in the health monitoring system for the steel beam connection bolts of the maglev maintenance base of the present invention;

[0061] Figure 3 This is a flowchart of the process of obtaining bolt force characterization values ​​in the steel beam connection bolt health monitoring system of the maglev maintenance base of the present invention. DETAILED DESCRIPTION

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0063] Example 1:

[0064] See also Figure 1 As shown, the maglev maintenance base steel beam connection bolt health monitoring system according to the embodiment of the present invention specifically includes: a data acquisition module, a data processing module, a failure diagnosis module and a cloud service platform;

[0065] Data acquisition module: including bolt status data acquisition unit, steel beam status data acquisition unit and vehicle working condition data acquisition unit;

[0066] Bolt status acquisition unit: used to monitor and acquire bolt status data, including two bolt monitoring sub-units, which are located in different positions;

[0067] In this embodiment, bolt status data is acquired through two bolt monitoring subunits. The manufacturing steps of one of the bolt monitoring subunits are as follows: the bolt head of the service bolt is polished and four strain gauges are attached. The strain grid direction of the strain gauges is arranged along the radial direction of the bolt head. The strain gauges are of the same specifications and are evenly spaced in a circular array.

[0068] Another manufacturing step for the bolt monitoring subunit is to grind the bolt head of the in-service bolt and attach a strain gauge with the strain gauge's grid direction arranged along the radial direction of the bolt head;

[0069] By installing strain gauges on the bolt heads for monitoring, there is no need to disassemble or destroy the bolts. Bolts in service can be monitored to ensure structural safety.

[0070] Another embodiment of the present invention is to obtain bolt status data using image recognition based on computer vision. The specific process is as follows:

[0071] Choose a high-resolution, high-sensitivity camera to ensure that it can clearly capture the details of the bolts. At the same time, adjust the camera parameters according to the shooting environment, including but not limited to: exposure time, focal length, to help obtain the best image quality;

[0072] Preprocessing is the first step in image processing. Its goal is to improve image quality, remove noise, and enhance useful information in the image, laying a good foundation for subsequent feature extraction and classification recognition.

[0073] Perform denoising operation on the bolt status image to eliminate noise in the bolt status image. The denoising operation uses filters and other methods to make the image smoother and clearer;

[0074] In order to highlight the key information in the image, such as texture and edges, image enhancement is required. The steel beam vibration image is enhanced by using methods such as histogram equalization and contrast stretching.

[0075] In order to simplify the image processing process, the grayscale image is converted into a binary image. By setting an appropriate threshold, the pixels in the steel beam vibration image are divided into foreground and background categories, which is convenient for subsequent feature extraction.

[0076] Image processing algorithms are used to extract the key features of the bolts. Feature extraction is the core step of image processing, which aims to extract the key features that can represent the vibration of the steel beam from the pre-processed image.

[0077] The texture of the bolt state is an important basis for judging its displacement. The gray-level co-occurrence matrix (GLCM) and Fourier transform of the image are calculated to extract the texture features of the bolt state. These features can include texture direction, texture period, texture uniformity, etc. Edge features:

[0078] Use edge detection algorithms (such as Canny edge detection and Sobel operator) to extract edge features of the bolt state. These features help determine the vibration displacement of the steel beam. Shape features: If the bolt state changes (such as bending, twisting, etc.), relevant features can be extracted through shape analysis algorithms.

[0079] Among them, Canny edge detection: uses the finite difference method to calculate the gradient magnitude and gradient direction of each pixel in the image, performs non-maximum suppression on the gradient magnitude image to refine the edge, sets two thresholds (high threshold and low threshold), and generates an initial edge image based on the gradient magnitude image. The high threshold is used to generate preliminary edges, and the low threshold is used to connect the broken parts in the preliminary edges. By connecting the pixels in the preliminary edges, a complete edge contour is formed;

[0080] A template image of the bolt in a tightened state is pre-established. The template image should include all key features of the bolt when tightened. The template image of the bolt in a tightened state is selected and set by a person skilled in the art based on multiple shots and extractions of images of the bolt in a tightened state.

[0081] Then, the captured image is matched with the template image, and whether the bolt is loose is determined by comparing the difference between the captured image and the template image, wherein the matching algorithm includes but is not limited to: a feature-based matching algorithm and a pixel-based matching algorithm;

[0082] In this example, the process of selecting a pixel-based matching algorithm to compare the difference between the collected image and the template image is as follows: for the collected image and the template image, the grayscale value or color value of the two is compared pixel by pixel, the difference value of each pixel is calculated, the difference values ​​of all pixels are combined into a difference map, where the value of each pixel represents the difference between the collected image and the template image at that position, the difference map is traversed, the number of pixels whose difference values ​​exceed a threshold is counted or the sum of the difference values ​​is calculated, and the degree of difference between the collected image and the template image is determined based on the sum value;

[0083] If the sum is greater than the threshold, the result of bolt loosening is output;

[0084] If the sum is less than or equal to the threshold, the bolt tightening judgment result is output;

[0085] Steel beam state acquisition unit: used to acquire steel beam state data, wherein the steel beam state data includes steel beam vibration parameter data and steel beam modal parameter data;

[0086] Exemplary methods for acquiring the steel beam vibration parameter data include, but are not limited to: acquiring by disposing a sensor in a cavity below the sliding surface of the steel beam, acquiring by image acquisition and processing;

[0087] Exemplarily, the process of obtaining the modal parameter data of the steel beam is as follows:

[0088] Modal analysis is divided into experimental modal analysis (EMA) and operational modal analysis (OMA);

[0089] Use the collected time series data to construct the Hankel matrix H, Where zi and zj are the dynamic response vectors of the measurement point at time i and j, and the H matrix is ​​divided into Zp and Zf, representing "past" and "future" respectively. Zp is the upper half of the Hankel matrix, and Zf is the lower half of the Hankel matrix.

[0090]

[0091] According to the formula O=Z f / Z p =Z f Z p T (Z p Z p T ) + Z p , project Zf onto Zp to obtain the projection matrix O, where the superscript “+” indicates pseudo-inverse;

[0092] According to the formula O = USV T Perform SVD decomposition on the O matrix, where S is a diagonal matrix consisting of singular values, and U and V are unitary matrices obtained by decomposition;

[0093] According to the formula, X i =S -0.5 U T O i , calculate the Kalman filter series Xi, re-divide the Hankel matrix, make Zp add one row, i.e., z0 to zi, total i+1 rows, and Zf reduce one row, i.e., zi+1 to z2i-1, total i-1 rows, and calculate Xi+1;

[0094] pass Calculate the discrete state space system matrix A. The relationship between the discrete state space system matrix A and the continuous state space system matrix Ac is as follows: As shown, fs is the sampling frequency, and the modal parameters can be calculated by performing eigenvalue decomposition on Ac;

[0095] Vehicle operating condition data acquisition unit: used to acquire the operating condition data of the maglev vehicle, wherein the operating condition data includes state change time, signal state change and acceleration;

[0096] In this embodiment, the process of obtaining the operating condition data of the maglev vehicle is as follows:

[0097] Maglev vehicle entry and exit monitoring: The sensor continuously emits signals. When the maglev vehicle stops on the base steel beam, the sensor signal is blocked. When the vehicle completely leaves the beam section, the sensor signal is unblocked. By monitoring the changes in the sensor signal state before and after the vehicle leaves or enters, the data processing system can record the precise time of the state change and the change in the signal state, thereby determining whether the maglev vehicle leaves or enters the beam section and determining the time when the maglev vehicle leaves or enters;

[0098] Maglev vehicle suspension monitoring: When the vehicle is parked on the base steel beam, the sensor signal is in an unobstructed state. When the vehicle begins to levitate, it will maintain a suspension gap of 8-10mm between the vehicle and the track. At this time, the suspension frame will move upward, which can block the signal emitted by the sensor, thereby realizing the monitoring of the vehicle's suspension state. The data processing system can record the precise time of the state change and the change in the signal state, thereby determining the specific time of levitation or landing;

[0099] It should be noted that the sensor needs to use a switching sensor, wherein the switching sensor includes but is not limited to: ultrasonic sensor, infrared sensor, laser sensor, etc.;

[0100] It should be explained that the vehicle operating condition data acquisition unit can perceive the vehicle's operating status in real time, including key information such as the vehicle's entry and exit times and the absolute time of suspension. This information provides important time node references for accurately extracting valid data segments related to bolt stress and steel beam dynamic response from a large amount of monitoring data, reducing the time required to manually search for these key data segments from the monitoring data and improving the efficiency and accuracy of data extraction.

[0101] In this embodiment, the process of obtaining the operating condition data of the maglev vehicle may also be:

[0102] Select an image acquisition device with high resolution, high sensitivity, and adaptability to the operating environment of the maglev vehicle, wherein the image acquisition device includes but is not limited to the use of an OV7670 image sensor;

[0103] It should be noted that the image sensor used should have the characteristics of low operating voltage, small size and built-in image processing unit, so as to be easily integrated into the monitoring system of the maglev vehicle;

[0104] Install image acquisition equipment at key locations where maglev vehicles enter and exit the depot (such as both sides of the base steel beams) and above the tracks for suspension monitoring;

[0105] During the installation of the image acquisition equipment, it is necessary to ensure that the viewing angle of the image acquisition equipment can cover the entire operating area of ​​the maglev vehicle and can clearly capture the state changes of the vehicle;

[0106] The image acquisition device continuously captures the running status of the maglev vehicle to generate a continuous image sequence;

[0107] The collected image data should include image information at key moments such as vehicle entry and exit, and changes in suspension status;

[0108] Preprocess the collected image data, including denoising and contrast enhancement, to improve image quality;

[0109] Use image recognition technologies, such as edge detection and contour extraction, to identify the position and shape of the maglev vehicle;

[0110] By analyzing the changes in the vehicle's position, the time points of the vehicle entering and exiting the warehouse and the suspended state are determined;

[0111] Use image analysis software to calculate the vehicle's acceleration and other motion parameters;

[0112] The image analysis software includes but is not limited to: image analysis software such as MATLAB and OpenCV;

[0113] Based on the image processing results, key information such as the entry and exit time of the maglev vehicle and the absolute time of suspension are extracted;

[0114] It should be noted that when acquiring the operating condition data of the maglev vehicle, you can choose to acquire data using either sensor or image acquisition, or you can choose to acquire data using both sensor and image acquisition simultaneously, and then compare and verify the data obtained by the two methods to ensure the accuracy of the data;

[0115] In this embodiment, it should be noted that if both sensor and image acquisition methods are selected to obtain the maglev vehicle operating condition data, it is necessary to ensure that the data of the two methods are synchronized in time;

[0116] Compare the data obtained by the sensor with the data obtained by image acquisition, and compare the differences in the vehicle entry and exit time and the suspension state transition time between the two;

[0117] If the data differ significantly, the cause needs to be analyzed. The reasons to be analyzed include but are not limited to: sensor failure, improper positioning of the image acquisition device, or inaccurate image processing algorithm;

[0118] Use known physical laws or empirical formulas to verify the data obtained by the two methods;

[0119] For example, the acceleration data calculated by the acceleration sensor and the image processing algorithm can be compared to verify the accuracy of both;

[0120] If the data obtained by the two methods are of comparable accuracy, then the two methods can be combined to provide more comprehensive and accurate data on the operating conditions of the maglev vehicle.

[0121] Among them, the data fusion methods include but are not limited to: through weighted averaging, Kalman filtering;

[0122] Exemplarily, the sensor data and the image data are fused by using an extended Kalman filter (EKF) nonlinear filtering method to process the nonlinear relationship between the sensor data and the image data;

[0123] Define the state vector, including but not limited to: vehicle position, velocity, acceleration and other key state variables;

[0124] Initialize the state vector and covariance matrix to reflect the uncertainty and correlation of the initial state;

[0125] Predict the state vector based on the vehicle's kinematic model or dynamic model;

[0126] Calculate the covariance matrix of the predicted state to reflect the uncertainty of the predicted state;

[0127] Preprocess the sensor and image data to extract observations related to the vehicle state; convert the observations into a form suitable for Kalman filtering, usually by defining an observation matrix;

[0128] Perform Taylor series expansion on the nonlinear state equation and observation equation and retain the first-order terms to achieve linearization;

[0129] Calculate the Jacobian matrix to reflect the linear relationship between the state vector and the observations;

[0130] Use the Kalman filter update formula to update the state vector and covariance matrix based on the observed and predicted data; calculate the Kalman gain to weigh the credibility of the predicted and observed values;

[0131] Predict and update the data at each moment to obtain continuous state estimation; check convergence to ensure the stability and accuracy of the state estimation;

[0132] If data anomalies or inconsistencies are found during the comparison and verification process, it is necessary to promptly troubleshoot the problem.

[0133] Troubleshooting includes, but is not limited to: checking the status of sensors and image acquisition equipment, checking the operation of the data processing system, and checking the correctness of the data processing algorithm;

[0134] The technical solution of the embodiment of the present invention is mainly as follows: by providing a strain gauge on the bolt head for monitoring, there is no need to disassemble or destroy the bolt, and the bolt in service state can be monitored without affecting the structural integrity, thereby reducing the risk and cost of the monitoring process;

[0135] The vehicle operating condition data acquisition unit can perceive the vehicle's operating status in real time, including key information such as the vehicle's entry and exit time and the absolute suspension time. This provides an important time node reference for accurately extracting valid data segments related to bolt stress and steel beam dynamic response from a large amount of monitoring data, improving the efficiency and accuracy of data extraction.

[0136] It not only monitors the status of the bolts, but also the status of the steel beams and the operating data of the maglev vehicle, providing comprehensive structural health monitoring information, which helps to fully evaluate the overall performance and safety of the structure.

[0137] Example 2:

[0138] Based on Example 1, please refer to Figure 2 - Figure 3 As shown, the maglev maintenance base steel beam connection bolt health monitoring system according to the embodiment of the present invention further includes:

[0139] Data processing module: including bolt status data processing unit, steel beam vibration data processing unit and steel beam modal data processing module;

[0140] Bolt status data processing unit: analyzes the bolt status data within the sampling period and outputs the bolt status representation value;

[0141] In some embodiments, a sampling frequency and a sampling period are preset to obtain bolt status data within the sampling period, wherein the bolt status data includes a bolt force value;

[0142] For example, if the sampling frequency is 100 Hz, data will be collected 100 times per second, that is, the sampling period is 0.01 second (1 / 100 second), then 100 data points will be collected in 0.01 second;

[0143] Compare the bolt force value with the bolt force threshold, where the bolt force threshold is set by those skilled in the art based on historical experimental data.

[0144] If the bolt force value is less than or equal to the bolt force threshold, it is marked as a normal force data point; if the bolt force value is greater than the bolt force threshold, it is marked as an abnormal force data point;

[0145] During the sampling period, the number of abnormal force data points is obtained, and the ratio of the number of abnormal data points to the total number of data points is processed to obtain the proportion of abnormal data points;

[0146] The bolt force value of the abnormal force data point is calculated by difference with the bolt force threshold to obtain the bolt force deviation value, the bolt force deviation value is processed by ratio with the bolt force threshold to obtain the bolt force degree ratio, and the bolt force degree ratios of all abnormal force data points are summed and averaged to obtain the average bolt force degree ratio;

[0147] Extract the maximum value of bolt stress ratio of all abnormal stress data points;

[0148] Substitute the formula SL = a1*ZB + a2*JZ + a3*ZD to calculate the bolt state representation value SL, where ZB represents the proportion of abnormal data points, JZ represents the average value of the bolt stress ratio, and ZD represents the maximum value of the bolt stress ratio. a1, a2, and a3 are weight coefficients, and a1+a2+a3=1.

[0149] It should be explained that the meaning reflected by the bolt state characterization value is: the bolt state characterization value is calculated by the proportion of abnormal data points, the average value of the bolt stress ratio and the maximum value of the bolt stress ratio, wherein the larger the proportion of abnormal data points, the more abnormal stress data points, and the worse the bolt state is during the sampling period, which may be in a loose or broken state; the larger the bolt stress ratio is, the larger the average value of the deviation between the bolt stress value of the abnormal stress data point and the bolt stress threshold, the worse the bolt state is during the sampling period, which may be in a loose or broken state; the maximum value of the bolt stress ratio, the greater the deviation between the bolt stress value at the corresponding abnormal stress data point and the bolt stress threshold, the greater the abnormality of the abnormal stress data point, and the worse the bolt state is during the sampling period, which may be in a loose or broken state;

[0150] Steel beam vibration parameter data processing unit: collects steel beam vibration parameter data within the sampling period and outputs acceleration index representation values;

[0151] In this embodiment, the steel beam vibration parameter data is obtained by an accelerometer;

[0152] In some implementations, the maximum value in the acceleration data is extracted to obtain an acceleration peak value, the acceleration peak value is subtracted from the acceleration standard value, the difference is taken as the absolute value to obtain an acceleration deviation value, and the acceleration deviation value is ratioed with the acceleration standard value to obtain an acceleration peak deviation ratio;

[0153] The standard value of acceleration is set by those skilled in the art based on the summary of multiple historical experimental data;

[0154] Substitute the acceleration data into the RMS formula to calculate the RMS value of acceleration;

[0155] Substitute the formula JS = s1 * FZ + s2 * JF to calculate the acceleration index representation value JS, where FZ represents the acceleration peak deviation ratio, JF represents the acceleration root mean square value, s1 and s2 are weight coefficients, and s1 + s2 = 1;

[0156] It should be explained that the meaning of the acceleration index representation value is as follows: the acceleration index representation value is calculated from the peak acceleration value and the root mean square acceleration value. Among them, the peak acceleration value represents the maximum impact force of the steel beam vibration, and the root mean square acceleration value represents the overall intensity of the vibration.

[0157] Steel beam modal parameter data processing module: analyzes the steel beam modal parameter data within the sampling period and outputs the steel beam modal characterization value;

[0158] Among them, the modal parameter data of the steel beam include: vibration shape, frequency and damping ratio;

[0159] In some embodiments, the modal frequency and the natural frequency are difference-calculated, the absolute value of the difference is taken, and the difference is ratio-processed with the natural frequency to obtain the modal frequency ratio;

[0160] Substitute into the formula The modal characterization value MT of the steel beam is calculated, where MP represents the modal frequency ratio, MZ represents the damping ratio, d1 and d2 are weight coefficients, and d1+d2=1;

[0161] It should be explained that the modal characterization value of the steel beam reflects the following meanings: the modal frequency ratio is the ratio obtained by comparing the actual modal frequency with the natural frequency. This ratio reflects the change in the modal frequency. If the actual modal frequency differs significantly from the natural frequency, it may mean that there is some degree of damage or change in the structure, such as looseness at the steel beam connection or material aging. The damping ratio indicates the degree of energy dissipation of the structure during vibration. It reflects the structure's damping capacity for vibration. The larger the damping ratio, the stronger the structure's damping capacity for vibration and the faster the vibration decays.

[0162] Failure diagnosis module: processes the bolt state representation value, acceleration index representation value and steel beam modal representation value, outputs the failure judgment value, and makes a judgment;

[0163] In some embodiments, the bolt state characterization value SL, the acceleration index characterization value JS and the steel beam modal characterization value MT are obtained and substituted into the formula The failure judgment value SX is calculated, where p1, p2, and p3 are preset proportional coefficients;

[0164] It should be explained that the meaning of the failure judgment value is as follows: the failure judgment value is calculated by the bolt state characterization value, the acceleration index characterization value and the steel beam modal characterization value. When the failure judgment value is less than or equal to the failure judgment threshold, it means that the bolt failure state is relatively minor, and an early warning signal is issued, and the staff conducts timely inspection and maintenance. When the failure judgment value is greater than the failure judgment threshold, it means that the bolt failure state is relatively serious, and an abnormal signal is issued, and the staff conducts maintenance and stops the vehicle from passing.

[0165] Obtaining a failure judgment value, and comparing the failure judgment value with a failure judgment threshold, wherein the failure judgment threshold is a critical value used to judge the degree of bolt failure, and is set by those skilled in the art based on a summary of multiple historical experimental data;

[0166] If the failure judgment value is less than or equal to the failure judgment threshold, a warning signal is generated;

[0167] If the failure judgment value is greater than the failure judgment threshold, an abnormal signal is generated;

[0168] The technical solution of the embodiment of the present invention is mainly as follows: the bolt state, steel beam vibration and modal parameters are analyzed by a data processing module, and the corresponding bolt state characterization value, acceleration index characterization value and steel beam modal characterization value are output; then, the failure diagnosis module calculates the failure judgment value according to the bolt state characterization value, acceleration index characterization value and steel beam modal characterization value, so as to judge the failure degree of the bolt; the failure degree of the bolt is diagnosed through a multi-source fusion bolt failure diagnosis scheme, thereby realizing comprehensive monitoring of the bolt state, steel beam vibration and modal parameters, and improving the accuracy and reliability of monitoring.

[0169] The size of the above threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the base number set by technical personnel in this field for each group of sample data; for example: in the actual acquisition process, there are many groups of bolt state characterization values, acceleration index characterization values ​​and steel beam modal characterization values. Many groups of bolt state characterization values, acceleration index characterization values ​​and steel beam modal characterization values ​​are processed to obtain the corresponding groups of failure judgment values. The staff evaluates the degree of bolt failure based on so many groups of failure judgment values, thereby obtaining a corresponding relationship between a failure judgment value and the degree of bolt failure, and then derives and divides the threshold of the failure judgment value according to the degree of bolt failure, thereby obtaining a failure judgment threshold, and compares the obtained failure judgment value with the failure judgment threshold, that is, completes the identification of the degree of bolt failure corresponding to the failure judgment value.

[0170] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. The health monitoring system for steel beam connection bolts at the maglev maintenance base is characterized by: Specifically include: Data acquisition module, data processing module, failure diagnosis module and cloud service platform; Data acquisition module: including bolt status data acquisition unit, steel beam status data acquisition unit and vehicle working condition data acquisition unit; Among them, the bolt status acquisition unit is used to monitor and obtain bolt status data. Bolt status data is obtained through two bolt monitoring sub-units, which are located in different positions; Steel beam state acquisition unit: used to acquire steel beam state data, wherein the steel beam state data includes steel beam vibration parameter data and steel beam modal parameter data; Vehicle operating condition data acquisition unit: used to acquire the operating condition data of the maglev vehicle, wherein the operating condition data includes the state change time and signal state change; Data processing module: including bolt status data processing unit, steel beam vibration data processing unit and steel beam modal data processing module; Output the bolt state representation value SL, acceleration index representation value JS and steel beam modal representation value MT through the data processing module; Failure diagnosis module: processes the bolt state characterization value, acceleration index characterization value and steel beam modal characterization value, outputs the failure judgment value, and judges the failure degree.

2. The maglev maintenance base steel beam connection bolt health monitoring system according to claim 1 is characterized by: The data processing unit specifically includes: Bolt status data processing unit: analyzes the bolt status data within the sampling period and outputs the bolt status representation value; Steel beam vibration parameter data processing unit: collects steel beam vibration data within the sampling period and outputs acceleration index representation values; Steel beam modal parameter data processing module: analyzes the steel beam modal parameter data within the sampling period and outputs the steel beam modal characterization value, where the steel beam modal parameter data includes: vibration mode, frequency and damping ratio.

3. The maglev maintenance base steel beam connection bolt health monitoring system according to claim 1 is characterized in that: The acquisition process of the two bolt monitoring subunits is as follows: The manufacturing steps for one of the bolt monitoring subunits are as follows: The bolt head of the in-service bolt is polished and four strain gauges are attached. The strain gauges are arranged along the radial direction of the bolt head. The strain gauges are of the same specifications and are evenly spaced in a circular array. Another manufacturing step of the bolt monitoring subunit is: grinding the bolt head of the service bolt and attaching a strain gauge, with the strain gauge's strain grid direction arranged along the radial direction of the bolt head.

4. The maglev maintenance base steel beam connection bolt health monitoring system according to claim 1 is characterized in that: The bolt status data can also be identified through images, and the specific process is as follows: Select a camera to capture images, pre-process the captured images, and use image processing algorithms to extract key features of the bolts; A template image of the bolt tightening state is created in advance, and the captured image is matched with the template image using a pixel-based matching algorithm. The specific process is as follows: For the collected image and the template image, the grayscale value or color value of the two is compared pixel by pixel, and the difference value of each pixel is calculated. The difference values ​​of all pixels are combined into a difference map, where the value of each pixel represents the difference between the collected image and the template image at that position. The difference map is traversed, and the sum of the difference values ​​that exceed the threshold is counted. Based on the sum value, the degree of difference between the collected image and the template image is determined; If the sum is greater than the threshold, the result of bolt loosening is output; If the sum is less than or equal to the threshold, the bolt tightening judgment result is output.

5. The maglev maintenance base steel beam connection bolt health monitoring system according to claim 1 is characterized in that: The process of obtaining the bolt status characterization value is as follows: Analyze the abnormal stress data points and bolt stress values ​​to obtain the abnormal data point ratio, the average bolt stress ratio, and the maximum bolt stress ratio; Substitute the formula SL=a1*ZB+a2*JZ+a3*ZD to calculate the bolt state characterization value SL, where ZB represents the proportion of abnormal data points, JZ represents the average value of the bolt stress ratio, ZD represents the maximum value of the bolt stress ratio, and a1, a2, and a3 are weight coefficients.

6. The maglev maintenance base steel beam connection bolt health monitoring system according to claim 5 is characterized in that: The process of obtaining the abnormal data point ratio, the average value of the bolt stress ratio, and the maximum value of the bolt stress ratio is as follows: Preset the sampling frequency and sampling period, and obtain the bolt status data within the sampling period, wherein the bolt status data includes the bolt force value; Compare the bolt force value with the bolt force threshold. If the bolt force value is less than or equal to the bolt force threshold, it is marked as a normal force data point. If the bolt force value is greater than the bolt force threshold, it is marked as an abnormal force data point. During the sampling period, the number of abnormal force data points is obtained, and the ratio of the number of abnormal force data points to the total number of data points is processed to obtain the proportion of abnormal data points; The bolt force value of the abnormal force data point is calculated by difference with the bolt force threshold to obtain the bolt force deviation value, the bolt force deviation value is processed by ratio with the bolt force threshold to obtain the bolt force degree ratio, and the bolt force degree ratios of all abnormal force data points are summed and averaged to obtain the average bolt force degree ratio; Extract the maximum value of the bolt stress ratio of all abnormal stress data points.

7. The maglev maintenance base steel beam connection bolt health monitoring system according to claim 2, characterized in that: The process of obtaining the acceleration index characterization value is as follows: Acquire acceleration data through an accelerometer, extract the maximum value in the acceleration data to obtain the acceleration peak value, calculate the difference between the acceleration peak value and the acceleration standard value, take the absolute value of the difference to obtain the acceleration deviation value, perform ratio processing on the acceleration deviation value and the acceleration standard value to obtain the acceleration peak deviation ratio; Substitute the acceleration data into the RMS formula to calculate the RMS value of acceleration; Substitute into the formula JS=s1*FZ+s2*JF to calculate the acceleration index representation value JS, where FZ represents the acceleration peak deviation ratio, JF represents the acceleration root mean square value, s1 and s2 are weight coefficients, and s1+s2=1.

8. The maglev maintenance base steel beam connection bolt health monitoring system according to claim 2, characterized in that: The process of obtaining the modal characterization value of the steel beam is as follows: Calculate the difference between the modal frequency and the natural frequency, take the absolute value of the difference, and compare it with the natural frequency to obtain the modal frequency ratio; Substitute into the formula The modal characterization value MT of the steel beam is calculated, where MP represents the modal frequency ratio, MZ represents the damping ratio, d1 and d2 are weight coefficients, and d1+d2=1.

9. The maglev maintenance base steel beam connection bolt health monitoring system according to claim 1, characterized in that: The process of obtaining the failure judgment value is as follows: Obtain the bolt state representation value SL, acceleration index representation value JS and steel beam modal representation value MT, and substitute them into the formula The failure judgment value SX is calculated, where p1, p2, and p3 are preset proportional coefficients.

10. The maglev maintenance base steel beam connection bolt health monitoring system according to claim 1, characterized in that: The process of determining the degree of failure is as follows: Obtaining a failure judgment value, and comparing the failure judgment value with a failure judgment threshold; If the failure judgment value is less than or equal to the failure judgment threshold, a warning signal is generated; If the failure judgment value is greater than the failure judgment threshold, an abnormal signal is generated.

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