Safety monitoring methods for grain steel silo structures

Abstract

This application belongs to the field of safety monitoring technology, specifically relating to a method for monitoring the structural safety of grain steel silos. The method includes: S10, acquiring raw point cloud data periodically collected by a three-dimensional data acquisition device, bolt length and crack detection results obtained by an ultrasonic flaw detection device, and strain data at preset points on the grain steel silo collected in real time by a strain acquisition device; S20, determining the structural data of the grain steel silo based on the raw point cloud data, including verticality, roundness, and silo body deformation in empty and full states; S30, conducting safety monitoring of the grain steel silo based on the structural data, bolt length, crack detection results, and strain data, and generating monitoring information. This method can effectively monitor the structural safety of grain steel silos, comprehensively understand the status information of vulnerable points in the silo shell structure, and help to detect potential safety hazards early, reducing sudden safety accidents.

Description

Technical Field

[0001] This application belongs to the field of safety monitoring technology, specifically relating to a method for safety monitoring of grain steel silo structures. Background Technology

[0002] Grain silos are warehouses for storing bulk grain. The use of silos can shorten the material loading and unloading process, reduce operating and maintenance costs, and facilitate mechanized and automated operations. However, steel silos are thin-walled cylindrical shell structures with complex stress and failure mechanisms. The steel structure of the silo shell, in particular, is highly susceptible to localized strain concentration and sudden deterioration of its condition under various natural or human influences, leading to cracking and failure, which seriously threatens the normal and safe use of the silo.

[0003] Traditional steel structure stress testing involves installing testing points at critical load-bearing locations on the steel structure. During testing, multiple points are measured simultaneously, but the stress state at each test point is determined independently, with no interdependence between the points. Each testing point undergoes only a one-dimensional linear evaluation, meaning a fixed threshold is set based on the material of the steel structure and compared with the measured value to determine if the point is functioning correctly. The test results only reflect the "absolute" result of the tested steel structure—whether damage has occurred—and cannot provide in-depth analysis of the damage trend or offer early warnings of potential failure. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] In view of the above-mentioned shortcomings and deficiencies of the prior art, this application provides a method for safety monitoring of grain steel silo structures.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, this application adopts the following technical solution:

[0008] This application provides a method for safety monitoring of a grain steel silo structure, the method comprising:

[0009] S10. Acquire raw point cloud data periodically collected by a 3D data acquisition device, bolt length and crack detection results obtained by an ultrasonic flaw detection device, and strain data of preset points on the grain steel silo collected in real time by a strain acquisition device.

[0010] S20. Determine the structural data of the grain steel silo based on the original point cloud data. The structural data includes verticality, roundness, and silo body shape variables in empty and full states.

[0011] S30. Based on the structural data, bolt length, crack detection results, and strain data, perform safety monitoring of the grain steel silo and generate monitoring information.

[0012] Optionally, S30 includes:

[0013] The structural relationship between each detection point is determined based on the pre-established steel plate silo steel structure calculation model, and the stress data of each detection point is determined based on the strain data.

[0014] Based on the coupling relationship between strain and stress data and theoretical analysis data at various detection points of the grain steel silo under no-load, loaded, and full-load conditions, structural safety prediction information is generated.

[0015] Optionally, in S10, environmental data around the grain steel silo is collected simultaneously with strain data; therefore, S30 also includes:

[0016] Safety monitoring of the grain steel silo structure is conducted based on the environmental data and the strain data.

[0017] Optionally, safety monitoring of the grain steel silo structure is performed based on the environmental data and the strain data, including:

[0018] The coupling relationship of stress data at each detection point is determined based on the structural relationship, and the correlation relationship between environmental data and stress data is determined based on the environmental data and the stress data.

[0019] The safety of the steel silo structure is assessed based on the coupling and correlation relationships, and assessment results are generated.

[0020] Optionally, S10 further includes: collecting vibration data of the grain steel silo; then, S30 further includes:

[0021] Based on the vibration data and the real-time collected strain data, the stress changes of the grain steel silo caused by vibration are monitored.

[0022] Optionally, the method further includes:

[0023] The safety of the steel silo structure is assessed based on the stress data and the pre-acquired stress design data, and an assessment result is generated.

[0024] Optionally, the method further includes:

[0025] It accepts user access commands, displays the real-time status of the monitored silos on the display terminal, and allows users to query and statistically analyze monitoring data based on time intervals and the number of the inspected facilities and equipment, and displays the query and statistical results.

[0026] Optionally, the method further includes:

[0027] Based on the strain data and the structural data, the causes of structural failure and vulnerable parts of the grain steel silo are analyzed using a pre-set expert knowledge base.

[0028] The strain data and the structural data are input into a trained risk prediction model to generate the risk category and risk level of the grain steel silo; wherein the risk prediction model is a classification model based on a deep neural network.

[0029] Optionally, determining the verticality of the grain steel silo based on the original point cloud data includes:

[0030] Based on the original point cloud data, the center of the circle at different elevation positions is obtained by fitting the point cloud data of slices at different elevation positions;

[0031] Calculate the verticality and tilt direction of the silo based on the distance between the centers and the positional deviation.

[0032] Optionally, determining the roundness of the grain steel silo based on the original point cloud data includes:

[0033] Based on the point cloud data of the grain steel silo, the maximum and minimum circles are fitted by the point cloud data of slices at different elevation positions, and the roundness index of the silo is calculated based on the fitted maximum and minimum circles.

[0034] (III) Beneficial Effects

[0035] The beneficial effects of this application are as follows: This application proposes a method for structural safety monitoring of grain steel silos, including: S10, acquiring raw point cloud data periodically collected by a three-dimensional data acquisition device, bolt length and crack detection results obtained by an ultrasonic flaw detection device, and strain data at preset points on the grain steel silo collected in real time by a strain acquisition device; S20, determining the structural data of the grain steel silo based on the raw point cloud data, including verticality, roundness, and silo body deformation in empty and full states; S30, conducting safety monitoring of the grain steel silo based on the structural data, bolt length, crack detection results, and strain data, and generating monitoring information. This method can effectively monitor the structural safety of grain steel silos, comprehensively grasp the status information of vulnerable points in the silo shell structure, help to detect silo safety hazards early, and reduce sudden safety accidents. Attached Figure Description

[0036] This application is described with reference to the following figures:

[0037] Figure 1 This is a schematic diagram of the safety monitoring method for a grain steel silo structure in one embodiment of this application;

[0038] Figure 2 This is an example diagram of real-time online monitoring of silos in another embodiment of this application;

[0039] Figure 3 This is an example diagram of an online data analysis interface in another embodiment of this application;

[0040] Figure 4 This is an example of an online data analysis spectrum diagram from another embodiment of this application;

[0041] Figure 5 This is an example diagram of a silo monitoring interface in another embodiment of this application;

[0042] Figure 6 This is a schematic diagram of a three-dimensional laser scanning process in yet another embodiment of this application;

[0043] Figure 7 This is a schematic diagram of the site layout in yet another embodiment of this application;

[0044] Figure 8 This is a schematic diagram of the control point layout in another embodiment of this application. Detailed Implementation

[0045] To better explain and facilitate understanding of the present invention, it is described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described below are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other; for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0046] Example 1

[0047] This application provides a method for safety monitoring of grain steel silo structures, which can be applied to safety monitoring servers. Figure 1 This is a schematic diagram of the safety monitoring method for a grain steel silo structure in one embodiment of this application, as shown below. Figure 1 As shown, the method includes:

[0048] S10. Acquire raw point cloud data periodically collected by a 3D data acquisition device, bolt length and crack detection results obtained by an ultrasonic flaw detection device, and strain data of preset points on the grain steel silo collected in real time by a strain acquisition device.

[0049] S20. Determine the structural data of the grain steel silo based on the original point cloud data. The structural data includes the verticality, roundness, and shape variables of the silo in the empty and full states.

[0050] S30. Based on structural data, bolt length, crack detection results, and strain data, conduct safety monitoring of grain steel silos and generate monitoring information.

[0051] The structural safety monitoring method for grain steel silos in this embodiment can effectively monitor the structural safety of grain steel silos, comprehensively grasp the status information of vulnerable points in the silo shell structure, detect potential safety hazards in the silos early, and take necessary reinforcement and repair measures in a timely manner. This greatly improves the silo safety monitoring capability, effectively enhances the speed and efficiency of accident hazard investigation, and significantly saves on manual monitoring costs.

[0052] To better understand the present invention, the steps in this embodiment will be described in detail below.

[0053] In this embodiment, the preset points include: points on the main load-bearing components such as the silo wall and the reinforcing support columns, points around the manhole and maintenance door, and key detection points.

[0054] Considering that the silo has only two structural forms along its height—a double-layered lower section and a single-layered upper section with thickened plates—eight measuring points should be set up in eight mutually perpendicular directions on the horizontal projection plane at the same height. These points should be arranged along the silo's height for five cycles, totaling 40 measuring points, which is sufficient to reflect the stress on the structure. Additionally, four measuring points can be set around each manhole on the silo wall, as well as at key measuring points and symmetrically placed points on both sides. Key measuring points can be areas with high stress as indicated by theoretical calculations or dangerous points discovered during on-site investigations.

[0055] To understand the stress on the warehouse wall panels at the manhole location, four strain sensors can be installed around the manhole and maintenance door. Considering that damage to the measuring points or wiring may lead to the loss of important data, some key measuring points can be appropriately arranged using symmetrical double-sided points.

[0056] By arranging detection points on the main load-bearing components such as the silo wall panels and the silo wall reinforcing support columns, complete stress characteristic data of the steel silo can be obtained.

[0057] In this embodiment, the strain acquisition device can be a strain sensor, and in this embodiment, the strain sensor can be a strain gauge. Preferably, the strain gauge can be a self-temperature-compensated strain gauge.

[0058] In this embodiment, determining the verticality of the grain steel silo based on the original point cloud data includes:

[0059] Based on the original point cloud data, the center of the circle at different elevation positions is obtained by fitting the point cloud data of slices at different elevation positions;

[0060] Calculate the verticality and tilt direction of the silo based on the distance between the centers and the positional deviation.

[0061] In this embodiment, determining the roundness of the grain steel silo based on the original point cloud data includes:

[0062] Based on the point cloud data of grain steel silos, the maximum and minimum circles are fitted by the point cloud data of slices at different elevation positions, and the roundness index of the silos is calculated based on the fitted maximum and minimum circles.

[0063] In this embodiment, the monitoring information may include: verticality, roundness, silo body deformation in empty and full states, bolt length, strain value compared with corresponding preset thresholds, and statistical information based on time intervals, equipment numbers, etc.

[0064] Example 2

[0065] This embodiment further explains the method for safety monitoring of grain steel silo structures based on Embodiment 1.

[0066] In this embodiment, S30 includes:

[0067] The structural relationship between each detection point is determined based on the pre-established steel plate silo steel structure calculation model, and the stress data of each detection point is determined based on the strain data;

[0068] Based on the coupling relationship between strain and stress data and theoretical analysis data at various detection points of grain steel silos under no-load, loaded, and full-load conditions, structural safety prediction information is generated.

[0069] It should be noted that when collecting strain data, the strain acquisition device can obtain the operating condition information of the silo under no-load, loaded, and full-load conditions through the flow meter.

[0070] In this embodiment, during S10, environmental data around the grain steel silo is collected simultaneously with strain data; therefore, S30 may further include:

[0071] Safety monitoring of grain steel silo structures is conducted based on environmental and strain data. Specifically, this includes:

[0072] The coupling relationship of stress data at each detection point is determined based on the structural relationship, and the correlation between environmental data and stress data is determined based on environmental data and stress data.

[0073] The safety of steel silo structures is assessed based on coupling and correlation relationships, and assessment results are generated.

[0074] In this embodiment, the environmental data includes environmental climate data collected by a barometric pressure sensor, a wind speed and direction sensor, and a temperature sensor installed at an unobstructed location on the top of the silo.

[0075] The ANSYS calculation model of steel structure facilities and equipment is dynamically mapped to stress data obtained from stress testing. This involves coupling the measured data from the field with theoretical analysis data to identify future trends. Specifically, based on traditional stress testing methods, the data from testing points is coupled and calculated according to the structural relationships within the ANSYS model. Furthermore, a local weather station is set up at the testing site to incorporate meteorological data into the structural calculation model. Therefore, the dynamic mapping stress testing method can comprehensively detect structural stress from multiple dimensions, including absolute test values ​​at testing points, the internal relationships between testing points, and meteorological factors.

[0076] The dynamic mapping detection method can comprehensively detect and analyze multiple dimensions, including stress values ​​at measuring points, structural interconnections at each measuring point, and meteorological conditions. This allows for a more accurate assessment of whether there are problems with the steel silo structure, and enables in-depth analysis of any existing issues, thereby guiding the safe use of the silos.

[0077] Meanwhile, parametric modeling of the steel silo shell structure was performed using ANSYS software, and static, transient, and modal analyses were conducted to obtain the initial stress of the structure at its zero-point state. The influence of factors such as the type of grain stored in the silo, the eccentricity of the material surface, shell openings, internal pressure, temperature, and wind load on the stress and strain of the silo shell structure was analyzed. Furthermore, the failure modes and causes of the steel silo shell structure were analyzed. Based on the survey results of grain silos, the usage status and failure modes of steel silos were comprehensively summarized, and the causes of structural failure and vulnerable areas were analyzed in detail according to the factors affecting the stress and strain of the shell structure.

[0078] By analyzing the dynamic mapping between the calculation model of the steel silo structure and the stress detection system, that is, by studying the coupling relationship between on-site measured data and theoretical analysis data, future development trends can be identified. This allows for accurate determination of whether there are problems with the steel silo structure and in-depth analysis of existing problems, thereby guiding the safe use of the silos.

[0079] In this embodiment, S10 further includes: collecting vibration data of the grain steel silo; therefore, S30 may further include:

[0080] Based on vibration data and real-time acquired strain data, stress changes in grain steel silos caused by vibration are monitored.

[0081] Vibration data can be collected by acceleration sensors installed on the bottom support and lower sidewall steel plates of the silo. When the steel silo is near a railway, the ground vibration caused by the passage of heavy-load trains is significant. Therefore, this embodiment monitors the stress caused by the vibration of heavy-load trains. When a heavy-load train passes, data from the vibration acceleration sensor and stress sensor are collected simultaneously, recording data under four operating conditions: empty and stationary, fully loaded and stationary, grain feeding, and grain discharging.

[0082] In this embodiment, the method may further include:

[0083] The safety of the steel silo structure is assessed based on stress data and pre-acquired stress design data, and assessment results are generated.

[0084] Specifically, the stress data includes:

[0085] a. Measure 24-hour steady-state monitoring data under no-load conditions of the silo to analyze the stress level of the steel silo under full load conditions;

[0086] b. During the production process, the stress data of the steel silos under the main load conditions during the silo loading process is measured to detect and analyze the stress performance of the steel silos under normal use.

[0087] c. Measure steady-state monitoring data for 24 to 48 hours under full load conditions of the silo to analyze the stress level of the steel silo under full load conditions;

[0088] d. During the production process, the unloading and discharge process of the steel silo is mainly carried out under reduced load conditions. Measurements are taken to detect and analyze the stress performance of the steel silo under normal use.

[0089] e. Under typhoon conditions, measurements are taken on unloaded or fully loaded steel silos to detect and analyze the stress performance of the steel silo structure under such wind load conditions.

[0090] By collecting the above stress data, we can understand the structural stress performance of steel silos under normal use conditions (strong wind or typhoon conditions, silos fully loaded or empty conditions), that is, the distribution of internal forces in the silo under the silo feeding and unloading load conditions during normal production.

[0091] The basic stress design data can be the stress design data of the main load-bearing components of the steel silo, such as the silo wall panels and the silo wall stiffener support columns, provided by the owner. By comparing and analyzing the data obtained from actual testing, the structural safety and normal use safety of the steel silo can be assessed. By comparing and analyzing the test results and design calculation results of the steel silo, the structural safety and normal use of the steel silo can be ensured.

[0092] An assessment of the overall safety status of the steel silo shall be conducted, and the assessment report shall include, but is not limited to, the following: the overall safety and technical status of the steel silo, the problems or weaknesses of the steel silo, rectification suggestions, usage requirements, and recommended remaining service life.

[0093] In this embodiment, the method further includes:

[0094] It accepts user access commands, displays the real-time status of the monitored silos on the display terminal, and allows users to query and statistically analyze monitoring data based on time intervals and the number of the inspected facilities and equipment, and displays the query and statistical results.

[0095] The terminal equipment is used for real-time monitoring, querying, displaying, and printing reports of the inspected facilities and equipment. The terminal equipment allows users to directly log in to the online monitoring system from the external web to view the real-time status of the inspected silos. Its main functions are as follows:

[0096] Dynamic query and statistics of test results. The terminal display module can perform query and statistics functions on test results based on information such as time interval and the number of the inspected facility or equipment.

[0097] Data monitoring and playback. The terminal display module can log in to the cloud server to view on-site detection data via web. The data can be displayed to users in various formats, and historical data can be remotely retrieved for playback.

[0098] Test results are published. The terminal display module can provide website and mobile phone publishing functions for test results.

[0099] Access control. The terminal display module enables role-based and permission-based responsibility and authorization management to achieve security control.

[0100] In this embodiment, the method further includes:

[0101] Based on strain data and structural data, the structural failure causes and vulnerable parts of the grain steel silo are analyzed using a pre-set expert knowledge base.

[0102] Strain data and structural data are input into a trained risk prediction model to generate risk categories and risk levels for grain steel silos; the risk prediction model is a classification model based on a deep neural network.

[0103] Figure 2 This is an example diagram of real-time online monitoring of silos in another embodiment of this application. Figure 3 This is an example diagram of an online data analysis interface in another embodiment of this application. Figure 4 This is an example of an online data analysis spectrum diagram in another embodiment of this application. Figure 5This is an example diagram of a silo monitoring interface from another embodiment of this application. Figure 2 As shown, the grain steel silo structure safety monitoring system can monitor the stress-time history and strain-time history data of each measuring point of the steel silo shell structure in real time under different service conditions, and detect local deformation in advance before an accident occurs. Figure 3 As shown, the structural safety monitoring system for grain steel silos can predict future development trends by coupling on-site measured data and theoretical analysis data; for example... Figure 4 As shown, by analyzing the additional stress caused by the vibration of a heavy-haul train passing by, the influence of the superposition of stress and original stress at different vibration frequencies on the steel silo is analyzed. Figure 5 As shown in the figure, the stress state at each detection point is displayed based on the sensor deployment location.

[0104] Therefore, the monitoring method in this embodiment, which utilizes the collected data for safety assessment, not only enables real-time monitoring of equipment usage but also predicts and analyzes equipment performance trends. It can provide timely alarms in case of problems or proactively alert users to equipment safety conditions, facilitating maintenance and repair. In the event of a sudden safety accident, the collected data can be used for precise analysis to identify the cause. The continuous recording, monitoring, and analysis of real-time collected data provides strong technical support for safe silo production.

[0105] Example 3

[0106] The following details the process of detecting raw point cloud data periodically acquired by a 3D data acquisition device and bolt length and cracks obtained by an ultrasonic flaw detection device in the method of this application.

[0107] Figure 6 This is a schematic diagram of a three-dimensional laser scanning process in another embodiment of this application, as shown below. Figure 6 As shown, the raw point cloud data collected periodically through 3D laser scanning of the steel silo using a 3D data acquisition device includes:

[0108] S1. Site survey.

[0109] A site survey was conducted to assess the scanning environment, focusing on key areas to be scanned, and the scanning stations were planned accordingly. The main principles are as follows:

[0110] (1) Maximize the scanning field of view. The station position should have as wide a field of view as possible over the scanning area to ensure that a single station can collect as much point cloud data as possible from the silo. At the same time, it should avoid points with too small an angle of incidence.

[0111] (2) The visibility between sites is good, and the data overlap rate is over 30%;

[0112] (3) The data of the scanned object is complete and without omission;

[0113] (4) The station position is stable and there is no vibration.

[0114] Figure 7 This is a schematic diagram of the site layout in another embodiment of this application, as shown below. Figure 7 As shown, based on the shape characteristics of the silo, the stations are mainly distributed in a circular pattern, surrounding the entire silo. The straight-line distance between the station and the silo wall should preferably be greater than 15 meters. The spacing between stations is approximately 10-15 meters.

[0115] S2. Control network layout

[0116] Considering the need to compare and analyze the deformation of the silo in its empty and full states, it is necessary to establish reference control points around the silo. The coordinate system of these reference points can be freely set, with the main purpose of ensuring a unified coordinate system for data from multiple scans. Figure 8 This is a schematic diagram of the control point layout in another embodiment of this application, as shown below. Figure 8 As shown, to ensure overall control accuracy, four control points need to be set up, with their positions evenly distributed in the four directions of east, west, south, and north of the silo. The straight-line distance from each control point to the silo wall is greater than 35 meters.

[0117] It should be noted that the selection of scanning sites and control points mentioned above is only for illustrative purposes. In actual field implementation, minor adjustments can be made based on the actual field environment.

[0118] S3, Field 3D Laser Scanning.

[0119] Based on the pre-designed site survey, the area to be scanned is scanned in detail. During the scanning process, the data resolution and quality of the 3D laser scanner are appropriately set to ensure data reliability. It is important to ensure that the spherical targets used as control points for coordinate unification are clearly identifiable during the scanning process.

[0120] S4, Data splicing processing.

[0121] The FARO SCENE point cloud data processing and analysis software was used to perform overall data stitching, noise reduction, coordinate unification, and overall sampling on the acquired data. During the stitching process, the overall stitching accuracy was repeatedly checked to ensure a final stitching precision better than 3mm. Finally, the data was converted and output according to a format compatible with various software programs.

[0122] The 3D laser scanner is either the FARO FOCUS S150 or the FARO FOCUS S350.

[0123] Complete 3D point cloud model data of steel silos is obtained by 3D laser scanning, and then the core indicators such as verticality, deformation and ellipticity of the silos are comprehensively detected and analyzed. At the same time, it is archived as a data file for future comparative analysis.

[0124] The following describes the bolt length and crack detection obtained by ultrasonic flaw detection equipment.

[0125] Based on the survey results of grain terminals, this study summarizes the usage and failure modes of steel silos, analyzes the causes of structural damage and vulnerable areas, and finds that bolt failure is invariably the first sign of silo failure. Therefore, from a usage perspective, bolted connections are the weakest link in the entire steel silo. Thus, inspecting the anchor bolts connecting the silo body to the concrete foundation, as well as the bolts connecting the silo walls to the reinforcing ribs, is crucial for ensuring the safe operation of the silos.

[0126] The following explanation uses the inspection of bolts on the silo wall as an example.

[0127] The bolts on the silo walls have small diameters, and some bolts have excessively high slenderness ratios, making on-site bolt testing technically very complex. The existing National Energy Administration's power industry standard DL / T694-2012, "Technical Guidelines for Ultrasonic Testing of High-Temperature Fastening Bolts," specifies the ultrasonic testing process and evaluation standards for bolts. However, in practice, to improve testing accuracy, it is necessary to constantly replace different types and specifications of probes, resulting in a significant workload and extremely tedious testing procedures.

[0128] This embodiment solves the challenge of performing high-precision, high-reliability on-site testing of silo sidewall bolts without disassembly by using non-destructive testing. Due to the large number of bolts requiring testing and the high-altitude nature of the work, the on-site testing workload is enormous. Considering on-site construction conditions and costs, the testing of silo sidewall bolts will only involve sampling.

[0129] Specifically, from approximately 40,000 bolts below the 13th ring of the silo, 5,000 representative bolts were selected from the silo wall and reinforcing ribs for testing. Ultrasonic testing technology was employed, utilizing both longitudinal wave straight probes and small-angle longitudinal wave angle probes to achieve non-destructive testing of the silo sidewall bolts. This solved the problem of completing high-precision, high-reliability on-site testing of silo bolts without disassembly, and guided the replacement of substandard bolts according to design specifications and requirements.

[0130] Selection of Testing Methods. Ultrasonic testing of bolts mainly employs the small-angle longitudinal wave method, the longitudinal wave direct probe method, and the transverse wave method. When both ends of the bolt are flat, or one end is flat and the other end has a flat surface with a width of not less than 5mm, the small-angle longitudinal wave method or the longitudinal wave direct probe method can be used. In this project, both ends of the bolts are flat, and the width of the flat surface is greater than 5mm; therefore, the longitudinal wave direct probe method is selected for testing in principle.

[0131] The testing instrument used was an ultrasonic flaw detector manufactured by Shantou Ultrasonic Instrument Research Institute; the probes used were longitudinal wave straight probes manufactured by Shantou Ultrasonic Instrument Research Institute and longitudinal wave straight probes manufactured by OLYMPUS.

[0132] Determination of defect indication length: The half-wave height (6dB) method is used to determine the defect indication length: Move the probe to find the strongest reflected wave of the defect, adjust the wave to 80% of the screen height, move the probe to the left (or right), and when the wave height drops to 40% of the screen height, mark the bolt corresponding to the center line of the probe. Then move the probe to the right (or left) and similarly reduce the wave height to 40% of the screen height and mark it again. The distance between the two marks is the defect indication length.

[0133] Crack detection: Longitudinal wave direct probe inspection. Crack detection of low-alloy steel bolts should comply with the following regulations:

[0134] a) Bolts without a center hole. If the defect signal is located on this side, its reflected wave amplitude is not less than Φ1mm-6dB reflection equivalent, and the indication length is not less than 6mm, it should be judged as a crack. If the defect signal is located on the opposite side, its reflected wave amplitude is not less than Φ1mm-16dB reflection equivalent, and the indication length is not less than 6mm, it should be judged as a crack.

[0135] b) There is a center-hole bolt. If the defect signal is located on this side, its reflected wave amplitude is not less than Φ1mm-12dB reflection equivalent, and the indication length is not less than 6mm, it should be judged as a crack.

[0136] Inspection of anchor bolts connecting the silo body to the concrete foundation

[0137] The anchor bolts connecting the silo body to the concrete foundation have an excessively large slenderness ratio, making on-site inspection using ordinary ultrasonic flaw detectors inadequate. This embodiment employs a phased array ultrasonic flaw detector for inspection.

[0138] Selection of testing method. The exposed end of the anchor bolt on the bearing platform is flat, with a diameter of Φ24mm and a length of 870mm. Phased array testing should be used. Ultrasonic phased array testing of anchor bolts mainly uses a 32-crystal phased array probe.

[0139] The testing instrument uses an ultrasonic phased array imaging system manufactured by OLYMPUS; the probe is a 32-crystal probe 5L32-A11 manufactured by OLYMPUS.

[0140] The methods for setting the detection sensitivity, determining the defect indication length, and identifying cracks are the same as those used in the inspection of silo wall bolts, and will not be repeated here.

[0141] Analysis of several silo collapse accidents revealed that bolts invariably failed before the silos themselves, indicating that bolts are a weak point in steel silo structures. Therefore, bolt testing is crucial, and the test data should be used to assess their safety and identify potential safety hazards in the silos as early as possible.

[0142] It should be noted that any reference numerals placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The words "a" or "an" preceding a component do not exclude the presence of a plurality of such components. The invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer.

[0143] Furthermore, it should be noted that in the description of this specification, the terms "one embodiment," "some embodiments," "embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0144] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the claims should be interpreted to include both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0145] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, then this invention should also include these modifications and variations.

Claims

1. A method for monitoring the safety of a grain steel silo structure, characterized by, The method comprises: S10, acquiring original point cloud data periodically collected by a three-dimensional data acquisition device, bolt length and crack detection results detected by an ultrasonic flaw detection device, and strain data of preset points on the grain steel silo collected in real time by a strain acquisition device; Specifically, the scanning site of the three-dimensional data acquisition device is laid out by surveying the scanning site, and the station position is more than 15 meters away from the silo wall in a straight line, and the data overlap rate between the stations is more than 30%; the reference control points are laid out around the silo, and the straight-line distance from the control point position to the silo wall is greater than 35 meters; the data obtained by the three-dimensional data acquisition device is spliced and denoised as a whole, the coordinates are unified, and the original point cloud data is output by using FARO SCENE point cloud data processing and analysis software; S20, determining structure data of the grain steel silo based on the original point cloud data, the structure data including perpendicularity, roundness, silo body deformation under empty warehouse state and full warehouse state; Determining the roundness of the grain steel silo based on the original point cloud data comprises: Based on the grain steel silo point cloud data, the maximum circle and the minimum circle are fitted by the point cloud data of different elevation position slices, and the roundness index of the silo is calculated based on the fitted maximum circle and minimum circle; Determining the perpendicularity of the grain steel silo based on the original point cloud data comprises: Based on the original point cloud data, the center of different elevation positions is fitted by the point cloud data of different elevation position slices; According to the distance and position deviation between the centers, the perpendicularity and inclination direction of the silo are calculated; S30, performing safety monitoring of the grain steel silo based on the structure data, the bolt length, the crack detection results and the strain data, and generating monitoring information.

2. The method of claim 1, wherein S30 comprises: Determining the structure relationship between each detection point according to a previously established steel silo steel structure calculation model, and determining the stress data of each detection point based on the strain data; Based on the coupling relationship between the strain data and the stress data of each detection point of the grain steel silo under empty load, loading load and full load load working conditions and the theoretical analysis data, generating structure safety prediction information.

3. The method of claim 2, wherein the method further comprises: In S10, the strain data is collected, and at the same time, the environmental data around the grain steel silo is also collected; then, S30 further comprises: Based on the environmental data and the strain data, the safety of the grain steel silo structure is monitored.

4. The method of claim 3, wherein the method further comprises: Based on the environmental data and the strain data, the safety of the grain steel silo structure is monitored, comprising: According to the structure relationship, the coupling relationship of the stress data of each detection point is determined, and the correlation between the environmental data and the stress data is determined based on the environmental data and the stress data; Based on the coupling relationship and the correlation, the safety of the steel silo structure is evaluated, and an evaluation result is generated.

5. The method of claim 2, wherein the method further comprises: In S10, the vibration data of the grain steel silo is also collected; then, S30 further comprises: Based on the vibration data and the strain data collected in real time, the stress change of the grain steel silo caused by vibration is monitored.

6. The method of claim 2, wherein The method further comprises: Based on the stress data and the pre-acquired stress design basis data, the safety of the steel silo structure is evaluated, and an evaluation result is generated.

7. The method of claim 1, wherein the method further comprises: The method further includes: accepting a user access instruction, displaying the real-time state of the monitored silo on the display terminal, and querying and statistically analyzing the monitoring data based on the time interval and the facility equipment number, and displaying the query and statistical analysis results.

8. The method of claim 1, wherein the method further comprises: The method further includes: Based on the strain data and the structure data, the structure damage reason and the weak position prone to damage of the grain steel silo are analyzed through a preset expert knowledge base; The strain data and the structure data are input into a trained risk prediction model to generate a risk category and a risk level of the grain steel silo; wherein the risk prediction model is a classification model established based on a deep neural network.

Images