A method for online monitoring of the camber change of the gantry crane girder

Abstract

This application relates to the technical field of large gantry cranes, and a method for online monitoring of the camber change of the gantry crane girder, including real-time data acquisition and normalization processing; dynamic attenuation calculation of elastic modulus; dynamic load-stress distribution calculation; non-linear coupling analysis of camber change; combination of data-driven and mechanism-driven correction and early warning. By introducing the "dynamic load-camber coupling analysis model" and based on the "dynamic coupling algorithm of elastic modulus attenuation function and load distribution", the present invention can comprehensively consider the coupling effects of various factors such as load, temperature, and vibration, accurately calculate the camber change of the gantry crane girder, and combine the dual advantages of data-driven and mechanism-driven methods. The present invention realizes dynamic correction and real-time update, solves the defects of low monitoring accuracy, poor real-time performance, and lack of flexibility existing in the prior art, and greatly improves the reliability and accuracy of monitoring.

Description

Technical Field

[0001] The present application relates to the technical field of large gantry cranes, and in particular to a method for online monitoring of the camber change of the gantry crane girder. Background Art

[0002] As a large-scale lifting equipment, gantry cranes are widely used in ports, railway freight yards, large warehouses and other places. Online monitoring of the camber of its girder is of great significance in ensuring the safe operation of the equipment, preventing safety accidents, extending the service life of the equipment and improving the operation efficiency of the equipment. The following defects exist in the prior art:

[0003] 1. Unable to accurately simulate the coupling effect of complex loads and environmental factors: Traditional methods often ignore the coupling effect of factors such as load, temperature, vibration, etc., resulting in inaccurate monitoring results. Existing monitoring technologies mostly rely on static or linear assumptions, ignoring the dynamic behavior of the gantry crane girder under complex working conditions and unable to comprehensively consider the interaction of all influencing factors;

[0004] 2. Insufficient monitoring accuracy: Most of the existing methods for monitoring the camber change of the girder are based on simplified models or analysis based on local single factors. These methods usually lack consideration of factors such as the attenuation of the elastic modulus and non-linear response of the girder under dynamic loads, resulting in low accuracy of the monitoring results and unable to reflect the complexity of the actual working environment;

[0005] 3. Lack of real-time and flexibility: In the prior art, although some methods can monitor the camber change in real time, they lack an adaptive adjustment mechanism for the real-time changes of multiple variables (such as dynamic load, temperature change, vibration, etc.) under complex working conditions. Traditional methods are usually based on fixed parameters or empirical models and cannot flexibly cope with changes under different environmental conditions;

[0006] 4. Lack of dynamic correction and update mechanism: In the prior art, in the monitoring of the camber change, there is a lack of dynamic correction based on real-time data and an adaptive model update function. The difference between the monitoring data and the model often cannot be corrected in time, resulting in the result deviating from the actual situation after long-term operation and reducing the accuracy of early warning;

[0007] 5. Insufficient combination of model and data: Many existing methods only rely on pure physical models or pure data-driven technologies and lack the ability to organically combine mechanism models with data-driven methods. Therefore, the accuracy and reliability of these methods are limited by the quality of the data and the simplified assumptions of the model, and comprehensive and accurate real-time monitoring cannot be achieved. Summary of the Invention

[0008] In order to overcome the problems existing in the prior art, the present application provides a method for online monitoring of the camber change of the gantry crane girder.

[0009] An online monitoring method for the camber change of the gantry crane girder provided by this application adopts the following technical solutions:

[0010] An online monitoring method for the camber change of the gantry crane girder includes the following steps:

[0011] Step 1: Real-time data acquisition and normalization processing. Real-time data of dynamic load, temperature field, vibration response, and initial camber change are collected through N high-precision sensors arranged on the gantry crane girder, and a data matrix is constructed: , where represents the dynamic load data at time t, represents the temperature field data at time t, represents the vibration response at time t; represents the initial camber distribution; the data is normalized: , eliminating the influence of the dimensions of different physical quantities, and the normalized data is used for subsequent model calculations;

[0012] Step 2: Dynamic attenuation calculation of elastic modulus. Based on the stress and temperature field collected in real time, a nonlinear coupling model is established to calculate the elastic modulus: : , where is the initial elastic modulus, describes the attenuation effect of stress on the modulus, describes the attenuation effect of temperature on the modulus, is the adjustment coefficient, is the environmental reference temperature, is the temperature fluctuation range, and the dynamic calculation result is used for subsequent stress distribution calculations;

[0013] Step 3: Dynamic load-stress distribution calculation. According to the dynamic elastic modulus of the real-time load , a stress distribution model is established to calculate the stress distribution : , where A is the cross-sectional area, and L are the starting point and ending point of the girder respectively, is the load weight distribution function, satisfying orthogonality: ;

[0014] Step 4: Nonlinear coupling analysis of camber change. Based on the calculated stress distribution and elastic modulus , the real-time change amount of the camber is deduced: , where is the initial camber, is the moment of inertia of the cross-section, which describes the bending resistance of the cross-section of the girder, represents the high-order non-linear coupling coefficient, and are the stress transient change and the time interval respectively. The calculation result of the camber change provides a basis for the subsequent correction steps;

[0015] Step Five: Combining data-driven and mechanism-driven correction, constructing a camber change prediction model through a long short-term memory network (LSTM): , where is the prediction function, are the model parameters, and dynamically correct the predicted value by combining with the mechanism model: , where K is the correction gain, is the corrected camber change value;

[0016] Step Six: Early warning, comparing the corrected camber change with the early warning value , and triggering an early warning when the following condition is met: , where is the probability function, is the preset critical value.

[0017] Preferably, the dynamic load is not less than 2000N.

[0018] Preferably, the initial elastic modulus ranges from 2.0×10 4 Pa - 2.1×10 5 Pa.

[0019] Preferably, the camber change does not exceed 3mm, and the early warning value ranges from 2.1 - 2.8mm.

[0020] Preferably, the range of the correction gain K is 0.6 - 0.8.

[0021] In summary, the present application includes at least one of the following beneficial technical effects:

[0022] By introducing the "dynamic load-camber coupling analysis model" and based on the "dynamic coupling algorithm of elastic modulus decay function and load distribution", the present invention can comprehensively consider the coupling effects of various factors such as load, temperature, and vibration, accurately calculate the camber change of the gantry crane girder, and combine the dual advantages of data-driven and mechanism-driven methods. The present invention realizes dynamic correction and real-time update, solves the defects of low monitoring accuracy, poor real-time performance, and lack of flexibility in the prior art, and greatly improves the reliability and accuracy of monitoring. Description of the Drawings

[0023] Figure 1 is a flowchart of a method for online monitoring of the camber change of the gantry crane girder. Detailed Implementation Manner

[0024] The following further describes the present application in detail with reference to the Figure 1 drawings.

[0025] The embodiment of the present application discloses a method for online monitoring of the camber change of the gantry crane girder.

[0026] Refer to Figure 1 , including Step 1: First, collect real-time data of dynamic load, temperature field, vibration response, and initial camber change through N high-precision sensors arranged on the gantry crane girder. The following is a sample of simulated data:

[0027] Construct a data matrix as follows: , and then normalize the data to eliminate the influence of dimensions, obtaining the normalized data matrix : , for example, for the data in the first row:

[0028] , the normalized data is:

[0029] Step 2: Calculate the dynamic attenuation of the elastic modulus. Based on the stress and temperature field collected in real time, establish a non-linear coupling model to calculate the elastic modulus :

[0030] ; Assume the initial elastic modulus , the adjustment coefficient , the stress and temperature are and respectively, and the calculated result is: , , so the elastic modulus is:

[0031] ,

[0032] Step 3: Calculate the dynamic load-stress distribution. According to the real-time load and elastic modulus , calculate the stress distribution :

[0033] , assume the cross-sectional area , the load , the elastic modulus , the stress distribution is calculated as:

[0034]

[0035] Step Four: Nonlinear Coupling Analysis of Camber Variation. Based on the calculated stress distribution and the elastic modulus , estimate the real-time variation of the camber :

[0036] , assume the initial camber is , the moment of inertia of the cross-section , the calculation result is:

[0037] , assume , the calculated result is:

[0038] Step Five: Combining Data-Driven and Mechanism-Driven Calibration. Build a camber variation prediction model through a Long Short-Term Memory Network (LSTM):

[0039] , assume that after training, the LSTM model predicts the camber variation as , combine the mechanism model to calibrate the predicted value:

[0040] , assume the calibration gain , then the calibrated camber variation is:

[0041]

[0042] Step Six: Real-Time Monitoring and Early Warning. Compare the calibrated camber variation with the early warning threshold . If it is detected that , then trigger the early warning.

[0043] In this case: , therefore, the early warning is not triggered at the current moment, and the camber variation of the gantry crane girder is within the safe range.

[0044] The above are all the preferred embodiments of this application. The protection scope of this application is not limited accordingly. Therefore, all equivalent changes made according to the structure, shape, and principle of this application should be covered within the protection scope of this application.

Claims

1. A method for online monitoring of the camber change of the girder of a gantry crane, characterized in that: It includes the following steps: Step 1: Real-time data acquisition and normalization processing. Real-time data of dynamic load, temperature field, vibration response and initial camber change are collected by N high-precision sensors arranged on the main girder of the gantry crane, and a data matrix is constructed: where P(t) represents the dynamic load data at time t, T(t) represents the temperature field data at time t, v(t) represents the vibration response at time t; Δf0(x) represents the initial camber distribution; the data is normalized as follows: The influence of the dimension of different physical quantities is eliminated, and the normalized data is used for subsequent model calculations; Step 2: Dynamic decay calculation of elastic modulus. Based on the stress σ(x,t) and temperature field T(x,t) collected in real time, a nonlinear coupling model is established to calculate the elastic modulus E(t,x): where E0 is the initial elastic modulus, describes the decay effect of stress on the modulus, describes the decay effect of temperature on the modulus, α and β are adjustment coefficients, T0 is the ambient reference temperature, ΔT is the temperature fluctuation range, and the dynamic calculation result E(t,x) is used for subsequent stress distribution calculation; Step 3: Dynamic load-stress distribution calculation. Based on the dynamic elastic modulus E(t, x) of the real-time load P(t), establish a stress distribution model and calculate the stress distribution σ(x, t): where A is the cross-sectional area, x0 and L are the starting point and the ending point of the girder respectively, and ψ(x’, t) is the load weight distribution function, which satisfies orthogonality: Step 4: Nonlinear coupling analysis of camber variation. Based on the calculated stress distribution σ(x, t) and elastic modulus E(t, x), the real-time variation Δf(t) of the camber is deduced as follows: where Δf0(x) is the initial camber, I(x) is the moment of inertia of the cross-section, which describes the flexural resistance of the cross-section of the girder, represents the high-order nonlinear coupling coefficient, Δσ and Δt are the transient stress variation and time interval respectively, and the calculation result of the camber variation provides a basis for the subsequent correction step; Step 5: Combine data-driven and mechanism-driven calibration, and construct a camber change prediction model through the long short-term memory network (LSTM): where is the prediction function, Θ is the model parameter, and the predicted value is dynamically calibrated by combining with the mechanism model: Δf * (t) = Δf(t) + K·(Δ^f(t) - Δf(t)), where K is the calibration gain, and Δf * (t) is the calibrated camber change value; Step Six: Early Warning. Compare the corrected camber change Δf * (t) with the early warning value Δf 临界 . Trigger an early warning when the following condition is met: where is the probability function, and Δf 临界 is the preset critical value.

2. The method for online monitoring of the camber change of the gantry crane girder according to claim 1, wherein: The dynamic load P(t) is not less than 2000 N.

3. The method for on-line monitoring the change of the camber of the gantry crane girder according to claim 1, characterized in that: The initial elastic modulus E0 ranges from 2.0×10 4 Pa to 2.1×10 5 Pa.

4. The method for on-line monitoring the camber change of the gantry crane girder according to claim 1, characterized in that: The camber change Δf * (t) does not exceed 3 mm, and the warning value Δf 临界 ranges from 2.1 to 2.8 mm.

5. The method for on-line monitoring of the camber change of the gantry crane girder according to claim 1, characterized in that: The range of the correction gain K is 0.6 - 0.8.

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