Method for monitoring camber change of gantry crane girder on line

Abstract

The invention relates to the technical field of large gantry cranes, in particular to a method for monitoring camber change of a gantry crane girder on line. Calculating elastic modulus dynamic attenuation; calculating dynamic load-stress distribution; performing non-linear coupling analysis on camber change; and data driving and mechanism driving are combined for correction and early warning. According to the method, a dynamic load-camber coupling analysis model is introduced, and based on an elastic modulus attenuation function and load distribution dynamic coupling algorithm, coupling effects of various factors such as load, temperature and vibration can be comprehensively considered, camber changes of the gantry crane girder can be accurately calculated, and the accuracy of the camber changes of the gantry crane girder can be improved. By combining the double advantages of a data driving method and a mechanism driving method, dynamic correction and real-time updating are realized, the defects of low monitoring precision, poor real-time performance and lack of flexibility in the prior art are overcome, and the monitoring reliability and accuracy are greatly improved.

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 changes in the camber of a gantry crane beam. Background Art

[0002] Gantry cranes are large lifting equipment and are widely used in ports, railway freight yards, large warehouses and other places. Online monitoring of the camber of the gantry crane 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 operating efficiency of the equipment. The existing technology has the following defects: 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, and vibration, resulting in inaccurate monitoring results. Existing monitoring technologies mostly rely on static or linear assumptions, ignoring the dynamic behavior of the gantry crane beam under complex working conditions, and are unable to comprehensively consider the interaction of all influencing factors; 2. Insufficient monitoring accuracy: Most existing beam camber change monitoring methods are based on simplified models or local single factor analysis. These methods usually fail to consider factors such as elastic modulus attenuation and nonlinear response of beams under dynamic loads, resulting in low monitoring accuracy and failure to reflect the complexity of the actual working environment; 3. Lack of real-time performance and flexibility: In existing technologies, although some methods can monitor the change of camber in real time, there is a lack of adaptive adjustment mechanism for 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 respond to changes under different environmental conditions; 4. Lack of dynamic correction and update mechanism: The existing technology lacks dynamic correction and adaptive model update functions based on real-time data in the monitoring of camber changes. The difference between monitoring data and models often cannot be corrected in time, resulting in the result deviating from the actual situation after a long period of operation, reducing the accuracy of early warning; 5. Insufficient integration of models and data: Many existing methods rely only 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 data and the simplified assumptions of the model, and cannot achieve comprehensive and accurate real-time monitoring. Summary of the invention

[0003] In order to overcome the problems existing in the prior art, the present application provides a method for online monitoring of the camber changes of a gantry crane beam.

[0004] The present application provides a method for online monitoring of gantry crane beam camber changes using the following technical solution: A method for online monitoring of gantry crane beam camber changes comprises the following steps: Step 1: Real-time data collection and normalization processing. N high-precision sensors arranged on the gantry crane beam collect real-time data of dynamic load, temperature field, vibration response and initial camber change to build a data matrix: ,in 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: , eliminate the dimensional effects of different physical quantities, and use the normalized data for subsequent model calculations; Step 2: Dynamic attenuation calculation of elastic modulus based on real-time collected stress and temperature field , establish a nonlinear coupling model and calculate the elastic modulus: : ,in is the initial elastic modulus, Describes the decaying effect of stress on the modulus, , describing the effect of temperature on the attenuation of the modulus, Adjustment coefficient, is the ambient reference temperature, is the temperature fluctuation range, dynamic calculation result Used for subsequent stress distribution calculations; Step 3: Dynamic load-stress distribution calculation, based on real-time load Dynamic elastic modulus , establish stress distribution model, calculate stress distribution : , where A is the cross-sectional area, and L are the starting point and end point of the beam respectively, is the load weight distribution function, satisfying orthogonality: ; Step 4: Nonlinear coupling analysis of camber variation based on calculated stress distribution and elastic modulus , calculate the real-time change of the camber : ,in is the initial camber, is the section moment of inertia, describing the bending resistance of the beam cross section, represents the high-order nonlinear coupling coefficient, and They are the transient stress change and time interval, respectively. The calculation results of the camber change provide a basis for the subsequent correction steps; Step 5: Combine data-driven and mechanism-driven corrections to build a camber change prediction model through a long short-term memory network (LSTM): ,in is the prediction function, As model parameters, the predicted values ​​are dynamically corrected in combination with the mechanism model: , where K is the correction gain, is the corrected camber change value; Step 6: Warning, the corrected camber changes With warning value By comparison, an alert is triggered when the following conditions are met: ,in is the probability function, is the preset critical value.

[0005] Preferably, dynamic load Not less than 2000N.

[0006] Preferably, the initial elastic modulus The range is 2.0×10 4 Pa-2.1×10 5 Pa.

[0007] Preferably, the camber changes No more than 3mm, warning value The range is 2.1-2.8mm.

[0008] Preferably, the correction gain K is in the range of 0.6-0.8.

[0009] In summary, the present application includes at least one of the following beneficial technical effects: This application introduces a "dynamic load-arch coupling analysis model" and is based on a "dynamic coupling algorithm of elastic modulus attenuation function and load distribution". The present invention can comprehensively consider the coupling effects of multiple factors such as load, temperature, vibration, etc., and accurately calculate the arch changes of the gantry crane beam. Combining the dual advantages of data-driven and mechanism-driven methods, the present invention realizes dynamic correction and real-time updating, 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 The present invention is a flow chart of a method for online monitoring of camber changes of a gantry crane beam. DETAILED DESCRIPTION

[0011] The following is combined with Figure 1 This application is described in further detail.

[0012] An embodiment of the present application discloses a method for online monitoring of changes in the camber of a gantry crane beam.

[0013] Reference Figure 1 , including step 1: First, N high-precision sensors arranged on the gantry crane beam collect real-time data on dynamic load, temperature field, vibration response and initial camber change. The following is a sample of simulation data:

[0014] Constructing the data matrix as follows: , and then normalize the data to eliminate the dimension effect and get the normalized data matrix : , for example, for the first row of data: , the normalized data is:

[0015] Step 2: Dynamic attenuation calculation of elastic modulus based on real-time collected stress and temperature field , establish a nonlinear coupling model and calculate the elastic modulus : ; Assuming the initial elastic modulus , adjustment coefficient , stress and temperature are and , we can calculate: , , so the elastic modulus is: ,

[0016] Step 3: Dynamic load-stress distribution calculation, based on real-time load and elastic modulus , calculate the stress distribution : , assuming the cross-sectional area ,load , elastic modulus , the stress distribution is calculated as:

[0017] Step 4: Nonlinear coupling analysis of camber variation based on calculated stress distribution and elastic modulus , calculate the real-time change of the camber : , assuming the initial camber is , section moment of inertia , we can calculate: , assuming , the calculated result is:

[0018] Step 5: Combine data-driven and mechanism-driven corrections to build a camber change prediction model through a long short-term memory network (LSTM): , assuming that after training, the LSTM model predicts the camber change to be , combined with the mechanism model to correct the predicted value: , assuming the correction gain , then the corrected camber change is:

[0019] Step 6: Real-time monitoring and early warning, the corrected camber changes and warning threshold If detected , then trigger the warning.

[0020] In this case: Therefore, no warning is triggered at the current moment, and the camber change of the gantry crane beam is within a safe range.

[0021] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for online monitoring of gantry crane beam camber changes, characterized in that: The following steps are involved: Step 1: Real-time data collection and normalization processing. N high-precision sensors arranged on the gantry crane beam collect real-time data of dynamic load, temperature field, vibration response and initial camber change to build a data matrix: ,in 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: , eliminate the dimensional effects of different physical quantities, and use the normalized data for subsequent model calculations; Step 2: Dynamic attenuation calculation of elastic modulus based on real-time collected stress and temperature field , establish a nonlinear coupling model and calculate the elastic modulus : ,in is the initial elastic modulus, Describes the decaying effect of stress on the modulus, , describing the effect of temperature on the attenuation of the modulus, is the adjustment coefficient, is the ambient reference temperature, is the temperature fluctuation range, dynamic calculation result Used for subsequent stress distribution calculations; Step 3: Dynamic load-stress distribution calculation, based on real-time load Dynamic elastic modulus , establish stress distribution model, calculate stress distribution : , where A is the cross-sectional area, and L are the starting point and end point of the beam respectively, is the load weight distribution function, satisfying orthogonality: ; Step 4: Nonlinear coupling analysis of camber variation based on calculated stress distribution and elastic modulus , calculate the real-time change of the camber : ,in is the initial camber, is the section moment of inertia, describing the bending resistance of the beam cross section, represents the high-order nonlinear coupling coefficient, and They are the transient stress change and time interval, respectively. The calculation results of the camber change provide a basis for the subsequent correction steps; Step 5: Combine data-driven and mechanism-driven corrections to build a camber change prediction model through a long short-term memory network (LSTM): ,in is the prediction function, is the model parameter, and the predicted value is dynamically corrected in combination with the mechanism model: , where K is the correction gain, is the corrected camber change value; Step 6: Warning, the corrected camber changes With warning value By comparison, an alert is triggered when the following conditions are met: ,in is the probability function, is the preset critical value.

2. A method for online monitoring of gantry crane beam camber changes, characterized in that: The dynamic load Not less than 2000N.

3. A method for online monitoring of gantry crane beam camber changes, characterized in that: The initial elastic modulus The range is 2.0×10 4 Pa-2.1×10 5 Pa.

4. A method for online monitoring of gantry crane beam camber changes, characterized in that: The crown changes No more than 3mm, warning value The range is 2.1-2.8mm.

5. A method for online monitoring of gantry crane beam camber changes, characterized in that: The correction gain K ranges from 0.6 to 0.8.

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