CN111591893A - Method for measuring hoisting load of automobile crane based on neural network - Google Patents
Method for measuring hoisting load of automobile crane based on neural network Download PDFInfo
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- CN111591893A CN111591893A CN202010461935.5A CN202010461935A CN111591893A CN 111591893 A CN111591893 A CN 111591893A CN 202010461935 A CN202010461935 A CN 202010461935A CN 111591893 A CN111591893 A CN 111591893A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C13/00—Other constructional features or details
- B66C13/16—Applications of indicating, registering, or weighing devices
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
- G01G19/14—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing suspended loads
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Abstract
The invention relates to a method for measuring the hoisting load of an automobile crane based on a neural network, and aims to solve the technical problems that the measurement process of the hoisting load of the automobile crane is easily influenced by uncontrollable factors such as dynamic moment change of an arm lever, multiple mechanical friction, steel wire rope droop and the like, the measurement is time-consuming and labor-consuming, and the measurement result is inaccurate. According to the invention, through the structural characteristics and geometric parameters of the crane boom of the automobile crane, a mechanical model of the boom under the load action is established, and the trained radial basis neural network is utilized, so that the hoisting load of the automobile crane under any environment, place and working condition can be quickly obtained through a microprocessor and a computer, thereby greatly saving the field actual measurement of the automobile crane, and the stability evaluation of the automobile crane is more convenient by obtaining the hoisting load in real time; the pressure and the elevation angle are utilized to obtain the real-time measurement of the hoisting load, the realization is easy, the arrangement is convenient, and the cost is low; the environmental interference and the system error are reduced, and the working efficiency is effectively improved.
Description
Technical Field
The invention belongs to the technical field of measurement of hoisting load of an automobile crane, and particularly relates to a method for measuring the hoisting load of the automobile crane based on a neural network.
Background
The automobile crane is a main tool for lifting materials at stations, wharfs and construction sites. The existing automobile crane does not have a mass metering device, and when the hoisting materials need to be metered, the materials are often weighed by a remote weighbridge, so that the time is consumed, and the operating cost is increased. In some intelligent early warning systems of cranes, the crane has great demand for real-time weighing of hoisting loads, so that the automobile crane has a metering function, is not limited by environment and places, and can accurately and rapidly meter in real time.
The radial basis function neural network is a feedforward neural network with excellent performance, can approach any nonlinear function with any precision, has global approach capability, fundamentally solves the local optimal problem of the BP network, has compact topological structure, can realize separate learning of structural parameters, has high convergence speed, and is widely applied to engineering prediction and control.
Disclosure of Invention
The invention aims to solve the technical problems that the measurement is time-consuming and labor-consuming and the measurement result is inaccurate because the measurement process of the lifting load of the automobile crane is easily influenced by uncontrollable factors such as dynamic moment change of a boom, multiple mechanical friction, steel wire rope droop and the like, and provides a method for measuring the lifting load of the automobile crane based on a neural network.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a method for measuring the lifting load of an automobile crane based on a neural network comprises the following steps:
(1) mechanical model for analyzing crane boom of automobile crane
Through the structural characteristics and the geometric parameters of a crane boom of the automobile crane, a mechanical model of the boom under the action of load is established, and the mutual influence relationship among all the parameters is analyzed, so that the basic function relationship among the hoisting load of the automobile crane, the pressure intensity of a hydraulic cylinder of the luffing cylinder and the elevation angle of the luffing cylinder is obtained;
(2) establishing a corresponding relation data sample of the pressure intensity of a hydraulic cylinder of the amplitude variation oil cylinder and the elevation angle of the amplitude variation oil cylinder during different hoisting loads
A pressure sensor and a displacement sensor are arranged on a hydraulic cylinder of the amplitude-variable oil cylinder, the pressure in the hydraulic cylinder is obtained through the pressure sensor, the stroke of the hydraulic cylinder is obtained through the displacement sensor, and the elevation angle of the amplitude-variable oil cylinder during working is obtained by combining the parameters of the automobile crane; collecting data of the pressure intensity of a hydraulic cylinder of the luffing cylinder along with the change of the elevation angle of the luffing cylinder under a certain fixed hoisting load, and establishing a data sample of the corresponding relation between the pressure intensity of the hydraulic cylinder of the luffing cylinder and the elevation angle of the luffing cylinder during different hoisting loads;
(3) obtaining learning sample data
Taking the pressure intensity of a hydraulic cylinder of the luffing cylinder and the elevation angle of the luffing cylinder calculated by the stroke of the luffing cylinder in the working state of the automobile crane as the input quantity of the learning sample, and the hoisting load of the automobile crane as the output quantity of the learning sample to obtain the learning sample data; the learning sample data is a database model established under typical arm lengths when the automobile crane is in a normal working state, so that the learning sample data conforms to the actual use working condition of the automobile crane;
(4) building and training radial basis function neural network model
Constructing a radial basis function neural network model through the learning sample data obtained in the step (3); selecting 1-2 groups of data as test data and the rest groups of data as learning samples according to the obtained radial basis function neural network model and the learning sample data, training the radial basis function neural network, outputting an equivalent hoisting load value of the learning sample through the trained radial basis function neural network model after the training is finished, comparing the equivalent hoisting load value with an actual hoisting value, and confirming the accuracy of the trained radial basis function neural network model; when the prediction error of the radial basis function neural network in the test sample data is lower than a specified level, the radial basis function neural network model of the optimal model parameter is obtained through the test, and the radial basis function neural network model can be used for measuring the real-time hoisting load of the automobile crane;
(5) method for measuring hoisting load of automobile crane by using neural network
Programming and writing the algorithm of the radial basis function neural network model established, trained and tested in the step (4) into a microprocessor and a computer, introducing the data sample of the corresponding relation between the pressure intensity of the hydraulic cylinder of the luffing cylinder and the elevation angle of the luffing cylinder during different hoisting loads obtained in the step (2) into the microprocessor and the computer for processing, and obtaining predicted values of the hoisting loads under different pressure intensities and elevation angles by utilizing the neural network.
Further, the basic functional relationship among the hoisting load Q of the truck crane obtained in the step (1), the pressure p of the hydraulic cylinder of the luffing cylinder and the elevation angle alpha of the luffing cylinder is as shown in formula one:
in the formula I, Q is the hoisting load G of the automobile cranebThe weight of a lifting arm, α the elevation angle of a luffing cylinder, l1The distance l from the starting point of the crane arm to the joint of the luffing cylinder2The distance l from the starting point of the crane boom to the gravity center of the crane boom3The total length of the boom during operation, p the pressure of the hydraulic cylinder of the luffing cylinder, and a the effective sectional area of the hydraulic cylinder in the stress direction.
Further, the radial basis function neural network model in the step (4) is a three-layer forward network composed of an input layer, a hidden layer and an output layer, wherein the hidden layer adopts a radial basis function as an excitation function, and the radial basis function is a gaussian function;
the radial basis function neural network model is a forward network constructed on the basis of a function approximation theory; the radial basis function network model is a network with only hierarchy; in the middle layer, the radial basis function of the layout response replaces the traditional excitation function of the global response, the number of nodes of the input layer is 2, and the number of nodes of the output layer is 1.
The invention has the beneficial effects that:
1. the invention aims at a method for calculating the hoisting load of the automobile crane by utilizing the pressure intensity of the hydraulic cylinder of the luffing cylinder and the elevation angle of the luffing cylinder of the automobile crane, introduces the description and correction of the radial basis function neural network, and can obtain more accurate hoisting load measurement data.
2. According to the invention, through the structural characteristics and the geometric parameters of the crane boom of the automobile crane, a mechanical model of the crane boom under the action of load is established, and the mutual influence relationship among all the parameters is analyzed. The trained radial basis function neural network is utilized, the hoisting load of the automobile crane under any environment, place and working condition can be rapidly obtained through the microprocessor and the computer, so that the field actual measurement of the automobile crane is greatly saved, and the stability evaluation of the automobile crane is more convenient by obtaining the hoisting load in real time; the pressure and the elevation angle are utilized to obtain the real-time measurement of the hoisting load, the realization is easy, the arrangement is convenient, and the cost is low; the environmental interference and the system error are reduced, and the working efficiency is effectively improved. The method is also suitable for the field of weight measurement of engineering machinery with a hydraulic arm except for an automobile crane.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a force diagram of a crane boom of a truck crane according to the present invention in the luffing plane;
FIG. 3 is a schematic view of the installation of the pressure sensor, displacement sensor, CPU and real-time operating condition display of the present invention;
FIG. 4 is a diagram of the communication mode of the pressure sensor, the displacement sensor, the CPU, the real-time operating mode display and the CAN bus according to the present invention;
FIG. 5 is a schematic diagram of the architecture of the radial basis function neural network of the present invention;
description of reference numerals: 1-pressure sensor, 2-displacement sensor, 3-central processor and 4-real-time working condition display.
Detailed Description
The invention is further described below with reference to examples and figures.
As shown in fig. 1, the method for measuring a lifting load of an automobile crane based on a neural network in this embodiment includes the following steps:
(1) mechanical model for analyzing crane boom of automobile crane
A mechanical model of the crane arm under the load action is established through the structural characteristics and the geometric parameters of the crane arm of the automobile crane, as shown in figure 2, wherein Q is the hoisting load and G of the automobile cranebIs the mass of the jib, theta is the elevation angle of the jib, α is the elevation angle of the luffing cylinder, l1The distance l from the starting point of the crane arm to the joint of the luffing cylinder2The distance l from the starting point of the crane boom to the gravity center of the crane boom3Analyzing the mutual influence relationship among all parameters so as to obtain the basic function relationship among the hoisting load Q of the automobile crane, the pressure p of the hydraulic cylinder of the luffing cylinder and the elevation angle α of the luffing cylinder, wherein the basic function relationship is as shown in the formula I:
in the formula I, Q is the hoisting load G of the automobile cranebThe weight of a lifting arm, α the elevation angle of a luffing cylinder, l1The distance l from the starting point of the crane arm to the joint of the luffing cylinder2The distance l from the starting point of the crane boom to the gravity center of the crane boom3The total length of the boom during operation, p the pressure of the hydraulic cylinder of the luffing cylinder, and a the effective sectional area of the hydraulic cylinder in the stress direction.
(2) Establishing a corresponding relation data sample of the pressure intensity of a hydraulic cylinder of the amplitude variation oil cylinder and the elevation angle of the amplitude variation oil cylinder during different hoisting loads
Installing a pressure sensor 1 and a displacement sensor 2 on the automobile crane, wherein as shown in figure 3, the pressure sensor 1 is installed in a hydraulic cylinder of the luffing cylinder, the displacement sensor 2 is installed on the luffing cylinder, the pressure in the hydraulic cylinder is obtained through the pressure sensor 1, the stroke of the hydraulic cylinder is obtained through the displacement sensor 2, and the elevation angle of the luffing cylinder during working is obtained by combining the parameters of the automobile crane, so that the overlarge direct measurement error is avoided; the central processor 3 and the real-time working condition display 4 are arranged in the cab. As shown in fig. 4, the pressure sensor 1, the displacement sensor 2, the central processing unit 3, and the real-time operating condition display 4 are communicated with each other by a CAN bus communication method. The method comprises the steps of collecting data of the pressure intensity of a hydraulic cylinder of the luffing cylinder along with the elevation angle of the luffing cylinder under a certain fixed hoisting load, and establishing a data sample of the corresponding relation between the pressure intensity of the hydraulic cylinder of the luffing cylinder and the elevation angle of the luffing cylinder during different hoisting loads.
(3) Obtaining learning sample data
Taking the pressure intensity of a hydraulic cylinder of the luffing cylinder and the elevation angle of the luffing cylinder calculated by the stroke of the luffing cylinder in the working state of the automobile crane as the input quantity of the learning sample, and the hoisting load of the automobile crane as the output quantity of the learning sample to obtain the learning sample data; the learning sample data is a database model established under typical arm lengths (the length of a main arm is generally 10.2m, 12.58m, 14.97m, 17.35m, 19.73m, 22.12m and 24.5m) when the automobile crane is in a normal working state, so that the learning sample data conforms to the actual use working condition of the automobile crane;
(4) building and training radial basis function neural network model
Constructing a radial basis function neural network model through the learning sample data obtained in the step (3); selecting 1-2 groups of data as test data and the rest groups of data as learning samples according to the obtained radial basis function neural network model and the learning sample data, training the radial basis function neural network, outputting an equivalent hoisting load value of the learning sample through the trained radial basis function neural network model after the training is finished, comparing the equivalent hoisting load value with an actual hoisting value, and confirming the accuracy of the trained radial basis function neural network model; when the prediction error of the radial basis function neural network in the test sample data is lower than a specified level, the radial basis function neural network model of the optimal model parameter is obtained through the test, and the radial basis function neural network model can be used for measuring the real-time hoisting load of the automobile crane;
as shown in fig. 5, the radial basis function neural network model in step (4) is a three-layer forward network composed of an input layer, a hidden layer and an output layer, the hidden layer adopts a radial basis function as an excitation function, and the radial basis function is a gaussian function;
the radial basis function neural network model is a forward network constructed on the basis of a function approximation theory; the radial basis function network model is a network with only hierarchy; in the middle layer, the radial basis function of the layout response replaces the traditional excitation function of the global response, the number of nodes of the input layer is 2, and the number of nodes of the output layer is 1.
Weight vector w1 for each neuron in the hidden layer connected with the input layeriAnd the input vector Xq(representing the qth input vector) is multiplied by a threshold value b1iAs an input of itself, the input of the ith neuron of the hidden layer can be derived therefrom
Output is as
The threshold b1 of the radial basis function can adjust the sensitivity of the function, but in practice another parameter C (called the spreading function) is more commonly used. In the toolbox of MATLAB neural networks, the relationship of b1 and C is
When the output of the hidden layer neuron becomes
The input of the output layer isWeighted summation of the neuron outputs of each hidden layer. Thus the output is
The training process of the radial basis function neural network model comprises two steps: the first step is the instructor-free learning, and the weight w1 between the training input layer and the hidden layer is determined; the second step is instructor-based learning, and determines weights w2 for training the hidden layer and the output layer. Before training, it is necessary to provide an input vector X, a corresponding output vector T and a spreading constant C of the radial basis functions. The goal of the training is to find the final weights w1, w2 and the thresholds b1, b2 for both layers (when the number of hidden layer cells equals the input vector, take b2 to 0). A radial base network is now designed with the Newrbe function. Its calling format is net ═ newrbe (P, T, good, spread, MN, DF); wherein the parameter P is a P multiplied by Q dimensional matrix formed by Q groups of input quantities; the parameter T is an S multiplied by Q dimensional matrix formed by Q groups of output quantities; the parameter gold is the expansion speed of the radial basis function; the parameter spread is the distribution of the radial basis functions; the parameter MN is the maximum number of neurons and is defaulted to Q; the parameter DF is the number of neurons added between the two displays. The output parameter net is the return value, a radial basis function neural network model.
(5) Method for measuring hoisting load of automobile crane by using neural network
Programming and writing the algorithm of the radial basis function neural network model established, trained and tested in the step (4) into a microprocessor and a computer, introducing the data sample of the corresponding relation between the pressure intensity of the hydraulic cylinder of the luffing cylinder and the elevation angle of the luffing cylinder during different hoisting loads obtained in the step (2) into the microprocessor and the computer for processing, and obtaining predicted values of the hoisting loads under different pressure intensities and elevation angles by utilizing the neural network.
The above-described embodiments are only for illustrating the technical ideas and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the contents of the present invention and to carry out the same, and the scope of the present invention is not limited only by the embodiments, i.e., all equivalent changes or modifications made in the spirit of the present invention are still within the scope of the present invention.
Claims (3)
1. A method for measuring the hoisting load of an automobile crane based on a neural network is characterized by comprising the following steps: the method comprises the following steps:
(1) mechanical model for analyzing crane boom of automobile crane
Through the structural characteristics and the geometric parameters of a crane boom of the automobile crane, a mechanical model of the boom under the action of load is established, and the mutual influence relationship among all the parameters is analyzed, so that the basic function relationship among the hoisting load of the automobile crane, the pressure intensity of a hydraulic cylinder of the luffing cylinder and the elevation angle of the luffing cylinder is obtained;
(2) establishing a corresponding relation data sample of the pressure intensity of a hydraulic cylinder of the amplitude variation oil cylinder and the elevation angle of the amplitude variation oil cylinder during different hoisting loads
A pressure sensor and a displacement sensor are arranged on a hydraulic cylinder of the amplitude-variable oil cylinder, the pressure in the hydraulic cylinder is obtained through the pressure sensor, the stroke of the hydraulic cylinder is obtained through the displacement sensor, and the elevation angle of the amplitude-variable oil cylinder during working is obtained by combining the parameters of the automobile crane; collecting data of the pressure intensity of a hydraulic cylinder of the luffing cylinder along with the change of the elevation angle of the luffing cylinder under a certain fixed hoisting load, and establishing a data sample of the corresponding relation between the pressure intensity of the hydraulic cylinder of the luffing cylinder and the elevation angle of the luffing cylinder during different hoisting loads;
(3) obtaining learning sample data
Taking the pressure intensity of a hydraulic cylinder of the luffing cylinder and the elevation angle of the luffing cylinder calculated by the stroke of the luffing cylinder in the working state of the automobile crane as the input quantity of the learning sample, and the hoisting load of the automobile crane as the output quantity of the learning sample to obtain the learning sample data; the learning sample data is a database model established under typical arm lengths when the automobile crane is in a normal working state, so that the learning sample data conforms to the actual use working condition of the automobile crane;
(4) building and training radial basis function neural network model
Constructing a radial basis function neural network model through the learning sample data obtained in the step (3); selecting 1-2 groups of data as test data and the rest groups of data as learning samples according to the obtained radial basis function neural network model and the learning sample data, training the radial basis function neural network, outputting an equivalent hoisting load value of the learning sample through the trained radial basis function neural network model after the training is finished, comparing the equivalent hoisting load value with an actual hoisting value, and confirming the accuracy of the trained radial basis function neural network model; when the prediction error of the radial basis function neural network in the test sample data is lower than a specified level, the radial basis function neural network model of the optimal model parameter is obtained through the test, and the radial basis function neural network model can be used for measuring the real-time hoisting load of the automobile crane;
(5) method for measuring hoisting load of automobile crane by using neural network
Programming and writing the algorithm of the radial basis function neural network model established, trained and tested in the step (4) into a microprocessor and a computer, introducing the data sample of the corresponding relation between the pressure intensity of the hydraulic cylinder of the luffing cylinder and the elevation angle of the luffing cylinder during different hoisting loads obtained in the step (2) into the microprocessor and the computer for processing, and obtaining predicted values of the hoisting loads under different pressure intensities and elevation angles by utilizing the neural network.
2. The method for measuring the hoisting load of the automobile crane based on the neural network as claimed in claim 1, wherein the method comprises the following steps: the basic functional relationship among the hoisting load Q of the truck crane obtained in the step (1), the pressure p of the hydraulic cylinder of the amplitude-variable oil cylinder and the elevation angle alpha of the amplitude-variable oil cylinder is as shown in a formula I:
in the formula I, Q is the hoisting load G of the automobile cranebThe weight of a lifting arm, α the elevation angle of a luffing cylinder, l1The distance l from the starting point of the crane arm to the joint of the luffing cylinder2The distance l from the starting point of the crane boom to the gravity center of the crane boom3The total length of the boom during operation, p the pressure of the hydraulic cylinder of the luffing cylinder, and a the effective sectional area of the hydraulic cylinder in the stress direction.
3. The method for measuring the hoisting load of the automobile crane based on the neural network as claimed in claim 1, wherein the method comprises the following steps: the radial basis function neural network model in the step (4) is a three-layer forward network composed of an input layer, a hidden layer and an output layer, wherein the hidden layer adopts a radial basis function as an excitation function, and the radial basis function is a Gaussian function;
the radial basis function neural network model is a forward network constructed on the basis of a function approximation theory; the radial basis function network model is a network with only hierarchy; in the middle layer, the radial basis function of the layout response replaces the traditional excitation function of the global response, the number of nodes of the input layer is 2, and the number of nodes of the output layer is 1.
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