CN112699501B - Cable tension monitoring system based on pulley shaft - Google Patents
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Abstract
The invention discloses a cable tension monitoring system based on a pulley shaft, which relates to the field of ship structure mechanics test, and comprises a pulley shaft assembly and a stress sensor, wherein a through hole is formed in the pulley shaft; establishing a three-dimensional equivalent model of the pulley shaft assembly, carrying out finite element analysis, determining stress data sets at all finite element grid nodes on the inner surface of the through hole, screening out monitoring measurement points from all the finite element grid nodes, and arranging a stress sensor at each monitoring measurement point; training the neural network model by using the vectorized sample set to obtain model parameters, and constructing to obtain a tension monitoring model; the system has the advantages that the actual measurement data set is collected in the working process of the pulley shaft assembly, the vectorized actual measurement data set is input into the tension monitoring model to obtain the tension of the cable, the tension on the cable can be timely obtained through the system, the existing common pulley shaft can be directly replaced, the application range is wide, and the system is suitable for batch production and low in cost.
Description
Technical Field
The invention relates to the field of ship structure mechanics test, in particular to a cable tension monitoring system based on a pulley shaft.
Background
The cable winch is a common connecting device for various ships and maritime work platforms, the cable is connected with the seabed or other fixed facilities so as to ensure that the ship is moored or operated at a fixed position, for large ships and special operation ships, the tension of the cable is generally higher than 2000KN, and in order to ensure the structural safety of an mooring system and reasonably adjust the mooring force, the tension of the cable acting on a winch pulley needs to be perceived.
Currently, the method for measuring the pulley cable tension mainly comprises the following steps: (1) The tension sensor is connected to one end of the cable, and the tension sensor is required to be arranged at the front end of the underwater or cabled pile, so that the arrangement is inconvenient and the durability is poor; (2) Clamping the strain gauge on the cable, but this approach can prevent normal retraction of the cable and requires field installation and debugging by a professional; (3) Other indirect measurement modes, such as arranging a sensor on a winch, indirectly acquiring the pulling force through calculation, have the disadvantages that the method needs customization, and needs professional on-site construction and debugging, and cannot realize generalization and batch deployment.
Disclosure of Invention
The invention provides a cable tension monitoring system based on a pulley shaft aiming at the problems and the technical requirements, and the technical scheme of the invention is as follows:
the cable tension monitoring system based on the pulley shaft comprises a pulley shaft assembly and a stress sensor, wherein the pulley shaft assembly comprises a pulley shaft and a pulley arranged on the pulley shaft, a cable is wound on the pulley, and a through hole is formed in the pulley shaft;
Establishing a three-dimensional equivalent model of the pulley shaft assembly, carrying out finite element analysis, determining stress data sets at all finite element grid nodes on the inner surface of the through hole, wherein the stress data set at each finite element grid node comprises stress values of the finite element grid node under N different loading forces, screening monitoring measurement points from all the finite element grid nodes according to the stress data sets at each finite element grid node and Pearson correlation coefficients between the stress data sets at any two finite element grid nodes, and arranging one stress sensor at each monitoring measurement point;
Applying n loading forces with different magnitudes in a preset loading range on the cable, respectively collecting stress measurement values of each stress sensor under each loading force to obtain a sample set, training a neural network model by using the sample set subjected to vectorization processing to obtain model parameters, and constructing to obtain a tension monitoring model;
And in the working process of the pulley shaft assembly, collecting stress measurement values of each stress sensor to obtain an actual measurement data set, and inputting the vectorized actual measurement data set into the tension monitoring model to obtain the corresponding cable tension.
According to a further technical scheme, the method for screening monitoring measurement points from all finite element grid nodes according to stress data sets at each finite element grid node and Pearson correlation coefficients between the stress data sets at any two finite element grid nodes comprises the following steps:
determining that the initial selected dataset includes stress datasets at all finite element mesh nodes;
selecting a finite element grid node corresponding to the largest stress data set from the selected data set as a monitoring measurement point;
Deleting the finite element grid nodes corresponding to the stress data sets, wherein the Pearson correlation coefficient between the selected data sets and the largest stress data set reaches the preset correlation coefficient, so as to obtain updated selected data sets;
And executing the step of selecting the finite element grid node with the largest corresponding stress data set from the selected data set as a monitoring measurement point again aiming at the selected data set until the selected data set is empty.
The further technical scheme is that the calculation formula of the Pearson correlation coefficient between stress data sets at any two finite element grid nodes is as follows:
r is a Pearson correlation coefficient between stress data sets X and Y at any two finite element mesh nodes, j is a parameter, X j represents the stress value in stress data set X at the jth loading force, Y j represents the stress value in stress data set Y at the jth loading force, Representing the average of all stress values in stress dataset X,/>The average of all stress values in the stress data set Y is represented.
The further technical scheme is that the training of the neural network model by using the vectorized sample set to obtain model parameters comprises the following steps:
inputting the sample set after vectorization as a model input value into a neural network model to obtain a corresponding model output value;
calculating according to the model input value and the model output value to obtain a corresponding loss function;
calculating according to the loss function and the network parameters of the ith layer network in the neural network model to obtain an error value of the ith layer network, and updating the network parameters of the ith layer network by using the error value of the ith layer network, wherein i is a parameter;
And updating the network parameters of each layer of network to obtain an iteratively updated neural network model, and executing the step of inputting the sample set after vectorization processing into the neural network model as an input value to obtain a corresponding output value on the iteratively updated neural network model until an iteration termination condition is reached.
According to the further technical scheme, the error value of the ith layer network is obtained by calculation according to the loss function and the network parameters of the ith layer network in the neural network model, and the network parameters of the ith layer network are updated by using the error value of the ith layer network, and the method comprises the following steps:
calculating the partial derivative between the loss function J and the network parameter H [i] of the ith network by a chained derivative method As an error value for the layer i network;
According to the formula And updating the network parameters of the i-layer network.
The expression of the ith layer network of the neural network model is as follows:
Z[i]＝W[i]A[i-1]+b[i]
A[i]＝g(Z[i])；
Z [i] represents the result of the i-layer network performing a linear operation on the input value A [i-1] of the i-layer network, g () represents the activation function, A [i] represents the output value of the i-layer network, the network parameters of the i-layer network include the network parameters W [i] and b [i], and the following formula Updating network parameters of the layer i network, including:
According to the formula Updating network parameters W [i] of the layer i network according to a formulaAnd updating the network parameter b [i] of the i-layer network.
The further technical scheme is that the expression of the activation function of the i-layer network is as follows:
According to a further technical scheme, the corresponding loss function is obtained by calculation according to the model input value and the model output value, and the method comprises the following steps of:
Wherein y q represents data corresponding to the q-th loading force in the sample set after vectorization processing, And the corresponding model output value obtained after y q is input into the neural network model is represented.
The further technical scheme is characterized in that the diameter of the through hole is smaller than one fourth of the diameter of the pulley shaft, and the depth of the through hole is smaller than one half of the length of the pulley shaft.
The technical scheme is that the through hole is provided with a bolt, and the inner wall of the through hole is provided with a threaded hole matched with the bolt.
The beneficial technical effects of the invention are as follows: the system can timely acquire the tension on the cable, can directly replace the existing common pulley shaft, has wide application range, does not influence the normal winding and unwinding of the cable system, and is suitable for batch production and low in cost.
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Fig. 1 is a schematic flow chart of the present application.
Detailed Description
The following describes the embodiments of the present invention further with reference to the drawings.
A cable tension monitoring system based on a pulley shaft comprises a pulley shaft assembly and a stress sensor, wherein the pulley shaft assembly comprises a pulley shaft and a pulley arranged on the pulley shaft, and a cable is wound on the pulley.
The pulley shaft is of a cylinder structure, the size, the strength and the shape of the pulley shaft are designed according to industry standards, a through hole is formed in the axis position of the pulley shaft, a stress sensor is arranged in the through hole, the stress value is converted into tension data of a cable through an algorithm, the diameter of the through hole is smaller than one fourth of the diameter of the pulley shaft, and the depth of the through hole is smaller than one half of the length of the pulley shaft.
Simultaneously, install the bolt on the through-hole of pulley shaft, be provided with on the inner wall of through-hole with bolt assorted screw hole, the bolt diameter of selecting for use is identical with the diameter of through-hole, mills out the screw thread on the inner wall of through-hole and makes the bolt can screw in a flexible way, forms integrated pulley shaft structure from this, has that the installation is simple and easy, water proofness is good and anticorrosive characteristics, on the other hand can guarantee the maintainability of the inside stress sensor of pulley shaft.
The stress sensor used in the system is a unidirectional stress sensor, and the structural deformation in the through hole is measured through the stress sensor to obtain the shearing force of the position. The unidirectional stress sensors are fixed on the intrados of the through hole, and the number of the unidirectional stress sensors is selected according to the subsequent finite element calculation result.
For each calculation process in the application, a three-dimensional equivalent model of the pulley shaft assembly is established and finite element analysis is carried out:
1. analysis is performed for the axle center hole and depth:
And calculating the influence of different opening positions and opening depths at the axle center on the structural strength of the pulley axle by using a finite element model of the pulley axle, and determining the size, the position and the depth of the through hole under the condition of ensuring the structural safety.
2. Finite element analysis is performed on a three-dimensional size model of the pulley shaft:
on one hand, checking whether the structural strength of the pulley shaft under the limit load meets the design requirement; on the other hand, the step is loaded to the limit to obtain stress distribution data of the surface of the opening, and the stress distribution data is used as input of a measuring point selection algorithm.
3. Judging whether the rigid material meets the design requirement:
When the position and depth of the hole of the pulley shaft through hole are selected, whether the current pulley shaft rigid body structure meets the hole opening requirement or not needs to be changed.
The three previous steps are the preliminary preparation of the present application, which is not the focus of the present application and will not be described in any greater detail.
4. Taking the stress distribution condition of the inner surface of the through hole, and selecting monitoring measuring points:
The inner surface of the through hole comprises m finite element grid nodes, k finite element grid nodes which can accurately invert the cable tension are selected from the m finite element grid nodes to form a monitoring measurement point group P= { P 1,P2,...,Pk }, and a stress sensor is arranged at each monitoring measurement point.
The method comprises the steps that nodes which are independent in linearity and have the largest stress value are selected to form a monitoring measurement point group P, the method is selected through Pearson correlation coefficients, the Pearson correlation coefficients are used for measuring the linear correlation degree of two variables, each finite element grid node obtains a stress data set containing N sample data under N different loading forces, the N loading forces are distributed in a stepped mode according to equal step size increase in a preset load range, wherein stress step loading means simulating different stresses, for example, the total 200 tons of pressure, and the stress of a cable is released through 40 step sizes, 5 tons of pressure is released under each step size, so that the pressure of the cable under various states is simulated, and the stress data set corresponding to each finite element grid node at the moment contains N sample data due to the N different loading forces, wherein each sample data is the sample data obtained by the finite element grid node under one loading force.
The calculation formula for Pearson correlation coefficients for any two stress data sets X and Y containing N samples is as follows:
As indicated by the above equation, the Pearson correlation coefficient for two variables is obtained by calculating the quotient of the covariance and standard deviation of the two variables, where r is the Pearson correlation coefficient between stress data sets X and Y at any two finite element mesh nodes, j is a parameter, X j represents the stress value in stress data set X at the jth loading force, Y j represents the stress value in stress data set Y at the jth loading force, Representing the average of all stress values in stress dataset X,/>The average of all stress values in the stress data set Y is represented.
The input sample stress value obtained by the finite element calculation refers to a stress component S3 with the highest overall stress level, and the unidirectional stress sensor is arranged in the direction.
If the Pearson correlation coefficient r is greater than the predetermined correlation coefficient value, for example, the predetermined correlation coefficient value is 0.95, that is, the Pearson correlation coefficient r is greater than or equal to 0.95, the two network nodes are considered to be strongly correlated, and one of the points needs to be eliminated.
In order to facilitate the subsequent data processing by using Pearson correlation coefficients, calculating Pearson correlation coefficients between every two nodes of all m finite element network nodes on the inner surface of the through hole to obtain a correlation coefficient matrix, wherein the matrix comprises m x m elements, represents the correlation coefficients of any node and all other nodes in m finite element grid nodes, and the Pearson correlation coefficient matrix is expressed as:
The selection step is described in detail below:
Step one: the stress data sets at the m finite element network nodes are stored in an initial selected data set.
Step two: and selecting a finite element grid node corresponding to the largest stress data set from the selected data set as a monitoring measurement point, and selecting all points which are strongly related to the monitoring measurement point and deleting the points by checking a correlation coefficient matrix to obtain an updated selected data set because the working conditions under each loading force are the same and the stress at all the finite element grid nodes becomes linear.
Step three: and step two, repeating the step two circularly until all the points are deleted, and finally obtaining k monitoring measurement points in the monitoring measurement point group P= { P 1,P2,...,Pk }, namely the point with the largest selected stress value and the linear uncorrelated point. At this time, the point selection work is completed, that is, k monitoring points are selected from all m finite element mesh nodes on the inner surface of the opening.
5. Cable tension inversion algorithm based on neural network model:
the method comprises the steps of training by adopting a neural network model to obtain model parameters, constructing a tension monitoring model, wherein a first layer is an input layer, and a last layer is an output layer, and for convenience of explanation, the neural network model comprises a three-layer neural network, k monitoring measurement points are substituted into the neural network model to be calculated, the stress response of the k monitoring measurement points under a loading force forms a sample set x= { X 1,x2,...,xk }, n sample sets are obtained under n loading forces, and the n sample sets are taken as data sets, wherein a sample characteristic matrix is expressed as x= { X (1), X (2), X (n) }.
If the computation of the entire data set is done by a single sample set, a large number of for loops must be passed, and therefore the for loops are replaced by vectorized computation.
The multi-layer neural network calculation steps are detailed below:
step one: forward propagation calculation: and taking the vectorized sample set as a model input value and inputting the model input value into a neural network model to obtain a corresponding model output value.
The neural network model is as follows:
Z[i]＝W[i]A[i-1]+b[i]
A[i]＝g(Z[i])；
Z [i] represents the result of the i-layer network performing a linear operation on the input value A [i-1] of the i-layer network, g () represents the activation function, A [i] represents the output value of the i-layer network, and the network parameters of the i-layer network include network parameters W [i] and b [i].
Specifically, the three-layer neural network is calculated as follows:
The three-layer hidden neural network design is adopted, the input layer has stress values of k monitoring measurement points under the action of n loading forces, the output layer is the resultant force of cable tension, and in order to prevent overfitting, a dropout layer is added in the network layer and used for randomly freezing certain node weights in the grid.
The activation function g [i](Z[i] used in the neural network model is ReLU (modified linear unit):
The expression is:
The ReLU activation function may be activated by a simple zero threshold matrix and is not affected by saturation. ReLU can greatly accelerate the convergence of random gradient descent algorithms when compared to Sigmoid, tanh functions, which is generally thought to be due to its linear, non-saturated form.
Step two: and calculating a loss function J according to the model input value and the model output value.
The loss function in the present application uses an MAE (mean absolute error) function,
The expression is:
Wherein y q represents data corresponding to the q-th loading force in the vectorized sample set, And the corresponding model output value obtained after y q is input into the neural network model is represented.
Step three: error back propagation: and calculating an error value of the ith network from the output layer to the input layer according to the loss function and the network parameters of the ith network in the neural network model.
Calculating the partial derivative between the loss function J and the network parameter H [i] of the ith network by a chained derivative methodAs the error value of the layer i network.
Step four: and updating the network parameters of the ith layer network by utilizing the error value of the ith layer network.
According to the formulaThe network parameters of the i-layer network are updated, since the network parameters of the i-layer network of the present application include W [i] and b [i].
Thus according to the formulaUpdating network parameters W [i] of the layer i network according to a formulaAnd updating the network parameter b [i] of the i-layer network.
The network parameters are subtracted by the back-propagated error because the back-propagated derivative represents the rate of change of the difference between the final output value and the true value. The counter-propagating error value may be positive or negative and thus it is understood that the value of the network parameter is decreased when the counter-propagating error value is positive and increased when the counter-propagating error value is negative. The purpose of this is two: so that the errors become smaller and the speed at which the errors become smaller is as fast as possible.
Step five: and iterating repeatedly until an iteration termination condition is reached, wherein the iteration termination condition comprises that the error value reaches a minimum value or a preset iteration number is reached.
Finally, a tension monitoring model is obtained, wherein the tension monitoring model is the mapping relation of resultant force T m of X= { X (1), X (2), X (n) } and the corresponding cable tension:
X＝{x(1),x(2),...,x(n)}→Tm；
In the working process of the pulley shaft assembly, the stress measurement value of each stress sensor is collected to obtain an actual measurement data set, and the vectorized actual measurement data set is input into the tension monitoring model to obtain the corresponding cable tension.
The above is only a preferred embodiment of the present application, and the present application is not limited to the above examples. It is to be understood that other modifications and variations which may be directly derived or contemplated by those skilled in the art without departing from the spirit and concepts of the present application are deemed to be included within the scope of the present application.
Claims (10)
1. The cable tension monitoring system based on the pulley shaft is characterized by comprising a pulley shaft assembly and a stress sensor, wherein the pulley shaft assembly comprises a pulley shaft and a pulley arranged on the pulley shaft, a cable is wound on the pulley, and a through hole is formed in the pulley shaft;
Establishing a three-dimensional equivalent model of the pulley shaft assembly, carrying out finite element analysis, determining stress data sets at all finite element grid nodes on the inner surface of the through hole, wherein the stress data set at each finite element grid node comprises stress values of the finite element grid node under N different loading forces, screening monitoring measurement points from all the finite element grid nodes according to the stress data sets at each finite element grid node and Pearson correlation coefficients between the stress data sets at any two finite element grid nodes, and arranging one stress sensor at each monitoring measurement point;
Applying n loading forces with different magnitudes in a preset loading range on the cable, respectively collecting stress measurement values of each stress sensor under each loading force to obtain a sample set, training a neural network model by using the sample set subjected to vectorization processing to obtain model parameters, and constructing to obtain a tension monitoring model;
And in the working process of the pulley shaft assembly, collecting stress measurement values of each stress sensor to obtain an actual measurement data set, and inputting the vectorized actual measurement data set into the tension monitoring model to obtain the corresponding cable tension.
2. The system of claim 1, wherein the screening of monitoring measurement points from all finite element mesh nodes based on stress data sets at each finite element mesh node and Pearson correlation coefficients between stress data sets at any two finite element mesh nodes comprises:
determining that the initial selected dataset includes stress datasets at all finite element mesh nodes;
selecting a finite element grid node corresponding to the largest stress data set from the selected data set as a monitoring measurement point;
Deleting the finite element grid nodes corresponding to the stress data sets, wherein the Pearson correlation coefficient between the selected data sets and the largest stress data set reaches the preset correlation coefficient, so as to obtain updated selected data sets;
And executing the step of selecting the finite element grid node with the largest corresponding stress data set from the selected data set as a monitoring measurement point again aiming at the selected data set until the selected data set is empty.
3. The system of claim 1, wherein the Pearson correlation between stress data sets at any two finite element mesh nodes is calculated as:
r is a Pearson correlation coefficient between stress data sets X and Y at any two finite element mesh nodes, j is a parameter, X j represents the stress value in stress data set X at the jth loading force, Y j represents the stress value in stress data set Y at the jth loading force, Representing the average of all stress values in stress dataset X,/>The average of all stress values in the stress data set Y is represented.
4. The system of claim 1, wherein training the neural network model using the vectorized sample set to obtain model parameters comprises:
inputting the sample set after vectorization as a model input value into a neural network model to obtain a corresponding model output value;
calculating according to the model input value and the model output value to obtain a corresponding loss function;
calculating according to the loss function and the network parameters of the ith layer network in the neural network model to obtain an error value of the ith layer network, and updating the network parameters of the ith layer network by using the error value of the ith layer network, wherein i is a parameter;
And updating the network parameters of each layer of network to obtain an iteratively updated neural network model, and executing the step of inputting the sample set after vectorization processing into the neural network model as an input value to obtain a corresponding output value on the iteratively updated neural network model until an iteration termination condition is reached.
5. The system of claim 4, wherein the calculating the error value of the i-th layer network according to the loss function and the network parameters of the i-th layer network in the neural network model, and updating the network parameters of the i-th layer network by using the error value of the i-th layer network comprises:
calculating the partial derivative between the loss function J and the network parameter H [i] of the ith network by a chained derivative method As an error value for the layer i network;
According to the formula And updating the network parameters of the i-layer network.
6. The system of claim 5, wherein the expression of the layer i network of the neural network model is:
Z[i]＝W[i]A[i-1]+b[i]
A[i]＝g(Z[i])；
Z [i] represents the result of the i-layer network performing a linear operation on the input value A [i-1] of the i-layer network, g () represents the activation function, A [i] represents the output value of the i-layer network, the network parameters of the i-layer network include the network parameters W [i] and b [i], and the following formula Updating network parameters of the layer i network, including:
According to the formula Updating network parameters W [i] of the i-layer network according to the formula/>And updating the network parameter b [i] of the i-layer network.
7. The system of claim 6, wherein the expression of the activation function of the layer i network is:
8. The system of claim 4, wherein said calculating a corresponding loss function from said model input values and model output values comprises calculating:
Wherein y q represents data corresponding to the q-th loading force in the sample set after vectorization processing, And the corresponding model output value obtained after y q is input into the neural network model is represented.
9. The system of claim 1, wherein the diameter of the through hole is less than one-fourth of the diameter of the pulley shaft and the depth of the through hole is less than one-half of the length of the pulley shaft.
10. The system of claim 1, wherein a bolt is mounted on the through hole, and a threaded hole matching the bolt is provided on an inner wall of the through hole.
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