CN117874479A - Heavy-duty locomotive coupler force identification method based on data driving - Google Patents
Heavy-duty locomotive coupler force identification method based on data driving Download PDFInfo
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Abstract
The invention discloses a heavy-duty locomotive coupler force identification method based on data driving, which relates to the technical field of heavy-duty locomotive coupler state monitoring and comprises the following steps: collecting longitudinal relative displacement signals of a coupler body of a heavy-duty train; smoothly processing a longitudinal relative displacement signal of the coupler body; intercepting the longitudinal relative displacement signals of the coupler body after filtering and smoothing by adopting a sliding window, extracting the relative speed characteristics in each section of relative displacement signals, and constructing a data set; normalizing the data set; establishing a DBO-ELM model, taking the number of neurons of an ELM hidden layer, penalty parameters and activation function parameters as optimization parameters, taking the mean square error of a verification set identification result as an fitness function, and carrying out iterative optimization on the DBO-ELM model parameters by using the verification set; training the model by using a training set after obtaining the optimal parameters of the model; inputting the test set sample into the trained model, and determining the accuracy of the trained model according to the error of the coupler force recognition result; the invention has the advantages of low cost and high efficiency.
Description
Technical Field
The invention relates to the technical field of heavy-duty locomotive coupler state monitoring, in particular to a heavy-duty locomotive coupler force identification method based on data driving.
Background
As one of two development directions of railways in the world, the heavy load of cargo transportation provides guarantee for the rapid development of economy and society. The transportation of the road goods has the characteristics of large transportation capacity, low energy consumption, high reliability and the like, and particularly rapid development in countries with large specific gravity of large quantities of the goods such as coal, ore and the like. However, under complex operation conditions and operation delay caused by air wave speed, as the marshalling is continuously enlarged and the axle weight is continuously increased, the traction braking operation difficulty is increased, the heavy-duty train operates to generate more intense longitudinal impulse, and the longitudinal impulse at the middle locomotive of the currently-used twenty-thousand ton heavy-duty train is particularly intense. The coupler bears random and alternating pulling force, compression force, impact force and other actions in the train operation, and many train accidents are caused by the fact that the coupler is stressed beyond relevant indexes.
At present, a common car coupler state monitoring method is to install a force measuring car coupler device, wherein strain gauges are arranged on two sides of a car coupler body to form a full-bridge circuit, a load is applied on a car coupler force calibration test bed to obtain a calibration coefficient, and then a car coupler force signal is obtained through acquisition and post-processing of the strain signal; the method is influenced by complex running conditions of the train, the problems of damage to equipment such as strain gauge damage, loose adhesion, complete falling off after long-term use and the like are easily caused, a special car coupler calibration test bed is required to be used for acquiring calibration coefficients, and the car coupler force measuring equipment needs to be manufactured again after damage, so that the problems of high monitoring cost, high maintenance difficulty and the like are caused.
Disclosure of Invention
In order to solve the problems in the prior art, the invention aims to provide a heavy-duty locomotive coupler force identification method based on data driving.
In order to achieve the above purpose, the invention adopts the following technical scheme: a method for recognizing the coupler force of a heavy-duty locomotive based on data driving comprises the following steps:
step 1, acquiring longitudinal relative displacement signals of a coupler body of a heavy-duty train under different working conditions;
step 2, adopting low-pass filtering and weighted average smoothing to process the longitudinal relative displacement signal of the coupler body, filtering out signal noise generated by external environment, and eliminating abnormal signal values;
step 3, intercepting the longitudinal relative displacement signals of the coupler body after filtering and smoothing by adopting a sliding window, extracting the relative speed characteristics in each section of relative displacement signals, and constructing a data set by utilizing the processed signals;
step 4, carrying out normalization processing on the data set and dividing a training set, a verification set and a test set;
step 5, establishing a DBO-ELM model, taking the number of neurons of an ELM hidden layer, penalty parameters and activation function parameters as optimization parameters, taking the mean square error of the identification result of the verification set as a fitness function, and carrying out iterative optimization on the DBO-ELM model parameters by using the verification set;
step 6, training the model by using a training set after obtaining optimal parameters of the model;
and 7, inputting the test set sample into the trained model, and determining the accuracy of the trained model according to the error of the coupler force recognition result.
As a further improvement of the present invention, in the step 3, the window length of the sliding window is 500 to 2000 data points, the moving step length of the sliding window is 10 to 50 data points, and the relative velocity is characterized by the relative velocity of each data point and the first data point in the relative displacement signal of the segment.
As a further improvement of the present invention, in the step 4, the normalization processing is specifically performed on the data set as follows:
；
in the method, in the process of the invention,for normalized data, ++>For the data to be normalized in the sample, < > a->As the maximum value in the data set,is the minimum in the dataset.
As a further improvement of the present invention, the step 5 specifically includes the steps of:
step 5.1, determining input variables and output variables of the DBO-ELM model;
step 5.2, ELM activation function selecting Sigmoid function with controllable parameters addedTo achieve a nonlinear mapping as shown in the following equation:
；
wherein:for inputting samples, < >>Controllable parameters added for Sigmoid functions;
step 5.3, determining parameters to be optimized: ELM hidden layer neuron numberPenalty parameter->And controllable parameters->The method comprises the steps of carrying out a first treatment on the surface of the Determining the population scale of dung beetles>Maximum number of iterations->Ratio of ball dung beetle, brood dung beetle, small dung beetle and thief, deflection coefficient ∈>Position updating parameter of ball dung beetle>Thieves update rule parameters +.>The method comprises the steps of carrying out a first treatment on the surface of the The updating formulas of the rolling ball dung beetles, brood dung beetles and thieves are as follows:
；
wherein:representing the current iteration number, +.>And->Respectively represent +.>First->Position information of rolling ball dung beetles, brood dung beetles, small dung beetles and thieves only, and (ii) the position information of the ball dung beetles, brood dung beetles and thieves only>Indicating deflection coefficient, +.>Representing the update parameters of the position of the ball dung beetles, < ->Representing natural coefficients->Representing the global worst position +_>Representing the optimal position of all individuals within the current population, < >>Indicating the best location of the thief individual within the current population,/-best location>Representing the lower limit of the range of values of the position information x,/-, for example>Represents the upper limit of the range of values of the position information x,/-, for example>And->Representing two independent random vectors of size 1 x D, D representing the dimension of the optimization problem,/->Representing random numbers subject to normal distribution, +.>Representing a random vector +.>Representing a random vector of size 1 x D following a normal distribution, ++>Representing a thief updating the rule parameters.
The beneficial effects of the invention are as follows:
1. the invention recognizes the coupler force by extracting the longitudinal relative displacement signal of the coupler body, overcomes the defect that the force measuring coupler device is required to be manufactured and installed in the traditional method, and has the advantages of low cost and high efficiency.
2. The DBO algorithm is used for optimizing the number of neurons in the ELM hidden layer, punishment parameters and activation function parameters, the mean square error of the actual result and the predicted result of the verification set is taken as the fitness function, the optimal model parameters of the ELM are obtained through repeated iterative optimization, the defect of low recognition accuracy caused by manual selection of the parameters of the ELM is overcome, and the method is further applied to coupler force recognition to improve the recognition accuracy.
Drawings
FIG. 1 is a flow chart of a method for recognizing the coupler force of a heavy-duty locomotive according to an embodiment of the invention;
FIG. 2 is an iterative graph of fitness in a coupler force recognition model parameter optimization process in an embodiment of the invention;
FIG. 3 is a diagram of the coupler force recognition result in an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Examples
In recent years, a mechanical learning algorithm is rapidly developed in the field of heavy-load railway fault diagnosis such as wheel polygon recognition, track shortwave damage detection and the like, and long-term monitoring of heavy-load trains is realized based on a data driving technology by collecting signals only through simple sensors such as displacement, acceleration and the like. In order to realize long-term monitoring of locomotive coupler force and reduce maintenance cost of coupler force measuring equipment, the embodiment provides a heavy-load locomotive coupler force identification method based on data driving.
As shown in fig. 1, the method specifically includes:
1) The method comprises the steps of collecting longitudinal relative displacement signals of a coupler body of a heavy-duty train under different working conditions;
in the specific implementation mode, the longitudinal relative displacement of the coupler body under the operation condition of the heavy-duty locomotive is collected at the sampling frequency of 200Hz by establishing a locomotive-truck coupling dynamics simulation model.
2) Adopting low-pass filtering and weighted average smoothing to process the longitudinal relative displacement signal of the coupler body, filtering out signal noise generated by external environment, and eliminating the deviation of the measured value of the longitudinal relative displacement of the coupler body caused by sensor test faults;
in the specific implementation, the cut-off frequency of the adopted low-pass filter is 70Hz, the weighted average smoothing parameter is 200, and the abnormal value of the longitudinal relative displacement of the coupler body is removed.
3) Intercepting the longitudinal relative displacement signal of the coupler body after filtering and smoothing by adopting a sliding window, wherein the window length of the sliding window is 1000 data points, and the moving step length of the sliding window is 50 data points; extracting the relative speed characteristics in each section of relative displacement signal, and constructing a data set by using the processed signals;
in particular, the relative velocity of each data point and the first data point in each segment of the displacement signal is selected as the relative velocity characteristic of the segment of the signal.
4) Carrying out normalization processing on the data set, and dividing a training set, a verification set and a test set; the data set normalization process is shown in formula (1):
（1）；
in the method, in the process of the invention,for normalized data, ++>For the data to be normalized in the sample, < > a->As the maximum value in the data set,is the minimum in the dataset.
In specific implementation, a training set in the data set is used for training a model, a verification set is used for optimizing model parameters, and a test set is used for testing the recognition effect of the model on the coupler force; the ratio of training set, validation set and test set is 4:1:1.
5) The DBO-ELM model is built, and the specific steps are as follows:
step 5.1: input and output variables of the DBO-ELM model are determined.
Step 5.2: the ELM activation function selects the Sigmoid function to which the controllable parameters are added to achieve a nonlinear mapping, as shown in the following equation.
（2）；
Wherein:for inputting samples, < >>Controllable parameters added for Sigmoid functions.
Step 5.3: determining parameters to be optimized; determining dung beetle population scaleMaximum number of iterations->Ratio of ball dung beetle, brood dung beetle, small dung beetle and thief, deflection coefficient ∈>Rolling upPosition updating parameter of ball dung beetle>Thieves update rule parameters +.>The method comprises the steps of carrying out a first treatment on the surface of the The updating formulas of the rolling ball dung beetles, brood dung beetles and thieves are as follows:
；
wherein:representing the current iteration number, +.>And->Respectively represent +.>First->Position information of rolling ball dung beetles, brood dung beetles, small dung beetles and thieves only, and (ii) the position information of the ball dung beetles, brood dung beetles and thieves only>Indicating deflection coefficient, +.>Representing the update parameters of the position of the ball dung beetles, < ->Representing natural coefficients->Representing the global worst position +_>Representing the optimality of all individuals within the current populationPosition (S)>Indicating the best location of the thief individual within the current population,/-best location>Representing the lower limit of the range of values of the position information x,/-, for example>Represents the upper limit of the range of values of the position information x,/-, for example>And->Representing two independent random vectors of size 1 x D, D representing the dimension of the optimization problem,/->Representing random numbers subject to normal distribution, +.>Representing a random vector +.>Representing a random vector of size 1 x D following a normal distribution, ++>Representing a thief updating the rule parameters.
In specific implementation, the population scale of the dung beetles30, maximum iteration number->100 parts of the ratio of the rolling ball dung beetles to the brood dung beetles to the small dung beetles to the thieves is 6:6:7:11, and the deflection coefficient is ∈10->0.1, the position of the ball dung beetle is updatedParameter->Updating rule parameter for 0.3 thief +.>0.5. And carrying out iterative optimization for 100 times according to the parameters to obtain an ELM optimal parameter combination, wherein the standard of the iteration end graph is that the iteration times reach 100 times, and the iteration result is shown in figure 2.
6) After the optimal parameters of the model are obtained, training is carried out on the model by utilizing a training set
In specific implementation, the optimized parameters in the model are set to optimized values, and then the training set is input into the ELM model to train the weight parameters between the hidden layer and the output layer.
7) And identifying the coupler force of the test set, and determining the accuracy of the model.
In a specific implementation, the test set sample is input into the trained model, and the mechanical energy of the output result is inversely normalized, so that the coupler force recognition result can be obtained, as shown in fig. 3. The mean square error of the coupler force recognition result is 8.586, the error is extremely small, and the constructed model can be used for recognizing the coupler force.
The foregoing examples merely illustrate specific embodiments of the invention, which are described in greater detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention.
Claims (4)
1. The method for recognizing the coupler force of the heavy-duty locomotive based on data driving is characterized by comprising the following steps of:
step 1, acquiring longitudinal relative displacement signals of a coupler body of a heavy-duty train under different working conditions;
step 2, adopting low-pass filtering and weighted average smoothing to process the longitudinal relative displacement signal of the coupler body, filtering out signal noise generated by external environment, and eliminating abnormal signal values;
step 3, intercepting the longitudinal relative displacement signals of the coupler body after filtering and smoothing by adopting a sliding window, extracting the relative speed characteristics in each section of relative displacement signals, and constructing a data set by utilizing the processed signals;
step 4, carrying out normalization processing on the data set and dividing a training set, a verification set and a test set;
step 5, establishing a DBO-ELM model, taking the number of neurons of an ELM hidden layer, penalty parameters and activation function parameters as optimization parameters, taking the mean square error of the identification result of the verification set as a fitness function, and carrying out iterative optimization on the DBO-ELM model parameters by using the verification set;
step 6, training the model by using a training set after obtaining optimal parameters of the model;
and 7, inputting the test set sample into the trained model, and determining the accuracy of the trained model according to the error of the coupler force recognition result.
2. The method of claim 1, wherein in step 3, the sliding window has a window length of 500 to 2000 data points, the sliding window has a moving step length of 10 to 50 data points, and the relative velocity is characterized by a relative velocity between each data point and the first data point in the segment of the relative displacement signal.
3. The method for recognizing the coupler force of the heavy-duty locomotive based on data driving according to claim 2, wherein in the step 4, the normalization processing of the data set is specifically as follows:
；
in the method, in the process of the invention,for normalized data, ++>For the data to be normalized in the sample, < > a->Maximum value in data set, +.>Is the minimum in the dataset.
4. The method for recognizing the coupler force of the heavy-duty locomotive based on data driving according to claim 1, wherein the step 5 specifically comprises the following steps:
step 5.1, determining input variables and output variables of the DBO-ELM model;
step 5.2, ELM activation function selecting Sigmoid function with controllable parameters addedTo achieve a nonlinear mapping as shown in the following equation:
；
wherein:for inputting samples, < >>Controllable parameters added for Sigmoid functions;
step 5.3, determining parameters to be optimized: ELM hidden layer neuron numberPenalty parameter->And controllable parameters->The method comprises the steps of carrying out a first treatment on the surface of the Determining the population scale of dung beetles>Maximum number of iterations->Ratio of ball dung beetle, brood dung beetle, small dung beetle and thief, deflection coefficient ∈>Position updating parameter of ball dung beetle>Thieves update rule parameters +.>The method comprises the steps of carrying out a first treatment on the surface of the The updating formulas of the rolling ball dung beetles, brood dung beetles and thieves are as follows:
；
wherein:representing the current iteration number, +.>And->Respectively represent +.>The first iterationPosition information of rolling ball dung beetles, brood dung beetles, small dung beetles and thieves only, and (ii) the position information of the ball dung beetles, brood dung beetles and thieves only>Indicating deflection coefficient, +.>Representing the update parameters of the position of the ball dung beetles, < ->Representing natural coefficients->Representing the global worst position +_>Representing the optimal position of all individuals within the current population, < >>Indicating the best location of the thief individual within the current population,/-best location>Representing the lower limit of the range of values of the position information x,/-, for example>Represents the upper limit of the range of values of the position information x,/-, for example>And->Representing two independent random vectors of size 1 x D, D representing the dimension of the optimization problem,/->Representing random numbers subject to normal distribution, +.>Representing a random vector +.>Representing a random vector of size 1 x D following a normal distribution, ++>Representing a thief updating the rule parameters.
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