CN115828086B - Heavy-duty locomotive coupler instability early warning method based on vehicle body lateral acceleration - Google Patents

Heavy-duty locomotive coupler instability early warning method based on vehicle body lateral acceleration Download PDF

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CN115828086B
CN115828086B CN202310093921.6A CN202310093921A CN115828086B CN 115828086 B CN115828086 B CN 115828086B CN 202310093921 A CN202310093921 A CN 202310093921A CN 115828086 B CN115828086 B CN 115828086B
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陈是扦
王开云
翟婉明
宋沛泽
谢博
凌亮
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Southwest Jiaotong University
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Abstract

The invention belongs to the technical field of heavy-duty locomotive coupler state monitoring, and discloses a heavy-duty locomotive coupler instability early warning method based on vehicle body lateral acceleration, which comprises the following steps: collecting vehicle body transverse acceleration response signals of the train under different working conditions; filtering the signal by adopting a low-pass filter, and eliminating abnormal acceleration values; extracting the time domain, frequency domain and time-frequency domain characteristics of the transverse vibration acceleration signals of the vehicle body; constructing a data set by using the extracted features, adding a classification label to the data set, carrying out normalization processing on the data set, and dividing a training set, a verification set and a test set; taking SVM punishment parameters and kernel function parameters as optimization parameters, and taking the classification accuracy of the verification set as an fitness function; selecting and optimizing parameters of the PSO-SVM model by using the verification set; the method and the device for testing the stability state of the coupler of the car have the advantages of low cost and high efficiency by identifying the stability state of the coupler of the car and judging whether the coupler is unstable.

Description

Heavy-duty locomotive coupler instability early warning method based on vehicle body lateral acceleration
Technical Field
The invention belongs to the technical field of heavy-duty locomotive coupler state monitoring, and particularly relates to a heavy-duty locomotive coupler instability early warning method based on vehicle body lateral acceleration.
Background
The heavy-load railway transportation has important significance for reducing the transportation cost and improving the transportation efficiency, and is a recognized railway freight development direction at home and abroad. Heavy haul is widely paid attention to all countries in the world because of the advantages of large traffic volume, low cost, high efficiency and the like, and a plurality of countries such as China, the United states, australia and the like all run heavy haul trains and commonly adopt modes of increasing the axle weight of the trains, expanding the grouping of the trains and the like to improve the transportation capacity of heavy haul railways. However, the increase in axle weight and consist length of heavy-duty trains also exacerbates the longitudinal impulse in the train operation, bringing great challenges to the service performance of locomotives and coupler buffers. The coupler is unstable easily due to the coupler pressing force generated by the coupler buffer device under the action of longitudinal impulse, and further an oversized coupler force transverse component is generated, and the coupler transverse force is transmitted to the wheel set through the vehicle body and the suspension, so that the wheel axle transverse force is increased, and the accident risks of wheel climbing, derailment and vehicle rollover are increased. At present, the universal safety limit value of the coupler swing angle of the 2 ten thousand ton heavy load train in China is
Figure SMS_1
The safety accidents of the multi-load railway all occur because the swing angle of the coupler of the train is overlarge and the coupler is unstable, so that the real-time and accurate monitoring of the coupler stable state is of great significance to the safe operation of the heavy-load train.
At present, a common car coupler state monitoring method is to arrange stay wire displacement sensors, cameras and other devices at the car coupler of the middle locomotive, and then collect displacement signals or image signals to judge the car coupler stable state. However, the method is affected by severe environments of lines, equipment damage such as broken wires is easy to cause, and the car coupler state monitoring method based on the image technology is affected by external environments such as weather and light, so that the problems of high monitoring cost, low identification accuracy and the like are caused. Because locomotive vibration condition and coupler steady state are closely related, and acceleration sensor sets up inside the locomotive, through automobile body vibration condition discernment coupler steady state can avoid above-mentioned problem, and has high efficiency, high accuracy's advantage. In recent years, machine learning algorithms have certain application in the field of fault diagnosis of heavy-duty trains, but are mostly used for track damage detection, wheel polygon recognition and the like, and some algorithm models are complex, have numerous parameters, are not suitable for on-line fault diagnosis and monitoring, have strong learning ability and few parameters of support vector machines (Support Vector Machine, SVM), have extremely strong generalization ability, and overcome the problems of long learning time, slow convergence speed and the like of the traditional neural network. Aiming at the characteristic that the SVM is greatly influenced by training parameters in the aspect of state recognition accuracy, a particle swarm optimization algorithm (Particle Swarm Optimization, PSO) is utilized to carry out global iterative optimization on SVM parameters, and the obtained optimal parameters are used for carrying out real-time recognition, so that the recognition accuracy is improved. In addition, the PSO-SVM has the advantages of high convergence speed and the like, and can be used for the problems of real-time monitoring and recognition of the coupler state of the railway heavy-duty locomotive.
Disclosure of Invention
In order to overcome the defects, the inventor of the invention continuously reforms and innovates through long-term exploration attempts and repeated experiments and efforts, and provides a heavy-load locomotive coupler instability early warning method based on the transverse acceleration of a locomotive body, which has the advantages of low cost and high efficiency by extracting the transverse acceleration signal characteristics of the locomotive body to identify whether the coupler state is unstable or not, and uses a PSO algorithm to optimize SVM punishment parameters and kernel function parameters so as to obtain optimal model parameters of the SVM, and then uses the optimal model parameters to identify the coupler stability, so that the identification accuracy is improved.
The technical scheme adopted by the invention for achieving the purpose is as follows: the method for early warning the coupler instability of the heavy-duty locomotive based on the transverse acceleration of the locomotive body is provided. The method comprises the following steps:
1) And (3) signal acquisition: collecting vehicle body transverse acceleration response signals of the train under different working conditions;
2) And (3) signal processing: the low-pass filter is adopted to carry out low-pass filtering on the vehicle body transverse acceleration response signal, so as to filter out signal noise generated by the external environment and remove abnormal acceleration values;
3) Extracting signal characteristics: extracting time domain, frequency domain and time-frequency domain characteristics of the transverse vibration acceleration signal of the vehicle body from the processed signals;
4) Data set construction: constructing a data set by using the extracted time domain, frequency domain and time-frequency domain characteristics, adding a classification label to the data set, carrying out normalization processing on the data set, and dividing a training set, a verification set and a test set; the training set and the verification set are used for optimizing model parameters, and the testing set is used for testing performance;
5) Building a training PSO-SVM model: taking SVM punishment parameters and kernel function parameters as optimization parameters, and taking the classification accuracy of the verification set as an fitness function;
6) PSO-SVM parameter optimization: performing iterative optimization on PSO-SVM model parameters by using a verification set;
7) And (3) state identification: and identifying the stability state of the coupler of the test set, and judging whether the coupler is unstable or not.
According to the heavy-duty locomotive coupler instability early warning method based on the vehicle body lateral acceleration, the further preferable technical scheme is as follows: 1) And acquiring the transverse acceleration of the electric locomotive under the conditions of 40km/h and 60km/h of running speed, different longitudinal impact forces and different traction/braking forces at 200Hz sampling frequency by establishing a locomotive-truck coupling dynamics simulation model.
According to the heavy-duty locomotive coupler instability early warning method based on the vehicle body lateral acceleration, the further preferable technical scheme is as follows: 2) In the above, the cut-off frequency of the low-pass filter is 80Hz, and the abnormal acceleration value refers to the measured value deviation caused by the sensor test fault.
According to the heavy-duty locomotive coupler instability early warning method based on the vehicle body lateral acceleration, the further preferable technical scheme is as follows: 3) Wherein the time domain features comprise maximum value, minimum value, average value, peak value, rectifying average value, variance, standard deviation, kurtosis, skewness, root mean square, margin factor, pulse factor, waveform factor and peak factor; the frequency domain features include: center of gravity frequency, root mean square frequency, frequency variance, frequency standard deviation; the time-frequency characteristics include: singular spectrum entropy, power spectrum entropy, energy entropy.
According to the heavy-duty locomotive coupler instability early warning method based on the vehicle body lateral acceleration, the further preferable technical scheme is as follows: 4) In the middle, the swing angle of the coupler is larger than
Figure SMS_2
Sample label is 1, and coupler swing angle is less than or equal to +.>
Figure SMS_3
The label is 0, and the training set sample is set to be +.>
Figure SMS_4
The normalization process is shown as formula (1):
Figure SMS_5
（1）
wherein:
Figure SMS_6
for normalized data, ++>
Figure SMS_7
For the data to be normalized in the sample, < > a->
Figure SMS_8
And->
Figure SMS_9
Respectively maximum and minimum in the dataset.
According to the heavy-duty locomotive coupler instability early warning method based on the vehicle body lateral acceleration, the further preferable technical scheme is as follows: optimizing model parameters by adopting a training set and a verification set, and testing the recognition effect of the model on the coupler swing angle instability early warning by adopting a test set; the training set, the verification set and the test set have the proportion of 320:50:40.
according to the heavy-duty locomotive coupler instability early warning method based on the vehicle body lateral acceleration, the further preferable technical scheme is as follows: 5) The PSO-SVM model is built, and the specific steps are as follows:
5.1: the SVM kernel function selects a gaussian kernel to implement the nonlinear mapping as shown in the following equation:
Figure SMS_10
（2）
wherein:
Figure SMS_11
for inputting samples, < >>
Figure SMS_12
Is Gaussian kernel parameters;
5.2: determining parameters to be optimized
Figure SMS_13
，/>
Figure SMS_14
The method comprises the steps of carrying out a first treatment on the surface of the Determining particle population size P, maximum iteration number N, inertial weight +.>
Figure SMS_15
Acceleration factor->
Figure SMS_16
、/>
Figure SMS_17
The method comprises the steps of carrying out a first treatment on the surface of the Speed of particles->
Figure SMS_18
Position->
Figure SMS_19
The updated formula of (2) is:
Figure SMS_20
（3）
Figure SMS_21
（4）
wherein:
Figure SMS_24
is an inertial weight; />
Figure SMS_26
And->
Figure SMS_29
The speed and the position of the kth iterative particle i are respectively; k is the current iteration number; acceleration factor->
Figure SMS_23
And->
Figure SMS_27
Is a non-negative constant; />
Figure SMS_30
And->
Figure SMS_31
Is distributed in->
Figure SMS_22
Random numbers in between; />
Figure SMS_25
Optimal position for individual particle history, < >>
Figure SMS_28
The best position for the particle population history.
According to the heavy-duty locomotive coupler instability early warning method based on the vehicle body lateral acceleration, the further preferable technical scheme is as follows: particle population size p=30; maximum number of iterations n=100; inertial weight
Figure SMS_32
=1；/>
Figure SMS_33
=1.8；
Figure SMS_34
=1.9。
Compared with the prior art, the technical scheme of the invention has the following advantages/beneficial effects:
1. by extracting the time domain, frequency domain and time domain characteristics of the transverse acceleration signal of the car body to identify whether the car coupler state is unstable, the defect that a test sensor needs to be installed at the car coupler in the operation environment in the traditional method is overcome, and the method has the advantages of low cost and high efficiency;
2. the PSO algorithm is used for optimizing the SVM punishment parameters and the kernel function parameters, the verification set classification accuracy is used as a PSO-SVM model fitness function, the optimal model parameters of the SVM are obtained through repeated iterative optimization, the defect of low recognition accuracy caused by manual parameter selection of the SVM is overcome, and the method is further applied to coupler stability recognition to improve the recognition accuracy.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some examples of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for early warning of coupler instability of a heavy-duty locomotive based on lateral acceleration of the locomotive body.
Fig. 2 is a graph of PSO algorithm fitness iterations.
FIG. 3 is a diagram of test set classification results.
FIG. 4 is a schematic diagram of the early warning of coupler instability of a heavy-duty locomotive of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, based on the embodiments of the invention, which are apparent to those of ordinary skill in the art without inventive faculty, are intended to be within the scope of the invention. Accordingly, the detailed description of the embodiments of the invention provided below is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus, once an item is defined in one figure, it may not be further defined and explained in the following figures.
Examples:
as shown in fig. 1, the workflow is as shown in fig. 1, and the specific steps are as follows:
1) Collecting vehicle body transverse acceleration signals of the electric locomotive under different working conditions, and extracting the vehicle body transverse acceleration signals through a time window;
in a specific embodiment, a locomotive-truck coupling dynamics simulation model is built, and the transverse acceleration of the electric locomotive under the conditions of 40km/h and 60km/h of running speed, different longitudinal impact forces and different traction/braking forces is collected at a sampling frequency of 200 Hz.
2) Denoising (filtering) the signal and removing abnormal data (removing abnormal acceleration values); since the measured values are subject to interference from the external environment in the field measured data, the measured values are filtered, the abnormal acceleration values are the measured value deviations caused by the sensor test faults,
in specific implementation, the cut-off frequency of the adopted low-pass filter is 80Hz, and the abnormal acceleration value is removed.
3) Extracting and collecting time domain, frequency domain and time-frequency domain characteristics of the transverse vibration acceleration signals of the vehicle body;
the time domain features comprise a maximum value, a minimum value, an average value, a peak value, a rectifying average value, a variance, a standard deviation, kurtosis, a skewness, a root mean square, a margin factor, a pulse factor, a waveform factor and a peak factor; the frequency domain features include: center of gravity frequency, root mean square frequency, frequency variance, frequency standard deviation; the time-frequency characteristics include: singular spectrum entropy, power spectrum entropy, energy entropy.
4) Adding a classification label to the data set, carrying out normalization processing on the data set, and dividing a training set, a verification set and a test set;
the swing angle of the coupler is larger than
Figure SMS_35
Sample label is 1, and coupler swing angle is less than or equal to +.>
Figure SMS_36
The label is 0, and the training set sample is set to be +.>
Figure SMS_37
The normalization process is shown as formula (1):
Figure SMS_38
（1）
wherein:
Figure SMS_39
for normalized data, ++>
Figure SMS_40
For the data to be normalized in the sample, < > a->
Figure SMS_41
And->
Figure SMS_42
Respectively maximum and minimum in the dataset.
The training set, the verification set and the test set are different samples in the data set, the training set and the verification set are used for carrying out parameter optimization on the SVM model, and the test set is used for testing the performance of the model after optimization; the training set, the verification set and the test set have the proportion of 320:50:40.
5) The PSO-SVM model is built, and the specific steps are as follows:
5.1: the SVM kernel function selects a gaussian kernel to implement the nonlinear mapping as shown in the following equation.
Figure SMS_43
（2）/>
Wherein:
Figure SMS_44
for inputting samples, < >>
Figure SMS_45
Is a gaussian kernel parameter.
5.2: determining parameters to be optimized
Figure SMS_46
，/>
Figure SMS_47
The method comprises the steps of carrying out a first treatment on the surface of the Determining particle population sizePMaximum number of iterationsNInertial weight->
Figure SMS_48
Acceleration factor->
Figure SMS_49
、/>
Figure SMS_50
The method comprises the steps of carrying out a first treatment on the surface of the Speed of particles->
Figure SMS_51
Position->
Figure SMS_52
The updated formula of (2) is:
Figure SMS_53
（3）
Figure SMS_54
（4）
wherein:
Figure SMS_56
is an inertial weight; />
Figure SMS_59
And->
Figure SMS_61
Respectively the firstkSub-iteration particlesiSpeed and position of (2);kthe current iteration number; acceleration factor->
Figure SMS_57
And->
Figure SMS_60
Is a non-negative constant; />
Figure SMS_63
And->
Figure SMS_64
Is distributed in->
Figure SMS_55
Random numbers in between; />
Figure SMS_58
Optimal position for individual particle history, < >>
Figure SMS_62
The best position for the particle population history.
In particular implementations, particle population sizeP=30; maximum number of iterationsN=100; inertial weight
Figure SMS_65
=1；/>
Figure SMS_66
=1.8；/>
Figure SMS_67
=1.9. And carrying out iterative optimization 100 times according to the parameters to obtain an SVM optimal parameter combination. The iterative results are shown in fig. 2.
6) And identifying the coupler stability state of the test set, and judging whether the coupler swing angle exceeds the stability limit value. The results are shown in FIG. 3 and FIG. 4.
In specific implementation, the coupler steady state identification is carried out by using a test set, and the accuracy rate of the test set identification is 97.5%. The method has higher recognition accuracy for judging whether the coupler is unstable or not, and can provide basis for judging the stability state of the coupler in the running process of the vehicle.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that the above-mentioned preferred embodiment should not be construed as limiting the invention, and the scope of the invention should be defined by the appended claims. It will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the spirit and scope of the invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.

Claims (7)

1. A heavy-duty locomotive coupler instability early warning method based on vehicle body lateral acceleration is characterized by comprising the following steps:
1) And (3) signal acquisition: collecting vehicle body transverse acceleration response signals of the train under different working conditions;
2) And (3) signal processing: the low-pass filter is adopted to carry out low-pass filtering on the vehicle body transverse acceleration response signal, so as to filter out signal noise generated by the external environment and remove abnormal acceleration values;
3) Extracting signal characteristics: extracting time domain, frequency domain and time-frequency domain characteristics of the transverse vibration acceleration signal of the vehicle body from the processed signals;
4) Data set construction: constructing a data set by using the extracted time domain, frequency domain and time-frequency domain characteristics, adding a classification label to the data set, carrying out normalization processing on the data set, and dividing a training set, a verification set and a test set;
5) Establishing a PSO-SVM model: the SVM punishment parameters and the kernel function parameters are used as optimization parameters, and the classification accuracy of the verification set is used as a fitness function, and the method comprises the following specific steps:
5.1: the SVM kernel function selects a gaussian kernel to implement the nonlinear mapping as shown in the following equation:
Figure QLYQS_1
（2）
wherein:
Figure QLYQS_2
for inputting samples, < >>
Figure QLYQS_3
Is Gaussian kernel parameters;
5.2: determining parameters to be optimized
Figure QLYQS_4
，/>
Figure QLYQS_5
The method comprises the steps of carrying out a first treatment on the surface of the Determining particle population sizePMaximum number of iterationsNInertial weight->
Figure QLYQS_6
Acceleration factor->
Figure QLYQS_7
、/>
Figure QLYQS_8
The method comprises the steps of carrying out a first treatment on the surface of the Speed of particles->
Figure QLYQS_9
Position->
Figure QLYQS_10
The updated formula of (2) is:
Figure QLYQS_11
（3）
Figure QLYQS_12
（4）
wherein:
Figure QLYQS_14
is an inertial weight; />
Figure QLYQS_16
And->
Figure QLYQS_21
Respectively the firstkSub-iteration particlesiSpeed and position of (2);kthe current iteration number; acceleration factor->
Figure QLYQS_15
And->
Figure QLYQS_18
Is a non-negative constant; />
Figure QLYQS_20
And->
Figure QLYQS_22
Is distributed in->
Figure QLYQS_13
Random numbers in between; />
Figure QLYQS_17
Optimal position for individual particle history, < >>
Figure QLYQS_19
The optimal position for particle population history;
6) Optimizing PSO-SVM model parameters: performing iterative optimization on parameters of the PSO-SVM model by using the verification set;
7) And (3) state identification: and identifying the stability state of the coupler of the test set, and judging whether the swing angle of the coupler exceeds the stability limit value, thereby judging whether the coupler is unstable.
2. The method for early warning of car coupler instability of a heavy-duty locomotive based on car body lateral acceleration according to claim 1, wherein in 1), car body lateral acceleration of an electric locomotive under the conditions of 40km/h, 60km/h, different longitudinal impact forces and different traction/braking forces is acquired at 200Hz sampling frequency by establishing a locomotive-truck coupling dynamics simulation model.
3. The method for early warning of coupler instability of a heavy-duty locomotive based on lateral acceleration of the locomotive body according to claim 1, wherein in 2), the cut-off frequency of the low-pass filter is 80Hz, and the abnormal acceleration value refers to a measured value deviation caused by a sensor test fault.
4. The method for early warning of coupler instability of a heavy-duty locomotive based on lateral acceleration of the locomotive body according to claim 1, wherein in 3), the time domain features comprise maximum value, minimum value, average value, peak value, rectified average value, variance, standard deviation, kurtosis, skewness, root mean square, margin factor, pulse factor, waveform factor and peak factor; the frequency domain features include: center of gravity frequency, root mean square frequency, frequency variance, frequency standard deviation; the time-frequency characteristics include: singular spectrum entropy, power spectrum entropy, energy entropy.
5. The method for early warning of coupler instability of heavy-duty locomotive based on lateral acceleration of the locomotive body as claimed in claim 1, wherein in 4), the swing angle of the coupler is larger than that of the coupler
Figure QLYQS_23
Sample label is 1, and coupler swing angle is less than or equal to +.>
Figure QLYQS_24
The label is 0, and the training set sample is set to be +.>
Figure QLYQS_25
The normalization process is shown as formula (1):
Figure QLYQS_26
（1）
wherein:
Figure QLYQS_27
for normalized data, ++>
Figure QLYQS_28
For the data to be normalized in the sample, < > a->
Figure QLYQS_29
And->
Figure QLYQS_30
Respectively maximum and minimum in the dataset.
6. The method for early warning of coupler instability of heavy-duty locomotive based on lateral acceleration of the locomotive body according to claim 1 or 5, wherein the proportion of training set, verification set and test set is 320:50:40.
7. the method for early warning of coupler instability of heavy-duty locomotive based on lateral acceleration of the locomotive body according to claim 1, wherein the particle population size p=30; maximum number of iterations n=100; inertial weight
Figure QLYQS_31
=1；/>
Figure QLYQS_32
=1.8；/>
Figure QLYQS_33
=1.9。/>
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