CN102496064B - Method for acquiring unevenness of track - Google Patents

Method for acquiring unevenness of track Download PDF

Info

Publication number
CN102496064B
CN102496064B CN201110393851.3A CN201110393851A CN102496064B CN 102496064 B CN102496064 B CN 102496064B CN 201110393851 A CN201110393851 A CN 201110393851A CN 102496064 B CN102496064 B CN 102496064B
Authority
CN
China
Prior art keywords
track
vehicle
irregularity
static
genetic algorithm
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201110393851.3A
Other languages
Chinese (zh)
Other versions
CN102496064A (en
Inventor
史红梅
余祖俊
朱力强
郭保青
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Jiaotong University
Original Assignee
Beijing Jiaotong University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Jiaotong University filed Critical Beijing Jiaotong University
Priority to CN201110393851.3A priority Critical patent/CN102496064B/en
Publication of CN102496064A publication Critical patent/CN102496064A/en
Application granted granted Critical
Publication of CN102496064B publication Critical patent/CN102496064B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Landscapes

  • Machines For Laying And Maintaining Railways (AREA)

Abstract

The invention relates to a method for acquiring unevenness of a track, which comprises the following steps that: vehicle vibration response data and orbit dynamic unevenness data detected by a track inspection vehicle are used for estimating the static unevenness of the track by adopting genetic algorithm to be combined with vehicle track coupling model and selecting the least square criterion instruction genetic algorithm, so sum of the error square between the theoretic output value of the calculation model and vehicle system displacement, speed, acceleration and track dynamic unevenness measured by the track inspection vehicle is minimized, and a best track static unevenness value can be obtained. Considering different vehicle track coupling models of different running trains, track vibration response which is possibly experienced by the train when the train passes through the track is obtained through simulation calculation, and then the track dynamic unevenness is calculated through the sum of the static unevenness and the steel track dynamic response, so scientific evidence can be provided for estimating the running safety and comfort of the train as well as track maintenance.

Description

A kind of acquisition methods of track irregularity
Technical field
The present invention relates to a kind of acquisition methods of track irregularity.
Background technology
Along with at a high speed, the developing rapidly of heavy haul railway, the dynamic action between rolling stock and track structure is strengthened day by day, the vehicle causing thus and track vibration, fatigue breakdown are more serious.Track irregularity is a kind of aggravation wheel-rail force, vibrative main outside excitation source to rolling stock system, and track dynamic irregularity detects the main regular detection that relies on track checking car and synthetic detection vehicle both at home and abroad at present.Can be divided into static track irregularity and dynamic track irregularity by detection mode.Static track irregularity refers to the track irregularity not having under rail wheeling action, and dynamic track irregularity refers to that the track irregularity under rail wheeling action detects.Dynamic track irregularity is the track condition that really affects train operating safety and stable operation.
Track dynamic irregularity detects the main regular detection that relies on track checking car and synthetic detection vehicle both at home and abroad at present.Because the various trains of track checking car, synthetic detection vehicle and actual operation there are differences in speed, axle weight, suspension parameter, the track irregularity data that therefore detect vehicle Dynamic Acquisition can not reflect track irregularity state when vehicle in use passes through.
Classical dynamics of vehicle is taking Vehicular system as research object, supposes that rail system is non-yielding prop, does not consider the dynamic effects of rail system to Vehicular system, and track irregularity is considered as to the outside excitation of Vehicular system.Along with the raising of train running speed, the aggravation of wheel track dynamic action, the research method that vehicle, two subsystems of track are independently got up can not solve the INTERACTION PROBLEMS of this complex large system.The a lot of scholars of recent domestic have carried out a large amount of theoretical researches about vehicle-orbit coupling dynamics field successively, have obtained many achievements.Zhai Wan penetrating judgment is awarded at the Space Coupling model of having set up passenger vehicle-track, lorry-track, locomotive-track, and wherein rail portion adopts rail-sleeper-railway roadbed three layer model; Feng Qingsong adopts analytical method to analyze under irregularity condition high speed railway track structural vibration and train speed, the impact of track irregularity on Ballast track structural vibration; Lei Xiaoyan teaches taking TGV vehicle as example, by setting up the vertical coupling model of stock rail, has analyzed the Vehicular vibration response of friction speed, different line conditions under the uneven excitation of track.Above-mentioned Most scholars is all to utilize stock rail coupling model to carry out the Response Analysis of stock rail, using track irregularity as known input stimulus.
Summary of the invention
The present invention proposes a kind of track irregularity acquisition methods, the estimation of track irregularity state while passing through to realize various trains.
Vehicular vibration response data and track dynamic irregularity data that first the present invention utilizes track checking car to detect, adopt genetic algorithm to combine with stock rail coupling model and estimate track static track irregularity.Then in conjunction with the stock rail coupling models of different operation trains, by simulation calculation obtains train by time the track dynamic irregularity that may experience, keep in repair scientific basis be provided for the security of assessment train operation and comfortableness and the maintenance of way.
Object of the present invention is achieved through the following technical solutions:
An acquisition methods for track irregularity, the method is as follows:
1) quality of initialization track checking car and track, rigidity, damping parameter matrix;
2) produce track static track irregularity initial population;
3), by static track irregularity initial population substitution stock rail coupling model, utilize numerical method to obtain the vibratory response of Vehicular system;
4) according to genetic algorithm objective function, i.e. error and minimum between the corresponding measured value of model output valve and model output valve, calculates the fitness of each individuality in population;
5) individuality in population is carried out to selection, intersection, the mutation operation of genetic algorithm, obtain population of new generation;
6) judge whether to arrive evolution end condition, obtain track static track irregularity optimal value;
7) utilize the track static track irregularity obtaining, in conjunction with stock rail coupling model, the vibration displacement while obtaining actual vehicle in use through track, obtains track dynamic irregularity.
Further, described step 1) in parameter comprise quality, damping and the stiffness matrix of track checking car, track.
Further, described step 3) in be to utilize criterion of least squares method to set up the genetic algorithm objective function of population.
Further, described step 6) in end condition be: judge whether the evolutionary generation in genetic algorithm equals 300.
The invention has the advantages that:
The present invention utilizes genetic algorithm to combine with stock rail coupling model and has proposed a kind of new obtain manner of track static track irregularity, the real track dynamic irregularity that may experience can obtain runing train operation accurately time, for maintenance of way maintenance and security of operation provide support.
Brief description of the drawings
The track static track irregularity of Fig. 1 based on genetic algorithm estimated flow process;
The track irregularity estimation scheme of Fig. 2 based on genetic algorithm and stock rail coupling model;
Fig. 3 track static track irregularity estimated result;
Fig. 4 track dynamic irregularity estimated result.
Embodiment
Parameter estimation is by the estimation function model to observation sample constructing variable, under a certain estimation criterion constraint condition, adopts the process of certain numerical value computing method estimation function Model Parameter.The function model that parameter estimation is constructed is generally divided into linear model and nonlinear model.Vehicle-orbit coupling model is a nonlinear system.Parameter Estimation of Nonlinear Model criterion and PARAMETERS IN THE LINEAR MODEL estimate similarly have: least-squares estimation criterion, maximum likelihood estimation criterion, Minimum Mean Square Error estimation criterion, Least absolute sum estimation criterion, Bayes' risk least estimated criterion etc.That be wherein most widely used is non-Linear least square estimation (LS).
Select non-linear least square estimation criterion herein, the track static track irregularity in the vertical coupling model of vehicle-track is estimated.Under the guidance of least-squares estimation criterion, the estimated result of static track irregularity can allow the error sum of squares minimum between output valve and the measured value of the vertical coupled system of vehicle-track.
The derivation algorithm of common non-linear least square parameter mainly contains: linear approximation method, process of iteration, search procedure, high-order local derviation direct method etc.The algorithm of leading for the nonlinear model the demand that can not lead all can not solve.Newton iteration is local convergence simultaneously, required parameter estimation local minimum often.Have a kind of algorithm of directly separating in model space random search can effectively overcome these problems, the method does not need differentiate, and generally has globally optimal solution.These methods mainly comprise Monte Carlo (Monte Carlo) method, simulated annealing (Simulated Annealing) and genetic algorithm (Genetic Algrithms).
The principle of the invention is Vehicular vibration response data and the track dynamic irregularity data of first utilizing track checking car to detect, adopt genetic algorithm to combine with stock rail coupling model, select criterion of least squares to instruct genetic algorithm to estimate track static track irregularity, make the theoretical output valve of computation model and the error sum of squares minimum of Vehicular system displacement, speed, acceleration and track dynamic irregularity that track checking car is measured, estimate track static track irregularity.Then in conjunction with the different stock rail coupling models of runing trains, by simulation calculation obtain train by time the track vibration response that may experience, and then calculate track dynamic irregularity by static track irregularity and rail dynamic response sum, for the assessment security of train operation and comfortableness and maintenance of way maintenance provide scientific basis.
Below in conjunction with accompanying drawing, the present invention is obtained to track irregularity and do specific description.
According to vehicle-orbit coupling kinetic model, system dynamics equation can be write as to following form:
[ M ] { X · · } + [ C ] { X · } + [ K ] { X } = { Q } { Q } = f ( { X } m { X · } , { X · · } , { Z 0 } ) - - - ( 1 )
{ Z in formula 0it is track static track irregularity value.
Because whole system is large-scale Nonlinear System of Equations, we cannot directly parse track static track irregularity { Z 0, but this problem can be converted into the Parameter Estimation Problem of model.In the case of mass matrix, damping matrix and the stiffness matrix of Vehicular system and rail system is definite, different track static track irregularities, can obtain the response of different vehicles-rail system by the numerical integration of second-order equation group.Therefore, vehicle-track coupling system simply can be described as:
X′=L(M,C,K,Z 0)+ΔE (2)
The measured value of the dynamic response of X ' expression vehicle-rail system in formula; L (M, C, K, Z 0) expression vehicle-track coupling system model; Δ E is error.So, track static track irregularity { Z 0estimation problem can be converted into the Parameter Estimation Problem based on vehicle-orbit coupling model.
If track static track irregularity Z 0scope be-10mm ~ 10mm.The detected value xk_r of displacement vector x (1: 10), velocity v (1: 10), acceleration a (1: 10) and the track dynamic irregularity of known vehicle system.The software flow pattern of genetic algorithm estimation track static track irregularity as shown in Figure 1.1. initialization track checking car and track are known quality, damping and stiffness matrix; 2. initialization wheel under track static track irregularity population; 3. adopt the method for criterion of least squares to evaluate each individuality, make system responses and measurement error quadratic sum minimum; 4. selection, intersection, the variation three of carrying out genetic algorithm operate greatly; 5. judge whether to arrive end condition.
After the track static track irregularity obtaining according to genetic algorithm optimization, using track static track irregularity as input, obtain the rail dynamic displacement under vehicle in use effect in conjunction with vehicle in use orbit coupling model, with the track static track irregularity of estimating be added can obtain operation train by time track irregularity state.
Below in conjunction with Fig. 2 and embodiment, the present invention is further detailed explanation.
The vertical nonlinear model of passenger vehicle-track that vehicle-orbit coupling model adopts in an embodiment of the present invention, is made up of auto model and model trajectory.Vehicle simulation is become to one in auto model and run on the multi-rigid-body system on track structure with speed v, by car body, trailing or leading bogie and wheel to forming, considered car body vertical vibration, nod, the vertical vibration of bogie, nod, take turns right vertical vibration, totally 10 degree of freedom.Model trajectory adopts rail-sleeper-railway roadbed-roadbed three-decker, and wherein rail adopts the point-supported endless Eular of elasticity beam, and sleeper adopts rigid unit, and railway roadbed is separated into railway roadbed module unit in the vertical.Vehicular system is realized by vertical contact of wheel track with the vertical coupling of rail system, is embodied in the vertical acting force between wheel track, and what adopt at embodiment is the most classical effective Hertz nonlinear elasticity contact model.
In embodiment, adopt hot-short parameter and high-speed line parameter as shown in table 1-table 2.
Table 1 hot-short parameter
Table 2 high-speed line parameter
Describe the acquisition process of track irregularity in detail below in conjunction with embodiment.
(1) produce initial population
In the space of separating, the individuality that produces at random some forms initial population, and individual number is exactly the size of population.The size of initial population and diversity directly have influence on the search efficiency of genetic algorithm.Population size n should not be too little also should not be too large.The too little meeting of population size n declines the Optimal performance of genetic algorithm, adopts larger population size can reduce genetic algorithm and is absorbed in the chance of locally optimal solution, but this means that computation complexity increases.The Population Size of track static track irregularity is taken as 20 in the present embodiment, and dimension NVAR is taken as 4, corresponding four wheels under track irregularity.
(2) calculate fitness function
According to the criterion of least squares of parameter estimation, set up the objective function of genetic algorithm.Objective function is namely according to the variance of the error of the theoretical value of the vertical coupling model output of the corresponding vehicle-track of individuality and measured value.Target function value is less, and individual adaptive value is just larger.The computing formula of objective function is:
fitfun = Δ E 2
= Σ i = 1 10 w x i ( x i ′ - x i ) 2 + w v i ( v i ′ - v i ) 2 + w A i ( A i ′ - A i ) 2 + w ( xk _ ei - xk _ r ) 2 - - - ( 3 )
In formula, x ' i, v ' i, A ' iwith xk_ei be that the vertical coupling model of i individual corresponding vehicle-track calculates Vehicular system displacement, speed, acceleration and the track dynamic irregularity of output, be to utilize Newmark prediction-correction method integration to obtain; x i, v i, A iwith xk_r be known Vehicular system displacement, speed, acceleration and track dynamic irregularity; W is weight matrix.
(3) select operation
Adopt even sequencing selection operator, adopt optimum conversation strategy, preserve in population 10% optimum individual number, these optimum individual numbers do not participate in crossing operation and variation computing, but with it replace the minimum individuality of fitness producing in Ben Dai colony after the operations such as intersection, variation.
(4) interlace operation
Adopt discrete intersection recombination form, discrete recombination can produce a mask table and decide which father is individual to contribute for filial generation.
(5) mutation operation
The genetic algorithms use that track static track irregularity is estimated be the operation of non-uniform mutation, original variable is being increased to a disturbed value.Computing formula is:
variable=variable+range×delta
Delta determines the precision of mutation operator energy optimum positioning value, and the minimum value of selecting delta is herein 2 -19.
The estimated result of the track static track irregularity obtaining according to genetic algorithm optimization as shown in Figure 3.Blue line is the track static track irregularity of estimating, red line is real track static track irregularity, can find out, estimated value and true value overlap substantially, and their related coefficient is 0.97, and standard error is 0.1752mm.
(6) track dynamic irregularity when calculating vehicle in use passes through
Using the track static track irregularity obtaining by genetic algorithm as initial conditions, in the stock rail coupling model of substitution vehicle in use, obtain the dynamic response of stock rail by numerical integration algorithm, can obtain the dynamic displacement of rail under vehicle in use effect, by with initial static track irregularity be added can obtain vehicle in use by time dynamic track irregularity.Fig. 4 is track dynamic irregularity estimated result.
The present invention can set up on the basis of stock rail coupling model and track checking car detection data, the estimation of track irregularity while realizing a kind of different train operation in conjunction with genetic algorithm, simulation result shows, the variation that adopts the track static track irregularity of this algorithm estimation and the dynamic response of vehicle-rail system can well catch up with true value, has realized the estimation of circuit time of day when dissimilar train is passed through.

Claims (3)

1. an acquisition methods for track irregularity, is characterized in that, the method is as follows:
1) quality of initialization track checking car and track, rigidity, damping parameter matrix;
2) produce track static track irregularity initial population;
3), by static track irregularity initial population substitution vehicle-orbit coupling model, utilize numerical method to obtain the vibratory response of Vehicular system;
Described vehicle-orbit coupling model state into:
X′=L(M,C,K,Z 0)+ΔE (2)
The measured value of the dynamic response of X ' expression vehicle-rail system in formula; L (M, C, K, Z 0) expression vehicle-track coupling system model; M, C, K, Z 0represent mass matrix, damping matrix, stiffness matrix and track static track irregularity value; Δ E is error;
4) according to genetic algorithm objective function, i.e. error and minimum between the corresponding measured value of model output valve and model output valve, calculates the fitness of each individuality in population;
5) individuality in population is carried out to selection, intersection, the mutation operation of genetic algorithm, obtain population of new generation;
6) judge whether to arrive evolution end condition, obtain track static track irregularity optimal value;
7) using the track static track irregularity obtaining by genetic algorithm as initial conditions, in vehicle-orbit coupling model of substitution vehicle in use, obtain the dynamic response of stock rail by numerical integration algorithm, can obtain the dynamic displacement of rail under vehicle in use effect, by with initial static track irregularity be added can obtain vehicle in use by time dynamic track irregularity;
The step that described genetic algorithm obtains track static track irregularity is:
1. initialization track checking car and track are known quality, damping and stiffness matrix;
2. initialization wheel under track static track irregularity population;
3. adopt the method for criterion of least squares to evaluate each individuality, make system responses and measurement error quadratic sum minimum;
4. selection, intersection, the variation three of carrying out genetic algorithm operate greatly;
5. judge whether to arrive end condition.
2. the acquisition methods of a kind of track irregularity according to claim 1, is characterized in that, described step 4) in be to utilize criterion of least squares method to set up the genetic algorithm objective function of population.
3. the acquisition methods of a kind of track irregularity according to claim 1, is characterized in that, described step 6) in end condition be: judge whether the evolutionary generation in genetic algorithm equals 300.
CN201110393851.3A 2011-12-01 2011-12-01 Method for acquiring unevenness of track Active CN102496064B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201110393851.3A CN102496064B (en) 2011-12-01 2011-12-01 Method for acquiring unevenness of track

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201110393851.3A CN102496064B (en) 2011-12-01 2011-12-01 Method for acquiring unevenness of track

Publications (2)

Publication Number Publication Date
CN102496064A CN102496064A (en) 2012-06-13
CN102496064B true CN102496064B (en) 2014-10-29

Family

ID=46187889

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201110393851.3A Active CN102496064B (en) 2011-12-01 2011-12-01 Method for acquiring unevenness of track

Country Status (1)

Country Link
CN (1) CN102496064B (en)

Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105139086B (en) * 2015-08-13 2018-09-21 杭州电子科技大学 Track transition Amplitude Estimation method based on optimization confidence rule-based reasoning
CN107264575A (en) * 2017-07-28 2017-10-20 合肥杰代机电科技有限公司 The detection method of track flatness
CN109639612B (en) * 2018-11-30 2021-03-30 兰州交通大学 ZPW-2000 signal demodulation method based on nonlinear least square method
CN110874450B (en) * 2019-11-20 2021-10-29 武汉理工大学 Railway bridge track irregularity calculation method based on vehicle-mounted monitoring
CN112766556B (en) * 2021-01-13 2022-04-01 北京交通大学 Automatic railway track historical maintenance identification method based on Bayesian information criterion
CN113032907B (en) * 2021-03-26 2023-07-25 北京交通大学 Method and system for correcting shaking car disease data deviation based on waveform correlation
CN113221212B (en) * 2021-04-27 2024-04-02 江苏韦尔汀轨道工程技术有限公司 Rail welding smoothness state evaluation management method
CN113446982B (en) * 2021-06-24 2023-01-24 中国铁道科学研究院集团有限公司 Dynamic calibration method and device for track geometry detection system
CN114819309B (en) * 2022-04-13 2023-04-18 成都理工大学 Steel rail smooth state optimization method
CN115906497B (en) * 2022-12-05 2024-03-26 中国铁路设计集团有限公司 Method for immediately acquiring track dynamic irregularity in track traffic line section
CN116258040B (en) * 2022-12-30 2024-01-23 武汉理工大学 Track irregularity detection method
CN115758061B (en) * 2023-01-10 2023-04-14 西南交通大学 Track irregularity fine adjustment method based on adjacent sleeper coupling analysis calculation

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101051393A (en) * 2007-05-18 2007-10-10 西南交通大学 Visual simulating method for dynamic contact of railway rolling stock wheel and rail
CN101697175A (en) * 2009-10-26 2010-04-21 华东交通大学 Simulated prediction method for rail transit noise
CN102032876A (en) * 2010-11-25 2011-04-27 北京交通大学 Method for detecting using state of multi-span continuous beam of existing railway

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7610152B2 (en) * 2005-05-04 2009-10-27 Lockheed Martin Corp. Train navigator with integral constrained GPS solution and track database compensation

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101051393A (en) * 2007-05-18 2007-10-10 西南交通大学 Visual simulating method for dynamic contact of railway rolling stock wheel and rail
CN101697175A (en) * 2009-10-26 2010-04-21 华东交通大学 Simulated prediction method for rail transit noise
CN102032876A (en) * 2010-11-25 2011-04-27 北京交通大学 Method for detecting using state of multi-span continuous beam of existing railway

Also Published As

Publication number Publication date
CN102496064A (en) 2012-06-13

Similar Documents

Publication Publication Date Title
CN102496064B (en) Method for acquiring unevenness of track
CN104155968B (en) A kind of small fault diagnostic method for bullet train suspension system executor
Zhang et al. Establishment and validation of a locomotive–track coupled spatial dynamics model considering dynamic effect of gear transmissions
CN103852269B (en) Bullet train runs kinetic parameter detection method
CN104765916A (en) Dynamics performance parameter optimizing method of high-speed train
Spiryagin et al. Control system for maximum use of adhesive forces of a railway vehicle in a tractive mode
Zhang et al. Symplectic random vibration analysis of a vehicle moving on an infinitely long periodic track
CN103246200B (en) A kind of motor train unit synchronization and tracking control method based on distributed model
WO2016098773A1 (en) Railway vehicle state monitoring device
Jin et al. Probabilistic evaluation approach for nonlinear vehicle–bridge dynamic performances
CN110377986B (en) Method for predicting side grinding of outer rail of small-radius curve of subway
Mızrak et al. Determining effects of wagon mass and vehicle velocity on vertical vibrations of a rail vehicle moving with a constant acceleration on a bridge using experimental and numerical methods
Zhang et al. A novel approach for decreasing driving energy consumption during coasting and cruise for the railway vehicle
CN111444574A (en) Sensor layout optimization method based on dynamic analysis
Hu et al. Determination of the critical defect and fatigue life of high-speed railway axles under variable amplitude loads
DUMITRIU INFLUENCE OF THE VERTICAL SUSPENSION ON THE VIBRATION BEHAVIOR IN THE RAILWAY VEHICLES.
Shi et al. Estimation of cement asphalt mortar disengagement degree using vehicle dynamic response
Zhou et al. LQG control for the integrated tilt and active lateral secondary suspension in high speed railway vehicles
CN103425827B (en) A kind of simulating analysis of train wheel flat
Sun et al. The parameter identification of metro rail corrugation based on effective signal extraction and inertial reference method
Li et al. Dynamical Effect Investigations of Component’s Internal Interface by Using Techniques of Rigid‐Flex Coupling Simulation
CN105365848B (en) EMU wheel set maintenance workbench integrated with flaw detection and technique
Wu et al. Train‐Bridge Dynamic Behaviour of Long‐Span Asymmetrical‐Stiffness Cable‐Stayed Bridge
CN105160103A (en) Collaborative optimization method of one-line and two-line vertical suspension damping ratio of high-speed railway vehicle
CN117313559B (en) Data-driven vehicle track coupling dynamics method

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C14 Grant of patent or utility model
GR01 Patent grant