CN113656985B

CN113656985B - Modeling method, fault diagnosis method and device for valve element in braking system

Info

Publication number: CN113656985B
Application number: CN202111021650.0A
Authority: CN
Inventors: 林硕; 李新桥
Original assignee: Shanghai Rentong Electronic Technology Co ltd
Current assignee: Shanghai Rentong Electronic Technology Co ltd
Priority date: 2021-09-01
Filing date: 2021-09-01
Publication date: 2024-03-15
Anticipated expiration: 2041-09-01
Also published as: CN113656985A

Abstract

The valve and a peripheral gas circuit of the valve are used as a valve system, a mathematical simulation model is built by utilizing a mathematical formula matched with the internal structure of the valve system based on the topological structure of the valve system, and a corresponding mathematical model is built according to the self structural characteristics of the valve system, so that the valve system simulation model accords with the structural characteristics of the valve system, and the accuracy of a simulation result is improved. The fault diagnosis method of the valve element is based on the valve system simulation model after correction under the normal state, and candidate fault item parameters are injected to obtain the valve system simulation model under the fault state. Calculating an optimal fault item parameter value through a genetic algorithm, and determining a fault type according to a fault item to which the optimal fault item parameter belongs; and meanwhile, determining the fault degree according to the fault item parameter value. According to the scheme, the fault tracing and fault degree tracing of the valve element are realized, and the maintenance cost of the brake system is effectively reduced.

Description

Modeling method, fault diagnosis method and device for valve element in braking system

Technical Field

The invention belongs to the technical field of software simulation, and particularly relates to a modeling method, a fault diagnosis method and a fault diagnosis device for valve elements in a braking system.

Background

In the running process of a vehicle (such as a rail transit vehicle), serious problems such as vehicle stop, off-line warehouse returning and the like are caused by the failure of valve elements in a vehicle braking system.

For the fault simulation reproduction of the valve, commercial software (such as AmeSim) is generally adopted for simulation, and the valve model base has a complete valve base part model base, so that modeling simulation can be carried out on various valves and even pneumatic systems simply, conveniently and quickly. The fault characteristics of valve elements in the braking system are reproduced by modifying the configurable parameters in the model, and a fault source is estimated according to the fault characteristics, so that the braking system design selection and the structural design of a single valve are guided.

However, for the fault simulation method based on commercial software, the defect is that the internal simulation model of the basic element library is fixed, parameters can only be configured, and the structure of the digital formula cannot be modified, so that the simulation model cannot be consistent with the actual running state of the valve, i.e. the built simulation model has low accuracy.

In addition, the commercial software can only simulate the fault phenomenon, qualitatively analyze the fault cause, and can not quantitatively analyze the fault cause.

Disclosure of Invention

In view of the above, the present application aims to provide a modeling method, a fault diagnosis method and a device for valve elements in a brake system, so as to solve the above technical problems, and the disclosed technical solution is as follows:

in a first aspect, the present application provides a method for modeling a valve-like element in a brake system, comprising:

based on a valve included in a valve system and a connection structure of peripheral gas circuit elements of the valve, obtaining a topological structure of the valve system, wherein the valve system is equivalent to a pipeline, an orifice, a volume chamber, a spring, a damping and a valve core;

modeling by using algebraic formulas of circular tube laminar flow, small hole throttling and ideal gas state equation aiming at a small flow channel of the valve system;

modeling with a thermodynamic differential equation describing a state quantity variation relationship for a mass flow channel of the valve system, wherein the state quantity includes a gas temperature, a density, and a pressure; the valve core, the spring and the damping are equivalent to a spring vibrator dynamic model;

and obtaining a valve system simulation model based on the mathematical model corresponding to the small flow channel and the large flow channel and the spring vibrator dynamic model.

Optionally, the method further comprises:

and calibrating the valve system simulation model in a normal state by utilizing a genetic algorithm to-be-calibrated key parameters to obtain a corrected valve system simulation model, wherein a simulation curve of valve outlet pressure of the valve system simulation model is matched with an actual measurement curve.

Optionally, the key parameters to be calibrated in the normal state include: the heat exchange coefficient of the inner wall of the brake cylinder, the valve port sectional area of the air inlet flow channel, the valve port sectional area of the exhaust flow channel and the length of a connecting pipeline between the throttle hole on the large flow channel and the brake cylinder.

Optionally, the calibrating the valve system simulation model in the normal state by using a genetic algorithm to calibrate the key parameters to be calibrated to obtain a corrected valve system simulation model, which includes:

establishing an initial population corresponding to the key parameters to be calibrated, wherein the initial population comprises a plurality of population individuals, and each population individual comprises a parameter value corresponding to the parameters to be calibrated;

calculating valve outlet pressure simulation data corresponding to each population of individuals by using the valve system simulation model;

calculating residual calculation results between simulation data of valve outlet pressure corresponding to each population individual and measured data of the valve system in a normal state based on the valve system simulation model;

Selecting, crossing and mutating population individuals in the initial population according to the residual error calculation result to obtain a new population, and repeatedly executing the step aiming at the new population until the residual error calculation result reaches a convergence requirement to obtain a target parameter value corresponding to the key parameter to be calibrated;

substituting the target parameter value corresponding to the key parameter to be calibrated into the valve system simulation model in a normal state to obtain a corrected valve system simulation model.

In a second aspect, the present application further provides a method for diagnosing a failure of a valve element in a brake system, including:

injecting a candidate fault item parameter based on the corrected valve system simulation model to obtain a valve system simulation model of a fault state, wherein the corrected valve system simulation model is obtained by using the method of a possible implementation manner in the first aspect;

and carrying out parameter optimization on candidate fault item parameters by utilizing a genetic algorithm aiming at the valve system simulation model of each fault state to obtain the optimal fault item parameters of which the simulation data and the measured data of the valve outlet pressure are matched.

Optionally, the valve system simulation model for each fault state performs parameter optimization on candidate fault parameters by using a genetic algorithm to obtain optimal fault parameters of which simulation data and measured data of valve outlet pressure are matched, including:

For any valve system simulation model in the fault state, carrying out parameter optimization on candidate fault item parameters of the valve system simulation model in the fault state by utilizing a genetic algorithm to obtain an optimal residual calculation result between simulation data and measured data of valve outlet pressure;

and determining the candidate fault item parameter corresponding to the optimal residual error calculation result with the minimum value as the optimal fault item parameter.

Optionally, the method further comprises:

determining a fault item corresponding to the optimal fault item parameter as a fault type of the valve system;

and determining the fault degree of the valve system based on the optimal parameter value of the optimal fault term parameter.

Optionally, for any valve system simulation model in the fault state, parameter optimization is performed on candidate fault item parameters of the valve system simulation model in the fault state by using a genetic algorithm to obtain an optimal residual calculation result between simulation data and measured data of valve outlet pressure, including:

establishing an initial population of the candidate fault item parameters, wherein the initial population comprises a plurality of population individuals, and each population individual comprises a parameter value corresponding to the candidate fault item parameters;

Calculating residual calculation results between simulation data of valve outlet pressure corresponding to each population individual and measured data of the fault state based on the valve system simulation model of the fault state;

and selecting, intersecting and mutating population individuals in the initial population according to the residual calculation result to obtain a new population, and repeatedly executing the step aiming at the new population until the residual calculation result reaches a convergence requirement to obtain an optimal residual calculation result corresponding to the valve system simulation model in the fault state.

In a third aspect, the present application further provides a modeling apparatus for a valve class element in a brake system, including:

the valve system structure acquisition module is used for acquiring the topological structure of the valve system based on the connection structure of a valve and a peripheral gas circuit element of the valve, wherein the valve system is equivalent to a pipeline, an orifice, a volume chamber, a spring, a damping and a valve core;

the small flow channel modeling module is used for modeling a pipeline, an orifice and a volume chamber which are included in a small flow channel of the valve system by using algebraic formulas of circular pipe laminar flow, orifice throttling and ideal gas state equation;

A mass flow channel modeling module for modeling, for a conduit, an orifice, and a volume chamber included in a mass flow channel of the valve system, using a thermodynamic differential equation describing a state quantity variation relationship, wherein the state quantity includes a gas temperature, a density, and a pressure;

the spring vibrator model building module is used for equivalent of the valve core, the spring and the damping into a spring vibrator dynamic model;

and the valve system model building module is used for obtaining a valve system simulation model based on the mathematical model corresponding to the small flow channel and the large flow channel and the spring vibrator dynamics model.

In a fourth aspect, the present application further provides a fault diagnosis device for a valve element in a brake system, including:

the fault state model building module is used for injecting a candidate fault item parameter based on the corrected valve system simulation model to obtain a valve system simulation model in a fault state, wherein the corrected valve system simulation model is obtained by the device in the third aspect;

and the fault item parameter optimizing module is used for carrying out parameter optimization on candidate fault item parameters by utilizing a genetic algorithm aiming at the valve system simulation model in each fault state to obtain the optimal fault item parameters of which the simulation data and the measured data of the valve outlet pressure are matched.

The invention provides a modeling method, a fault diagnosis method and a device for valve elements in a braking system, wherein the modeling method for the valve elements takes a valve and peripheral gas circuit elements thereof as a system, and the structure of the valve system is equivalent to elements such as a pipeline, an orifice, a volume chamber, a spring, a damping valve core and the like. And further obtaining the topological structure of the valve system based on the structural relationship between the valve body and the peripheral gas circuit elements. The large-flow through valve comprises a small-flow channel and a large-flow channel, and the small-flow channel is modeled by algebraic formulas such as circular pipe laminar flow, small hole throttling, ideal gas state equation and the like. For large flow channels, modeling is performed using thermodynamic differential equations describing the state quantity change relationships. The spring, the damping and the valve core are equivalent to a spring vibrator dynamic model, and finally the valve system simulation model is obtained. Therefore, according to the scheme, a mathematical simulation model is built according to the internal structure of the valve body and the peripheral gas circuit of the valve body and a mathematical formula matched with the internal structure, and a corresponding mathematical model is built according to the self structural characteristics of the valve system, so that the valve system simulation model built by the scheme accords with the structural characteristics of the valve system, and the accuracy of a simulation result is improved.

Further, parameter calibration is carried out on key parameters of the simulation model in a normal state, so that a more accurate valve system simulation model is obtained, and the accuracy of a simulation result of the valve system is further improved.

According to the fault diagnosis method for the valve element, the candidate fault parameters are injected based on the corrected valve system simulation model in the normal state, and the valve system simulation model in the fault state is obtained. Then, calculating an optimal fault item parameter value through a genetic algorithm, and determining a fault type according to a fault item to which the optimal fault item parameter belongs; and meanwhile, determining the fault degree according to the fault item parameter value. By utilizing the scheme, the fault tracing and fault degree tracing of the valve element are realized, and the maintenance cost of the brake system is effectively reduced.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1A is a flow chart of a method of modeling a valve-like element provided in an embodiment of the present application;

FIG. 1B is a flow chart of a method for diagnosing a failure of a valve-like element according to an embodiment of the present application;

FIG. 2 is a schematic diagram of a high flow path of a relay valve according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of a small flow path of a relay valve according to an embodiment of the present disclosure;

FIG. 4 is an equivalent topology of a relay valve provided in an embodiment of the present application;

FIG. 5 is a schematic diagram of errors in residual calculation according to an embodiment of the present application;

FIG. 6 is a schematic diagram of a comparison between a test curve and a simulation curve corresponding to outlet pressures of a low brake position brake cylinder of a relay valve under a plurality of working conditions according to an embodiment of the present application;

FIG. 7 is a schematic diagram of a comparison between a test curve and a simulation curve corresponding to outlet pressures of a high brake position brake cylinder of a relay valve under a plurality of working conditions according to an embodiment of the present application;

FIG. 8 is a schematic diagram of a relay valve diaphragm leakage provided by an embodiment of the present application;

FIG. 9 is a schematic diagram of measured curves and simulation curves of the valve outlet pressure of the relay valve at fault A;

FIG. 10 is a schematic diagram of measured curves and simulation curves of the valve outlet pressure of the relay valve at fault B;

FIG. 11 is a block diagram of a modeling apparatus for a valve class component provided in an embodiment of the present application;

fig. 12 is a block diagram of a failure diagnosis apparatus for a valve-like element provided in an embodiment of the present application.

Detailed Description

In order to solve the technical problems, the application provides a modeling method, a fault diagnosis method and a device for valve elements in a brake system. And then, calculating by a genetic algorithm to obtain an optimal fault item parameter value in a fault state and a residual error calculated value between a simulation curve and an actually measured curve, determining the fault degree according to the fault item parameter value, and determining the fault type according to the residual error calculated value, thereby realizing the fault tracing and fault degree tracing of the valve element of the braking system and effectively reducing the maintenance cost of the braking system.

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Referring to fig. 1A, a flowchart of a modeling method for a valve element in a brake system according to an embodiment of the present application is shown, where the method may include the following steps:

s110, obtaining the topological structure of the valve system based on the valve included in the valve system and the connection structure of the peripheral gas circuit elements of the valve.

The accuracy of the model simulation results depends on knowledge of the internal structural dimensions of the valve, as well as the structural dimensions of the piping to which the valve is externally connected, and therefore the valve and its peripheral gas circuit components (i.e., piping to which the valve is externally connected) need to be modeled as a system (i.e., valve system).

Wherein the valve system is equivalent to a pipe, an orifice, a volume chamber, a spring, a damper, and a spool.

The spring element here includes a physical spring in the valve, as well as a spring element obtained by equivalent of a sealing element in the valve (a component in the valve body that acts as a seal).

According to the internal structure of the valve and the connection structure of the peripheral gas circuit elements, a topological structure between equivalent elements of the valve system is constructed, and further, according to the topological structure of the equivalent elements of the valve system and equivalent equations corresponding to the equivalent elements, a mathematical simulation model of the whole valve system is obtained, wherein the simulation model is composed of a mathematical equation set.

Valve elements with large-flow through capacity usually adopt a small-flow gas circuit to realize the inflation, exhaust and pressure maintaining control of a large-flow channel. Thus, a high flow capacity valve includes a low flow passage and a high flow passage.

Modeling is carried out on three gas path elements included in the large-flow channel and the small-flow channel respectively. In addition, the spring, the damping and the valve core are equivalent to a spring vibrator dynamic model.

S120, modeling is conducted on a small flow channel of the valve system by using algebraic formulas of circular tube laminar flow, small hole throttling and ideal gas state equation.

For example, a mathematical model of the piping elements of the small flow channel is constructed using a circular tube laminar flow-pressure equation. And constructing a mathematical model of the orifice element of the small flow channel by using a thin-wall orifice flow-pressure equation. A mathematical model of the volume chamber of the small flow channel is constructed using a variable volume chamber pressure-flow equation. It can be seen that the mathematical model of the small flow channel comprises algebraic equation sets consisting of circular tube laminar flow-pressure equation, thin-wall orifice flow-pressure equation and variable volume chamber pressure-flow equation.

S130, modeling is performed on a large flow channel of the valve system by using a thermodynamic differential equation describing the change relation of state quantities, wherein the state quantities comprise gas temperature, density and pressure.

For the large flow passage, the outlet of the valve is connected with the brake cylinder, and the modeling process of the large flow passage and the brake cylinder needs to consider a heat transfer process and a thermodynamic differential equation describing the state quantity change relation of gas temperature, density, pressure and the like. The effect of temperature variations on the brake cylinder pressure will determine the accuracy of the simulation.

For example, the mathematical model of the large flow channel is composed of a differential equation set formed by a pipeline flow thermodynamic differential equation set, an orifice pressure difference-flow differential equation set and a brake cylinder wall heat transfer equation set. Specifically, a mathematical model of a pipe element in a mass flow channel is constructed using a system of thermodynamic differential equations of the pipe flow. And constructing a mathematical model of the orifice in the large-flow channel by using an orifice differential pressure-flow differential equation set. And constructing a mathematical model of the volume chamber of the large-flow channel by using a brake cylinder wall heat transfer equation set.

And S140, the valve core, the spring and the damping of the valve are equivalent to a spring vibrator dynamic model.

The valve spool herein is a broad concept and may include the main piston, the valve disc and the valve spool of the transducer in the valve body. And the valve core-spring-damping is equivalent to a spring vibrator dynamic model.

S150, obtaining a valve system simulation model based on mathematical models corresponding to the small flow channel and the large flow channel and a spring vibrator dynamics model.

The mathematical simulation model of the whole valve system comprises the mathematical models of the small flow channel and the large flow channel and the spring vibrator dynamics model.

Furthermore, as shown in fig. 1, in order to keep the constructed simulation model consistent with the real valve system, in other embodiments of the present application, the following steps may be further included:

and S160, calibrating the key parameters to be calibrated by using a genetic algorithm for the valve system simulation model in a normal state to obtain a corrected valve system simulation model.

The simulation curve of the valve outlet pressure corresponding to the corrected valve system simulation model is matched with the actual measurement curve, wherein the matching means that the error between the simulation data and the actual measurement data is within a preset threshold range. The preset threshold range can be set according to actual conditions.

The normal state refers to a state in which the valve system can normally operate. The fault diagnosis depends on an accurate valve system simulation model, so that before the constructed valve system simulation model is used for fault diagnosis, the key parameter calibration is required to be carried out on the simulation model in a normal state, and a more accurate valve system simulation model is obtained.

In the specific implementation, the influence degree of each parameter in the model parameter set on the valve outlet pressure can be analyzed first to obtain key parameters to be calibrated under the normal state, for example, the key parameters comprise the heat exchange coefficient of the inner wall of the brake cylinder, the valve outlet sectional area of the air inlet flow channel, the valve outlet sectional area of the air outlet flow channel and the length of the connecting pipeline. The length of the connecting line here refers to the length of the connecting line between the throttle bore on the large-flow passage of the valve and the brake cylinder.

After the key parameters to be calibrated in the normal state are determined, a genetic algorithm is utilized to find out the target values of the corresponding key parameters to be calibrated when the simulation data of the obtained valve outlet pressure curve are basically consistent with the measured data under the multiple working conditions of the valve system in the normal state. When the key parameters to be calibrated of the valve system simulation model take corresponding target values, the simulation value of the valve outlet pressure output by the model is basically consistent with the actual measurement value of the valve port pressure of the valve system.

Substituting the target parameter value corresponding to the key parameter to be calibrated into the valve system simulation model in a normal state to obtain a corrected valve system simulation model. Therefore, parameters in the valve system simulation model can be consistent with the real state of the valve system, and the more accurate valve system simulation model can be obtained.

And packaging the simulation model as a function, and identifying key parameters in a normal state through a genetic algorithm. The key parameter identification process by using the genetic algorithm is as follows:

an initial population is established for a set of key parameters (including a plurality of key parameters), each individual in the population representing parameter values for each key parameter, for example, one individual including values for four key parameters of brake cylinder inner wall heat exchange coefficient, valve outlet cross-sectional area of the intake flow passage, valve outlet cross-sectional area of the exhaust flow passage, and length of the connecting line.

And (3) inputting the upstream pressure and the control pressure of the corresponding physical interfaces of the valve system simulation model, calculating simulation values of the valve outlet pressure for each individual (namely, parameter values of key parameters) in the population, and finally obtaining residual calculation results between the valve outlet pressure simulation curve and the actually measured valve outlet pressure curve of each individual in the population. Then, a new population is obtained through selection, crossing, mutation and recombination, and an optimal key parameter set is obtained through iterative calculation.

The modeling method of the valve element provided by the application takes the valve and the peripheral gas circuit element thereof as a system, wherein the valve system is equivalent to a pipeline, an orifice, a volume chamber, a spring, a damping and a valve core. And constructing a valve system simulation model based on the topological structure of the valve system. The large-flow through valve generally comprises a small-flow channel and a large-flow channel, and algebraic formulas such as circular tube laminar flow, small-hole throttling, ideal gas state equation and the like are used for modeling the small-flow channel. For large flow channels, modeling is performed using thermodynamic differential equations describing the state quantity change relationships. The spring, the damping and the valve core are equivalent to a spring vibrator dynamic model, and finally the valve system simulation model is obtained. Therefore, according to the scheme, a mathematical simulation model is built according to the structure of the valve body and the peripheral gas circuit thereof and a mathematical formula matched with the internal structure, and a corresponding mathematical model is built according to the self structural characteristics of the valve system, so that the valve system simulation model built by the scheme accords with the structural characteristics of the valve system, and the accuracy of a simulation result is improved.

Further, parameter calibration is carried out on key parameters of the simulation model in a normal state, so that key parameter values enabling a simulation curve of valve outlet pressure of the valve system simulation model to be basically consistent with an actual measurement curve are obtained, the corrected valve system simulation model in the normal state is finally obtained, and accuracy of a valve system simulation result is further improved.

After an accurate valve system simulation model is obtained, parameter optimization is carried out on the valve system in a fault state by utilizing the simulation model, so that the fault type of the valve is obtained through simulation.

Referring to fig. 1B, a flowchart of a method for diagnosing a failure of a valve element according to an embodiment of the present application is shown, where the method may include the following steps:

s210, injecting a candidate fault item parameter based on the corrected valve system simulation model to obtain a valve system simulation model of a fault state.

The corrected valve system simulation model is obtained by calibrating key parameters of the valve system simulation model in a normal state through the modeling method of the valve element.

After the corrected valve system simulation model is obtained, a possible fault term parameter (i.e., candidate fault term parameter) is injected to obtain a valve system simulation model of a fault state. Several candidate fault parameters can be used to obtain a simulation model of the valve system for several fault conditions. For example, the 2 candidate fault parameters are primarily determined through the fault phenomenon of the valve body, and then the valve system simulation model in 2 fault states is obtained through fault injection.

And S220, carrying out parameter optimization on candidate fault item parameters by utilizing a genetic algorithm aiming at the valve system simulation model of each fault state to obtain the optimal fault item parameters of which the simulation data of the valve outlet pressure is matched with the measured data.

In one embodiment of the present application, for any valve system simulation model in the fault state, parameter optimization is performed on candidate fault item parameters of the valve system simulation model in the fault state by using a genetic algorithm, so as to obtain an optimal residual calculation result between simulation data and measured data of the valve outlet pressure. Further, determining the candidate fault item parameter corresponding to the optimal residual error calculation result with the minimum value as the optimal fault item parameter.

Wherein the process of identifying the fault parameters using the genetic algorithm comprises: and calculating a simulation curve of the valve outlet pressure according to any one of the populations corresponding to the fault item parameters, and finally obtaining a residual calculation result between the valve outlet pressure simulation curve of each population individual and the valve outlet pressure curve obtained through actual measurement. And obtaining a new population through selection, crossing, mutation and recombination, and obtaining a simulation curve which is basically consistent with the valve outlet pressure actual measurement curve of the valve system in the fault state through iterative calculation, wherein the fault item parameter value corresponding to the simulation curve is an optimal fault item parameter value, and the optimal fault item parameter value is used for indicating the fault degree. And meanwhile, determining a fault item corresponding to the optimal fault item parameter value, namely the fault type of the fault membership.

According to the fault diagnosis method for the valve element, provided by the embodiment, the candidate fault item parameters are injected based on the corrected valve system simulation model in the normal state, so that the valve system simulation model in the fault state is obtained. Then, calculating an optimal fault item parameter value through a genetic algorithm, and determining a fault type according to a fault item to which the optimal fault item parameter belongs; and meanwhile, determining the fault degree according to the fault item parameter value. By utilizing the scheme, the fault tracing and fault degree tracing of the valve element are realized, and the maintenance cost of the brake system is effectively reduced.

The fault diagnosis method of the valve element provided in the present application will be described below by taking a relay valve as an example.

Referring to fig. 2 and 3, fig. 2 shows a schematic diagram of a large flow path of the relay valve, and fig. 3 shows a schematic diagram of a small flow path of the relay valve.

As shown in fig. 2 and 3, the R port is an upstream air inlet, the C port is connected with a downstream brake cylinder, the F port is connected with an air inlet interface of the converter, the Cv port is connected with pilot pressure, and the O ₃ Mouth, O ₁ Mouth, O ₂ The ports are respectively connected with the atmosphere. a. The parts b and h represent three diaphragms on the main piston, separating three variable volume chambers S, cv and C.

The black bold line in fig. 2 shows the large flow channel, i.e. the large flow main channel of the valve, and the black bold line in fig. 3 shows the small flow channel, i.e. the small flow control air path of the valve.

Wherein the main piston is O ₁ The valve disc portion at the channel and the valve core portion of the transducer are equivalent to a spring vibrator model.

Referring to fig. 4, an equivalent topology of a relay valve is shown.

As shown in fig. 4, the equivalent topology of the small flow channel gas circuit is as follows: the control of the passage switch between the port C and the variable volume chamber S is equivalent to the thin-walled orifice 1. O is added with ₃ The passage switching control between the port to the variable volume chamber S is equivalent to the thin-walled orifice 2.

F port pressure can change the position of a valve core of the converter, and a rubber sealing ring on the valve core is used for controlling a C port or O port ₃ The port communicates with the S chamber. The thin-wall orifice is turned on or off by adjusting the effective through sectional area of the thin-wall orifice.

C _v Port to variable volume chamber C _v The passage between them is equivalent to circular tube laminar flow. The gas passage between the variable volume chamber C and the valve disc is equivalently a fixed orifice.

The small flow channel gas circuit further comprises a spring oscillator model corresponding to the main piston, namely a main piston spring oscillator model, and a spring oscillator model corresponding to the valve disc, namely a valve disc spring oscillator model.

As shown in fig. 4, the equivalent topology of the high flow channel gas circuit is as follows:

valve disk and valve seat v ₂ The slit between the valve disc and the tail of the main piston rod is equivalent to a variable throttle 1, and the slit between the valve disc and the tail of the main piston rod is equivalent to a variable throttle 2.

When the main piston drives the valve disc to move to the right side integrally until the valve disc is separated from the valve seat v ₂ When the variable orifice 1 is opened (when the variable orifice 2 is in a closed state), the upstream gas enters the brake cylinder through the R port, the valve seat v2, and the C port. The main piston moves leftwards until the main piston is separated from the valve disc, the variable orifice 2 is opened (the variable orifice 1 is in a closed state) at the moment, and the gas in the brake cylinder passes through the variable orifice 2 and O from the port C ₁ The port flows out into the atmosphere.

Because of the high flow path to charge and exhaust the brake cylinder, there is a temperature change in the gas, and therefore modeling this part of the system requires consideration of thermodynamic and thermal equations.

1. Construction of valve system simulation model

(1) Simulation model establishment of small-flow channel gas circuit

For the gas circuit loop of the small flow channel, a circular tube laminar flow formula, a small hole throttling formula and an ideal gas state equation can be used for modeling the pipeline element, the throttling element and the volume chamber respectively.

1) Circular tube laminar flow-pressure equation:

in the formula (1), Q _mass For mass flow, ΔP _r Is the pressure difference between two ends of the pipeline. V is the kinematic viscosity of air fluid, d is the inner diameter of the equivalent circular tube, and l is the length of the circular tube.

2) Thin wall orifice flow-pressure equation:

In formula (2), Δp=p ₂ -p ₁ ，q _z For free air volume flow, S _eqv Is the effective through cross-sectional area of the orifice, p ₁ For absolute pressure upstream of orifice, p ₂ Is the absolute pressure downstream of the orifice. T (T) ₁ On the orificeThe thermodynamic temperature of the stream, such as may be set to the ambient temperature at the time of the test.

3) Variable volume chamber pressure-flow equation:

in the formula (3), p ₀ For initial air pressure in the chamber, p ₁ For the volume of the chamber after the end of inflation, R is the ideal gas constant 283, T is the temperature in the chamber, Q _mass Mass flow into the plenum, S _pist o _n Is the effective acting area of the piston surface, x ₀ The initial piston position of the variable-volume air chamber is that x is the current position of the air chamber piston, and x-x ₀ I.e. the displacement of the piston.

The simulation model of the small flow channel gas circuit is built based on the small flow channel gas circuit topological graph corresponding to the valve body shown in fig. 2 and the formulas (1) - (3), and is essentially an algebraic equation set containing the formulas (1) - (3), so that the solution of the equation set can be solved through algorithms such as a dichotomy and a secant method.

(2) Simulation model establishment of high-flow channel gas circuit

For a large flow passage, a valve outlet is connected with a brake cylinder, and a heat transfer process and a thermodynamic differential equation describing the state quantity change relation of gas temperature, density, pressure and the like are required to be considered in the modeling process of the main flow passage and the brake cylinder. The effect of temperature variations on the brake cylinder pressure will determine the accuracy of the simulation.

1) A system of pipe flow thermodynamic differential equations:

in the formula (4) of the present invention,is a bias of the mass of gas in the steady state flow process conduit to the pressure of the gas. />Is the partial conductance of the gas mass in the pipeline to the gas temperature in the steady-state flow process. P is p _l Is the gas pressure of the node in the pipeline, T _l For the gas temperature of the nodes in the pipeline, +.>And->The mass flow is the mass flow of Port A and Port B, wherein Port A is the inlet of a throttling element (the throttling element refers to a throttling hole and a part of pipelines connected with two ends of the throttling hole), and Port B is the outlet of the throttling element. />Is the partial guide of the internal energy of the gas in the pipeline to the pipeline pressure in the steady-state flow process. />Is the partial guide of the internal energy of the gas in the pipeline to the temperature of the gas in the pipeline in the steady-state flow process. Phi _A And phi is phi _B The energy flows into and out of the Port a and Port B Port flows. Phi _H Is the energy flowing in from the wall surface of the pipeline through a heat transfer mode. P is p _A 、p _B 、p _l Is the pressure of the node l in the pipeline, port a, port B. ρ _A 、ρ _B 、ρ _l Is the gas density at port a, port B and node i within the pipeline. S is S _pipe Is the cross-sectional area of the pipeline. Δp _Al And Δp _Bl Is the head loss caused by gas viscosity.

Below the limit of the reynolds number, i.e. laminar flow, Δp _Al And Δp _Bl The calculation can be performed by the following formula,

Formula (5)In f _shape Is a laminar flow shape factor, u _l The dynamic viscosity of the node l in the pipeline, ρ _l Is the density of the node l in the pipeline, D _h Is the hydraulic diameter of the pipeline, S _pipe Is the cross-sectional area of the pipeline L _pipe For the length of the pipeline L _eqv Equivalent line length for local drag loss.

Above the limit of the Reynolds number, i.e. turbulence, Δp _Al And Δp _Bl The calculation can be performed by the following formula,

in the formula (6), f _Darcy Is the Darcy friction factor.

2) Orifice differential pressure-flow differential equation set:

in the formula (7), C _d Flow coefficient, so, of orifice restriction _rifice Is the cross-sectional area at the inlet Port a and outlet Port B of the orifice restriction element. Δw _ABtur Is the amount of change in flow from PortA to PortB (i.e., the inlet of the throttling element) and the amount of change in flow from the throttling orifice to PortB (i.e., the outlet of the throttling element) during turbulent flow. Δw _BAtur Is the amount of flow rate change from inlet (PortA) to orifice and from orifice to outlet (PortB) during the flow from PortB to PortA in turbulent conditions. Δw _lam In the case of laminar flow, the flow rate varies from inlet to orifice and orifice to outlet.

3) Heat transfer equation for brake cylinder wall:

for the thermodynamic process of a fixed volume reservoir, only the mass balance equation and the energy balance equation in equation (4) are needed. The heat transfer equations for the brake cylinder walls are supplemented here. The heat transferred by the convection of heat between the brake cylinder wall and the inner gas and between the brake cylinder wall and the outer atmosphere can be calculated by equation (8):

Q _HC ＝k _cvt ·A·(T _A -T _B ) (8)

In the formula (8), k _cvt A is the equivalent convection heat exchange area of the brake cylinder, T is the convection heat exchange coefficient _A 、T _B Is the temperature on both sides of the convective interface.

4) Spring matrix dynamics differential equation:

the valve core-spring-damping equivalent in the valve body in the formula (9) is a spring vibrator dynamics model. In the above, m _p C is vibrator mass _p For damping, k _p For spring rate x _p For the displacement of vibrator F _w Is an external force.

The main piston spring oscillator model, the valve disc spring oscillator model and the converter valve core spring oscillator model shown in fig. 4 are all built by adopting the formulas shown in fig. 9.

For the large-flow channel gas circuit, a simulation model of the large-flow channel gas circuit is built based on a large-flow channel gas circuit topological diagram corresponding to the valve body shown in fig. 2 and the formulas (4) - (9). The nature of the simulation model belongs to a differential equation set, and after the integral form is required to be converted and discretized, a backward Euler method and a Dragon-Gregorian tower method are adopted for solving.

2. Correction of valve system simulation model under normal state

By analyzing the influence degree of each parameter in the model parameter set on the valve port output pressure (namely, the pressure of the C port), key parameters (namely, key parameters to be calibrated) in a normal state can be obtained, for example, the key parameters comprise: the heat exchange coefficient of the inner wall of the brake cylinder, the valve port sectional area of the air inlet flow channel, the valve port sectional area of the exhaust flow channel and the length of the connecting pipeline.

Wherein the heat exchange coefficient of the inner wall of the brake cylinder represents the heat convection coefficient between the wall surface of the brake cylinder and the internal gas, i.e., k in the formula (8) _cvt . Inlet air flow passageThe valve port sectional area represents the inlet sectional area of the variable orifice 1, and the valve port sectional area of the exhaust flow passage represents the inlet sectional area of the variable orifice 2, as in S in the formula (7) _orifice . The length of the connecting line represents the length of the connecting line between the variable orifice 1,2 and the brake cylinder, i.e., L in the formulas (5), (6) _pipe 。

The process of optimizing key parameters by using a genetic algorithm is as follows:

1) And establishing an initial population for the key parameters in the normal state.

For example, key parameters include: the heat exchange coefficient of the inner wall of the brake cylinder, the valve port sectional area of the air inlet flow channel, the valve port sectional area of the exhaust flow channel and the length of the connecting pipeline are selected randomly from the upper limit range and the lower limit range of each key parameter to be used as individuals in the initial population.

Wherein, a group of key parameters comprises a thermal convection coefficient kcvt, the sectional area Sorifice of the inlet of the variable orifice 1,2, and the length Lpipe of the connecting pipeline between the variable orifice 1,2 and the brake cylinder. A set of key parameters may be assigned different values, with a set of values representing an individual in a population. For example, individual 1 (kcvt, sorface, lpipe) = (1, 2, 3), individual 2 (kcvt, sorifice, lpipe) = (2, 3, 4). If the population has 20 individuals, there are 20 different groups (kcvt, sorifice, lpipe).

2) And (5) calling a valve system simulation model to calculate valve outlet pressure simulation data of each population of individuals.

The simulation model of the valve system can be written by using a C language and packaged as a function, a group of key parameter values corresponding to any group of individuals are assigned to the function, the input of the function is the upstream pressure and the control pressure of the corresponding physical interface, and the assigned function is used for calculating a simulation curve of the valve outlet (namely C port) pressure corresponding to the group of individuals.

Wherein the upstream pressure and the control pressure of the corresponding physical interface are time-varying curves, and therefore, the output valve outlet pressure is also a time-varying curve, i.e. a simulation data curve of the valve outlet pressure.

Further, residual calculation results (such as a sum of squares of residual) between the valve outlet pressure simulation curve and the valve outlet pressure measured curve of each population individual are calculated and returned to the genetic algorithm.

In one embodiment of the present application, the sum of squares of residuals is calculated as follows:

in the formula (10), as shown in FIG. 5, a solid curve represents a curve of test data, a broken curve represents a curve of simulation data, wherein i represents an i-th data point in the curve, E _vi A difference value between the brake cylinder pressure at the ith point on the test curve and the brake cylinder pressure of the simulation curve is represented, E _li The difference in the lateral axis is represented when the brake cylinder pressure at the i-th point on the test curve is equal to the brake cylinder pressure of the simulation curve. Further, a moderating factor for each individual in the population is calculated based on the sum of squares of the residuals, the moderating factor affecting the selection of individuals in the subsequent population and the outcome of the individual winnings and winnings.

3) And selecting, crossing and mutating the generation population, calling a valve system simulation model, and calculating the moderate factors of the new individuals.

4) The new population is recombined with the original population.

5) Repeating the steps 3) and 4) until the residual calculation result reaches the convergence requirement, and obtaining the optimal key parameter value in the normal state.

For example, a numerical range of the residual square sum may be preset, when the calculated residual square sum is within the numerical range, it is determined that the residual calculation result reaches the convergence requirement, and population individuals with target residual reaching the convergence requirement are determined as optimal key parameter values corresponding to the valve system in a normal state, that is, the valve system simulation model when each key parameter respectively takes the corresponding optimal key parameter value is more fit to the actual valve system, that is, the valve system simulation model at this time is more accurate.

Referring to fig. 6 and 7, fig. 6 is a schematic diagram of comparing a test curve corresponding to the outlet pressure of a low brake position brake cylinder of the relay valve under a plurality of working conditions with a simulation curve, and fig. 7 is a schematic diagram of comparing a test curve corresponding to the outlet pressure of a high brake position brake cylinder of the relay valve under a plurality of working conditions with a simulation curve.

The simulation curves in fig. 6 and 7 are obtained based on the corrected valve system simulation model in the normal state.

As shown in fig. 6 and fig. 7, after the valve system simulation model in the normal state is corrected, simulation data and measured data of the valve outlet pressure curve corresponding to the relay valve in the normal state respectively in a plurality of different working conditions (when the pilot pressure Cv of the relay valve is different in value) are all attached together, that is, the simulation curves and the measured curves corresponding to the working conditions are good in consistency.

3. Fault item parameter identification in fault conditions

Before the fault item parameters are identified, determining fault parameters possibly related to the fault phenomenon through the fault phenomenon. A plurality of possible fault parameters exist in a fault phenomenon, and the possible fault parameters (namely candidate fault parameters) are injected into the valve system simulation model after correction in a normal state one by one to obtain the valve system simulation model in the fault state. And carrying out parameter optimization on each possible fault item parameter one by utilizing a genetic algorithm, comparing residual square sums corresponding to each fault parameter, and determining the fault item parameter with the minimum residual square sum value as the optimal fault item parameter, namely determining the fault type affiliated to the fault phenomenon according to the residual calculation value, namely what fault the current valve outlet pressure response affiliated to.

Further, according to the optimal fault term parameter value, determining the fault degree, for example, the greater the deviation between the parameter value and the normal parameter range is, the greater the fault degree is; conversely, the smaller the deviation of the parameter value from the normal parameter range, the smaller the degree of failure.

The following describes the fault parameter identification process with the diaphragm leakage and the main piston spring rate anomaly of the relay valve:

for leakage caused by aging of the diaphragm element, fault injection is achieved by adding a flow passage in the form of an orifice between two variable volume chambers, as shown in fig. 8, simulating a leakage orifice on the diaphragm, adding an orifice flow passage (i.e., a vent flow passage) between the S chamber and the Cv chamber, i.e., adding an orifice flow-pressure equation in the original equation set, because of the addition of a vent flow, the corresponding flow term needs to be added to the piping connected to the orifice.

For aged spring elements and sealing elements, then the stiffness parameters in the spring vibrator dynamics model are modified.

For example, simulate a diaphragm leak hole by punching a hole in the a diaphragm, replacing the main piston spring with a higher rate spring simulates a main piston stiffness anomaly failure.

The valve outlet pressure curve (C port position) of the relay valve in the failure state was recorded by the test. And respectively identifying the aperture of the leakage hole of the membrane and the stiffness of the main piston spring through a genetic algorithm for the same fault curve.

a the aperture of the diaphragm leakage orifice represents the effective through-sectional area S of the orifice in (2) _eqv And converting the cross-sectional area into an aperture; main piston stiffness represents the stiffness coefficient k in (9) _p 。

Similar to the process of optimizing key parameters in a normal state, the process of optimizing fault item parameters by using a genetic algorithm is as follows:

1) An initial population is established for fault term parameters in a simulation model of a valve system in a fault state.

The key parameters in the fault state include newly added parameters after fault injection. For example, in fig. 8, a valve port cross-sectional area of an orifice flow passage is added between the newly added S air chamber and Cv air chamber.

2) The valve system simulation model after fault injection (i.e., the valve system simulation model for the fault condition above) is invoked to calculate valve outlet pressure simulation data for each individual.

After the valve system simulation model in the fault state is obtained, the valve system simulation model is packaged into a function, a group of key parameter values are assigned to the function, and then a simulation curve of the valve outlet pressure corresponding to the group of key parameter values is calculated.

And then, calculating a valve outlet pressure simulation curve corresponding to each population individual, and feeding back to a genetic algorithm.

And further calculating a moderate factor of each individual according to the residual square sum, wherein the moderate factor influences the selection of the individual in the subsequent population and the individual winner and winner result.

3) And selecting, crossing and mutating the current generation population, calling a valve system simulation model in a fault state, and calculating the moderate factor of the new individual.

4) The new population is recombined with the original population.

5) Repeating the steps 3) and 4) until the target residual reaches the convergence requirement, and obtaining the optimal fault item parameter value matched with the actual fault state.

The recognition results of the parameters for the main piston stiffness anomaly, a diaphragm leakage are shown in table 1:

TABLE 1

As shown in table 1, for fault a, the sum of squares residuals for the primary piston spring rate identification parameter is 0.3188429 and the sum of squares residuals for the a-diaphragm leakage Kong Shibie parameter is 0.3844163. The sum of the squares residuals of the primary piston spring rate identification parameters is smaller, thus determining fault a as the primary piston spring rate anomaly. Similarly, failure B was determined to be a diaphragm leak.

Referring to fig. 9, a schematic diagram of an actual measurement curve and each simulation curve of the valve outlet pressure under the fault a is shown, and the simulation curve corresponding to the primary piston spring stiffness parameter identification result is more fit to the actual measurement curve, so that the fault a is determined to be the primary piston spring stiffness abnormality.

Referring to fig. 10, a schematic diagram of a measured curve and each simulation curve of the valve outlet pressure at the fault B is shown, and the simulation curve corresponding to the a-diaphragm leakage hole parameter identification result is more fit to the measured curve, so that the fault B is determined to be a-diaphragm leakage.

According to the fault diagnosis method of the relay valve, a simulation model is built for the relay valve and the peripheral gas circuit elements thereof. And (3) carrying out key parameter calibration on the simulation model according to the valve system in the normal state to obtain a corrected valve system simulation model in the normal state, thereby providing a more accurate simulation model for subsequent fault diagnosis. Then, aiming at a certain fault phenomenon, single fault injection is carried out on the corrected simulation models one by one to obtain a valve system simulation model in a fault state, and parameter optimization is carried out on each possible fault parameter by utilizing a genetic algorithm to obtain a residual calculation result corresponding to each possible fault parameter. Further, the optimal fault item parameter value is obtained by comparing residual calculation results corresponding to all possible fault parameters. According to the residual calculation results corresponding to the fault parameters, the fault reasons corresponding to the fault phenomenon, namely the fault types to which the fault phenomenon belongs, can be determined.

Further, the fault degree is determined according to the optimal fault item parameter value, and the fault tracing and fault degree tracing of the relay valve of the braking system are realized according to the scheme, so that the maintenance cost of the braking system is effectively reduced.

Corresponding to the modeling method embodiment of the valve element, the application also provides a modeling device embodiment of the valve element.

Referring to fig. 11, a block diagram of a modeling apparatus for a valve class element in a brake system according to an embodiment of the present application is shown, where the apparatus may include:

the valve system structure obtaining module 110 is configured to obtain a topology structure of a valve system based on a connection structure of a valve included in the valve system and a peripheral gas path element of the valve.

The valve system comprises a valve and a peripheral gas path element of the valve, and the valve system can be equivalent to a pipeline, an orifice, a volume chamber, a spring, a damping and a valve core.

The small flow channel modeling module 120 is configured to model, for a pipe, an orifice, and a volume chamber included in a small flow channel of the valve system, using algebraic formulas of a circular pipe laminar flow, a small orifice, and an ideal gas state equation.

A mass flow channel modeling module 130 for modeling, for a conduit, an orifice, and a volume chamber comprised by a mass flow channel of the valve system, using thermodynamic differential equations describing the state quantity change relationships, wherein the state quantities include gas temperature, density, and pressure.

And the spring oscillator model construction module 140 is used for equivalent of the valve core, the spring and the damping of the valve into a spring oscillator dynamic model.

The valve system model building module 150 is configured to obtain the valve system simulation model based on the mathematical models corresponding to the small flow channel and the large flow channel, and the spring vibrator dynamics model.

In another embodiment of the present application, as shown in fig. 11, the modeling apparatus for a valve element described above may further include:

and the parameter calibration module is used for calibrating the valve system simulation model in a normal state by utilizing a genetic algorithm to-be-calibrated key parameters to obtain a corrected valve system simulation model which enables a simulation curve of the valve outlet pressure of the valve system simulation model to be matched with the actual measurement curve.

In one embodiment of the present application, the key parameters to be calibrated in the normal state include: the heat exchange coefficient of the inner wall of the brake cylinder, the valve port sectional area of the air inlet flow channel and the valve port sectional area of the air outlet flow channel, and the length of a connecting pipeline between the throttle hole on the large flow channel and the brake cylinder.

In yet another embodiment of the present application, the parameter calibration module is specifically configured to:

The modeling device for the valve type element provided by the application takes the valve and the peripheral gas path element thereof as a system, wherein the valve system can be equivalent to effective elements such as a pipeline, an orifice, a volume chamber, a spring, a damping element, a valve core and the like. And further obtaining the topological structure of the valve system based on the structural relation of the valve system. The large-flow through valve comprises a small-flow channel and a large-flow channel, and the small-flow channel is modeled by algebraic formulas such as circular pipe laminar flow, small hole throttling, ideal gas state equation and the like. For large flow channels, modeling is performed using thermodynamic differential equations describing the state quantity change relationships. The spring, the damping and the valve core are equivalent to a spring vibrator dynamic model, and finally the valve system simulation model is obtained. Therefore, according to the scheme, a mathematical simulation model is built according to the internal structure of the valve body and the peripheral gas circuit of the valve body and a mathematical formula matched with the internal structure, and a corresponding mathematical model is built according to the self structural characteristics of the valve system, so that the valve system simulation model built by the scheme accords with the structural characteristics of the valve system, and the accuracy of a simulation result is improved.

Further, parameter calibration is carried out on key parameters of the simulation model in a normal state, so that key parameter values enabling a simulation curve of valve outlet pressure of the valve system simulation model to be basically consistent with an actual measurement curve are obtained, the corrected valve system simulation model in the normal state is finally obtained, and the simulation result accuracy of the valve system simulation model is further improved.

Corresponding to the above-mentioned embodiments of the method for diagnosing a failure of a valve element, the present application also provides embodiments of a device for diagnosing a failure of a valve element.

Referring to fig. 12, a block diagram of a fault diagnosis device for a valve element in a brake system according to an embodiment of the present application is shown, where the device may include:

the fault state model building module 210 is configured to inject a candidate fault term parameter based on the corrected valve system simulation model to obtain a valve system simulation model of the fault state.

The valve system simulation model after correction is constructed by using the modeling device of the valve element;

the fault term parameter optimizing module 220 is configured to perform parameter optimization on candidate fault term parameters by using a genetic algorithm for each valve system simulation model in the fault state, so as to obtain an optimal fault term parameter that matches simulation data and measured data of the valve outlet pressure.

And determining the fault item corresponding to the optimal fault item parameter as the fault type of the valve system. And determining the fault degree of the valve system based on the optimal parameter value of the optimal fault term parameter.

In one embodiment of the present application, the fault term parameter optimizing module is specifically configured to:

In another embodiment of the present application, the process of parameter optimizing candidate fault parameters of a valve system simulation model of the fault state using a genetic algorithm includes:

According to the valve element fault diagnosis device provided by the embodiment, the valve system simulation model in the fault state is obtained by injecting candidate fault item parameters based on the corrected valve system simulation model in the normal state. Then, calculating an optimal fault item parameter value through a genetic algorithm, and determining a fault type according to a fault item to which the optimal fault item parameter belongs; and meanwhile, determining the fault degree according to the fault item parameter value. By utilizing the scheme, the fault tracing and fault degree tracing of the valve element are realized, and the maintenance cost of the brake system is effectively reduced.

The embodiment of the invention provides an electronic device, which comprises a processor, a memory and a program stored in the memory and capable of running on the processor, wherein the modeling method of the valve type element or the fault diagnosis method of the valve type element is realized when the processor executes the program.

The present application also provides a storage medium executable by a computing device, in which a program is stored, which when executed by the computing device, implements the above-described modeling method of a valve-like element, or a failure diagnosis method of a valve-like element.

For the foregoing method embodiments, for simplicity of explanation, the methodologies are shown as a series of acts, but one of ordinary skill in the art will appreciate that the present invention is not limited by the order of acts, as some steps may, in accordance with the present invention, occur in other orders or concurrently. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present invention.

It should be noted that the technical features described in each embodiment in this specification may be replaced or combined with each other, and each embodiment is mainly described in a different manner from other embodiments, and identical and similar parts between the embodiments are referred to each other. For the apparatus class embodiments, the description is relatively simple as it is substantially similar to the method embodiments, and reference is made to the description of the method embodiments for relevant points.

The steps in the methods of the embodiments of the present application may be sequentially adjusted, combined, and pruned according to actual needs.

The modules and sub-modules in the device and the terminal in the embodiments of the present application may be combined, divided, and deleted according to actual needs.

In the embodiments provided in the present application, it should be understood that the disclosed terminal, apparatus and method may be implemented in other manners. For example, the above-described terminal embodiments are merely illustrative, and for example, the division of modules or sub-modules is merely a logical function division, and there may be other manners of division in actual implementation, for example, multiple sub-modules or modules may be combined or integrated into another module, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.

The modules or sub-modules illustrated as separate components may or may not be physically separate, and components that are modules or sub-modules may or may not be physical modules or sub-modules, i.e., may be located in one place, or may be distributed over multiple network modules or sub-modules. Some or all of the modules or sub-modules may be selected according to actual needs to achieve the purpose of the embodiment.

In addition, each functional module or sub-module in each embodiment of the present application may be integrated in one processing module, or each module or sub-module may exist alone physically, or two or more modules or sub-modules may be integrated in one module. The integrated modules or sub-modules may be implemented in hardware or in software functional modules or sub-modules.

Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims

1. A method of modeling a valve-like element in a braking system, comprising:

based on a valve included in a valve system and a connection structure of peripheral gas circuit elements of the valve, obtaining a topological structure of the valve system, wherein the valve system is equivalent to a pipeline, an orifice, a volume chamber, a spring, a damping and a valve core; the spring comprises a physical spring in the valve and a spring element obtained by equivalent of a sealing element in the valve;

modeling with a thermodynamic differential equation describing a state quantity variation relationship for a mass flow channel of the valve system, wherein the state quantity includes a gas temperature, a density, and a pressure;

the valve core, the spring and the damping are equivalent to a spring vibrator dynamic model;

2. The method according to claim 1, wherein the method further comprises:

3. The method according to claim 2, wherein the key parameters to be calibrated in the normal state include: the heat exchange coefficient of the inner wall of the brake cylinder, the valve port sectional area of the air inlet flow channel, the valve port sectional area of the exhaust flow channel and the length of a connecting pipeline between the throttle hole on the large flow channel and the brake cylinder.

4. A method according to claim 2 or 3, wherein said calibrating the valve system simulation model in a normal state using a genetic algorithm to calibrate key parameters to be calibrated to obtain a corrected valve system simulation model comprises:

5. A method for diagnosing a failure of a valve-like element in a brake system, comprising:

injecting a candidate fault item parameter based on the corrected valve system simulation model to obtain a valve system simulation model of a fault state, wherein the corrected valve system simulation model is obtained by the method of any one of claims 2-4;

6. The method of claim 5, wherein the performing parameter optimization on candidate fault term parameters by using a genetic algorithm for the valve system simulation model of each fault state to obtain an optimal fault term parameter in which simulation data of valve outlet pressure matches measured data, comprises:

7. The method according to claim 5 or 6, characterized in that the method further comprises:

8. The method of claim 6, wherein for any of the valve system simulation models in the fault state, performing parameter optimization on candidate fault term parameters of the valve system simulation model in the fault state by using a genetic algorithm to obtain an optimal residual calculation result between simulation data and measured data of valve outlet pressure, including:

9. A modeling apparatus for a valve-like element in a brake system, comprising:

the valve system structure acquisition module is used for acquiring the topological structure of the valve system based on the connection structure of a valve and a peripheral gas circuit element of the valve, wherein the valve system is equivalent to a pipeline, an orifice, a volume chamber, a spring, a damping and a valve core; the spring comprises a physical spring in the valve and a spring element obtained by equivalent of a sealing element in the valve;

10. A failure diagnosis apparatus for a valve-like element in a brake system, comprising:

a fault state model construction module, configured to inject a candidate fault item parameter based on a corrected valve system simulation model to obtain a valve system simulation model of a fault state, where the corrected valve system simulation model is obtained using the apparatus of claim 9;