CN117454508A

CN117454508A - Train running part wheel health management method and system

Info

Publication number: CN117454508A
Application number: CN202311394136.0A
Authority: CN
Inventors: 张志亮; 黄贵发; 陈湘; 王智; 李修文
Original assignee: Tangzhi Science & Technology Hunan Development Co ltd; Beijing Tangzhi Science & Technology Development Co ltd
Current assignee: Tangzhi Science & Technology Hunan Development Co ltd; Beijing Tangzhi Science & Technology Development Co ltd
Priority date: 2023-10-25
Filing date: 2023-10-25
Publication date: 2024-01-26

Abstract

The application provides a method and a system for managing the health of wheels of a train running part, wherein the method comprises the following steps: vibration data, impact data, fault diagnosis data, wheel set state data and wheel set production maintenance data are obtained; preprocessing vibration data, impact data, fault diagnosis data, wheel set state data and wheel set production maintenance data to obtain first processing data; according to the first processing data, performing whole vehicle out-of-round fault analysis, whole wheel set size overrun analysis and whole vehicle non-out-of-round fault analysis to respectively obtain a first fault grade, a second fault grade and a third fault grade; calculating to obtain a fault type index according to the first fault level, the second fault level and the third fault level; and acquiring the residual life prediction data of the train wheels, and performing maintenance decision analysis according to the fault type index and the residual life prediction data to obtain maintenance guidance suggestions. The method and the device can accurately and effectively evaluate the health state of the wheels of the running gear of the train and conduct maintenance guidance.

Description

Train running part wheel health management method and system

Technical Field

The application relates to the technical field of rail transportation safety monitoring, in particular to a train running part wheel health management method and system.

Background

The wheel pair is a key component of a running part of the train, is used as a bearing end in long-term contact with a track, and is greatly influenced by road and ground conditions. Conventional wheel set health assessment and maintenance are based on geometric measurements, with maintenance standards varying from place to place. Some cities are subjected to simple statistical analysis such as manual measurement, actual running-in and the like, maintenance standards and maintenance strategies are determined empirically, wheel tread turning is carried out, the maintenance mode is large in data statistical analysis workload, mathematical models are not formed, requirements on the data strategies and the professional level of analysts are high, a unified method is not formed, the analysis output standard results are different, and the maintenance strategy results are influenced by the level capability of the analysts. The traditional wheel set health evaluation and maintenance method is easy to cause larger wheel set loss of the railway vehicle, and increases the maintenance cost of enterprises.

Therefore, how to accurately and effectively evaluate and maintain the health state of the wheels of the running gear of the train is a technical problem to be solved by those skilled in the art.

Disclosure of Invention

In order to solve the technical problems, the application provides a train running part wheel health management method which can accurately and effectively evaluate the health state of the train running part wheel and guide maintenance. The application also provides a train running part wheel health management system which has the same technical effect.

A first object of the present application is to provide a method of train running gear wheel health management.

The first object of the present application is achieved by the following technical solutions:

a method for managing wheel health of a train running gear, comprising:

vibration data, impact data and fault diagnosis data in a train running part vehicle-mounted fault diagnosis system are obtained, wheel set state data in a wheel set geometric dimension detection system and wheel set production maintenance data are obtained, and the fault diagnosis data are obtained by performing fault diagnosis analysis on the vibration data and the impact data by the running part vehicle-mounted fault diagnosis system;

preprocessing the vibration data, the impact data, the fault diagnosis data, the wheel set state data and the wheel set production maintenance data to obtain first processing data;

according to the first processing data, carrying out whole vehicle out-of-round fault analysis to obtain a first fault grade;

performing whole wheel set size overrun analysis according to the first processing data to obtain a second fault level;

according to the first processing data, carrying out whole vehicle non-out-of-round fault analysis to obtain a third fault level;

Calculating to obtain a fault type index according to the first fault level, the second fault level and the third fault level;

and acquiring the residual life prediction data of the train wheels, and performing maintenance decision analysis according to the fault type index and the residual life prediction data to obtain maintenance guidance suggestions.

Preferably, in the method for managing wheel health of a running unit of a train, the first processing data includes first analysis data, wherein the first analysis data includes a out-of-round fault type, an out-of-round fault alarm position, an out-of-round fault alarm level, an out-of-round fault feature, a vibration trend change rate, an out-of-round detection value, a radial circle runout and a radial circle runout average value; and according to the first processing data, performing whole vehicle out-of-round fault analysis to obtain a first fault level, including:

acquiring the first analysis data from the first processing data;

according to the first analysis data and a preset first weight calculation rule, calculating a first weight of each measuring point of the whole vehicle;

determining the preset out-of-round state of each measuring point of the whole vehicle according to the first analysis data and the preset out-of-round state, and calculating a first ratio of the number of measuring point positions belonging to each preset out-of-round state in the whole vehicle to the number of all measuring points of the whole vehicle;

And calculating a first fault level according to the first weight of each measuring point, the first ratio corresponding to each preset out-of-round state and a preset first level calculation rule.

Preferably, in the method for managing wheel health of a running gear of a train, the preset first weight calculation rule specifically includes:

when out-of-round fault characteristics or out-of-round fault alarms exist at the measuring point positions, and out-of-round detection values are obtained<a ₀ First weight A of the measuring point at mm ₀ =1/3×out-of-round coefficient×vibration trend change rate;

when out-of-round fault alarm exists at the measuring point position, the out-of-round detection value of the measuring point position is more than or equal to a ₀ mm and<a ₁ first weight A of the measuring point at mm ₁ =2/3×out-of-round coefficient×vibration trend change rate;

when out-of-round fault alarm exists at the measuring point position, the out-of-round detection value of the measuring point position is more than or equal to a ₁ First weight A of the measuring point at mm ₂ =out-of-round coefficient x vibration trend change rate;

wherein, the out-of-round coefficient=out-of-round fault alarm level×out-of-round detection value, and the out-of-round fault alarm level takes a value of b when no fault alarm is output ₀ The out-of-round fault alarm level takes a value of b when the alarm level is output at one level ₁ The out-of-round fault alarm level takes a value of b when the alarm second-level output ₂ ，a ₀ And a ₁ As measured out-of-roundnessPreset threshold, b ₀ 、b ₁ And b ₂ The preset value of the out-of-round fault alarm level is obtained.

Preferably, in the method for managing wheel health of a running unit of a train, the determining the preset out-of-round state to which each measuring point of the whole train belongs according to the first analysis data and the preset out-of-round state, and calculating a first ratio of the number of measuring point positions belonging to each preset out-of-round state in the whole train to the number of all measuring points of the whole train, specifically including:

determining a preset out-of-round state of each measuring point of the whole vehicle according to the first analysis data and the preset out-of-round state, wherein the preset out-of-round state comprises a first out-of-round state, a second out-of-round state and a third out-of-round state; when the tread of the measuring point is out of round normally, or radial circle jumps<c ₀ When the measurement point is mm, the position of the measurement point is in the first out-of-round state; when the position of the measuring point is out of round, the first-level output is alarmed, or the radial circle runout is more than or equal to c ₀ mm is less than or equal to c ₁ When the measurement point is in mm, the position of the measurement point is in the second out-of-round state; when the position of the measuring point is out of round fault, the second level output is given out of round fault alarm, or the first level output is given out of round fault alarm and the radial circle jumps>c ₁ When mm, the measuring point position is in the third out-of-round state, wherein c ₀ And c ₁ A preset threshold value for radial circle runout;

according to the preset out-of-round state to which each measuring point belongs, a first ratio B of the number of the measuring point positions belonging to the first out-of-round state in the whole vehicle to the number of all measuring points of the whole vehicle is calculated ₀ The number of the measuring point positions belonging to the second out-of-round state in the whole vehicle accounts for a first ratio B of the number of all measuring points of the whole vehicle ₁ And a first ratio B of the number of the positions of the measuring points belonging to the third out-of-round state in the whole vehicle to the number of all measuring points of the whole vehicle ₂ 。

Preferably, in the method for managing wheel health of a running gear of a train, the preset first level calculation rule specifically includes:

when the first out-of-round state corresponds to a first ratio B ₀ >d ₀ When the first failure level=radial circle run-out average value×a ₀ '×(1-B ₀ ) Wherein A is ₀ ' is the first weight A in the whole vehicle ₀ Average value of (2);

when the first out-of-round state corresponds to a first ratio B ₀ ≤d ₀ And the first ratio B corresponding to the second out-of-round state ₁ A first ratio B corresponding to the third out-of-round state ₂ Sum of>d ₁ At this time, the first failure level=radial circle runout average value× ((a) ₁ '+A ₂ ')/2)×(0.5+B ₁ +B ₂ ) Wherein A is ₁ ' is the first weight A in the whole vehicle ₁ Average value of A ₂ ' is the first weight A in the whole vehicle ₂ Average value of (2);

when the first out-of-round state corresponds to a first ratio B ₀ ≤d ₀ And the first ratio B corresponding to the second out-of-round state ₁ A first ratio B corresponding to the third out-of-round state ₂ The sum is less than or equal to d ₁ At this time, the first failure level=radial circle runout average value× ((a) ₁ '+A ₂ ')/2)×(B ₁ +B ₂ )；

Wherein d ₀ And d ₁ A preset threshold for the first ratio.

Preferably, in the method for managing wheel health of a running gear of a train, the first processing data further includes second analysis data, wherein the second analysis data includes a rim thickness wear rate, a rim thickness average wear rate, a wheel diameter difference, an average wheel diameter difference, and a wheel diameter value; and performing overall wheel set size overrun analysis according to the first processing data to obtain a second fault level, wherein the method comprises the following steps of:

acquiring the second analysis data from the first processing data;

according to the second analysis data and a preset second weight calculation rule, calculating a second weight of each measuring point of the whole vehicle;

determining the preset abrasion state of each measuring point of the whole vehicle according to the second analysis data and the preset abrasion state, and calculating a second ratio of the number of measuring point positions belonging to each preset abrasion state in the whole vehicle to the number of all measuring points of the whole vehicle;

and calculating a second fault level according to the second weight of each measuring point, the second ratio corresponding to each preset abrasion state and a preset second level calculation rule.

Preferably, in the method for managing wheel health of a running gear of a train, the preset second weight calculation rule specifically includes:

rim thickness wear rate at the site of the test point>e ₀ mm/ten thousand km, or wheel diameter difference>f ₀ mm, or wheel diameter value<g ₀ The second weight C of the measuring point at mm ₁ =1- (1/(4-fold rim thickness wear rate+2-fold wheel diameter difference+ (wheel diameter value/840)));

the thickness abrasion rate of the rim at the position of the measuring point is less than or equal to e ₀ mm/ten thousand kilometers and not less than e ₁ mm/ten thousand kilometers, and f ₁ The diameter difference of the wheel is less than or equal to mm and less than or equal to f ₀ mm, and the wheel diameter value is more than or equal to g ₀ The second weight C of the measuring point at mm ₂ =1- (1/(2-fold rim thickness wear rate+1-fold wheel diameter difference+ (wheel diameter value/840)));

rim thickness wear rate at the site of the test point<e ₁ mm/ten thousand kilometers or wheel diameter difference<f ₁ mm, and the wheel diameter value is more than or equal to g ₀ The second weight C of the measuring point at mm ₃ =1- (1/(1-fold rim thickness wear rate+0.5-fold wheel diameter difference+ (wheel diameter value/840)));

wherein e ₀ And e ₁ F is a preset threshold value of the wear rate of the thickness of the rim ₀ And f ₁ G is a preset threshold value of wheel diameter difference ₀ Is a preset threshold value of the wheel diameter value.

Preferably, in the method for managing wheel health of a running unit of a train, according to the second analysis data and a preset abrasion state, determining the preset abrasion state to which each measurement point of the whole train belongs, and calculating a second ratio of the number of measurement point positions belonging to each preset abrasion state in the whole train to the number of all measurement points of the whole train, including:

Determining the preset abrasion state of each measuring point of the whole vehicle according to the second analysis data and the preset abrasion state, wherein the preset abrasion state comprises a first abrasion state and a second abrasion stateA state and a third wear state; rim thickness wear rate at the site of the test point>e ₀ mm/ten thousand km, or wheel diameter difference>f ₀ mm, or wheel diameter value<g ₀ When the measuring point is in mm, the position of the measuring point is in the first abrasion state; the thickness abrasion rate of the rim at the position of the measuring point is less than or equal to e ₀ mm/ten thousand kilometers and not less than e ₁ mm/ten thousand kilometers, and f ₁ The diameter difference of the wheel is less than or equal to mm and less than or equal to f ₀ mm, and the wheel diameter value is more than or equal to g ₀ When the measuring point is in mm, the position of the measuring point is in the second abrasion state; rim thickness wear rate at the site of the test point<e ₁ mm/ten thousand kilometers or wheel diameter difference<f ₁ mm, and the wheel diameter value is more than or equal to g ₀ When the measurement point is in mm, the position of the measurement point is in the third abrasion state;

according to the preset abrasion state of each measuring point, calculating a second ratio D of the number of measuring point positions belonging to the first abrasion state in the whole vehicle to the number of all measuring points of the whole vehicle ₀ The number of the measuring point positions belonging to the second abrasion state in the whole vehicle accounts for a second ratio D of the number of all measuring points of the whole vehicle ₁ The number of measuring point positions belonging to the third abrasion state in the whole vehicle accounts for a second ratio D of the number of all measuring points of the whole vehicle ₂ 。

Preferably, in the method for managing wheel health of a running gear of a train, the preset second level calculation rule specifically includes:

a second ratio D corresponding to the first abrasion state ₀ ≥h ₀ When the second failure level=average wear rate of rim thickness×average wheel diameter difference×c ₁ '×D ₀ The method comprises the steps of carrying out a first treatment on the surface of the Wherein C is ₁ ' is the second weight C in the whole vehicle ₁ Average value of (2);

a second ratio D corresponding to the second abrasion state ₁ >h ₁ And a second ratio D corresponding to the first abrasion state ₀ <h ₀ When the second failure level=average wear rate of rim thickness×average wheel diameter difference×c ₂ '×D ₁ The method comprises the steps of carrying out a first treatment on the surface of the Wherein C is ₂ ' is the second weight C in the whole vehicle ₂ Average value of (2);

a second ratio D corresponding to the third wear state ₂ >h ₂ And (2) anda second ratio D corresponding to the second wear state ₁ ≤h ₁ And a second ratio D corresponding to the first wear state ₀ <h ₀ And a second ratio D corresponding to the first abrasion state ₀ A second ratio D corresponding to the second wear state ₁ Sum of<h ₃ When the second failure level=average wear rate of rim thickness×average wheel diameter difference×c ₃ '×D ₂ The method comprises the steps of carrying out a first treatment on the surface of the Wherein C is ₃ ' is the second weight C in the whole vehicle ₃ Average value of (2);

wherein h is ₀ 、h ₁ 、h ₂ And h ₃ A preset threshold for the second ratio.

Preferably, in the train running part wheel health management method, the first processing data further comprises third analysis data, wherein the third analysis data comprises a non-out-of-round fault type, a non-out-of-round fault alarm position, a non-out-of-round fault alarm level, a non-out-of-round fault feature, an abrasion stripping area and a stripping depth; and according to the first processing data, performing the whole vehicle non-out-of-round fault analysis to obtain a third fault level, including:

Acquiring the third analysis data from the first processing data;

according to the third analysis data and a preset third weight calculation rule, calculating a third weight of each measuring point of the whole vehicle;

determining the preset scratch state of each measuring point of the whole vehicle according to the third analysis data and the preset scratch state, and calculating a third ratio of the number of measuring point positions belonging to each preset scratch state in the whole vehicle to the number of all measuring points of the whole vehicle;

and according to the third weight of each measuring point, the third ratio corresponding to each preset scratch state and a preset third grade calculation rule, calculating a third fault grade.

Preferably, in the method for managing wheel health of a running gear of a train, the preset third weight calculation rule specifically includes:

when there is a non-out-of-round fault alarm at the measuring point position, and the measuring point positionScratch peel area is greater than or equal to i ₀ mm*i ₀ mm, and the stripping depth is greater than or equal to j ₀ In mm, the third weight E of the measuring point ₁ =non-out-of-round fault alert level x scratch peel area x peel depth;

when there is a non-out-of-round fault alarm at the measuring point position, and the scratch stripping area of the measuring point position<i ₀ mm*i ₀ mm, and peel depth <j ₀ In mm, the third weight E of the measuring point ₂ =2/3×non-out-of-round fault alert level×scratch peel area×peel depth;

when the non-loss-of-circle fault feature exists or the non-loss-of-circle fault alarm exists at the measuring point position, the stripping area is scratched<i ₀ mm*i ₀ mm, and peel depth<j ₀ In mm, the third weight E of the measuring point ₃ =1/3×non-out-of-round fault alert level×scratch peel area×peel depth;

wherein, the non-loss-of-circle fault alarm level takes the value k when the non-loss-of-circle fault feature or the non-fault alarm output is output ₀ The non-out-of-round fault alarm level takes the value k when the primary alarm is output ₁ The non-out-of-round fault alarm level takes the value k when the secondary alarm is output ₂ ，i ₀ Preset threshold value for scratch peeling area, j ₀ K is a preset threshold value of the stripping depth ₀ 、k ₁ And k ₂ The preset value of the alarm level of the non-out-of-round fault is obtained.

Preferably, in the method for managing wheel health of a running unit of a train, the determining the preset scratch state to which each measurement point of the whole train belongs according to the third analysis data and the preset scratch state, and calculating a third ratio of the number of measurement point positions belonging to each preset scratch state in the whole train to the number of all measurement points of the whole train, includes:

determining the preset scratch state of each measuring point of the whole vehicle according to the third analysis data and the preset scratch state, wherein the preset scratch state comprises a first scratch and peel state, a second scratch and peel state and a third scratch and peel state; when a non-out-of-round fault alarm exists at the measuring point position, the scratch stripping area of the measuring point position is more than or equal to i ₀ mm*i ₀ mm, and the stripping depth is greater than or equal to j ₀ When mm, the position of the measuring point is in the first scratch stripping state; when there is a non-out-of-round fault alarm at the measuring point position, and the scratch stripping area of the measuring point position<i ₀ mm*i ₀ mm, and peel depth<j ₀ When mm, the position of the measuring point is in the second scratch stripping state; when the non-loss-of-circle fault feature exists or the non-loss-of-circle fault alarm exists at the measuring point position, the stripping area is scratched<i ₀ mm*i ₀ mm, and peel depth<j ₀ When mm, the position of the measuring point is in the third scratch stripping state;

according to the preset scratch state of each measuring point, calculating a third ratio F of the number of measuring point positions belonging to the first scratch stripping state in the whole vehicle to the number of all measuring points of the whole vehicle ₁ The number of the measuring point positions belonging to the second scratch and peel state in the whole vehicle accounts for a third ratio F of the number of all measuring points of the whole vehicle ₂ The number of the measuring point positions belonging to the third scratch and peel state in the whole vehicle accounts for a third ratio F of the number of all measuring points of the whole vehicle ₃ 。

Preferably, in the method for managing wheel health of a running gear of a train, the preset third level calculation rule specifically includes:

when the first scratch peeling state corresponds to the third ratio F ₁ ≥l ₀ When third failure level=e ₁ '×F ₁ The method comprises the steps of carrying out a first treatment on the surface of the Wherein E is ₁ ' is the third weight E in the whole vehicle ₁ Average value of (2);

when the first scratch peeling state corresponds to the third ratio F ₁ <l ₀ And a third ratio F corresponding to the second scratch-off state ₂ ≥l ₁ When the third failure level=2/3×e ₂ '×F ₂ The method comprises the steps of carrying out a first treatment on the surface of the Wherein E is ₂ ' is the third weight E in the whole vehicle ₂ Average value of (2);

when the first scratch peeling state corresponds to the third ratio F ₁ <l ₀ And a third ratio F corresponding to the second scratch-off state ₂ <l ₁ And a third ratio corresponding to the third scratch-off stateF ₃ ≤l ₂ When the third failure level=1/3×e ₂ '×F ₂ ；

When the first scratch peeling state corresponds to the third ratio F ₁ <l ₀ And a third ratio F corresponding to the second scratch-off state ₂ <l ₁ And a third ratio F corresponding to the third scratch-off state ₃ >l ₂ When the third failure level=1/3×e ₃ '×(1-F ₃ ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein E is ₃ ' is the third weight E in the whole vehicle ₃ Average value of (2);

wherein l ₀ 、l ₁ And l ₂ A preset threshold for the third ratio.

Preferably, in the method for managing wheel health of a running gear of a train, the performing maintenance decision analysis according to the failure type index and the residual life prediction data to obtain maintenance guidance advice includes:

comparing the fault type index with a preset threshold value to obtain a comparison result;

And outputting maintenance guidance suggestions according to the comparison result and the residual life prediction data.

A second object of the present application is to provide a train running gear wheel health management system.

The second object of the present application is achieved by the following technical solutions:

a train running gear wheel health management system comprising:

the system comprises a data acquisition unit, a wheel set geometric dimension detection system and a wheel set production maintenance unit, wherein the data acquisition unit is used for acquiring vibration data, impact data and fault diagnosis data in a train running part vehicle-mounted fault diagnosis system, and the fault diagnosis data is obtained by performing fault diagnosis analysis on the vibration data and the impact data by the running part vehicle-mounted fault diagnosis system;

the data processing unit is used for preprocessing the vibration data, the impact data, the fault diagnosis data, the wheel set state data and the wheel set production maintenance data to obtain first processing data;

the first analysis unit is used for carrying out whole vehicle out-of-round fault analysis according to the first processing data to obtain a first fault grade;

the second analysis unit is used for carrying out over-limit analysis on the size of the whole wheel set according to the first processing data to obtain a second fault level;

The third analysis unit is used for carrying out the whole vehicle non-out-of-round fault analysis according to the first processing data to obtain a third fault level;

the index calculating unit is used for calculating and obtaining a fault type index according to the first fault level, the second fault level and the third fault level;

and the operation and maintenance decision unit is used for acquiring the residual life prediction data of the train wheels, and carrying out maintenance decision analysis according to the fault type index and the residual life prediction data to obtain maintenance guidance suggestions.

According to the technical scheme, vibration data, impact data and fault diagnosis data in the vehicle-mounted fault diagnosis system of the train running part are obtained, wheel set state data in the wheel set geometric dimension detection system and wheel set production maintenance data are obtained, preprocessing is carried out, first processing data fused with the data are obtained, wherein the first processing data are used for subsequent train wheel fault analysis, and accuracy of the wheel fault analysis is improved; further, according to the first processing data, the whole train out-of-round fault analysis, the whole train wheel set size overrun analysis and the whole train non-out-of-round fault analysis are respectively carried out to obtain a first fault grade, a second fault grade and a third fault grade, and a fault type index is calculated, so that the overall assessment of the whole train and the whole train state of a line is realized; and finally, carrying out maintenance decision analysis according to the fault type index and the residual life prediction data of the train wheels to obtain maintenance guidance suggestions, improving the scientificity of maintenance decision instructions and being beneficial to improving the operation and maintenance efficiency and the service life of the train wheels. In summary, the technical scheme can accurately and effectively evaluate and maintain the health state of the wheels of the train running part.

Drawings

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

Fig. 1 is a schematic flow chart of a method for managing wheel health of a running gear of a train according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of a wheel health management system for a running gear of a train in an embodiment of the application.

Detailed Description

In order to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.

In the embodiments provided in the present application, it should be understood that the disclosed method and system may be implemented in other manners. The system embodiments described below are merely illustrative, and for example, the division of units and modules is merely a logical function division, and other divisions may be implemented in practice such as: multiple units or modules may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or modules, whether electrically, mechanically, or otherwise.

In addition, each functional unit in each embodiment of the present application may be integrated in one processor, or each unit may be separately used as one device, or two or more units may be integrated in one device; the functional units in the embodiments of the present application may be implemented in hardware, or may be implemented in a form of hardware plus a software functional unit.

Those of ordinary skill in the art will appreciate that: all or part of the steps of implementing the method embodiments described below may be performed by program instructions and associated hardware, and the foregoing program instructions may be stored in a computer readable storage medium, which when executed, perform steps comprising the method embodiments described below; and the aforementioned storage medium includes: a mobile storage device, a Read Only Memory (ROM), a magnetic disk or an optical disk, or the like, which can store program codes.

It should be appreciated that the terms "system," "apparatus," "unit," and/or "module," if used herein, are merely one method for distinguishing between different components, elements, parts, portions, or assemblies at different levels. However, if other words can achieve the same purpose, the word can be replaced by other expressions.

Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, the meaning of "a plurality" or "a number" is two or more, unless explicitly defined otherwise.

If a flowchart is used in the present application, the flowchart is used to describe the operations performed by the system according to embodiments of the present application. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.

It should also be noted that, in this document, terms such as "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that an 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 article or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in an article or apparatus that comprises such element.

The embodiment of the application is written in a progressive manner.

As shown in fig. 1, an embodiment of the present application provides a method for managing wheel health of a running gear of a train, including:

s101, vibration data, impact data and fault diagnosis data in a train running part vehicle-mounted fault diagnosis system are obtained, wheel set state data in a wheel set geometric dimension detection system and wheel set production maintenance data are obtained, and the fault diagnosis data are obtained by performing fault diagnosis analysis on the vibration data and the impact data by the running part vehicle-mounted fault diagnosis system;

in S101, the vehicle-mounted fault diagnosis system for the running part is a safety monitoring device for the running part of the railway vehicle, which performs real-time collection and processing of multiple physical quantities through a composite sensor deployed at the position of the running part, performs comprehensive calculation and analysis through an embedded software diagnosis module, outputs a fault diagnosis result, realizes real-time monitoring of the fault state of the running part, identifies early faults and timely alarms and prompts, can effectively prevent running accidents caused by the fault of the running part, and ensures improvement of operation quality. The existing train running part vehicle-mounted fault diagnosis system generally comprises a vehicle-mounted diagnostic instrument, a preprocessor, a compound sensor, a rotating speed pulse signal, a wireless data transmission terminal and the like. The system communicates with a vehicle TCMS (Train Control and Management System ) through an ethernet or MVB (Multifunction Vehicle Bus, multifunctional vehicle bus) network, and transmits data to a vehicle section hierarchical travel part health management system or a data center through a train ethernet switch or a wireless data transmission terminal. In some embodiments, vibration data, impact data, and fault diagnosis data obtained by performing fault diagnosis analysis on the vibration data and the impact data of the train wheels can be obtained from an existing train running part vehicle-mounted fault diagnosis system; the vibration data and the impact data can be vibration signals and impact signals acquired by a composite sensor (such as a vibration impact detection sensor) in the vehicle-mounted fault diagnosis system of the travelling part, and the composite sensor can be arranged at a designated position (such as an axle box, a bogie, a vehicle body, a gear box, a motor axle box and the like) on a train according to actual requirements; the fault diagnosis data can be obtained by performing fault diagnosis analysis on vibration data and impact data by a vehicle-mounted diagnostic instrument in a traveling part vehicle-mounted fault diagnosis system through a preset fault diagnosis algorithm; in other embodiments, the fault diagnosis data includes, but is not limited to: scratch peeling alarm, tread out-of-round alarm, internal defect alarm, vibration effective value trend data, out-of-round trend data and the like. The common tread faults of the train running part generally comprise tread scratch and peeling faults, tread out-of-round faults, tread internal defect faults and the like, and the train running part vehicle-mounted fault diagnosis system can generate corresponding scratch and peeling alarms, tread out-of-round alarms, internal defect alarms and the like according to the tread fault types; the most direct expression of the wheel-rail acting force is a vibration effective value, when a vehicle runs on a wave-milled road section or the vehicle wheels per se have out-of-round, the vibration effective value intuitively reflects the wheel-rail state of the vehicle on the wave-milled road section or the vehicle wheels out-of-round, the vibration effective value trend data is that the collected original vibration sample data is subjected to effective value (average root) calculation to obtain trend data which changes along with time, the trend can reflect the trend change of the wheel-rail state, and the magnitude of the vibration effective value trend data correspondingly increases along with the development and deterioration of the wave-milled road section; the out-of-round trend data is trend data which is obtained by twice integrating vibration original data and changes along with time, the trend can reflect the development and change of the out-of-round trend, the corresponding out-of-round trend data also increases along with the increase of the out-of-round degree, the train runs on a more wave-worn road section for a long time, the out-of-round of a vehicle wheel set can be induced, and the deterioration and the development of the wave-worn road section can be aggravated.

The wheel pair geometric dimension detection system is special equipment for detecting wheel abrasion of a railway vehicle, is arranged on two sides of a railway, can detect the dimension of each relevant part of the wheel pair of the railway vehicle on line, and can rapidly and accurately detect important data such as the rim thickness, the rim height, the tread shape, the wheel diameter, the inner side distance of the wheel and the like of the wheel when a train passes through the system. The overall architecture of the existing wheelset geometry detection system consists of 3 parts: rail side equipment, detection station equipment and a terminal display system. The rail edge equipment mainly comprises wheel set size measurement, vehicle number identification and wheel detection magnetic steel. When the train runs to different magnetic steel positions, each functional module of the system performs corresponding data acquisition, processing, storage, discrimination and other works, and when the train completely passes through the detection area, the system completes detection work, forms relevant data, communicates with the terminal display system and automatically stores the relevant data, and completes the detection process. In some embodiments, the wheel set status data may be obtained from existing wheel set geometry detection systems, including, but not limited to: rim thickness, rim height, wheel diameter difference (coaxial, co-frame, co-vehicle), tread wear, out-of-roundness, passing speed, QR value, alarm type, alarm level, alarm times, etc., wherein rim thickness refers to the thickness of the rim of a wheel, which is the vertical thickness of the rim interface; the rim height is the vertical distance from the top and bottom of the rim to the rim height measuring line; wheel diameter difference is the difference of 2 wheel diameters, can get the wheel pair on same epaxial wheel pair, 2 wheel pairs on same bogie, 2 wheel pairs on same festival car, and different motorcycle type different position standard is different, and mainstream motorcycle type standard: coaxial: 1mm, same frame: 3mm, same car: 6mm; tread wear refers to the wear of the wheel pair tread per ten thousand kilometers; the out-of-roundness refers to out-of-round radial runout of the wheel set; the QR value is the horizontal distance between the intersection point of the vertical line at the position 10mm above the reference line of the rolling circular tread and the inner measurement of the rim and the intersection point of the vertical line at the top of the rim, which is downward and 2mm, and the inner measurement of the rim; the alarm type can be rim overrun, including rim thickness and height overrun; the alarm level is divided into a first level and a second level according to the overrun degree; as the number of times the train passes through the wheelset geometry detection system increases, the number of alarms increases cumulatively.

Wheel set production service data, which may be actual measured data of the wheel set prior to repair, in some embodiments includes, but is not limited to: rim thickness, wheel diameter value, QR value, etc. The wheel set production maintenance data can be obtained from the existing maintenance information system, and the specific obtaining mode does not influence the realization of the embodiment.

S102, preprocessing vibration data, impact data, fault diagnosis data, wheel set state data and wheel set production maintenance data to obtain first processing data;

in S102, in order to improve accuracy of the subsequent failure analysis, vibration data, impact data, failure diagnosis data, wheel set status data, and wheel set production maintenance data are preprocessed to obtain first processed data, where the preprocessing process may use an existing data preprocessing method, such as data verification, data cleaning, data fusion, and so on. In some embodiments, the data verification may be verification of collection time of various data, and the data cleaning may be cleaning of irrelevant data, repeated data and smooth noise data in various data, and data affecting the data fusion effect, such as abnormal value, missing value, etc., in the data is removed; the data fusion can be to mutually fuse and supplement similar fault data in various data; other types of data preprocessing methods can be reasonably adopted in the preprocessing process, and the application is not limited to the preprocessing method.

S103, carrying out whole vehicle out circle failure analysis according to the first processing data to obtain a first failure grade;

in S103, tread faults mainly include two fault types: the out-of-round fault and the non-out-of-round fault can be specifically obtained by screening first analysis data for out-of-round fault analysis including all wheel measuring points of the whole train (for example, for a train with six carriages, the whole train includes 48 wheels corresponding to 48 measuring points respectively) according to the first processing data, and obtaining a first fault level reflecting the severity of the out-of-round fault of the whole train by comprehensively analyzing the first analysis data.

In some embodiments, the first processed data comprises first analysis data, wherein the first analysis data comprises out-of-round fault type, out-of-round fault alert location, out-of-round fault alert level, out-of-round fault signature, vibration trend change rate, out-of-round detection value, radial circle runout, and radial circle runout average; the out-of-round fault type, the out-of-round fault alarm position and the out-of-round fault alarm level can be generated by a train running part vehicle-mounted fault diagnosis system; the out-of-round fault feature is that the traveling part vehicle-mounted fault diagnosis system performs fault qualitative judgment on the collected vibration and impact original data through a fault diagnosis algorithm to obtain an early out-of-round fault which does not reach an alarm output level; the vibration trend change rate can be the difference value of vibration effective value trend data of units Mo Gong, wherein the vibration effective value trend data obtains trend data changing along with time by calculating effective values (average roots) of collected original vibration sample data; the out-of-roundness detection value can be obtained by measuring the out-of-roundness of the tread of the wheel by an out-of-roundness measuring instrument, and can also be obtained by measuring a geometric dimension detection system of the wheel set; radial circle runout refers to the maximum fluctuation of the distance between each point on the actual surface of the measured rotating surface in the same cross section and a reference axis; the radial runout average value refers to a weighted average value of radial runout values of all measuring points of a train.

One implementation manner of the step specifically comprises the following steps:

s1031, acquiring first analysis data from the first processing data;

s1032, calculating the first weight of each measuring point of the whole vehicle according to the first analysis data and a preset first weight calculation rule;

in S1032, specifically, the preset first weight calculation rule may be set to:

when out-of-round fault alarm exists at the measuring point position, the out-of-round detection value of the measuring point position is more than or equal to a ₀ mm and<a ₁ mmwhen the first weight A of the measuring point ₁ =2/3×out-of-round coefficient×vibration trend change rate;

the non-loss-of-circle fault alarm indicates the non-loss-of-circle fault alarm output of the traveling part vehicle-mounted fault diagnosis system; the out-of-round coefficient is determined by both the out-of-round fault alarm level and the out-of-round detection value, the out-of-round coefficient=the out-of-round fault alarm level×the out-of-round detection value, and the out-of-round fault alarm level takes a value of b when the out-of-round fault alarm is output ₀ The out-of-round fault alarm level takes a value of b when the alarm level is output at one level ₁ The out-of-round fault alarm level takes a value of b when the alarm second-level output ₂ ；a ₀ And a ₁ B is a preset threshold value of the out-of-roundness detection value ₀ 、b ₁ And b ₂ The preset value of the out-of-round fault alarm level is obtained.

In a specific embodiment, a ₀ 、a ₁ 、b ₀ 、b ₁ And b ₂ The value of (a) can be flexibly set according to the actual application requirement, for example, a ₀ ＝0.3、a ₁ ＝0.5、b ₀ ＝1、b ₁ ＝2、b ₃ =3, the present application is not limited thereto.

Based on the first analysis data and a preset first weight calculation rule, the first weight of each measuring point of the whole vehicle can be calculated.

S1033, determining a preset out-of-round state of each measuring point of the whole vehicle according to the first analysis data and the preset out-of-round state, and calculating a first ratio of the number of measuring point positions belonging to each preset out-of-round state in the whole vehicle to the number of all measuring points of the whole vehicle;

in S1033, specifically, the preset out-of-round state may include a first out-of-round state, a second out-of-round state, and a third out-of-round state, where: the first out-of-round state may be: tread out-of-round normal or radial run-out<c ₀ mm; the second out-of-round state may be: out-of-round fault alert primary output or (c) ₀ Radial round jump with mm less than or equal toThe movement is less than or equal to c ₁ mm); the third out-of-round state may be: the out-of-round fault alarm secondary output, or (out-of-round fault alarm primary output and radial circle runout) >c ₁ mm); wherein c ₀ And c ₁ A preset threshold value for radial circle runout; one implementation manner of the step specifically comprises the following steps:

determining a preset out-of-round state of each measuring point of the whole vehicle according to the first analysis data and the preset out-of-round state, wherein the preset out-of-round state comprises a first out-of-round state, a second out-of-round state and a third out-of-round state; when the tread of the measuring point is out of round normally, or radial circle jumps<c ₀ When the measurement point is mm, the position of the measurement point is in a first out-of-round state; when the position of the measuring point is out of round, the first-level output is alarmed, or the radial circle runout is more than or equal to c ₀ mm is less than or equal to c ₁ When the measurement point is mm, the position of the measurement point is in a second out-of-round state; when the position of the measuring point is out of round fault, the second level output is given out of round fault alarm, or the first level output is given out of round fault alarm and the radial circle jumps>c ₁ When the measurement point is mm, the position of the measurement point is in a third out-of-round state;

according to the preset out-of-round state of each measuring point, a first ratio B of the number of measuring point positions belonging to the first out-of-round state in the whole vehicle to the number of all measuring points of the whole vehicle is calculated ₀ The number of the positions of the measuring points belonging to the second out-of-round state in the whole vehicle accounts for the first ratio B of the number of all measuring points of the whole vehicle ₁ The number of the positions of the measuring points belonging to the third out-of-round state in the whole vehicle accounts for a first ratio B of the number of all measuring points of the whole vehicle ₂ 。

In a specific embodiment, c ₀ And c ₁ The value of (c) can be flexibly set according to the actual application requirement, for example, c ₀ ＝0.3、c ₁ =0.5, the present application is not limited thereto.

S1034, according to the first weight of each measuring point, a first ratio corresponding to each preset out-of-round state and a preset first grade calculation rule, calculating a first fault grade.

In S1034, specifically, a first ratio corresponding to the first out-of-round state is set as B ₀ The first ratio corresponding to the second out-of-round state is B ₁ First corresponding to third out-of-round stateThe ratio is B ₂ The method comprises the steps of carrying out a first treatment on the surface of the The preset first level calculation rule may be set as:

a first ratio B corresponding to the first out-of-round state ₀ >d ₀ When the first failure level=radial circle run-out average value×a ₀ '×(1-B ₀ ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein A is ₀ ' is the first weight A in the whole vehicle ₀ Average value of (A), i.e. A ₀ ' all first weights A corresponding to the whole vehicle measuring point ₀ Taking an average value to obtain;

a first ratio B corresponding to the first out-of-round state ₀ ≤d ₀ And a first ratio B corresponding to the second out-of-round state ₁ First ratio B corresponding to third out-of-round state ₂ Sum of>d ₁ At this time, the first failure level=radial circle runout average value× ((a) ₁ '+A ₂ ')/2)×(0.5+B ₁ +B ₂ ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein A is ₁ ' is the first weight A in the whole vehicle ₁ Average value of A ₂ ' is the first weight A in the whole vehicle ₂ Average value of (A), i.e. A ₁ ' all first weights A corresponding to the whole vehicle measuring point ₁ Taking average value to obtain A ₂ ' all first weights A corresponding to the whole vehicle measuring point ₂ Taking an average value to obtain;

other cases (i.e. when the first out-of-round state corresponds to the first ratio B ₀ ≤d ₀ And a first ratio B corresponding to the second out-of-round state ₁ First ratio B corresponding to third out-of-round state ₂ The sum is less than or equal to d ₁ When), first failure level=radial circle run out average value× ((a) ₁ '+A ₂ ')/2)×(B ₁ +B ₂ ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein A is ₁ ' is the first weight A in the whole vehicle ₁ Average value of measuring points A ₂ ' first weight A in the whole vehicle ₂ Measuring point average value;

wherein d ₀ And d ₁ A preset threshold for a first ratio;

in a specific embodiment, d ₀ And d ₁ The value of (2) can be flexibly set according to the actual application requirement, for example, d ₀ ＝90％、d ₁ =20%, to which the present application is not limited. Generally, if the system does not output an alarm orThe radial circle runout is smaller than 0.3mm (namely the first ratio B corresponding to the first out-of-round state ₀ ) Above 90%, the train is considered to be free of bulk out of round, and accordingly the value of the first failure level is relatively small. The first fault level calculated through the implementation steps can accurately reflect the severity degree of the out-of-round fault of the whole vehicle.

S104, performing overall wheel set size overrun analysis according to the first processing data to obtain a second fault level;

In S104, specifically, second analysis data for performing wheel set size overrun analysis including all wheel measurement points of the whole train may be screened according to the first processing data, and by performing comprehensive analysis on the second analysis data, a second failure level reflecting the severity of the abrasion failure of the whole train may be obtained.

In some embodiments, the first process data further comprises second analysis data, wherein the second analysis data comprises rim thickness wear rate, rim thickness average wear rate, wheel diameter difference, average wheel diameter difference, wheel diameter value; wherein: the wheel rim thickness abrasion rate, the wheel rim thickness average abrasion rate, the wheel diameter difference, the average wheel diameter difference and the wheel diameter value can be generated by a wheel set geometric dimension detection system; the average wear rate of the rim thickness is the weighted average of the wear amounts of the rim thickness of all wheel sets of the train per Mo Gong mileage, and the average wheel diameter difference is the weighted average of the coaxial wheel diameter differences of the train; in other embodiments, the second analysis data may also include a large and small wheel failure type, rim overrun alarm data (including rim thickness and height overrun), etc., which is not limited in this application.

S1041, acquiring second analysis data from the first processing data;

s1042, calculating a second weight of each measuring point of the whole vehicle according to the second analysis data and a preset second weight calculation rule;

in S1042, specifically, the preset second weight calculation rule may be set to:

rim thickness wear rate at the site of the test point>e ₀ mm/ten thousand kilometers, or large wheel diameter difference>f ₀ mm, or wheel diameter value<g ₀ The second weight C of the measuring point at mm ₁ =1- (1/(4-fold rim thickness wear rate+2-fold wheel diameter difference+ (wheel diameter value/840)));

rim thickness wear rate at the site of the test point<e ₁ mm/ten thousand kilometers or wheel diameter difference<f ₁ mm, and the wheel diameter value is more than or equal to g ₀ mm, the second weight C of the measuring point ₃ =1- (1/(1-fold rim thickness wear rate+0.5-fold wheel diameter difference+ (wheel diameter value/840)));

And based on the second analysis data and a preset second weight calculation rule, calculating the second weight of each measuring point of the whole vehicle.

S1043, determining a preset abrasion state of each measuring point of the whole vehicle according to the second analysis data and the preset abrasion state, and calculating a second ratio of the number of measuring point positions belonging to each preset abrasion state in the whole vehicle to the number of all measuring points of the whole vehicle;

in S1043, specifically, the preset wear state may include: a first abrasion state, a second abrasion state and a third abrasion state. Wherein: the first wear state may be: rim thickness wear rate>e ₀ mm/ten thousand kilometers or wheel diameter difference>f ₀ mm or wheel diameter value<g ₀ mm; the second wear state may be: the thickness abrasion rate of the wheel rim is less than or equal to e ₀ mm/ten thousand kilometers and not less than e ₁ mm/ten thousand kilometers, and f ₁ The diameter difference of the wheel is less than or equal to mm and less than or equal to f ₀ mm, and the wheel diameter value is more than or equal to g ₀ mm; the third wear state may be: rim thickness wear rate<e ₁ mm/ten thousand kilometers or wheel diameter difference<f ₁ mm, and the wheel diameter value is more than or equal to g ₀ mm; one implementation manner of the step specifically comprises the following steps:

determining a preset abrasion state of each measuring point of the whole vehicle according to the second analysis data and the preset abrasion state, wherein the preset abrasion state comprises a first abrasion state, a second abrasion state and a third abrasion state; rim thickness wear rate at the site of the test point>e ₀ mm/ten thousand km, or wheel diameter difference >f ₀ mm, or wheel diameter value<g ₀ When the measuring point is in mm, the position of the measuring point is in a first abrasion state; the thickness abrasion rate of the rim at the position of the measuring point is less than or equal to e ₀ mm/ten thousand kilometers and not less than e ₁ mm/ten thousand kilometers, and f ₁ The diameter difference of the wheel is less than or equal to mm and less than or equal to f ₀ mm, and the wheel diameter value is more than or equal to g ₀ When the measuring point is in mm, the position of the measuring point is in a second abrasion state; rim thickness wear rate at the site of the test point<e ₁ mm/ten thousand kilometers or wheel diameter difference<f ₁ mm, and the wheel diameter value is more than or equal to g ₀ When the measurement point is in mm, the position of the measurement point is in a third abrasion state;

according to the preset abrasion state of each measuring point, calculating a second ratio D of the number of measuring point positions belonging to the first abrasion state in the whole vehicle to the number of all measuring points of the whole vehicle ₀ The number of measuring points in the second abrasion state in the whole vehicle accounts for a second ratio D of the number of all measuring points of the whole vehicle ₁ The number of measuring point positions belonging to the third abrasion state in the whole vehicle accounts for the second ratio D of the number of all measuring points of the whole vehicle ₂ 。

In a specific embodiment, e ₀ 、e ₁ 、f ₀ 、f ₁ And g ₀ The value of (2) can be flexibly set according to the actual application requirement, for example, e ₀ ＝2、e ₁ ＝1、f ₀ ＝3、f ₁ ＝1、g ₀ =800, the present application is not limited thereto.

S1043, calculating a second fault grade according to a second weight of each measuring point, a second ratio corresponding to each preset abrasion state and a preset second grade calculation rule.

In S1043, specifically, a second ratio corresponding to the first wear state is set as D ₀ Second wearing state pairThe second ratio of the reaction is D ₁ The second ratio corresponding to the third abrasion state is D ₂ The method comprises the steps of carrying out a first treatment on the surface of the In some embodiments, the preset second level calculation rule may be set to:

a second ratio D corresponding to the first wear state ₀ ≥h ₀ When the second failure level=average wear rate of rim thickness×average wheel diameter difference×c ₁ '×D ₀ The method comprises the steps of carrying out a first treatment on the surface of the Wherein C is ₁ ' is the second weight C in the whole vehicle ₁ Average value of (C) ₁ ' from all the corresponding second weights C in the whole vehicle measuring point ₁ Taking an average value to obtain;

a second ratio D corresponding to the second abrasion state ₁ >h ₁ And a second ratio D corresponding to the first abrasion state ₀ <h ₀ When the second failure level=average wear rate of rim thickness×average wheel diameter difference×c ₂ '×D ₁ The method comprises the steps of carrying out a first treatment on the surface of the Wherein C is ₂ ' is the second weight C in the whole vehicle ₂ Average value of (C) ₂ ' from all the corresponding second weights C in the whole vehicle measuring point ₂ Taking an average value to obtain;

a second ratio D corresponding to the third abrasion state ₂ >h ₂ And a second ratio D corresponding to the second abrasion state ₁ ≤h ₁ A second ratio D corresponding to the first wear state ₀ <h ₀ And a second ratio D corresponding to the first abrasion state ₀ A second ratio D corresponding to a second wear state ₁ Sum of <h ₃ When the second failure level=average wear rate of rim thickness×average wheel diameter difference×c ₃ '×D ₂ The method comprises the steps of carrying out a first treatment on the surface of the Wherein C is ₃ ' is the second weight C in the whole vehicle ₃ Average value of (C) ₃ ' from all the corresponding second weights C in the whole vehicle measuring point ₃ Taking an average value to obtain;

In a specific embodiment, h ₀ 、h ₁ 、h ₂ And h ₃ The value of (2) can be flexibly set according to the actual application requirement, for example, h ₀ ＝20％、h ₁ ＝30％、h ₂ ＝50％、h ₃ =50%, the present application is not limited thereto. In general, if the rim thickness wear rate overrun ratio exceeds a certain limit (i.e., the second ratio D corresponding to the first wear state) ₀ More than or equal to 20 percent or the second abrasion state corresponds to the second ratio D ₁ And more than 30 percent), the train is considered to have universal abrasion, the whole train abrasion acceleration can be accelerated, and the second fault grade assignment is larger.

S105, carrying out whole vehicle non-out-of-round fault analysis according to the first processing data to obtain a third fault level;

in S105, specifically, third analysis data for performing non-out-of-round fault analysis including all wheel measurement points of the whole train may be screened according to the first processing data, and by performing comprehensive analysis on the third analysis data, a third fault level reflecting severity of scratch stripping fault of the whole train may be obtained.

In some embodiments, the first processed data further comprises third analysis data, wherein the third analysis data comprises a non-out-of-round fault type, a non-out-of-round fault alert location, a non-out-of-round fault alert level, a non-out-of-round fault feature, an scratch peel area, and a peel depth; the non-out-of-round fault type, the non-out-of-round fault alarm location, the non-out-of-round fault alarm level, the scratch strip area data, and the strip depth may be generated by a train running part on-board fault diagnosis system. The scratch stripping area is the product of the length and the width of scratch stripping on the surface of the wheel pair, the stripping depth is the stripping depth of the surface of the wheel pair, namely the stripping amount of the surface of the wheel pair, and the non-out-of-round fault is characterized in that the vehicle-mounted fault diagnosis system of the running part carries out fault qualitative judgment on the collected vibration and impact original data through a fault diagnosis algorithm to obtain an early non-out-of-round fault which does not reach the alarm output level;

s1051, acquiring third analysis data from the first processing data;

s1052, calculating a third weight of each measuring point of the whole vehicle according to the third analysis data and a preset third weight calculation rule;

In S1052, specifically, the preset third weight calculation rule may be set to:

when a non-out-of-round fault alarm exists at the measuring point position, the scratch stripping area of the measuring point position is more than or equal to i ₀ mm*i ₀ mm, and the stripping depth is greater than or equal to j ₀ In mm, the third weight E of the measuring point ₁ =non-out-of-round fault alert level x scratch peel area x peel depth;

when there is a non-out-of-round fault alarm at the measuring point position, and the scratch stripping area of the measuring point position<i ₀ mm*i ₀ mm, and peel depth<j ₀ In mm, the third weight E of the measuring point ₂ =2/3×non-out-of-round fault alert level×scratch peel area×peel depth;

wherein, the non-loss-of-circle fault alarm level takes the value k when the non-loss-of-circle fault feature or the non-fault alarm output is output ₀ The non-out-of-round fault alarm level takes the value k when the primary alarm is output ₁ The non-out-of-round fault alarm level takes the value k when the secondary alarm is output ₂ The method comprises the steps of carrying out a first treatment on the surface of the The non-loss-of-circle fault alarm indicates that the running part vehicle-mounted fault diagnosis system has no non-loss-of-circle fault alarm output, i ₀ Preset threshold value for scratch peeling area, j ₀ K is a preset threshold value of the stripping depth ₀ 、k ₁ And k ₂ The preset value of the alarm level of the non-out-of-round fault is obtained.

Based on the third analysis data and a preset third weight calculation rule, a third weight of each measuring point of the whole vehicle can be calculated.

S1053, determining a preset scratch state of each measuring point of the whole vehicle according to the third analysis data and the preset scratch state, and calculating a third ratio of the number of measuring point positions belonging to each preset scratch state in the whole vehicle to the number of all measuring points of the whole vehicle;

in S1053, specifically, the preset scratch states may include a first scratch-off state, a second scratch-off state, and a third scratch-off state. Wherein: the first scratch-off state may be: the non-out-of-round fault alarm exists, and the scratch stripping area of the measuring point position is more than or equal to i ₀ mm*i ₀ mm, and the stripping depth is greater than or equal to j ₀ mm; the second scratch-off state may be: when there is a non-out-of-round fault alarm at the measuring point position, and the scratch stripping area of the measuring point position<i ₀ mm*i ₀ mm, and peel depth<j ₀ mm; the third scratch-off state may be: when the non-loss-of-circle fault feature exists or the non-loss-of-circle fault alarm exists at the measuring point position, the stripping area is scratched <i ₀ mm*i ₀ mm, and peel depth<j ₀ mm; one implementation manner of the step specifically comprises the following steps:

determining a preset scratch state of each measuring point of the whole vehicle according to the third analysis data and the preset scratch state, wherein the preset scratch state comprises a first scratch and peel state, a second scratch and peel state and a third scratch and peel state; when a non-out-of-round fault alarm exists at the measuring point position, the scratch stripping area of the measuring point position is more than or equal to i ₀ mm*i ₀ mm, and peel depth>j ₀ When the thickness is mm, the position of the measuring point is in a first scratch stripping state; when there is a non-out-of-round fault alarm at the measuring point position, and the scratch stripping area of the measuring point position<i ₀ mm*i ₀ mm, and peel depth<j ₀ When the thickness is mm, the position of the measuring point is in a second scratch stripping state; when the non-out-of-round fault feature or the non-out-of-round fault alarm exists at the position of the measuring point, the stripping area is scratched<i ₀ mm*i ₀ mm, and peel depth<j ₀ When the thickness is mm, the position of the measuring point is in a third scratch stripping state;

according to the preset scratch state of each measurement point, calculating a third ratio F of the number of measurement point positions belonging to the first scratch stripping state in the whole vehicle to the number of all measurement points of the whole vehicle ₁ The number of the measuring points belonging to the second scratch and peel state in the whole vehicle accounts for the third ratio F of the number of all the measuring points of the whole vehicle ₂ Belonging to a third scratch and peel state in the whole vehicleThe number of the measuring points accounts for the third ratio F of the number of all the measuring points of the whole vehicle ₃ 。

In a particular embodiment, i ₀ 、j ₀ 、k ₀ 、k ₁ And k ₂ The value of (2) can be flexibly set according to the actual application requirement, for example, i ₀ ＝3、j ₀ ＝1、k ₀ ＝1、k ₁ ＝2、k ₂ =3, the present application is not limited thereto.

S1054, calculating a third fault level according to a third weight of each measuring point, a third ratio corresponding to each preset scratch state and a preset third level calculation rule.

In S1054, specifically, a third ratio corresponding to the first scratch-off state is set to be F ₁ Setting the third ratio corresponding to the second scratch-off state as F ₂ Setting the third ratio corresponding to the third scratch-off state as F ₃ The method comprises the steps of carrying out a first treatment on the surface of the In some embodiments, the preset third level calculation rule may be set to:

a third ratio F corresponding to the first scratch-off state ₁ ≥l ₀ When third failure level=e ₁ '×F ₁ The method comprises the steps of carrying out a first treatment on the surface of the Wherein E is ₁ ' is the third weight E in the whole vehicle ₁ Average value of (E) ₁ ' all the third weights E corresponding to the whole vehicle measuring point ₁ And taking an average value.

A third ratio F corresponding to the first scratch-off state ₁ <l ₀ And a third ratio F corresponding to the second scratch-off state ₂ ≥l ₁ When the third failure level=2/3×e ₂ '×F ₂ The method comprises the steps of carrying out a first treatment on the surface of the Wherein E is ₂ ' is the third weight E in the whole vehicle ₂ Average value of (E) ₂ ' all the third weights E corresponding to the whole vehicle measuring point ₂ And taking an average value.

A third ratio F corresponding to the first scratch-off state ₁ <l ₀ And a third ratio F corresponding to the second scratch-off state ₂ <l ₁ And a third ratio F corresponding to the third scratch-off state ₃ ≤l ₂ When the third failure level=1/3×e ₂ '×F ₂ ；

A third ratio F corresponding to the first scratch-off state ₁ <l ₀ And a third ratio F corresponding to the second scratch-off state ₂ <l ₁ And a third ratio F corresponding to a third scratch-off state ₃ >l ₂ When the third failure level=1/3×e ₃ '×(1-F ₃ ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein E is ₃ ' is the third weight E in the whole vehicle ₃ Average value of (E) ₃ ' all the third weights E corresponding to the whole vehicle measuring point ₃ Taking an average value to obtain;

wherein l ₀ 、l ₁ And l ₂ A preset threshold for the third ratio.

In a specific embodiment, l ₀ 、l ₁ And l ₂ The value of (2) can be flexibly set according to the actual application requirement, for example, l ₀ ＝20％、l ₁ ＝20％、l ₂ =80%, the present application is not limited thereto. In general, turning is primarily related to the scratch-off area and depth, if the first scratch-off condition corresponds to a third ratio exceeding a certain ratio (i.e., third ratio F ₁ Not less than 20%), the scratch depth is very serious, the maintenance action is required to be unfolded, and the third failure level is assigned as E ₁ ' to third ratio F ₁ Is a product of (3).

The execution order of S103, S104, and S105 may be interchanged or may be executed simultaneously, which does not affect the implementation of the present embodiment.

S106, calculating to obtain a fault type index according to the first fault level, the second fault level and the third fault level;

in S106, specifically, the failure type index may be calculated according to the following formula:

D＝A*X+B*Y+C*Z

wherein D represents a failure type index, X represents a first failure level, Y represents a second failure level, Z represents a third failure level, and A, B, C are weighting coefficients corresponding to the failure levels, respectively; in a specific embodiment, the value of the weighting coefficient A, B, C may be flexibly set according to practical application requirements, for example, the weighting coefficient A, B, C may be set to 1, 4.5, and 1, respectively, which is not limited in this application. The calculated fault type index is used for subsequent operation and maintenance decision analysis, and the calculation process of the fault type index considers all wheelset states of the whole train and realizes the overall assessment of the states of the whole train and the whole train of the line.

S107, acquiring the residual life prediction data of the train wheels, and performing maintenance decision analysis according to the fault type indexes and the residual life prediction data to obtain maintenance guidance suggestions.

In S107, specifically, the remaining life prediction data may be a remaining mileage prediction value of a train wheel, which may be obtained from an existing remaining life prediction system of a train wheel, which is not limited in this application. By combining the fault type index and the residual life prediction data, the maintenance decision analysis is carried out, so that the scientificity of maintenance decision instructions can be improved, and the operation and maintenance efficiency and the service life of the train wheels can be improved. In some embodiments, one implementation of this step includes:

s1071, comparing the fault type index with a preset threshold value to obtain a comparison result;

s1072, outputting maintenance guidance suggestions according to the comparison result and the residual life prediction data.

Specifically, the preset threshold may be set according to actual application requirements, for example, the preset threshold may include a first threshold and a second threshold, where the first threshold is smaller than the second threshold, the fault type index is compared with the first threshold and the second threshold, and when the fault type index is smaller than or equal to the first threshold, a corresponding maintenance guidance suggestion is output: and turning and repairing only the position with the single measuring point exceeding the standard, wherein the maintenance time is the last repairing course of the residual mileage life in the residual life prediction data. When the fault type index is greater than the first threshold and less than the second threshold, outputting corresponding maintenance guidance suggestions: and (5) turning and repairing the whole vehicle. The first threshold and the second threshold may be set according to actual application requirements, which is not limited in this application.

Conventional wheel set health assessment and maintenance are based on geometric measurements, with maintenance standards varying from place to place. Some cities are subjected to simple statistical analysis such as manual measurement, actual running-in and the like, maintenance standards and maintenance strategies are determined empirically, wheel tread turning is carried out, the maintenance mode is large in data statistical analysis workload, mathematical models are not formed, requirements on the data strategies and the professional level of analysts are high, a unified method is not formed, the analysis output standard results are different, and the maintenance strategy results are influenced by the level capability of the analysts. The traditional wheel set health evaluation and maintenance method is easy to cause larger wheel set loss of the railway vehicle, and increases the maintenance cost of enterprises.

According to the embodiment, vibration data, impact data and fault diagnosis data in the vehicle-mounted fault diagnosis system of the train running part are obtained, wheel set state data in the wheel set geometric dimension detection system and wheel set production maintenance data are obtained, and preprocessing is carried out to obtain first processing data fused with the data, wherein the first processing data are used for subsequent train wheel fault analysis, and accuracy of the wheel fault analysis is improved; further, according to the first processing data, the whole train out-of-round fault analysis, the whole train wheel set size overrun analysis and the whole train non-out-of-round fault analysis are respectively carried out to obtain a first fault grade, a second fault grade and a third fault grade, and a fault type index is calculated, so that the overall assessment of the whole train and the whole train state of a line is realized; and finally, carrying out maintenance decision analysis according to the fault type index and the residual life prediction data of the train wheels to obtain maintenance guidance suggestions, improving the scientificity of maintenance decision instructions and being beneficial to improving the operation and maintenance efficiency and the service life of the train wheels. In summary, the above embodiments can accurately and effectively perform health status evaluation and maintenance guidance on wheels of a running gear of a train.

As shown in fig. 2, in another embodiment of the present application, there is provided a train running gear wheel health management system including:

the data acquisition unit 10 is used for acquiring vibration data, impact data and fault diagnosis data in the vehicle-mounted fault diagnosis system of the train running part, wheel set state data in the wheel set geometric dimension detection system and wheel set production maintenance data, wherein the fault diagnosis data is obtained by performing fault diagnosis analysis on the vibration data and the impact data by the vehicle-mounted fault diagnosis system of the running part;

a data processing unit 11, configured to pre-process vibration data, impact data, fault diagnosis data, wheel set status data, and wheel set production maintenance data, to obtain first processing data;

the first analysis unit 12 is configured to perform a whole vehicle out-of-round fault analysis according to the first processing data, so as to obtain a first fault level;

a second analysis unit 13, configured to perform overall wheel set size overrun analysis according to the first processing data, to obtain a second failure level;

a third analysis unit 14, configured to perform a non-out-of-round fault analysis on the whole vehicle according to the first processing data, to obtain a third fault level;

an index calculating unit 15, configured to calculate a fault type index according to the first fault level, the second fault level, and the third fault level;

The operation and maintenance decision unit 16 is configured to obtain the predicted remaining life data of the wheels of the train, and perform maintenance decision analysis according to the failure type index and the predicted remaining life data, so as to obtain maintenance guidance advice.

In other embodiments of the present application, in the wheel health management system of the train running part, the first processing data includes first analysis data, where the first analysis data includes a out-of-round fault type, an out-of-round fault alarm position, an out-of-round fault alarm level, an out-of-round fault feature, a vibration trend change rate, an out-of-round detection value, a radial circle runout, and a radial circle runout average value; the first analysis unit 12 is specifically configured to, when performing the whole vehicle out-of-round fault analysis according to the first processing data to obtain a first fault level:

acquiring first analysis data from the first processing data;

determining a preset out-of-round state of each measuring point of the whole vehicle according to the first analysis data and the preset out-of-round state, and calculating a first ratio of the number of measuring point positions belonging to each preset out-of-round state in the whole vehicle to the number of all measuring points of the whole vehicle;

And according to the first weight of each measuring point, a first ratio corresponding to each preset out-of-round state and a preset first grade calculation rule, calculating a first fault grade.

In other embodiments of the present application, in the train running part wheel health management system, the first processing data further includes second analysis data, where the second analysis data includes a rim thickness wear rate, a rim thickness average wear rate, a wheel diameter difference, an average wheel diameter difference, a wheel diameter value; the second analysis unit 13 is specifically configured to, when performing the overall wheel set size overrun analysis according to the first processing data to obtain the second failure level:

acquiring second analysis data from the first processing data;

according to the second analysis data and the preset abrasion state, determining the preset abrasion state of each measuring point of the whole vehicle, and calculating a second ratio of the number of measuring point positions belonging to each preset abrasion state in the whole vehicle to the number of all measuring points of the whole vehicle;

and calculating a second fault grade according to a second weight of each measuring point, a second ratio corresponding to each preset abrasion state and a preset second grade calculation rule.

In other embodiments of the present application, in the train running part wheel health management system, the first processing data further includes third analysis data, wherein the third analysis data includes a non-out-of-round fault type, a non-out-of-round fault alarm location, a non-out-of-round fault alarm level, a non-out-of-round fault feature, an abrasion stripping area, and a stripping depth; the third analysis unit 14 is specifically configured to, when performing the whole vehicle non-out-of-round fault analysis according to the first processing data to obtain a third fault level:

acquiring third analysis data from the first processing data;

according to the third analysis data and the preset scratch states, determining the preset scratch state of each measuring point of the whole vehicle, and calculating a third ratio of the number of measuring point positions belonging to each preset scratch state in the whole vehicle to the number of all measuring points of the whole vehicle;

and according to the third weight of each measuring point, the third ratio corresponding to each preset scratch state and the preset third grade calculation rule, calculating a third fault grade.

In other embodiments of the present application, in the wheel health management system of the running gear of the train, the maintenance decision unit 16 is specifically configured to, when performing maintenance decision analysis according to the failure type index and the remaining life prediction data, obtain maintenance guidance advice:

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. 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 application. Thus, the present application 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.

Claims

1. A method for managing wheel health of a train running gear, comprising:

2. The method of claim 1, wherein the first process data comprises first analysis data, wherein the first analysis data comprises out-of-round fault type, out-of-round fault alert location, out-of-round fault alert level, out-of-round fault signature, vibration trend change rate, out-of-round detection value, radial circle run out, and radial circle run out average; and according to the first processing data, performing whole vehicle out-of-round fault analysis to obtain a first fault level, including:

Acquiring the first analysis data from the first processing data;

3. The method as claimed in claim 2, wherein the preset first weight calculation rule is specifically:

wherein, the out-of-round coefficient=out-of-round fault alarm level×out-of-round detection value, and the out-of-round fault alarm level takes a value of b when no fault alarm is output ₀ The out-of-round fault alarm level takes a value of b when the alarm level is output at one level ₁ The out-of-round fault alarm level takes a value of b when the alarm second-level output ₂ ，a ₀ And a ₁ B is a preset threshold value of the out-of-roundness detection value ₀ 、b ₁ And b ₂ The preset value of the out-of-round fault alarm level is obtained.

4. A method as claimed in claim 3, wherein the determining the preset out-of-round state to which each measurement point of the whole vehicle belongs according to the first analysis data and the preset out-of-round state, and calculating a first ratio of the number of measurement point positions belonging to each of the preset out-of-round states in the whole vehicle to the number of all measurement points of the whole vehicle, specifically includes:

according to the first analysis data and the preset out-of-round state, each measuring point of the whole vehicle is determinedThe preset out-of-round state comprises a first out-of-round state, a second out-of-round state and a third out-of-round state; when the tread of the measuring point is out of round normally, or radial circle jumps <c ₀ When the measurement point is mm, the position of the measurement point is in the first out-of-round state; when the position of the measuring point is out of round, the first-level output is alarmed, or the radial circle runout is more than or equal to c ₀ mm is less than or equal to c ₁ When the measurement point is in mm, the position of the measurement point is in the second out-of-round state; when the position of the measuring point is out of round fault, the second level output is given out of round fault alarm, or the first level output is given out of round fault alarm and the radial circle jumps>c ₁ When mm, the measuring point position is in the third out-of-round state, wherein c ₀ And c ₁ A preset threshold value for radial circle runout;

5. The method as claimed in claim 4, wherein the preset first level calculation rule is specifically:

Wherein d ₀ And d ₁ A preset threshold for the first ratio.

6. The method of claim 1, wherein the first process data further comprises second analysis data, wherein the second analysis data comprises rim thickness wear rate, rim thickness average wear rate, wheel diameter difference, average wheel diameter difference, wheel diameter value; and performing overall wheel set size overrun analysis according to the first processing data to obtain a second fault level, wherein the method comprises the following steps of:

acquiring the second analysis data from the first processing data;

7. The method as claimed in claim 6, wherein the preset second weight calculation rule is specifically:

rim thickness wear rate at the site of the test point>e ₀ mm/ten thousand kilometers, orWheel diameter difference>f ₀ mm, or wheel diameter value<g ₀ The second weight C of the measuring point at mm ₁ =1- (1/(4-fold rim thickness wear rate+2-fold wheel diameter difference+ (wheel diameter value/840)));

8. The method as set forth in claim 7, wherein determining the preset wear state to which each measurement point of the whole vehicle belongs according to the second analysis data and the preset wear state, and calculating a second ratio of the number of measurement point positions belonging to each preset wear state in the whole vehicle to the number of all measurement points of the whole vehicle, includes:

determining the preset abrasion state of each measuring point of the whole vehicle according to the second analysis data and the preset abrasion state, wherein the preset abrasion state comprises a first abrasion state, a second abrasion state and a third abrasion state; rim thickness wear rate at the site of the test point>e ₀ mm/ten thousand km, or wheel diameter difference>f ₀ mm, or wheel diameter value<g ₀ When the measuring point is in mm, the position of the measuring point is in the first abrasion state; the thickness abrasion rate of the rim at the position of the measuring point is less than or equal to e ₀ mm/ten thousand kilometers and not less than e ₁ mm/ten thousand kilometers, and f ₁ The diameter difference of the wheel is less than or equal to mm and less than or equal to f ₀ mm, and the wheel diameter value is more than or equal to g ₀ mm, the measuring point positionAttributing to the second abrasion state; rim thickness wear rate at the site of the test point<e ₁ mm/ten thousand kilometers or wheel diameter difference<f ₁ mm, and the wheel diameter value is more than or equal to g ₀ When the measurement point is in mm, the position of the measurement point is in the third abrasion state;

9. The method as claimed in claim 8, wherein the preset second level calculation rule is specifically:

a second ratio D corresponding to the third wear state ₂ >h ₂ And a second ratio D corresponding to the second abrasion state ₁ ≤h ₁ And a second ratio D corresponding to the first wear state ₀ <h ₀ And a second ratio D corresponding to the first abrasion state ₀ A second ratio D corresponding to the second wear state ₁ Sum of<h ₃ When the second failure level=average wear rate of rim thickness×average wheel diameter difference×c ₃ '×D ₂ The method comprises the steps of carrying out a first treatment on the surface of the Wherein C is ₃ ' is the second weight C in the whole vehicle ₃ Average value of (2);

10. The method of claim 1, wherein the first process data further comprises third analysis data, wherein the third analysis data comprises a non-out-of-round fault type, a non-out-of-round fault alert location, a non-out-of-round fault alert level, a non-out-of-round fault signature, an scratch peel area, and a peel depth; and according to the first processing data, performing the whole vehicle non-out-of-round fault analysis to obtain a third fault level, including:

acquiring the third analysis data from the first processing data;

11. The method as claimed in claim 10, wherein the preset third weight calculation rule is specifically:

when there is a non-out-of-round fault alarm at the measuring point position, the scratch stripping surface at the measuring point positionProduct of<i ₀ mm*i ₀ mm, and peel depth<j ₀ In mm, the third weight E of the measuring point ₂ =2/3×non-out-of-round fault alert level×scratch peel area×peel depth;

12. The method as set forth in claim 11, wherein determining the preset scratch state to which each measurement point of the whole vehicle belongs according to the third analysis data and the preset scratch state, and calculating a third ratio of the number of measurement point positions belonging to each of the preset scratch states in the whole vehicle to the number of all measurement points of the whole vehicle, includes:

determining the preset scratch state of each measuring point of the whole vehicle according to the third analysis data and the preset scratch state, wherein the preset scratch state comprises a first scratch and peel state, a second scratch and peel state and a third scratch and peel state; when a non-out-of-round fault alarm exists at the measuring point position, the scratch stripping area of the measuring point position is more than or equal to i ₀ mm*i ₀ mm, and the stripping depth is greater than or equal to j ₀ When mm, the position of the measuring point is in the first scratch stripping state; when there is a non-out-of-round fault alarm at the measuring point position, and the scratch stripping area of the measuring point position<i ₀ mm*i ₀ mm, and peel depth<j ₀ mm, theThe position attribution of the measuring point is in the second scratch stripping state; when the non-loss-of-circle fault feature exists or the non-loss-of-circle fault alarm exists at the measuring point position, the stripping area is scratched<i ₀ mm*i ₀ mm, and peel depth<j ₀ When mm, the position of the measuring point is in the third scratch stripping state;

13. The method as claimed in claim 12, wherein the preset third level calculation rule is specifically:

when the first scratch peeling state corresponds to the third ratio F ₁ <l ₀ And a third ratio F corresponding to the second scratch-off state ₂ <l ₁ And a third ratio F corresponding to the third scratch-off state ₃ ≤l ₂ When the third failure level=1/3×e ₂ '×F ₂ ；

When the first scratch peeling state corresponds to the third ratio F ₁ <l ₀ And the second scratch is peeled offThird ratio F of state correspondence ₂ <l ₁ And a third ratio F corresponding to the third scratch-off state ₃ >l ₂ When the third failure level=1/3×e ₃ '×(1-F ₃ ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein E is ₃ ' is the third weight E in the whole vehicle ₃ Average value of (2);

wherein l ₀ 、l ₁ And l ₂ A preset threshold for the third ratio.

14. The method of claim 1, wherein said performing a maintenance decision analysis based on said fault type index and said residual life prediction data to obtain maintenance guidance recommendations comprises:

15. A train running gear wheel health management system, comprising: