A train running gear axle box defect detection and analysis method and related device

Abstract

The present application provides a method for finding and analyzing axle box defects of a train running gear and a related device, the method comprising: step 100, obtaining the vibration pulse signal of each axle box of the train running gear; step 200, calculating the correlation coefficient function of the vibration pulse signal of any two axle boxes, and traversing all axle boxes; step 300, judging whether there is rail damage according to the maximum peak number of each correlation coefficient function; step 400, if there is rail damage, filtering out the rail damage pulse data, and obtaining the axle box damage pulse data; step 500, judging whether there is an axle box defect according to the axle box damage pulse data. By analyzing the correlation coefficient function of the vibration pulse signal between any two axle boxes, judging whether there is rail damage according to the maximum peak number, and if there is rail damage, filtering out the rail damage pulse data, thereby improving the accuracy of judging the axle box damage.

Description

Technical Field

[0001] The invention relates to a rail train axle box damage detection technology, and in particular to a train running gear axle box disease detection and analysis method and related devices. Background Art

[0002] The running gear is the part of the train that runs along the line under the action of traction power. The function of the running gear is to ensure that the vehicle runs flexibly, safely and smoothly along the rails and passes through curves; reliably bear various forces acting on the vehicle and transmit them to the rails; alleviate the mutual impact between the vehicle and the rails, reduce vehicle vibration, ensure sufficient running stability and good running quality; have a reliable braking mechanism, so that the vehicle has a good braking effect. The component that is sleeved on the axle neck to connect the wheelset and the bogie frame or the two-axle vehicle body is referred to as the axle box. Its function is to transfer the weight and load of the vehicle body to the wheelset, lubricate the axle neck, reduce friction, and reduce running resistance.

[0003] When the axle box is damaged, a characteristic spectrum will be generated during train operation. By analyzing the characteristic spectrum, the fault characteristics can be found, so as to detect the axle box fault early. However, due to the strong interference caused by various random vibrations during train operation, the existing patent CN201210089445.2 proposes a method for detecting bearings using bearing fault vibration signals, which filters out unstable signals through multiple sampling and correlation calculation, leaving low-frequency pulse signals, including bearing fault signals.

[0004] However, when the rail is also damaged, since it is also a stable low-frequency pulse signal, this method will process the pulse signal generated by the rail damage as a bearing fault signal, causing the method to fail. Summary of the invention

[0005] The purpose of the present invention is to provide a method and a related device for finding and analyzing axle box defects of a train running gear, which can improve the accuracy of judging axle box damage by filtering out interference caused by rail damage.

[0006] A brief summary of one or more aspects is given below to provide a basic understanding of these aspects. This summary is not an exhaustive overview of all conceived aspects, and is neither intended to identify the key or critical elements of all aspects nor to define the scope of any or all aspects. Its only purpose is to give some concepts of one or more aspects in a simplified form as a prelude to a more detailed description that will be given later.

[0007] According to one aspect of the present invention, a method for finding and analyzing axle box defects of a train running gear is provided, comprising:

[0008] Step 100, obtaining vibration pulse signals of each axle box of the train running gear;

[0009] Step 200, calculating the correlation coefficient function of the vibration pulse signals of any two axle boxes, and traversing all axle boxes;

[0010] Step 300: judging whether there is rail damage according to the maximum peak number of each correlation coefficient function;

[0011] Step 400: If there is rail damage, filter out the rail damage pulse data to obtain the axle box damage pulse data;

[0012] Step 500: Determine whether there is an axle box defect based on the axle box damage pulse data.

[0013] In one embodiment, in step 200, the correlation coefficient function formula of the vibration pulse signals of any two axle boxes is:

[0014] F ab (t) = ∫f a (τ-t)×f b (τ)×dτ,

[0015] where f a (τ-t) is the vibration pulse signal function of the ath axle box with τ as the independent variable, f b (τ) is the vibration pulse signal function of the b-th axle box with τ as the independent variable.

[0016] In one embodiment, step 300 specifically includes: analyzing the maximum peak number of each correlation coefficient function, and if there is a correlation coefficient function with 0 maximum peak, it is determined that there is no rail damage.

[0017] In one embodiment, the step 300 further includes:

[0018] If there is no correlation coefficient function with 0 maximum peak value, determine whether the maximum peak numbers of the correlation coefficient functions of each axle box are the same; if they are the same, it is determined that only rail damage exists and step 400 is not performed; if they are different, it is determined that both rail damage and axle box damage exist and step 400 is entered.

[0019] In one embodiment, step 400 includes: performing autocorrelation analysis on the axle box with the maximum peak value, and obtaining axle box damage pulse data through a filtering algorithm.

[0020] In one embodiment, step 500 includes: judging whether there is an axle box disease according to the peak characteristics of the axle box damage pulse signal.

[0021] According to a second aspect of the present invention, there is provided a disease analysis device, comprising:

[0022] A data acquisition module is used to acquire vibration pulse signals of each axle box of the train running gear;

[0023] A correlation coefficient function calculation module is used to calculate the correlation coefficient function of the vibration pulse signals of any two axle boxes and traverse all axle boxes;

[0024] A judgment module, used for judging whether there is rail damage according to the maximum peak number of each correlation coefficient function;

[0025] A data filtering module is used to filter out rail damage pulse data when there is rail damage, and obtain axle box damage pulse data;

[0026] The disease analysis module is used to determine whether there is an axle box disease based on the axle box damage pulse data.

[0027] According to a third aspect of the present invention, there is provided a device comprising a memory and a processor; the memory is used to store a computer program; the processor is used to implement the train running gear axle box defect detection and analysis method as described in the first aspect when executing the computer program.

[0028] According to a fourth aspect of the present invention, a readable storage medium is provided, on which a program is stored. When the program is executed by a processor, the method for finding and analyzing defects in an axle box of a train running gear as described in the first aspect is implemented.

[0029] The beneficial effect of the embodiment of the present invention is: by analyzing the correlation coefficient function of the vibration pulse signal between any two axle boxes, it is determined whether there is rail damage based on the maximum peak number. If there is rail damage, the rail damage pulse data is filtered out, thereby improving the accuracy of the axle box damage judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0031] The above features and advantages of the present invention can be better understood after reading the detailed description of the embodiments of the present disclosure in conjunction with the following drawings. In the drawings, the components are not necessarily drawn to scale, and components with similar related properties or features may have the same or similar reference numerals.

[0032] Figure 1 is a schematic diagram of the method flow of an embodiment of the present application;

[0033] Figure 2 It is a schematic diagram of a device module of an embodiment of the present application. DETAILED DESCRIPTION

[0034] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. Note that the aspects described below in conjunction with the accompanying drawings and specific embodiments are only exemplary and should not be construed as limiting the scope of protection of the present invention in any way.

[0035] like Figure 1 As shown, the embodiment of the present application provides a method for finding and analyzing axle box defects of a train running gear, comprising:

[0036] Step 100, obtaining vibration pulse signals of each axle box of the train running gear;

[0037] Step 200, calculating the correlation coefficient function of the vibration pulse signals of any two axle boxes, and traversing all axle boxes;

[0038] The correlation coefficient function formula of the vibration pulse signals of any two axle boxes is:

[0039] F ab (t) = ∫f a (τ-t)×f b (τ)×dτ,

[0040] where f a (τ-t) is the vibration pulse signal function of the ath axle box with τ as the independent variable, f b (τ) is the vibration pulse signal function of the b-th axle box with τ as the independent variable.

[0041] Step 300: judging whether there is rail damage according to the maximum peak number of each correlation coefficient function;

[0042] Step 300 specifically includes:

[0043] The maximum peak numbers of the correlation coefficient functions are analyzed. If there is a correlation coefficient function with 0 maximum peak, it is determined that there is no rail damage.

[0044] If there is no correlation coefficient function with 0 maximum peak value, determine whether the maximum peak numbers of the correlation coefficient functions of each axle box are the same; if they are the same, it is determined that only rail damage exists and step 400 is not performed; if they are different, it is determined that both rail damage and axle box damage exist and step 400 is entered.

[0045] For example, if 2 axle boxes are damaged and the rail is not damaged, there will be a maximum peak between the two damaged axle boxes, and the correlation coefficient function of other axle boxes will not have peaks. If 0 axle boxes are damaged and the rail is 1 damaged, there will be 1 maximum peak in the correlation coefficient function of any two axle boxes. If 2 axle boxes are damaged and the rail is also damaged, there will be 3 maximum peaks between the two damaged axle boxes, and 1 maximum peak will appear between the other axle boxes.

[0046] It can be seen that this method is applicable to the situation where at least two axle boxes are damaged. For the situation where only one axle box is damaged, it is necessary to use the existing axle box disease detection method (such as the axle temperature detection method) to make a judgment.

[0047] Step 400: If there is rail damage, filter out the rail damage pulse data to obtain the axle box damage pulse data;

[0048] For the axle box with the largest peak value, autocorrelation analysis is performed, and the axle box damage pulse data is obtained through filtering algorithm.

[0049] Step 500: Determine whether there is an axle box defect based on the axle box damage pulse data.

[0050] Determine whether there is an axle box defect based on the peak characteristics of the axle box damage pulse signal.

[0051] Corresponding to the above method, the embodiment of the present application further provides a train running gear axle box disease detection and analysis device, comprising:

[0052] The data acquisition module 201 is used to acquire the vibration pulse signal of each axle box of the train running gear;

[0053] A correlation coefficient function calculation module 202 is used to calculate the correlation coefficient function of the vibration pulse signals of any two axle boxes and traverse all axle boxes;

[0054] A judgment module 203, used to judge whether there is rail damage according to the maximum peak number of each correlation coefficient function;

[0055] The data filtering module 204 is used to filter out the rail damage pulse data when there is rail damage, and obtain the axle box damage pulse data;

[0056] The disease analysis module 205 is used to determine whether there is an axle box disease based on the axle box damage pulse data.

[0057] It is easy to understand that the embodiment of the present application also provides a train running gear axle box disease detection and analysis device, including a memory and a processor;

[0058] The memory may be used to store instructions, programs, codes, code sets or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the above-mentioned train running gear axle box defect detection and analysis method, etc.; the data storage area may store data involved in the above-mentioned train running gear axle box defect detection and analysis method, etc.

[0059] The processor may include one or more processing cores. The processor calls the data stored in the memory by running or executing the instructions, programs, code sets or instruction sets stored in the memory, performs various functions of the present application and processes data. The processor may be at least one of a special purpose integrated circuit, a digital signal processor, a digital signal processing device, a programmable logic device, a field programmable gate array, a central processing unit, a controller, a microcontroller and a microprocessor. It is understandable that for different devices, the electronic device used to implement the above-mentioned processor function can also be other, and the embodiments of the present application are not specifically limited.

[0060] If the above method of the embodiment of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read Only Memory), a magnetic disk or an optical disk. In this way, the embodiment of the present invention is not limited to any specific combination of hardware and software.

[0061] In summary, the train running gear axle box defect detection and analysis method and device provided in the embodiments of the present application can filter out the pulse signal caused by rail damage when both rail damage and axle box damage exist, thereby improving the accuracy of the axle box damage signal analysis.

[0062] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0063] The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein, but should be granted the widest scope consistent with the principles and novel features disclosed herein.

[0064] The above description is only a preferred example of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for finding and analyzing axle box defects of a train running gear, characterized in that: include: Step 100, obtaining vibration pulse signals of each axle box of the train running gear; Step 200, calculating the correlation coefficient function of the vibration pulse signals of any two axle boxes, and traversing all axle boxes; Step 300, judging whether there is rail damage according to the maximum peak number of each correlation coefficient function; Step 400: If there is rail damage, filter out the rail damage pulse data to obtain the axle box damage pulse data; Step 500: Determine whether there is an axle box defect based on the axle box damage pulse data.

2. The train running gear axle box defect detection and analysis method according to claim 1 is characterized in that: In step 200, the correlation coefficient function formula of the vibration pulse signals of any two axle boxes is: F ab (t)=∫f a (τ-t)×f b (τ)×dτ, where f a (τ-t) is the vibration pulse signal function of the ath axle box with τ as the independent variable, f b (τ) is the vibration pulse signal function of the b-th axle box with τ as the independent variable.

3. The train running gear axle box defect detection and analysis method according to claim 2 is characterized in that: The step 300 specifically includes: analyzing the maximum peak number of each correlation coefficient function, and if there is a correlation coefficient function with 0 maximum peak, it is determined that there is no rail damage.

4. The train running gear axle box defect detection and analysis method according to claim 3 is characterized in that: The step 300 further includes: If there is no correlation coefficient function with 0 maximum peak value, determine whether the maximum peak numbers of the correlation coefficient functions of each axle box are the same; if they are the same, it is determined that only rail damage exists and step 400 is not performed; if they are different, it is determined that both rail damage and axle box damage exist and step 400 is entered.

5. The train running gear axle box defect detection and analysis method according to claim 4 is characterized in that: The step 400 includes: performing autocorrelation analysis on the axle box with the maximum peak value, and obtaining axle box damage pulse data through a filtering algorithm.

6. The train running gear axle box defect detection and analysis method according to claim 5 is characterized in that: The step 500 includes: judging whether there is an axle box disease according to the peak characteristics of the axle box damage pulse signal.

7. A disease analysis device, comprising: A data acquisition module is used to acquire vibration pulse signals of each axle box of the train running gear; A correlation coefficient function calculation module is used to calculate the correlation coefficient function of the vibration pulse signals of any two axle boxes and traverse all axle boxes; A judgment module, used for judging whether there is rail damage according to the maximum peak number of each correlation coefficient function; A data filtering module is used to filter out rail damage pulse data when there is rail damage, and obtain axle box damage pulse data; The disease analysis module is used to determine whether there is an axle box disease based on the axle box damage pulse data.

8. A device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to implement the train running gear axle box defect detection and analysis method as described in any one of claims 1 to 6 when executing the computer program.

9. A readable storage medium, characterized in that: The readable storage medium stores a program, and when the program is executed by the processor, the train running gear axle box defect detection and analysis method as described in any one of claims 1 to 6 is implemented.

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