Motor train unit axle temperature abnormity automatic detection method and related product

Abstract

The invention discloses a motor train unit axle temperature anomaly automatic detection method and related products, and belongs to the technical field of motor train unit axle temperature detection. According to the method, the axle temperature value is collected in real time and stored in the server, a complete and reliable axle temperature data basis is constructed, the problems of missing detection, error detection and insufficient detection frequency existing in manual inspection are avoided, and data integrity and accuracy are guaranteed; on-line running motor train units are identified based on the current-day motor train unit running plan, so that accurate target positioning and monitoring, non-running vehicle interference elimination, invalid data processing amount reduction and detection efficiency improvement are realized; the axle temperature data of the on-line motor train unit are obtained from the server regularly, the axle temperature characteristic data are calculated, abnormity judgment is carried out according to the preset threshold value range, and through real-time monitoring and intelligent analysis, axle temperature abnormity can be found in time, and major safety accidents are effectively prevented. According to the method, the abnormal state of the axle temperature of the motor train unit can be accurately and efficiently identified in real time, so that effective guidance is provided for overhaul and maintenance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of motor train unit axle temperature detection, and particularly relates to a motor train unit axle temperature anomaly automatic detection method and related products. BACKGROUND

[0002] With the rapid development of high-speed railways, motor train units as important means of transportation, their operation safety and reliability are of great concern. The axle temperature of a motor train unit is a key parameter reflecting its health status, and axle temperature anomalies can cause serious faults such as hot axle and cut axle, and even cause safety accidents. Therefore, real-time and accurate monitoring and analysis of the axle temperature of a motor train unit is an important link to ensure the safe operation of the motor train unit.

[0003] Currently, the axle temperature detection system of a motor train unit mainly collects axle temperature data through temperature sensors installed at specific positions of the axle, and the data is transmitted to a PHM (Prognostics and Health Management) system for storage and query. However, the existing PHM health management system only has an axle temperature data query function, and the analysis of axle temperature data completely relies on manual observation of temperature curves. This method has the following disadvantages: each motor train unit is equipped with hundreds of axle temperature sensors, and a large amount of axle temperature data is generated during daily operation, and the efficiency and accuracy of manual analysis are low, and this method is a post-detection method, that is, the axle temperature data is analyzed after the operation of the motor train unit ends, and it is impossible to detect abnormal states in real time during the operation of the motor train unit, and it is difficult to discover and handle potential faults in time.

[0004] Therefore, how to realize real-time detection of the axle temperature of a motor train unit and improve the detection efficiency and accuracy has become a technical problem to be solved by those skilled in the art. SUMMARY

[0005] The present application aims to provide a motor train unit axle temperature anomaly automatic detection method and related products to overcome the problem of low efficiency and accuracy of manual analysis of motor train unit axle temperature data in the prior art, and the inability to detect axle temperature anomaly states in real time.

[0006] The present application solves the above technical problems by the following technical solutions: A motor train unit axle temperature anomaly automatic detection method, comprising the following steps: Real-time collection of axle temperature values of all measurement sites of each carriage of all motor train units and storage in a server; Obtaining a motor train unit operation plan for the day and identifying motor train units that are online and running; Multiple axle temperature values ​​of each measuring position in each carriage of the EMU in operation are obtained from the server at regular intervals, and the axle temperature characteristic data of the same measuring position in the same carriage at the same time are calculated. The axle temperature characteristic data are judged to be abnormal based on the preset threshold range.

[0007] A further improvement of the present invention is that the preset threshold range is set according to the "EMU Running Gear Axle Temperature Monitoring Standards and Handling Requirements".

[0008] A further improvement of the present invention is that it also includes the following steps: providing maintenance suggestions based on the abnormality determination results.

[0009] A further improvement of the present invention is that all the measuring parts include: 1st side axle box bearing, 2nd side axle box bearing, large gearbox wheel side, large gearbox motor side, small gearbox wheel side, small gearbox motor side, motor transmission end, motor non-transmission end and motor stator.

[0010] A further improvement of the present invention is that the method of periodically obtaining multiple axle temperature values ​​of each measuring position of each carriage of the EMU running on the line from the server is specifically obtaining at least four axle temperature values ​​of each measuring position of each carriage of the EMU running on the line from the server every five minutes.

[0011] A further improvement of the present invention is that the axle temperature characteristic data includes first axle temperature characteristic data, second axle temperature characteristic data, third axle temperature characteristic data and fourth axle temperature characteristic data, wherein the first axle temperature characteristic data is the maximum axle temperature of the same measuring position in the same compartment at the same time, the second axle temperature characteristic data is the difference between the maximum and minimum axle temperatures of the same measuring position in the same compartment at the same time, the third axle temperature characteristic data is the maximum value of the average difference between each axle temperature value and the remaining axle temperature values ​​at the same measuring position in the same compartment at the same time, and the fourth axle temperature characteristic data is the maximum value of the axle temperature change rate at the same measuring position in the same compartment at the same time.

[0012] The present invention also provides an automatic detection system for abnormal axle temperature of an EMU, comprising: The first module is used to collect the axle temperature values ​​of all measuring parts of each carriage of all EMUs in real time and store them in the server; The second module is used to obtain the EMU operation plan for the day and identify the EMUs that are running online; The third module is used to periodically obtain multiple axle temperature values ​​of each measuring position in each carriage of the EMU running online from the server, calculate the axle temperature characteristic data of the same measuring position in the same carriage at the same time, and make abnormal judgments on the axle temperature characteristic data based on the preset threshold range.

[0013] The application further provides a computer device comprising a memory and a processor, the memory stores a computer program, and the processor realizes the steps of the motor train unit axle temperature abnormality automatic detection method when executing the computer program.

[0014] The application further provides a computer readable storage medium, which stores a computer program, and the computer program realizes the steps of the motor train unit axle temperature abnormality automatic detection method when executed by a processor.

[0015] The application further provides a computer program product comprising a computer program, and the computer program realizes the steps of the motor train unit axle temperature abnormality automatic detection method when executed by a processor.

[0016] Compared with the prior art, the positive progress effect of the application is that: The motor train unit axle temperature abnormality automatic detection method provided by the application can realize real-time, accurate and efficient identification of the axle temperature abnormality state of the motor train unit, thereby providing effective guidance for maintenance and repair.

[0017] Further, four axle temperature characteristic data, i.e., maximum value, maximum-minimum difference value, maximum average difference value and maximum jump value, are used to construct an analysis model, so as to comprehensively judge the axle temperature state from multiple dimensions such as single extreme value, temperature difference fluctuation and trend change, compared with the traditional single threshold value judgment, the potential abnormality (such as local overheating, temperature mutation or continuous temperature difference, etc.) can be more comprehensively identified. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings are included to provide a further understanding of the application, and constitute a part of the application. The illustrative embodiments of the application and their description serve to explain the application without limiting the application.

[0019] Figure 1 FIG. 1 is a flowchart of the motor train unit axle temperature abnormality automatic detection method of the application; Figure 2 FIG. 2 is a schematic diagram of the alarm condition in an embodiment of the application; Figure 3 FIG. 3 is a schematic diagram of the acquisition of the motor train unit operation condition on the same day in Embodiment One of the application. DETAILED DESCRIPTION

[0020] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of the present application.

[0021] In the description of the present application, it should be understood that the terms “including” and “comprising” indicate the presence of the described features, integers, steps, operations, elements, and / or components but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0022] It should also be understood that the terms used in the specification of the present application are only for the purpose of describing particular embodiments and are not intended to limit the present application. As used in the specification and the appended claims of the present application, the singular forms “a”, “an” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] It should be understood that, although the terms first, second, third, etc. can be used in the embodiments of the present application to describe a certain range, etc., these certain ranges should not be limited to these terms. These terms are only used to distinguish the certain ranges from each other. For example, the first certain range can also be called the second certain range, and similarly, the second certain range can also be called the first certain range without departing from the scope of the embodiments of the present application.

[0024] Depending on the context, the word “if’ as used herein can be interpreted to mean “when” or “while” or “in response to determining” or “in response to detecting.” Similarly, the phrase “if it is determined” or “if (a stated condition or event) is detected” can be interpreted to mean “when it is determined” or “in response to determining” or “when (a stated condition or event) is detected” or “in response to detecting (a stated condition or event)”.

[0025] Various structural schematic diagrams according to the disclosed embodiments of the present application are shown in the accompanying drawings. These diagrams are not drawn to scale, in which certain details are exaggerated for the purpose of clarity, and certain details can be omitted. The shapes of various regions, layers shown in the drawings and their relative sizes and positional relationships are only exemplary, and in actuality, there can be deviations due to manufacturing tolerances or technical limitations, and regions / layers with different shapes, sizes, relative positions can be additionally designed by those skilled in the art according to actual needs.

[0026] The application will be further described in detail below in combination with the drawings and specific embodiments, which are an explanation rather than a limitation of the application.

[0027] Referring to Figure 1 An EMU axle temperature abnormality automatic detection method, comprising the following steps: Real-time collection of axle temperature values of all measurement sites of each car of all EMUs and storage to a server; Obtaining a daily EMU operation plan and identifying an online running EMU; Timely obtaining of multiple axle temperature values of each car of the online running EMU from the server, calculation of axle temperature characteristic data of the same car and the same measurement site at the same time, and abnormality determination of the axle temperature characteristic data according to a preset threshold range.

[0028] Through real-time collection of axle temperature values and storage to the server, a complete and reliable axle temperature data basis is constructed, avoiding the problems of missed inspection, wrong inspection and insufficient inspection frequency of artificial inspection, ensuring data integrity and accuracy; based on the daily EMU operation plan, the online running EMU is identified, the monitoring target is accurately positioned, the interference of non-running vehicles is excluded, the invalid data processing amount is reduced, and the detection efficiency is improved; the axle temperature data of the online EMU is obtained from the server at regular intervals, the axle temperature characteristic data is calculated, and the abnormality is determined according to the preset threshold range, through real-time monitoring and intelligent analysis, the axle temperature abnormality can be found in time, and the occurrence of major safety accidents can be effectively prevented. The method of the application can identify the axle temperature abnormality state of the EMU in real time, accurately and efficiently, thereby providing effective guidance for maintenance.

[0029] Specifically, the preset threshold range is set according to the EMU running part axle temperature monitoring standard and disposal requirement.

[0030] Specifically, it further comprises the following steps: giving a maintenance suggestion according to the abnormality determination result.

[0031] Specifically, the all measurement sites include: 1st side axle box bearing, 2nd side axle box bearing, large gear box wheel side, large gear box motor side, small gear box wheel side, small gear box motor side, motor transmission end, motor non-transmission end and motor stator.

[0032] Specifically, the timely obtaining of multiple axle temperature values of each car of the online running EMU from the server is specifically obtaining at least four axle temperature values of each car of the online running EMU from the server every five minutes.

[0033] Specifically, the axle temperature characteristic data includes first axle temperature characteristic data, second axle temperature characteristic data, third axle temperature characteristic data and fourth axle temperature characteristic data, wherein the first axle temperature characteristic data is the maximum axle temperature value of the same measuring part in the same carriage at the same time, the second axle temperature characteristic data is the difference between the maximum axle temperature value and the minimum axle temperature value of the same measuring part in the same carriage at the same time, the third axle temperature characteristic data is the maximum average difference between each axle temperature value and the remaining axle temperature value of the same measuring part in the same carriage at the same time, and the fourth axle temperature characteristic data is the maximum axle temperature change rate of the same measuring part in the same carriage at the same time.

[0034] The four axle temperature characteristic data, i.e., the maximum value, the maximum-minimum difference value, the maximum average difference value and the maximum jump value, are used to construct an analysis model, so that the axle temperature state is comprehensively judged from multiple dimensions such as single extreme value, temperature difference fluctuation and trend change, and compared with the traditional single threshold value judgment, the potential abnormality (such as local overheating, temperature mutation or continuous temperature difference) can be more comprehensively identified.

[0035] The application also provides a high-speed train set axle temperature abnormality automatic detection system, comprising: A first module is configured to collect axle temperature values of all measuring parts of each carriage of all high-speed train sets in real time and store the axle temperature values in a server. A second module is configured to obtain a high-speed train set operation plan for the day and identify high-speed train sets that are in operation. A third module is configured to obtain multiple axle temperature values of each measuring part of each carriage of the high-speed train sets that are in operation from the server at regular time intervals, calculate axle temperature characteristic data of the same measuring part in the same carriage at the same time, and determine the abnormality of the axle temperature characteristic data according to a preset threshold range.

[0036] Embodiment I A high-speed train set axle temperature abnormality automatic detection method comprises the following steps: Step 1: A temperature sensor installed at a specific position of a high-speed train set axle is remotely transmitted to a high-speed train set health management system server, wherein the specific position includes 1st side axle box bearing, 2nd side axle box bearing, large gear box wheel side, large gear box motor side, small gear box wheel side, small gear box motor side, motor transmission end, motor non-transmission end and motor stator.

[0037] Step 2: Referring to Figure 3 , the high-speed train set health management system is used to obtain the high-speed train set operation for the day and identify high-speed train sets that are in operation. Step 3: The high-speed train set health management system is used to obtain axle temperature data of the high-speed train sets that are in operation at regular time intervals, wherein the regular time interval is 5 minutes.

[0038] Step 4: Calculate the axle temperature characteristic data by analyzing the axle temperature data of the online running motor train unit; wherein the axle temperature characteristic data comprises: first axle temperature characteristic data: calculate the maximum value of the four temperature values of the same measurement site in the same carriage at the same time; second axle temperature characteristic data: calculate the difference between the maximum value and the minimum value of the four temperature values of the same measurement site in the same carriage at the same time, which is referred to as the maximum minimum difference value; third axle temperature characteristic data: calculate the maximum value of the difference between each temperature value and the average value of the remaining three temperature values of the four temperature values of the same measurement site in the same carriage at the same time, which is referred to as the maximum average difference value; fourth axle temperature characteristic data: calculate the maximum value of the change rate of the four temperature values of the same measurement site in the same carriage at the same time, which is referred to as the maximum jump value; Step 5: According to the "prediction", "early warning", "alarm" three levels, the abnormal axle temperature characteristic data is identified, and the maintenance suggestion corresponding to the warning level is given; wherein the abnormal axle temperature characteristic data is as follows: The first axle temperature characteristic data: the maximum value is within the preset temperature range; The second axle temperature characteristic data: the maximum minimum difference value is within the preset temperature difference range; The third axle temperature characteristic data: the maximum average difference value is within the preset temperature difference range; The fourth axle temperature characteristic data: the maximum jump value is within the preset change rate range.

[0039] The motor train unit axle temperature abnormality automatic detection method provided in the embodiment can realize timely discovery of the abnormal running state of the motor train unit, issue a warning and a fault disposal suggestion, guide the maintenance personnel to maintain the abnormal part, and ensure the safe operation of the motor train unit, by automatically counting the motor train units running on the same day, automatically acquiring the axle temperature data at regular intervals, and analyzing the axle temperature data in real time, taking the maximum value, the maximum minimum difference value, the maximum average difference value, and the maximum jump value as the judgment basis.

[0040] The preset threshold range corresponding to the four kinds of abnormal axle temperature characteristics for judging the warning level, and the maintenance suggestion corresponding to the warning level can be dynamically adjusted according to the "motor train unit running part axle temperature monitoring standard and disposal requirement", and can adapt to the maintenance work of motor train units of different types and under different conditions.

[0041] In a specific embodiment of the present application, the preset threshold range corresponding to the abnormal axle temperature characteristic data in step 5 can be dynamically adjusted according to the "motor train unit running part axle temperature monitoring standard and disposal requirement", and the preset threshold range of each item of the axle box temperature "alarm" warning of the CRH380AL type motor train unit is taken as an example: The maximum value is within [140℃, +∞), the maximum average difference value is within [65℃, +∞), and the maximum jump value is within [15℃ / min, +∞). See Figure 2When the maximum jump value reaches 15℃ / min, an alarm is given.

[0042] In a specific embodiment of the present application, the warning is given according to three levels of "prediction", "early warning" and "alarm", and the maintenance suggestion corresponding to the warning level is given, which can be dynamically adjusted according to the "standard for monitoring and disposal of the running part of the EMU" for the "prediction" warning of the axle box bearing temperature of the CRH380AL EMU. The vehicle mechanic controls the temperature, the command center and the EMU allocation workshop monitor the temperature through the PHM system, and prepare the fault escalation disposal scheme. If the temperature continues to rise to 115℃ or the temperature difference reaches 45℃, the driver is immediately notified to run at a speed of 260km / h, and after 5 minutes of speed reduction, if the axle temperature still shows an upward trend, the train is immediately controlled to run at a speed of 240km / h, and after 5 minutes of speed reduction, if the axle temperature still shows an upward trend, the speed is gradually reduced by 20km / h, and each speed reduction lasts for 5 minutes. When the temperature of the axle box bearing is less than 110℃ and the temperature difference is less than 40℃, the speed is gradually increased by 20km / h, and each speed increase lasts for 5 minutes, until the normal speed operation is restored.

[0043] Based on the same inventive concept, the embodiments of the present application provide a computer device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the automatic detection method for the abnormal axle temperature of the EMU when executing the computer program. The memory can include a memory, such as a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk memory. The processor, network interface and memory are connected to each other through an internal bus, which can be an industry standard architecture bus, a peripheral component interconnect standard bus, an extended industry standard architecture bus, etc. The bus can be divided into an address bus, a data bus and a control bus. The memory is used to store programs, and specifically, the programs can include program codes, and the program codes include computer operation instructions. The memory can include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0044] Based on the same inventive concept, the embodiments of the present application provide a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the automatic detection method for the abnormal axle temperature of the EMU. Specifically, the computer readable storage medium includes but is not limited to, for example, a volatile memory and / or a non-volatile memory. The volatile memory can include RAM (random access memory) and / or cache memory, etc. The non-volatile memory can include ROM (read only memory), hard disk, flash memory, optical disk, magnetic disk, etc.

[0045] Based on the same inventive concept, the embodiment of the present application provides a computer program product, which comprises a computer program stored on a computer readable storage medium, and the computer program comprises program instructions, which, when executed by a computer device, cause the computer device to perform the steps of the motor train unit axle temperature abnormality automatic detection method.

[0046] Those skilled in the art should understand that the embodiments of the present application can be provided as a method or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM (Compact Disc Read-Only Memory), optical storage, etc.) containing computer-usable program code.

[0047] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the processor of the computer device or other programmable data processing devices produce a device implemented in the flowcharts and / or block diagrams. Figure 1 The device that realizes the functions specified in one flow or multiple flows and / or one block or multiple blocks.

[0048] These computer program instructions can also be stored in a computer readable memory capable of guiding the computer device or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product comprising instruction devices, which realize the functions specified in one flow or multiple flows and / or one block or multiple blocks in the flowcharts and / or block diagrams. Figure 1 The device that realizes the functions specified in one flow or multiple flows and / or one block or multiple blocks. Figure 1 The device that realizes the functions specified in one flow or multiple flows and / or one block or multiple blocks.

[0049] These computer program instructions can also be loaded into the computer device or other programmable data processing device, so that a series of operation steps are performed on the computer device or other programmable device to produce a processing implemented by the computer device, so that the instructions executed on the computer device or other programmable device provide steps for realizing the functions specified in one flow or multiple flows and / or one block or multiple blocks in the flowcharts and / or block diagrams. Figure 1 The device that realizes the functions specified in one flow or multiple flows and / or one block or multiple blocks. Figure 1 The device that realizes the functions specified in one flow or multiple flows and / or one block or multiple blocks.

[0050] While the preferred embodiments of the application have been described, additional variations and modifications can be made to these embodiments by those skilled in the art once they have the benefit of the present disclosure without departing from the spirit and scope of the application. Accordingly, it is intended that the appended claims include all such modifications and variations as fall within the scope of the present application.

[0051] It is apparent that those skilled in the art can make various changes and modifications to the application without departing from the spirit and scope of the application. It is therefore intended that the present application cover all such changes and modifications that are within its scope.

Claims

1. A method for automatically detecting abnormal axle temperature of an EMU, characterized in that: The following steps are involved: Collect the axle temperature values ​​of all measuring parts of each carriage of all EMUs in real time and store them on the server; Obtain the daily train operation plan and identify the trains in operation; Multiple axle temperature values ​​of each measuring position in each carriage of the EMU in operation are obtained from the server at regular intervals, and the axle temperature characteristic data of the same measuring position in the same carriage at the same time are calculated. The axle temperature characteristic data are judged to be abnormal based on the preset threshold range.

2. The method for automatically detecting abnormal axle temperature of an EMU according to claim 1, characterized in that: The preset threshold range is set according to the "EMU Running Gear Axle Temperature Monitoring Standards and Handling Requirements".

3. The method for automatically detecting abnormal axle temperature of an EMU according to claim 1, characterized in that: The following steps are also included: Provide maintenance suggestions based on abnormality judgment results.

4. The method for automatically detecting abnormal axle temperature of an EMU according to claim 1, characterized in that: All the measurement parts include: 1st side axle box bearing, 2nd side axle box bearing, large gearbox wheel side, large gearbox motor side, small gearbox wheel side, small gearbox motor side, motor transmission end, motor non-transmission end and motor stator.

5. The method for automatically detecting abnormal axle temperature of an EMU according to claim 4, characterized in that: The method of regularly obtaining multiple axle temperature values ​​of each measuring position of each carriage of the EMU running on the line from the server specifically obtains at least four axle temperature values ​​of each measuring position of each carriage of the EMU running on the line from the server every five minutes.

6. The method for automatically detecting abnormal axle temperature of an EMU according to claim 1, characterized in that: The axle temperature characteristic data includes first axle temperature characteristic data, second axle temperature characteristic data, third axle temperature characteristic data and fourth axle temperature characteristic data, wherein the first axle temperature characteristic data is the maximum axle temperature of the same measuring position in the same compartment at the same time, the second axle temperature characteristic data is the difference between the maximum and minimum axle temperatures of the same measuring position in the same compartment at the same time, the third axle temperature characteristic data is the maximum value of the average difference between each axle temperature value and the remaining axle temperature values ​​at the same measuring position in the same compartment at the same time, and the fourth axle temperature characteristic data is the maximum value of the axle temperature change rate of the same measuring position in the same compartment at the same time.

7. An automatic detection system for abnormal axle temperature of an EMU, characterized by: include: The first module is used to collect the axle temperature values ​​of all measuring parts of each carriage of all EMUs in real time and store them in the server; The second module is used to obtain the EMU operation plan for the day and identify the EMUs that are running online; The third module is used to periodically obtain multiple axle temperature values ​​of each measuring position in each carriage of the EMU running online from the server, calculate the axle temperature characteristic data of the same measuring position in the same carriage at the same time, and make abnormal judgments on the axle temperature characteristic data based on the preset threshold range.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for automatically detecting abnormal axle temperature of an EMU as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for automatically detecting abnormal axle temperature of an EMU as described in any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for automatically detecting abnormal axle temperature of an EMU as described in any one of claims 1 to 6 are implemented.

Images