Method and device for determining the health status of rolling bearings of EMU

Abstract

The present invention provides a method and device for determining the health status of rolling bearings of EMUs, the method comprising: obtaining a bearing acoustic feature database based on historical bearing acoustic monitoring data and bearing maintenance information; inputting the bearing acoustic feature database into a pre-established bearing fault diagnosis model to obtain relevant feature parameters; determining a bearing health index based on the relevant feature parameters, and using the bearing health index to obtain a bearing health index database; inputting newly added acoustic monitoring data into the bearing fault diagnosis model to obtain a health index, and comparing the health index with the bearing health index database to determine the bearing health status. The present invention solves the problem of monitoring the health status of rolling bearings, breaks through the limitations of existing rolling bearing safety monitoring equipment, realizes the assessment of the health status of rolling bearings, provides support for rolling bearing improvements and rolling bearing safety monitoring optimization, and has strong engineering application value.

Description

Technical Field

[0001] The invention relates to the technical field of railway vehicle monitoring and detection, and in particular to a method and a device for determining the health state of a rolling bearing of a train set. Background Art

[0002] Rolling bearings are one of the main sources of failure in the running gear of EMUs and are also one of the key components that affect the safety of EMU operation. Strengthening the status monitoring and fault diagnosis technology of EMU rolling bearings, giving full play to the use efficiency of EMU rolling bearings, and avoiding safety accidents are higher requirements for the use and maintenance of EMU rolling bearings in the new era.

[0003] EMU rolling bearings operate in a high-speed, high-impact environment most of the time and are one of the components with the worst working environment in EMUs. Due to the characteristics of their operating environment, once a rolling bearing fails, if it is not discovered and handled in time, it will cause accidents such as hot axles, burning axles, and shearing axles. Therefore, in the daily use of EMUs, EMU rolling bearings are equipped with on-board axle temperature sensors to monitor abnormal bearing operation temperatures. In addition, the EMU rolling bearing early fault trackside acoustic diagnosis system (TADS-1) that is being planned can also monitor rolling bearings online for safety. However, at present, neither of these two technical solutions can evaluate the health status of EMU rolling bearings and can only serve as a guarantee for EMU driving safety. The process from the occurrence of a fault to the burning of a hot axle in EMU rolling bearings is relatively long. Faults such as the peeling of the outer ring, inner ring, and roller surface of the bearing will not show obvious high temperatures at the beginning. Abnormal high temperatures in the bearing operation are basically in the middle and late stages of the fault. Therefore, on-board axle temperature monitoring cannot evaluate the health status of the bearing.

[0004] At present, although TADS-1 can detect faulty bearings earlier than the on-board axle temperature monitoring system, it is found through actual application that TADS-1 also has problems such as missed reports and unstable alarm levels. It has insufficient ability to diagnose inner ring and roller faults, and TADS-1 does not have the function of evaluating the health status of bearings. Summary of the invention

[0005] In view of the problems existing in the prior art, the main purpose of the embodiments of the present invention is to provide a method and device for determining the health status of a rolling bearing of an EMU, so as to evaluate the health status of the rolling bearing of the EMU.

[0006] In order to achieve the above object, an embodiment of the present invention provides a method for determining the health status of a rolling bearing of an EMU, the method comprising:

[0007] Acquiring historical bearing acoustic monitoring data and bearing maintenance information, and obtaining a bearing acoustic feature database based on the historical bearing acoustic monitoring data and bearing maintenance information;

[0008] Inputting the bearing acoustic feature database into a pre-established bearing fault diagnosis model to obtain relevant feature parameters;

[0009] Determine a bearing health index according to the relevant characteristic parameters, and obtain a bearing health index database using the bearing health index;

[0010] The newly added acoustic monitoring data is input into the bearing fault diagnosis model, and the health index is calculated, and the health index is compared with the bearing health index database to determine the bearing health status.

[0011] Optionally, in one embodiment of the present invention, the historical bearing acoustic monitoring data includes the bearing installation position, the vehicle number of the bearing, the monitoring time, the passing speed, the vehicle running direction and the bearing acoustic original signal file.

[0012] Optionally, in one embodiment of the present invention, the bearing maintenance information includes the bearing accumulated mileage and the bearing maintenance record.

[0013] Optionally, in an embodiment of the present invention, the step of inputting the bearing acoustic feature database into a pre-established bearing fault diagnosis model to obtain relevant feature parameters includes:

[0014] The bearing acoustic feature database is input into a pre-established bearing fault diagnosis model, and a plurality of sub-frequency bands are obtained by using a wavelet algorithm;

[0015] The bearing fault diagnosis model performs fault information identification and bandpass filtering on the sub-frequency band to obtain a fault frequency band, and calculates characteristic parameters of the fault frequency band to obtain relevant characteristic parameters; wherein the relevant characteristic parameters include dimensional characteristic parameters and dimensionless characteristic parameters.

[0016] The embodiment of the present invention further provides a device for determining the health status of a rolling bearing of an EMU, the device comprising:

[0017] A data acquisition module, used to acquire historical bearing acoustic monitoring data and bearing maintenance information, and obtain a bearing acoustic feature database based on the historical bearing acoustic monitoring data and bearing maintenance information;

[0018] A diagnostic model module, used to input the bearing acoustic feature database into a pre-established bearing fault diagnosis model to obtain relevant feature parameters;

[0019] A health index module, used to determine the bearing health index according to the relevant characteristic parameters, and obtain a bearing health index database using the bearing health index;

[0020] The health status module is used to input the newly added acoustic monitoring data into the bearing fault diagnosis model, calculate the health index, and compare the health index with the bearing health index database to determine the bearing health status.

[0021] Optionally, in one embodiment of the present invention, the historical bearing acoustic monitoring data includes the bearing installation position, the vehicle number of the bearing, the monitoring time, the passing speed, the vehicle running direction and the bearing acoustic original signal file.

[0022] Optionally, in one embodiment of the present invention, the bearing maintenance information includes the bearing accumulated mileage and the bearing maintenance record.

[0023] Optionally, in one embodiment of the present invention, the diagnosis model module includes:

[0024] A sub-frequency band unit, used to input the bearing acoustic feature database into a pre-established bearing fault diagnosis model, and obtain multiple sub-frequency bands using a wavelet algorithm;

[0025] The characteristic parameter unit is used for the bearing fault diagnosis model to perform fault information identification and bandpass filtering on the sub-frequency band to obtain the fault frequency band, and to calculate the characteristic parameters of the fault frequency band to obtain relevant characteristic parameters; wherein the relevant characteristic parameters include dimensional characteristic parameters and dimensionless characteristic parameters.

[0026] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the program.

[0027] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for executing the above method.

[0028] The present invention determines the health status of EMU rolling bearings based on acoustic signals, solves the problem of rolling bearing health status monitoring, breaks through the limitations of existing rolling bearing safety monitoring equipment, realizes the assessment of rolling bearing health status, and provides support for rolling bearing improvement and rolling bearing safety monitoring optimization, and has strong engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1This is a flow chart of a method for determining the health status of a rolling bearing of an EMU according to an embodiment of the present invention;

[0031] Figure 2 A flow chart of a bearing fault diagnosis model obtained in an embodiment of the present invention;

[0032] Figure 3 A flowchart for determining the health status of a rolling bearing of an EMU in a specific embodiment of the present invention;

[0033] Figure 4 This is a schematic structural diagram of a device for determining the health status of a rolling bearing of an EMU according to an embodiment of the present invention;

[0034] Figure 5 This is a schematic diagram of the structure of the diagnostic model module in an embodiment of the present invention;

[0035] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The embodiment of the present invention provides a method and device for determining the health status of a rolling bearing of an EMU.

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] like Figure 1 The flowchart of a method for determining the health status of a rolling bearing of an EMU according to an embodiment of the present invention is shown. The execution subject of the method for determining the health status of a rolling bearing of an EMU provided by the embodiment of the present invention includes but is not limited to a computer. The method shown in the figure includes:

[0039] Step S1, acquiring historical bearing acoustic monitoring data and bearing maintenance information, and obtaining a bearing acoustic feature database based on the historical bearing acoustic monitoring data and bearing maintenance information.

[0040] Among them, the EMU rolling bearing trackside acoustic monitoring system obtains no less than a certain amount of historical bearing acoustic monitoring data within a maintenance cycle. Specifically, the historical bearing acoustic monitoring data includes the bearing installation position, the vehicle number where the bearing is located, the monitoring time, the passing speed, the vehicle running direction and the bearing acoustic original signal file.

[0041] Furthermore, the bearing maintenance information is obtained from the EMU Management Information System (EMIS). Specifically, the bearing maintenance information includes the cumulative mileage of the bearing, the bearing maintenance record, etc.

[0042] Furthermore, the bearing installation position is used as the data matching object, and the historical bearing acoustic monitoring data is cross-matched with the bearing maintenance information to obtain the bearing acoustic feature database.

[0043] Step S2: input the bearing acoustic feature database into a pre-established bearing fault diagnosis model to obtain relevant feature parameters.

[0044] Among them, the bearing fault diagnosis model performs time and frequency analysis on the bearing acoustic signal to extract relevant characteristic parameters. The bearing fault diagnosis model first splits the original signal into a series of sub-bands using the wavelet algorithm, and then uses indicators such as kurtosis to identify fault information on the decomposed sub-bands. Then, the target frequency band is bandpass filtered to obtain the fault frequency band, and finally the characteristic parameters of the fault frequency band are calculated. The relevant characteristic parameters include dimensional fault characteristic parameters and dimensionless characteristic parameters. Dimensional characteristic parameters include peak-to-peak value, effective value, average value, etc. Dimensionless characteristic parameters include skewness, kurtosis, peak factor, pulse factor and margin factor.

[0045] Step S3, determining a bearing health index according to the relevant characteristic parameters, and using the bearing health index to obtain a bearing health index database.

[0046] Among them, the bearing health index is constructed using relevant characteristic parameters, and a bearing health index database based on big data calculation is obtained. The health index can be constructed by dimensional characteristic parameters and dimensionless characteristic parameters, for example:

[0047] Health index = a·peak value + b·effective value + c·kurtosis + d·pulse factor...

[0048] Among them, a, b, c, and d represent weighting coefficients respectively. The specific values ​​are not shown and are only used for illustration. Furthermore, the bearing health index is divided into four sections: healthy, sub-healthy, unhealthy, and dangerous in combination with relevant bearing maintenance information. The historical health index of faulty bearings found in general maintenance is low, while the historical health index of normal bearings is high. Matching the maintenance results with the calculated bearing health index can determine the health index ranges corresponding to serious faults, minor faults, and normal bearings, respectively, and thus obtain the threshold for dividing the sections.

[0049] Step S4: input the newly added acoustic monitoring data into the bearing fault diagnosis model, obtain the health index through calculation, and compare the health index with the bearing health index database to determine the bearing health status.

[0050] Among them, the acoustic monitoring data of the bearing is collected as acoustic monitoring data, and the acoustic monitoring data is input into the bearing fault diagnosis model, and its health index is calculated by the bearing fault diagnosis model. Then, the calculation result is compared with the threshold value of each section in the bearing health index database, and finally the bearing health assessment result is obtained.

[0051] As an embodiment of the present invention, the historical bearing acoustic monitoring data includes the bearing installation position, the vehicle number of the bearing, the monitoring time, the passing speed, the vehicle running direction and the bearing acoustic original signal file.

[0052] As an embodiment of the present invention, the bearing maintenance information includes the bearing accumulated mileage and the bearing maintenance record.

[0053] As an embodiment of the present invention, Figure 2 As shown, the bearing acoustic feature database is input into the pre-established bearing fault diagnosis model, and the relevant feature parameters obtained include:

[0054] Step S21, inputting the bearing acoustic feature database into a pre-established bearing fault diagnosis model, and obtaining a plurality of sub-frequency bands using a wavelet algorithm;

[0055] In step S22, the bearing fault diagnosis model performs fault information identification and bandpass filtering on the sub-frequency band to obtain a fault frequency band, and calculates characteristic parameters of the fault frequency band to obtain relevant characteristic parameters; wherein the relevant characteristic parameters include dimensional characteristic parameters and dimensionless characteristic parameters.

[0056] Among them, the bearing fault diagnosis model performs time and frequency analysis on the bearing acoustic signal to extract relevant characteristic parameters. The bearing fault diagnosis model first splits the original signal into a series of sub-bands using the wavelet algorithm, and then uses indicators such as kurtosis to identify fault information on the decomposed sub-bands. Then, the target frequency band is bandpass filtered to obtain the fault frequency band, and finally the characteristic parameters of the fault frequency band are calculated. The relevant characteristic parameters include dimensional fault characteristic parameters and dimensionless characteristic parameters. Dimensional characteristic parameters include peak-to-peak value, effective value, average value, etc. Dimensionless characteristic parameters include skewness, kurtosis, peak factor, pulse factor and margin factor.

[0057] Furthermore, the bearing fault diagnosis model performs time and frequency analysis on the bearing acoustic signal to extract relevant characteristic parameters. The relevant characteristic parameters include dimensional fault characteristic parameters and dimensionless characteristic parameters. The dimensional characteristic parameters include peak-to-peak value, effective value, average value, etc. The dimensionless characteristic parameters include skewness, kurtosis, peak factor, pulse factor and margin factor.

[0058] In a specific embodiment of the present invention, in order to prevent the rolling bearings of EMUs from failing due to failure and causing accidents such as hot cutting, burning axles, and derailment, the on-board axle temperature monitoring is currently the main means of daily safety monitoring of rolling bearings, and the TADS-1 detection station is used at a certain frequency as an auxiliary safety monitoring means, and a maintenance system is implemented in which the bearings are disassembled for overhaul after a cumulative running of 1.2 million kilometers and are scrapped after 2.4 million kilometers. However, with the proposal for optimizing the maintenance schedule and system of EMUs and the development of EMU fault prediction and health management (PHM) technology, it will gradually become possible to reasonably extend the life of EMU rolling bearings. In order to ensure the safe operation of the EMU during the bearing life extension period, it is necessary to conduct a health status assessment of the rolling bearings. Figure 3 The flowchart of determining the health status of the rolling bearing of a train set in a specific embodiment of the present invention is shown in the figure. The process shown in the figure specifically includes:

[0059] a. Obtain no less than a certain amount of historical bearing acoustic monitoring data within a maintenance cycle through the EMU rolling bearing trackside acoustic monitoring system; the monitoring data includes the bearing installation position, the vehicle number where the bearing is located, the monitoring time, the passing speed, the vehicle running direction and the bearing acoustic original signal file.

[0060] b. Obtain the operation and maintenance information of the bearing from the EMU Management Information System (EMIS) (such as the cumulative mileage of the bearing, bearing maintenance records, etc.).

[0061] c. Taking bearings as the research object, cross-match the bearing acoustic monitoring data with the bearing operation and maintenance information to obtain the bearing acoustic feature database.

[0062] d. Perform big data calculation on the feature database based on the bearing fault diagnosis model. The bearing fault diagnosis model performs time and frequency analysis on the bearing acoustic signal to extract relevant feature parameters. The relevant feature parameters include dimensional fault feature parameters and dimensionless feature parameters. The dimensional feature parameters include peak-to-peak value, effective value, average value, etc. The dimensionless feature parameters include skewness, kurtosis, peak factor, pulse factor and margin factor.

[0063] e. Use characteristic parameters to construct the bearing health index, and finally obtain a bearing health index database based on big data calculation. Combined with the use and maintenance information of relevant bearings, the bearing health index is divided into four sections: healthy, sub-healthy, unhealthy, and dangerous.

[0064] f. For the newly added bearing acoustic monitoring data, its health index can be calculated through the bearing fault diagnosis model, and then the calculation result can be compared with the threshold values ​​of each section in the bearing health index database to finally obtain the bearing health assessment result.

[0065] The present invention determines the health status of rolling bearings of EMUs based on acoustic signals, solves the problem of monitoring the health status of rolling bearings, breaks through the limitations of existing rolling bearing safety monitoring equipment, and realizes the assessment of the health status of rolling bearings by extracting the time-frequency parameter features of rolling bearings, providing support for the improvement of rolling bearings and the optimization of rolling bearing safety monitoring, and has strong engineering application value. In addition, the evaluation sample of the present invention is large, and the evaluation sample obtained meets the actual application conditions of EMU axle box bearings, and the cost of evaluating the health status of EMU axle box bearings is low.

[0066] like Figure 4 The figure shows a schematic diagram of the structure of a device for determining the health status of a rolling bearing of an EMU according to an embodiment of the present invention. The device shown in the figure includes:

[0067] The data acquisition module 10 is used to acquire historical bearing acoustic monitoring data and bearing maintenance information, and obtain a bearing acoustic feature database based on the historical bearing acoustic monitoring data and bearing maintenance information.

[0068] Among them, the EMU rolling bearing trackside acoustic monitoring system obtains no less than a certain amount of historical bearing acoustic monitoring data within a maintenance cycle. Specifically, the historical bearing acoustic monitoring data includes the bearing installation position, the vehicle number where the bearing is located, the monitoring time, the passing speed, the vehicle running direction and the bearing acoustic original signal file.

[0069] Furthermore, the bearing installation position is used as a data matching object to obtain the bearing maintenance information from the EMU management information system (EMIS). Specifically, the bearing maintenance information includes the cumulative mileage of the bearing, the bearing maintenance record, etc.

[0070] Furthermore, the historical bearing acoustic monitoring data is cross-matched with the bearing maintenance information to obtain a bearing acoustic feature database.

[0071] The diagnosis model module 20 is used to input the bearing acoustic feature database into a pre-established bearing fault diagnosis model to obtain relevant feature parameters.

[0072] Among them, the bearing fault diagnosis model performs time and frequency analysis on the bearing acoustic signal to extract relevant characteristic parameters. The bearing fault diagnosis model first splits the original signal into a series of sub-bands using the wavelet algorithm, and then uses indicators such as kurtosis to identify fault information on the decomposed sub-bands. Then, the target frequency band is bandpass filtered to obtain the fault frequency band, and finally the characteristic parameters of the fault frequency band are calculated. The relevant characteristic parameters include dimensional fault characteristic parameters and dimensionless characteristic parameters. Dimensional characteristic parameters include peak-to-peak value, effective value, average value, etc. Dimensionless characteristic parameters include skewness, kurtosis, peak factor, pulse factor and margin factor.

[0073] The health index module 30 is used to determine the bearing health index according to the relevant characteristic parameters, and obtain a bearing health index database using the bearing health index.

[0074] Among them, the bearing health index is constructed using relevant characteristic parameters, and a bearing health index database based on big data calculation is obtained.

[0075] Furthermore, combined with relevant bearing maintenance information, the bearing health index is divided into four sections: healthy, sub-healthy, unhealthy, and dangerous. The historical health index of faulty bearings found in general maintenance is low, while the historical health index of normal bearings is high. Matching the maintenance results with the calculated bearing health index can determine the health index ranges corresponding to serious faults, minor faults, and normal bearings, respectively, and thus obtain the threshold for dividing the sections.

[0076] The health status module 40 is used to input the newly added acoustic monitoring data into the bearing fault diagnosis model, obtain the health index through calculation, and compare the health index with the bearing health index database to determine the bearing health status.

[0077] Among them, the acoustic monitoring data of the bearing is collected as acoustic monitoring data, and the acoustic monitoring data is input into the bearing fault diagnosis model, and its health index is calculated by the bearing fault diagnosis model. Then, the calculation result is compared with the threshold value of each section in the bearing health index database, and finally the bearing health assessment result is obtained.

[0078] As an embodiment of the present invention, the historical bearing acoustic monitoring data includes the bearing installation position, the vehicle number of the bearing, the monitoring time, the passing speed, the vehicle running direction and the bearing acoustic original signal file.

[0079] As an embodiment of the present invention, the bearing maintenance information includes the bearing accumulated mileage and the bearing maintenance record.

[0080] As an embodiment of the present invention, Figure 5 As shown, the diagnostic model module 20 includes:

[0081] A sub-frequency band unit 21 is used to input the bearing acoustic feature database into a pre-established bearing fault diagnosis model and obtain multiple sub-frequency bands using a wavelet algorithm;

[0082] The characteristic parameter unit 22 is used for the bearing fault diagnosis model to perform fault information identification and bandpass filtering on the sub-frequency band to obtain the fault frequency band, and to calculate the characteristic parameters of the fault frequency band to obtain relevant characteristic parameters; wherein the relevant characteristic parameters include dimensional characteristic parameters and dimensionless characteristic parameters.

[0083] Based on the same application concept as the above-mentioned method for determining the health status of a rolling bearing of an EMU, the present invention also provides the above-mentioned device for determining the health status of a rolling bearing of an EMU. Since the principle of solving the problem by the device for determining the health status of a rolling bearing of an EMU is similar to that of the method for determining the health status of a rolling bearing of an EMU, the implementation of the device for determining the health status of a rolling bearing of an EMU can refer to the implementation of the method for determining the health status of a rolling bearing of an EMU, and the repeated parts will not be repeated.

[0084] The present invention determines the health status of EMU rolling bearings based on acoustic signals, solves the problem of rolling bearing health status monitoring, breaks through the limitations of existing rolling bearing safety monitoring equipment, and realizes the assessment of rolling bearing health status by extracting the time-frequency parameter features of rolling bearings. It provides support for rolling bearing improvements and rolling bearing safety monitoring optimization, and has strong engineering application value.

[0085] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the program.

[0086] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for executing the above method.

[0087] like Figure 6 As shown, the electronic device 600 may further include: a communication module 110, an input unit 120, an audio processing unit 130, a display 160, and a power supply 170. It is worth noting that the electronic device 600 does not necessarily have to include Figure 6 In addition, the electronic device 600 may also include Figure 6 For components not shown, reference may be made to the prior art.

[0088] like Figure 6 As shown, the central processor 100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor devices and / or logic devices. The central processor 100 receives inputs and controls the operations of various components of the electronic device 600.

[0089] The memory 140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 100 may execute the program stored in the memory 140 to implement information storage or processing.

[0090] The input unit 120 provides input to the CPU 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0091] The memory 140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 140 may also be some other type of device. The memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application / function storage unit 142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 600 through the central processor 100.

[0092] The memory 140 may also include a data storage unit 143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 144 of the memory 140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0093] The communication module 110 is a transmitter / receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter / receiver) 110 is coupled to the central processor 100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0094] Based on different communication technologies, multiple communication modules 110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module (transmitter / receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide an audio output via the speaker 131 and receive an audio input from the microphone 132, thereby realizing a common telecommunication function. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 130 is also coupled to the central processor 100, so that the sound can be recorded on the local machine through the microphone 132, and the sound stored on the local machine can be played through the speaker 131.

[0095] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may 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, optical storage, etc.) containing computer-usable program code.

[0096] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0099] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for determining the health status of a rolling bearing of an EMU, characterized in that: The method comprises: Acquiring historical bearing acoustic monitoring data and bearing maintenance information, and obtaining a bearing acoustic feature database based on the historical bearing acoustic monitoring data and bearing maintenance information; Inputting the bearing acoustic feature database into a pre-established bearing fault diagnosis model to obtain relevant feature parameters; Determine a bearing health index according to the relevant characteristic parameters, and obtain a bearing health index database using the bearing health index; The newly added acoustic monitoring data is input into the bearing fault diagnosis model, and a health index is obtained by calculation, and the health index is compared with the bearing health index database to determine the health status of the bearing; The bearing acoustic feature database is input into a pre-established bearing fault diagnosis model to obtain relevant feature parameters, including: The bearing acoustic feature database is input into a pre-established bearing fault diagnosis model, and a plurality of sub-frequency bands are obtained by using a wavelet algorithm; The bearing fault diagnosis model performs fault information identification and bandpass filtering on the sub-frequency band to obtain a fault frequency band, and calculates characteristic parameters of the fault frequency band to obtain relevant characteristic parameters; wherein the relevant characteristic parameters include dimensional characteristic parameters and dimensionless characteristic parameters; Wherein, determining the bearing health index according to the relevant characteristic parameters, and using the bearing health index to obtain a bearing health index database includes: The bearing health index is constructed using relevant characteristic parameters, and a bearing health index database based on big data calculation is obtained; the health index is constructed by dimensional characteristic parameters and dimensionless characteristic parameters. The dimensional characteristic parameters include peak-to-peak value, effective value, and average value, and the dimensionless characteristic parameters include skewness, kurtosis, peak factor, pulse factor, and margin factor.

2. The method according to claim 1, characterized in that The historical bearing acoustic monitoring data includes the bearing installation position, the vehicle number where the bearing is located, the monitoring time, the passing speed, the vehicle running direction and the bearing acoustic original signal file.

3. The method according to claim 1, characterized in that The bearing maintenance information includes the bearing accumulated mileage and the bearing maintenance record.

4. A device for determining the health status of rolling bearings of a train set, characterized in that: The device comprises: A data acquisition module, used to acquire historical bearing acoustic monitoring data and bearing maintenance information, and obtain a bearing acoustic feature database based on the historical bearing acoustic monitoring data and bearing maintenance information; A diagnostic model module, used to input the bearing acoustic feature database into a pre-established bearing fault diagnosis model to obtain relevant feature parameters; A health index module, used to determine the bearing health index according to the relevant characteristic parameters, and obtain a bearing health index database using the bearing health index; A health status module, used to input the newly added acoustic monitoring data into the bearing fault diagnosis model, obtain a health index by calculation, and compare the health index with the bearing health index database to determine the bearing health status; Wherein, the diagnostic model module includes: A sub-frequency band unit, used to input the bearing acoustic feature database into a pre-established bearing fault diagnosis model, and obtain multiple sub-frequency bands using a wavelet algorithm; A characteristic parameter unit, used for the bearing fault diagnosis model to perform fault information identification and bandpass filtering on the sub-frequency band to obtain a fault frequency band, and to calculate characteristic parameters on the fault frequency band to obtain relevant characteristic parameters; wherein the relevant characteristic parameters include dimensional characteristic parameters and dimensionless characteristic parameters; Wherein, determining the bearing health index according to the relevant characteristic parameters, and using the bearing health index to obtain a bearing health index database includes: The bearing health index is constructed using relevant characteristic parameters, and a bearing health index database based on big data calculation is obtained; the health index is constructed by dimensional characteristic parameters and dimensionless characteristic parameters. The dimensional characteristic parameters include peak-to-peak value, effective value, and average value, and the dimensionless characteristic parameters include skewness, kurtosis, peak factor, pulse factor, and margin factor.

5. The device according to claim 4, characterized in that The historical bearing acoustic monitoring data includes the bearing installation position, the vehicle number where the bearing is located, the monitoring time, the passing speed, the vehicle running direction and the bearing acoustic original signal file.

6. The device according to claim 4, characterized in that The bearing maintenance information includes the bearing accumulated mileage and the bearing maintenance record.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 3 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program for executing the method according to any one of claims 1 to 3.

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