Methods, devices, vehicles, and storage media for identifying abnormal noises in vehicles.

Abstract

This application relates to a method, apparatus, vehicle, and storage medium for identifying abnormal noises in vehicles. The method includes: acquiring a video of the abnormal noise from a test vehicle; converting the video into audio of the abnormal noise; and confirming the time and speed at which the abnormal noise occurs. A noise waterfall plot is obtained from the audio of the abnormal noise, and the order of the abnormal noise is identified by combining the time and speed of the abnormal noise. The order of the abnormal noise is calculated for the motor and reducer components of the test vehicle; the order of the abnormal noise is compared with the order of the motor and reducer components of the test vehicle; and the faulty component is identified based on the comparison result. Embodiments of this application can determine the source of the abnormal noise by comparing the order of the motor and reducer components of the test vehicle with the order of the abnormal noise, thereby accurately identifying the source of the abnormal noise when diagnosing motor noise problems and improving maintenance efficiency.

Description

Technical Field

[0001] This application relates to the field of fault identification technology, and in particular to a method, device, vehicle, and storage medium for identifying abnormal noises in vehicles. Background Technology

[0002] With the development of new energy vehicles and their increasing market share, the issue of how to quickly and efficiently handle abnormal noises from motors and reducers in new energy vehicles has also emerged. Without a reasonable and accurate testing method to pinpoint the source of the fault, relying solely on the experience of individual repair personnel and frequently replacing parts to find the cause will undoubtedly increase after-sales maintenance costs. Furthermore, since new energy vehicles from major automakers are sold nationwide, if R&D engineers had to travel to the site every time a customer reported an abnormal noise issue, it would reduce troubleshooting efficiency and increase costs.

[0003] In related technologies, objective standards can replace human experience to determine the abnormal noise problem of motors. However, these technologies cannot identify the source of the abnormal noise from the motor or reducer, such as which bearing or gear shaft is faulty. The source of the fault still needs to be investigated, which is not conducive to improving maintenance efficiency and is not suitable for handling after-sales abnormal noise problems. It needs to be improved. Summary of the Invention

[0004] This application provides a method, device, vehicle, and storage medium for identifying abnormal noises in vehicles, in order to solve the technical problem in the related art that the source of the abnormal noise cannot be identified when determining abnormal noise problems in motors, resulting in low maintenance efficiency.

[0005] The first aspect of this application provides a method for identifying abnormal noises in a vehicle, comprising the following steps: acquiring a video of the abnormal noise from a test vehicle, converting the video into audio of the abnormal noise, and confirming the time and speed at which the abnormal noise occurs; obtaining a noise waterfall plot from the audio of the abnormal noise, and identifying the order of the abnormal noise based on the time and speed at which the abnormal noise occurs; calculating the order of the motor and reducer-related components of the test vehicle, comparing the order of the abnormal noise with the order of the motor and reducer-related components of the test vehicle, and identifying the faulty component based on the comparison result.

[0006] Based on the above-mentioned technical means, the embodiments of this application can accurately identify the fault source that causes abnormal noise when determining the problem of abnormal noise in motors, thereby improving maintenance efficiency.

[0007] Optionally, in one embodiment of this application, after identifying the faulty part, the method further includes: replacing the faulty part; reacquiring the audio data of the test vehicle driving within a preset time period, and identifying whether there are abnormal noises in the audio data.

[0008] Based on the above technical means, the embodiments of this application can replace the faulty parts after confirming the source of the fault, and confirm whether the vehicle still has abnormal noise problems after the parts are replaced.

[0009] Optionally, in one embodiment of this application, obtaining the noise waterfall diagram from the abnormal noise audio includes: performing a short-time Fourier transform on the audio signal of the abnormal noise audio to generate the noise waterfall diagram.

[0010] Based on the above technical means, the embodiments of this application can generate a noise waterfall diagram through short-time Fourier transform.

[0011] Optionally, in one embodiment of this application, calculating the order of the motor and reducer-related components of the test vehicle includes: obtaining the design parameters of the motor and reducer of the test vehicle; and calculating the order of the motor and reducer-related components of the test vehicle based on the design parameters, wherein the motor and reducer-related components include a motor shaft, an input shaft, an intermediate shaft, an output shaft, and / or various bearings.

[0012] Based on the above-mentioned technical means, the embodiments of this application can calculate the order of the relevant components of the motor and reducer of the test vehicle based on the design parameters of the motor and reducer of the test vehicle, thereby facilitating the subsequent determination of the fault source.

[0013] Optionally, in one embodiment of this application, after acquiring the abnormal noise video of the test vehicle, the method further includes: re-examining the abnormal noise video to determine whether there is an abnormal noise in the video; if there is no abnormal noise in the video, or if the preset abnormal noise condition is not met, then the abnormal noise video of the test vehicle is acquired again.

[0014] Based on the above technical means, the embodiments of this application can re-inspect abnormal noise videos to avoid poor recording quality affecting data analysis results.

[0015] A second aspect of this application provides a vehicle abnormal noise identification device, comprising: an acquisition module for acquiring a video of an abnormal noise from a test vehicle, converting the video into an audio recording of the abnormal noise, and confirming the time and speed at which the abnormal noise occurs; a first identification module for obtaining a noise waterfall plot from the audio recording of the abnormal noise, and identifying the order of the abnormal noise based on the time and speed at which the abnormal noise occurs; and a second identification module for calculating the order of the motor and reducer-related components of the test vehicle, comparing the order of the abnormal noise with the order of the motor and reducer-related components of the test vehicle, and identifying faulty parts based on the comparison results.

[0016] Optionally, in one embodiment of this application, it further includes: a repair module for replacing the faulty part; and a third identification module for reacquiring the audio data of the test vehicle driving within a preset time period and identifying whether there are abnormal noises in the audio data.

[0017] Optionally, in one embodiment of this application, the first identification module includes: a generation unit, configured to perform a short-time Fourier transform on the audio signal of the abnormal noise audio to generate the noise waterfall diagram.

[0018] Optionally, in one embodiment of this application, the second identification module includes: an acquisition unit for acquiring design parameters of the motor and reducer of the test vehicle; and a calculation unit for calculating the order of the motor and reducer-related components of the test vehicle based on the design parameters, wherein the motor and reducer-related components include a motor shaft, an input shaft, an intermediate shaft, an output shaft, and / or various bearings.

[0019] Optionally, in one embodiment of this application, it further includes: a re-inspection module, used to re-inspect the abnormal noise video to determine whether there is an abnormal noise in the abnormal noise video; and a judgment module, used to re-acquire the abnormal noise video of the test vehicle when there is no abnormal noise in the abnormal noise video or the preset abnormal noise condition is not met.

[0020] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle abnormal noise identification method as described in the above embodiments.

[0021] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for identifying abnormal vehicle noises.

[0022] The beneficial effects of the embodiments of this application are as follows:

[0023] (1) The embodiments of this application can determine the source of abnormal noise by comparing the order of the motor and reducer related components of the test vehicle with the order of the abnormal noise, thereby accurately identifying the source of abnormal noise when judging the motor abnormal noise problem and improving maintenance efficiency.

[0024] (2) The embodiments of this application can re-inspect the video of abnormal noise to avoid poor recording effect and affect the data analysis results.

[0025] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0026] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0027] Figure 1 A flowchart illustrating a method for identifying abnormal vehicle noises according to an embodiment of this application;

[0028] Figure 2 A noise waterfall diagram of a vehicle abnormal noise identification method according to an embodiment of this application;

[0029] Figure 3 This is a flowchart of a method for identifying abnormal vehicle noises according to another embodiment of this application;

[0030] Figure 4 This is a schematic diagram of the structure of a vehicle abnormal noise identification device according to an embodiment of this application;

[0031] Figure 5 This is a structural schematic diagram of a vehicle provided according to an embodiment of this application.

[0032] Among them, 10-vehicle abnormal noise identification device; 100-acquisition module; 200-first identification module; 300-second identification module. Detailed Implementation

[0033] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0034] The following description, with reference to the accompanying drawings, outlines a method, apparatus, vehicle, and storage medium for identifying vehicle abnormal noises according to embodiments of this application. Addressing the technical problem mentioned in the background section of the related art, where the source of abnormal noise cannot be identified when determining motor abnormal noise problems, leading to low repair efficiency, this application provides a method for identifying vehicle abnormal noises. In this method, based on a video recording of abnormal noise from a test vehicle, an audio recording of the abnormal noise is obtained, and the time and speed of the abnormal noise occurrence are confirmed. A noise waterfall diagram obtained from the abnormal noise audio is used to identify the order of the abnormal noise occurrence. The order of the abnormal noise occurrence is compared with the order of the motor and reducer components of the test vehicle to determine the source of the abnormal noise. This method accurately identifies the source of abnormal noise when determining motor abnormal noise problems, improving repair efficiency. It is highly practical and easy to promote and apply. Therefore, it solves the technical problem in the related art where the source of abnormal noise cannot be identified when determining motor abnormal noise problems, leading to low repair efficiency.

[0035] Specifically, Figure 1This is a flowchart illustrating a method for identifying abnormal noises in a vehicle, as provided in an embodiment of this application.

[0036] like Figure 1 As shown, the method for identifying abnormal noises in this vehicle includes the following steps:

[0037] In step S101, the abnormal noise video of the test vehicle is acquired, the abnormal noise video is converted into abnormal noise audio, and the time and speed at which the abnormal noise occurs are confirmed.

[0038] In actual execution, before acquiring the video of abnormal noise from the test vehicle, this application embodiment can pre-determine whether the test vehicle has an abnormal noise problem and confirm whether the abnormal noise problem is caused by a fault in the motor or reducer.

[0039] Once the abnormal noise problem is confirmed to exist and is caused by a malfunction in the motor or reducer, this embodiment of the application can first determine the method of filming the abnormal noise video, such as filming while the test vehicle is driving on the road, filming while the test vehicle is on a lift, or filming while the vehicle is fixed on a rotating hub. Specifically, to fully represent the abnormal noise, this embodiment of the application can, under different filming methods and according to the filming requirements, confirm the status of the test vehicle, start the test vehicle, and begin filming the abnormal noise video. During the video recording process, when an abnormal noise occurs, this embodiment of the application can record the moment of the abnormal noise.

[0040] Furthermore, in this embodiment of the application, the recorded video of abnormal noise can be converted into audio of abnormal noise using a method such as an audio converter, and the time when the abnormal noise occurs in the audio of abnormal noise and the vehicle speed at which the abnormal noise occurs can be confirmed.

[0041] Optionally, in one embodiment of this application, after acquiring the abnormal noise video of the test vehicle, the method further includes: re-examining the abnormal noise video to determine whether there is an abnormal noise in the video; if there is no abnormal noise in the video, or if the preset abnormal noise conditions are not met, then the abnormal noise video of the test vehicle is acquired again.

[0042] As one possible implementation, this application embodiment can re-examine the abnormal noise video after acquiring the video of the test vehicle to determine whether there is an abnormal noise in the video. If there is an abnormal noise in the video and the preset abnormal noise condition is met, that is, the abnormal noise decibel is higher than the preset threshold, then the abnormal noise video can be used. If there is no abnormal noise in the video, or the preset abnormal noise condition is not met, that is, the abnormal noise decibel is lower than the preset threshold, this application embodiment can re-acquire the abnormal noise video of the test vehicle to avoid poor recording effect and affecting the data analysis results.

[0043] It should be noted that the preset threshold can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0044] In step S102, a noise waterfall diagram is obtained from the abnormal noise audio, and the order of the abnormal noise is identified by combining the time of occurrence of the abnormal noise and the vehicle speed.

[0045] In some embodiments, the present application embodiments can obtain a noise waterfall diagram based on the obtained abnormal noise audio, and combine it with the pre-obtained time of occurrence of the abnormal noise and vehicle speed to identify the order of occurrence of the abnormal noise in the noise waterfall diagram, which is convenient for subsequent fault source finding.

[0046] Optionally, in one embodiment of this application, obtaining a noise waterfall diagram from the abnormal noise audio includes: performing a short-time Fourier transform on the audio signal of the abnormal noise audio to generate a noise waterfall diagram.

[0047] Specifically, embodiments of this application can perform a short-time Fourier transform on the audio signal in the abnormal noise audio to obtain, as shown below. Figure 2 The noise waterfall plot shown can be represented by the following Fourier transform formula:

[0048]

[0049] This application embodiment can find the fault order curve from the waterfall plot, and confirm the time and vehicle speed when the abnormal noise occurs by combining the audio playback. This application embodiment can draw a straight line on the waterfall plot with the time as the coordinate based on the time when the abnormal noise occurs in the audio, find the intersection point of the time line and the abnormal order curve, identify the frequency f of the abnormal noise through the intersection point, and then calculate the fault order by combining the vehicle speed or motor speed when the abnormal noise occurs with the identified abnormal noise frequency, thus completing the abnormal noise order detection.

[0050] In step S103, the order of the motor and reducer components of the test vehicle is calculated, the order of the abnormal noise is compared with the order of the motor and reducer components of the test vehicle, and the faulty parts are identified based on the comparison results.

[0051] In actual implementation, the embodiments of this application can calculate the order of the motor and reducer related components of the test vehicle and compare it with the order of the motor and reducer related components of the test vehicle to identify the source of the fault and determine the faulty parts, thereby facilitating subsequent parts replacement and repair and improving maintenance efficiency.

[0052] Optionally, in one embodiment of this application, calculating the order of the motor and reducer-related components of the test vehicle includes: obtaining the design parameters of the motor and reducer of the test vehicle; and calculating the order of the motor and reducer-related components of the test vehicle based on the design parameters, wherein the motor and reducer-related components include a motor shaft, an input shaft, an intermediate shaft, an output shaft, and / or various bearings.

[0053] As one possible approach, embodiments of this application can calculate the order of related components (motor shaft, input shaft, intermediate shaft, output shaft, bearings, etc.) of the motor and reducer by using design parameters of the motor and reducer (such as the number of pole pairs of the motor rotor, the number of stator slots, the number of teeth of the reducer gears, the number of balls of the bearings, etc.).

[0054] Furthermore, the frequency and corresponding order of the drive shaft:

[0055]

[0056] Cage frequency and corresponding order:

[0057]

[0058] Ball bearing frequency and corresponding order:

[0059]

[0060] Bearing outer ring frequency and corresponding order:

[0061]

[0062] Bearing inner ring frequency and corresponding order:

[0063]

[0064] Where k is the number of teeth on the corresponding gear on the bearing, d is the ball diameter, D is the diameter at the center of the ball, α is the radial contact angle, m is the number of balls, and N is the motor speed. n is the order: f represents the frequency of the abnormal noise, T represents the duration of the abnormal noise, V represents the corresponding vehicle speed, and N represents the frequency of the abnormal noise. 10 The motor speed is the speed of the motor when the vehicle speed is 10 km / h.

[0065] Optionally, in one embodiment of this application, after identifying the faulty part, the method further includes: replacing the faulty part; reacquiring the audio data of the test vehicle driving within a preset time period, and identifying whether there are abnormal noises in the audio data.

[0066] In some embodiments, after replacing the faulty parts, the audio data of the test vehicle driving within a preset time period can be reacquired, and the presence of abnormal noises in the audio data can be identified again. If there are no abnormal noises, it indicates that the motor and reducer of the current test vehicle have been repaired. If there are still abnormal noises, it indicates that there are other sources of failure in the motor and reducer of the current test vehicle, and abnormal noise identification needs to be performed again.

[0067] Combination Figure 2 and Figure 3As shown, the working principle of the vehicle abnormal noise identification method of this application embodiment is explained in detail with reference to one embodiment.

[0068] like Figure 3 As shown, embodiments of this application may include the following steps:

[0069] Step S301: Problem Analysis. Before acquiring the video of abnormal noise from the test vehicle during actual execution, this embodiment of the application can pre-determine whether the test vehicle has an abnormal noise problem and confirm whether the abnormal noise problem is caused by a fault in the motor or reducer, such as by determining it through after-sales information provided by the user.

[0070] Step S302: Prepare the test vehicle, such as the vehicle that the user sent back for repair with abnormal noise.

[0071] Step S303: Determine the video recording method for abnormal noise. Based on the test conditions or problem reproduction requirements, this embodiment of the application can select a video recording method for abnormal noise, such as: recording while the vehicle is driving on the road, recording while the vehicle is on a lift, or recording while the vehicle is fixed to a rotating hub.

[0072] The shooting requirements for each testing method are as follows:

[0073] 1. Taking photos while the vehicle is driving on the road:

[0074] Vehicle status: Doors and windows are closed, and there are no loose items inside the vehicle;

[0075] Camera: The motor speed is displayed on the vehicle's dashboard and is always directly facing the vehicle speedometer;

[0076] Operating conditions: a) Gradually accelerate from a standstill to 50 km / h; b) Maintain a constant speed of 50 km / h (lasting approximately 10 seconds); c) Coast from 50 km / h to 10 km / h (the 50 km / h speed can be adjusted to 30 km / h or 70 km / h, but the abnormal noise must be reproduced).

[0077] 2. The vehicle was photographed from the lift:

[0078] Vehicle status: Doors and windows are closed, and there are no loose items inside the vehicle;

[0079] Recording inside the vehicle: The motor speed is displayed on the vehicle's dashboard, and the camera is pointed directly at the vehicle's speedometer throughout the recording.

[0080] Recording from under the vehicle: Simply point the camera at the electric drive assembly.

[0081] 3. Secure the vehicle to the rotating hub for photography:

[0082] Vehicle status: Doors and windows are closed, and there are no loose items inside the vehicle;

[0083] Recording inside the vehicle: The motor speed is displayed on the vehicle's dashboard, and the camera is pointed directly at the vehicle's speedometer throughout the recording.

[0084] Recording from under the vehicle: Simply point the camera at the electric drive assembly.

[0085] Step S304: Reproduce the abnormal noise problem and record a video. This embodiment of the application can, under different shooting methods and according to shooting requirements, confirm the status of the test vehicle, start the test vehicle, and begin recording the abnormal noise video. During the video recording process, when an abnormal noise occurs, this embodiment of the application can record the moment of the abnormal noise.

[0086] As one possible implementation, this application embodiment can re-examine the abnormal noise video after acquiring the video of the test vehicle to determine whether there is an abnormal noise in the video. If there is an abnormal noise in the video and the preset abnormal noise condition is met, that is, the abnormal noise decibel is higher than the preset threshold, then the abnormal noise video can be used. If there is no abnormal noise in the video, or the preset abnormal noise condition is not met, that is, the abnormal noise decibel is lower than the preset threshold, this application embodiment can re-acquire the abnormal noise video of the test vehicle to avoid poor recording effect and affecting the data analysis results.

[0087] It should be noted that the preset threshold can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0088] Step S305: Extract the abnormal noise audio. In this embodiment, the recorded abnormal noise video can be converted into abnormal noise audio using a method such as an audio converter, and the video format can be adjusted to the audio MAV format.

[0089] Step S306: Process the abnormal noise audio data and analyze the abnormal noise order. Specifically, in this embodiment of the application, a short-time Fourier transform can be performed on the audio signal in the abnormal noise audio to obtain, as shown below. Figure 2 The noise waterfall plot shown can be represented by the following Fourier transform formula:

[0090]

[0091] This application embodiment can find the fault order curve from the waterfall plot, and confirm the time and vehicle speed when the abnormal noise occurs by combining the audio playback. This application embodiment can draw a straight line on the waterfall plot with the time as the coordinate based on the time when the abnormal noise occurs in the audio, find the intersection point of the time line and the abnormal order curve, identify the frequency f of the abnormal noise through the intersection point, and then calculate the fault order by combining the vehicle speed or motor speed when the abnormal noise occurs with the identified abnormal noise frequency, thus completing the abnormal noise order detection.

[0092] Step S307: Calculate the order of key components of the motor and reducer. As one possible implementation, embodiments of this application can calculate the order of relevant components (motor shaft, input shaft, intermediate shaft, output shaft, bearings, etc.) of the motor and reducer based on design parameters of the motor and reducer (such as the number of pole pairs of the motor rotor, the number of stator slots, the number of teeth of the reducer gears, the number of balls in the bearings, etc.).

[0093] Furthermore, the frequency and corresponding order of the drive shaft:

[0094]

[0095] Cage frequency and corresponding order:

[0096]

[0097] Ball bearing frequency and corresponding order:

[0098]

[0099] Bearing outer ring frequency and corresponding order:

[0100]

[0101] Bearing inner ring frequency and corresponding order:

[0102]

[0103] Where k is the number of teeth on the corresponding gear on the bearing, d is the ball diameter, D is the diameter at the center of the ball, α is the radial contact angle, m is the number of balls, and N is the motor speed. n is the order: f represents the frequency of the abnormal noise, T represents the duration of the abnormal noise, V represents the corresponding vehicle speed, and N represents the frequency of the abnormal noise. 10 The motor speed is the speed of the motor when the vehicle speed is 10 km / h.

[0104] Step S308: Identify the source of abnormal noise and replace the faulty part. This embodiment of the application can calculate the order of the motor and reducer-related components of the test vehicle and compare it with the order of the motor and reducer-related components of the test vehicle to identify the source of the fault and determine the faulty part, thereby facilitating subsequent part replacement and repair and improving repair efficiency.

[0105] The vehicle abnormal noise identification method proposed in this application obtains the abnormal noise audio based on the abnormal noise video of the test vehicle, confirms the time and speed of the abnormal noise occurrence, and identifies the order of the abnormal noise by combining the noise waterfall diagram obtained from the abnormal noise audio. By comparing the order of the abnormal noise occurrence with the order of the relevant components of the test vehicle's motor and reducer, the fault source causing the abnormal noise is determined. This method accurately identifies the fault source of the abnormal noise when diagnosing motor abnormal noise problems, improving repair efficiency, and is highly practical and easy to promote and apply. Therefore, it solves the technical problem in related technologies where the fault source of abnormal noise cannot be identified when diagnosing motor abnormal noise problems, leading to low repair efficiency.

[0106] Next, the vehicle abnormal noise identification device according to the embodiments of this application is described with reference to the accompanying drawings.

[0107] Figure 4 This is a block diagram of a vehicle abnormal noise identification device according to an embodiment of this application.

[0108] like Figure 4 As shown, the vehicle abnormal noise identification device 10 includes: an acquisition module 100, a first identification module 200, and a second identification module 300.

[0109] Specifically, the acquisition module 100 is used to acquire video of abnormal noises from the test vehicle, convert the video of abnormal noises into audio of abnormal noises, and confirm the time and speed at which the abnormal noises occur.

[0110] The first identification module 200 is used to obtain a noise waterfall diagram from the abnormal noise audio, and identify the order of the abnormal noise by combining the time of the abnormal noise occurrence and the vehicle speed.

[0111] The second identification module 300 is used to calculate the order of the motor and reducer related components of the test vehicle, compare the order of the abnormal noise with the order of the motor and reducer related components of the test vehicle, and identify the faulty parts based on the comparison results.

[0112] Optionally, in one embodiment of this application, the vehicle abnormal noise identification device 10 further includes a maintenance module and a third identification module.

[0113] The maintenance module is used to replace faulty parts.

[0114] The third identification module is used to reacquire the audio data of the test vehicle during a preset driving time and identify whether there are any abnormal noises in the audio data.

[0115] Optionally, in one embodiment of this application, the first identification module 200 includes a generation unit.

[0116] The generation unit is used to perform a short-time Fourier transform on the audio signal of the abnormal noise to generate a noise waterfall diagram.

[0117] Optionally, in one embodiment of this application, the second identification module 300 includes an acquisition unit and a calculation unit.

[0118] The acquisition unit is used to acquire the design parameters of the motor and reducer of the test vehicle.

[0119] The calculation unit is used to calculate the order of the motor and reducer related components of the test vehicle based on the design parameters. The motor and reducer related components include the motor shaft, input shaft, intermediate shaft, output shaft and / or various bearings.

[0120] Optionally, in one embodiment of this application, the vehicle abnormal noise identification device 10 further includes a re-inspection module and a judgment module.

[0121] The re-inspection module is used to re-inspect the abnormal noise video to determine whether there is an abnormal noise in the video.

[0122] The judgment module is used to reacquire the abnormal noise video of the test vehicle when there is no abnormal noise in the abnormal noise video or the preset abnormal noise conditions are not met.

[0123] It should be noted that the explanation of the above-mentioned method for identifying abnormal vehicle noises also applies to the vehicle noise identification device of this embodiment, and will not be repeated here.

[0124] The vehicle abnormal noise identification device proposed in this application obtains the abnormal noise audio based on the abnormal noise video of the test vehicle, confirms the time and speed of the abnormal noise occurrence, and identifies the order of the abnormal noise by combining the noise waterfall diagram obtained from the abnormal noise audio. By comparing the order of the abnormal noise occurrence with the order of the relevant components of the test vehicle's motor and reducer, the fault source causing the abnormal noise is determined. This accurately identifies the fault source when diagnosing motor abnormal noise problems, improving repair efficiency, and offering high practicality and ease of widespread application. Therefore, it solves the technical problem in related technologies where the fault source of abnormal noise cannot be identified when diagnosing motor abnormal noise problems, leading to low repair efficiency.

[0125] Figure 5 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include:

[0126] The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0127] When the processor 502 executes the program, it implements the vehicle abnormal noise identification method provided in the above embodiments.

[0128] Furthermore, the vehicle also includes:

[0129] Communication interface 503 is used for communication between memory 501 and processor 502.

[0130] The memory 501 is used to store computer programs that can run on the processor 502.

[0131] The memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0132] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0133] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0134] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0135] This embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for identifying abnormal vehicle noises.

[0136] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0137] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0138] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0139] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0140] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0141] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0142] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0143] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method of identifying an abnormal sound of a vehicle, characterized by, The method comprises the following steps: obtaining an abnormal sound video of a test vehicle, converting the abnormal sound video into abnormal sound audio, and confirming the time and vehicle speed at which the abnormal sound occurs; obtaining a noise waterfall chart from the abnormal sound audio, identifying the order at which the abnormal sound occurs in combination with the time and vehicle speed at which the abnormal sound occurs; and calculating the orders of motor and reducer related parts of the test vehicle, comparing the order at which the abnormal sound occurs with the orders of the motor and reducer related parts of the test vehicle, and identifying a faulty part according to the comparison result. The calculation of the orders of the motor and reducer related parts of the test vehicle comprises: obtaining design parameters of the motor and reducer of the test vehicle; based on the design parameters, calculating the orders of the motor and reducer related parts of the test vehicle, wherein the motor and reducer related parts include motor shaft, input shaft, intermediate shaft, output shaft and / or bearings. After the faulty part is identified, the method further comprises:

2. The method of claim 1, wherein, replacing the faulty part; re-obtaining audio data of the test vehicle running within a preset time period, and identifying whether there is an abnormal sound in the audio data. The obtaining of the noise waterfall chart from the abnormal sound audio comprises:

3. The method of claim 1, wherein, performing short-time Fourier transform on the audio signal of the abnormal sound audio to generate the noise waterfall chart. After the abnormal sound video of the test vehicle is obtained, the method further comprises:

4. The method of claim 1, wherein, re-inspecting the abnormal sound video to determine whether there is an abnormal sound in the abnormal sound video; if there is no abnormal sound in the abnormal sound video or a preset abnormal sound condition is not met, re-obtaining the abnormal sound video of the test vehicle. The method comprises:

5. An apparatus for identifying an abnormal sound of a vehicle, characterized by comprising: an obtaining module configured to obtain an abnormal sound video of a test vehicle, convert the abnormal sound video into abnormal sound audio, and confirm the time and vehicle speed at which the abnormal sound occurs; a first identifying module configured to obtain a noise waterfall chart from the abnormal sound audio, identify the order at which the abnormal sound occurs in combination with the time and vehicle speed at which the abnormal sound occurs; and a second identifying module configured to calculate the orders of motor and reducer related parts of the test vehicle, compare the order at which the abnormal sound occurs with the orders of the motor and reducer related parts of the test vehicle, and identify a faulty part according to the comparison result. The second identifying module comprises: an obtaining unit configured to obtain design parameters of the motor and reducer of the test vehicle; a calculation unit configured to calculate the orders of the motor and reducer related parts of the test vehicle based on the design parameters, wherein the motor and reducer related parts include motor shaft, input shaft, intermediate shaft, output shaft and / or bearings. The method further comprises: a maintenance module configured to replace the faulty part; 6. The apparatus of claim 5, wherein, a third identifying module configured to re-obtain audio data of the test vehicle running within a preset time period, and identify whether there is an abnormal sound in the audio data. The first identifying module comprises: a generation unit configured to perform short-time Fourier transform on the audio signal of the abnormal sound audio to generate the noise waterfall chart.

7. The apparatus of claim 5, wherein, The method comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for identifying vehicle abnormal sound according to any one of claims 1-4.

8. A vehicle characterized by comprising: ​ ​ 9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor for implementing the method for identifying vehicle abnormal sound as claimed in any one of claims 1-4.

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