Abnormal sound diagnosis system

Abstract

To optimize a diagnosis range for abnormal sound in an abnormal diagnosis system to more improve diagnosis accuracy for the abnormal sound.SOLUTION: An abnormal sound diagnosis system of the present disclosure includes: an arithmetic processing unit that acquires a spectrogram indicating a relation among a time, a frequency and a sound pressure from sound time-series data; a display unit that displays the spectrogram; a diagnosis range adjustment unit that, when a minimum frequency of a diagnosis range for the spectrogram selected by a user on the display unit is equal to or more than a predetermined threshold value, maintains the diagnosis range as it is, and when the minimum frequency is less than the threshold value, corrects the diagnosis range so that the minimum frequency is a frequency having a predetermined order component calculated on the basis of a maximum rotational frequency of a driving source of a vehicle in the diagnosis range acquired in synchronization with the sound data; and a diagnosis unit that diagnoses a cause for abnormal sound generated in the vehicle on the sound time-series data and the diagnosis range.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present disclosure relates to an abnormal sound diagnosis system that diagnoses abnormal sounds generated in a vehicle. [Background technology]

[0002] Conventionally, an abnormal sound identification device including a calculation device that can access a trained model of artificial intelligence and an output device is known (see, for example, Patent Document 1). The calculation device of this abnormal sound identification device identifies frequency-time data indicating a time change in the frequency spectrum of a sound recorded in a vehicle, and displays the identified frequency-time data on a monitor. In addition, a user of the abnormal sound identification device selects a first time range in which an abnormal sound is considered to be included and a second time range in which an abnormal sound is considered not to be included from the frequency-time data on the monitor. Next, the calculation device inputs the identified frequency-time data into the trained model. The trained model identifies one or more abnormal sounds occurring within the first time range as a first abnormal sound, and identifies one or more abnormal sounds occurring within the second time range as a second abnormal sound. Here, it is highly likely that no abnormal sound has occurred in the second time range, and that the second abnormal sound is an abnormal sound that has not been recognized by a person. Therefore, a first abnormal sound that matches the type of the second abnormal sound is highly likely to be an abnormal sound that has not been recognized by a person. Based on this, the computing device displays on the monitor the type of the first abnormal sound that does not match the type of the second abnormal sound, thereby making it possible to identify the type of abnormal sound contained in the sound recorded in the vehicle. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2023-012335 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the above-described abnormal sound identification device, the frequency range of the first and second time ranges is a wide range that includes 0 Hz, so that sounds with relatively low frequencies that would not normally be recognized as abnormal sounds may be erroneously determined as abnormal sounds by the trained model, which may reduce the accuracy of identifying abnormal sounds.

[0005] Therefore, a primary object of the present disclosure is to further improve the accuracy of abnormal sound diagnosis by optimizing the abnormal sound diagnosis range in an abnormal sound diagnosis system. [Means for solving the problem]

[0006] The abnormal sound diagnosis system disclosed herein is an abnormal sound diagnosis system that diagnoses an abnormal sound generated in a vehicle based on time-series data of a sound emitted from the vehicle, and includes a calculation processing unit, a diagnosis range adjustment unit, and a diagnosis unit. The calculation processing unit acquires a spectrogram showing the relationship between time, frequency, and sound pressure from the time-series data of the sound. The diagnosis range adjustment unit maintains the diagnosis range as it is when the minimum frequency of the diagnosis range of the spectrogram selected by the user on the display unit is equal to or greater than a predetermined threshold value. In addition, when the minimum frequency of the selected range of the spectrogram is less than the threshold value, the diagnosis range adjustment unit corrects the diagnosis range so that the minimum frequency becomes the frequency of a predetermined order component calculated based on the maximum rotation speed of the drive source of the vehicle in the diagnosis range acquired in synchronization with the sound data. Furthermore, the diagnosis unit diagnoses the cause of the abnormal sound generated in the vehicle based on the time-series data of the sound and the diagnosis range. As a result, when the minimum frequency of the diagnosis range of the spectrogram selected by the user is less than the threshold value, the minimum frequency of the diagnosis range is raised, thereby preventing the diagnosis unit from erroneously determining that a relatively low-frequency sound that is not actually recognized as an abnormal sound is an abnormal sound. As a result, it is possible to optimize the diagnostic range of the abnormal sound in the abnormal sound diagnosis system and further improve the accuracy of diagnosing the abnormal sound. Note that the diagnosing unit may be constructed by machine learning so as to diagnose the cause of the abnormal sound based on the sound time-series data and the diagnostic range. [Brief description of the drawings]

[0007] [Figure 1]1 is a schematic configuration diagram showing an abnormal sound diagnosis system according to the present disclosure. [Diagram 2] 4 is a flowchart showing a series of processes executed in a terminal constituting the abnormal sound diagnosis system of the present disclosure. [Diagram 3] 5(a) and 5(b) are explanatory diagrams showing examples of displays on a display unit of a terminal constituting the abnormal sound diagnosis system of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Next, an embodiment of the present disclosure will be described with reference to the drawings.

[0009] 1 is a schematic diagram showing an abnormal sound diagnosis system 1 according to the present disclosure. The abnormal sound diagnosis system 1 shown in the figure is for diagnosing the cause of abnormal sound generated in a vehicle V, such as a vehicle equipped with only an engine as a drive source, a hybrid vehicle (HEV, PHEV), or an electric vehicle (BEV, FCEV), and includes a mobile terminal 10 and a server 20 as a diagnostic device capable of exchanging information with the mobile terminal 10 through communication.

[0010] The mobile terminal 10 is used by a user of the abnormal sound diagnosis system 1, who is, for example, an operator at a vehicle dealership or a maintenance shop, when responding to a user (owner) of the vehicle V in which an abnormal sound has occurred, or when performing a reproduction test in which the vehicle V is driven (operated) on a roadway or a test bench to reproduce the abnormal sound. In this embodiment, the mobile terminal 10 is a smartphone including an SoC, a ROM, a RAM, a GPS module (location information acquisition unit), an auxiliary storage device (flash memory) M, a display unit 11, a communication module 12, a microphone (not shown), and the like. An abnormal sound diagnosis support application (program) is also installed in the mobile terminal 10. As shown in FIG. 1, the mobile terminal 10 includes a medical interview information acquisition unit 13, a sound acquisition unit 14, a vehicle state acquisition unit 15, a calculation processing unit 16, an extraction unit 17, and a display control unit 18, which are constructed by cooperation between the abnormal sound diagnosis support application (software) and hardware such as the SoC of the mobile terminal 10.

[0011] The display unit 11 of the mobile terminal 10 includes a touch panel type liquid crystal panel or an organic EL panel. The communication module 12 exchanges various information with the electronic control device of the vehicle V via short-distance wireless communication or a cable (dongle), and can exchange various information with the server 20 via a network such as the Internet. The medical interview information acquisition unit 13 acquires information indicating the state of the vehicle V at the time of the occurrence of the abnormal sound (hereinafter referred to as "medical interview information") provided by the user of the vehicle V via the display unit 11. The sound acquisition unit 14 acquires time axis data of the sound (sound pressure) when the reproduction test is performed by the operator. The vehicle state acquisition unit 15 acquires information indicating the state of the vehicle V (hereinafter referred to as "vehicle state data") in synchronization with the acquisition of the time axis data of the sound by the sound acquisition unit 14 when the reproduction test is performed. The vehicle state data includes a plurality of physical quantities (vehicle speed, engine speed, motor speed, etc.) corresponding to the items of the medical interview information. The calculation processing unit 16 executes an analysis process of the time axis data of the sound acquired by the sound acquisition unit 14. The extraction unit 17 narrows down the analysis results by the calculation processing unit 16 in accordance with the selection by the operator, etc. The display control unit 18 controls the display unit 11.

[0012] The server 20 of the abnormal sound diagnosis system 1 is a computer (information processing device) including a CPU, ROM, RAM, input / output devices, a communication module, etc., and is installed and managed, for example, by an automobile manufacturer that manufactures the vehicle V. The server 20 is configured with an abnormal sound diagnosis unit 21 that diagnoses abnormal sounds generated in the vehicle V by cooperation between hardware such as a CPU and a pre-installed abnormal sound diagnosis application. The abnormal sound diagnosis unit 21 includes a neural network (convolutional neural network) configured by supervised learning (machine learning) so as to diagnose the cause of the abnormal sound generated in the vehicle V and the part that is the source of the abnormal sound based on the interview information and sound time axis data acquired by the mobile terminal 10. Furthermore, in the server 20, when it is found that a new abnormal sound has occurred in the vehicle V, re-learning is performed for the abnormal sound diagnosis unit 21 using the sound time axis data acquired for the new abnormal sound and the contents of each item of the interview information as teacher data.

[0013] Furthermore, the server 20 includes a storage device 22 that stores a database that stores information on a plurality of abnormal sounds that are known to occur in a vehicle for each vehicle model. The database stores information such as time axis data of the sound, the cause of the abnormal sound, the part that is the source of the sound, the contents of the medical interview information provided by the user, etc., and measures to eliminate the abnormal sound, for each of the plurality of abnormal sounds, by linking them to the information. The server 20 also updates the database based on information acquired from many vehicles including the vehicle V, and information on newly discovered abnormal sounds transmitted from automobile manufacturers (developers, etc.), vehicle dealers, repair shops, etc.

[0014] Next, the abnormal sound diagnosis procedure performed by the abnormal sound diagnosis system 1 will be described.

[0015] When a worker at a vehicle dealership or a repair shop is requested by a user of a vehicle V to eliminate an abnormal sound, the worker listens to the inquiry information from the user and then performs a reproduction test to obtain information necessary for diagnosing the abnormal sound. When performing the reproduction test, the worker connects the mobile terminal 10 to an electronic control device of the vehicle V, and places or fixes the mobile terminal 10 or an external microphone connected to the mobile terminal 10 in an appropriate position on the vehicle V. The worker also starts an abnormal sound diagnosis support application and turns on the start switch of the vehicle V. Accordingly, the mobile terminal 10 obtains information such as the vehicle identification number or chassis number of the vehicle V from the electronic control device. Furthermore, the worker taps a recording start button displayed on the display unit 11 and drives (operates) the vehicle V on a roadway or a test stand to reproduce the driving state in which the abnormal sound occurred based on the inquiry information from the user of the vehicle V.

[0016] While the vehicle V is running (operating), the sound acquisition unit 14 of the mobile terminal 10 acquires time axis data of the sound emitted from the vehicle V at predetermined time intervals (micro time) and stores the data in the auxiliary storage device M, and the vehicle state acquisition unit 15 acquires vehicle state data specified by the worker in response to medical interview information from the user of the vehicle V at predetermined time intervals (micro time) from the electronic control device of the vehicle V in synchronization with the acquisition of the sound time axis data by the sound acquisition unit 14 and stores the vehicle state data in the auxiliary storage device M. Then, when the worker taps the recording stop button displayed on the display unit 11, the acquisition of the sound time axis data and vehicle state data is completed, and the mobile terminal 10 executes a series of processes shown in FIG.

[0017] 2, after the reproduction test is completed, the calculation processing unit 16 of the mobile terminal 10 acquires the time axis data of the sound acquired by the sound acquisition unit 14 (step S100), and performs STFT (Short-Time Fourier Transform) on the acquired time axis data of the sound to acquire a spectrogram (acoustic spectrogram) showing the relationship between time, frequency, and sound pressure (step S110). In addition, the display control unit 18 of the mobile terminal 10 causes the display unit 11 to display the spectrogram acquired by the calculation processing unit 16 (step S120). In this embodiment, the spectrogram is a color map in which the horizontal axis is the time axis and the vertical axis is the frequency axis, and the sound pressure level is color-coded to show the relationship between time and sound pressure level for each frequency.

[0018] When the spectrogram is displayed on the display unit 11 of the mobile terminal 10, the worker selects (specifies) on the display unit 11 a range of the spectrogram that should be diagnosed (analyzed) by the abnormal sound diagnosis unit 21 (server 20) (hereinafter referred to as a "diagnosis range"), as shown by double lines in FIG. 3(a). The diagnosis range specifies a time range and a frequency range that should be diagnosed (analyzed) by the abnormal sound diagnosis unit 21 (server 20). In response to the worker's operation on the screen, the extraction unit 17 obtains the diagnosis range selected by the worker and instructs the display control unit 18 to display the diagnosis range on the display unit 11 (step S130). In step S130, the display control unit 18 displays the spectrogram on the display unit 11 so that only the diagnosis range selected by the worker is visible, for example by graying out the parts not selected by the worker.

[0019] Next, the extraction unit 17 extracts the minimum frequency fmin and the maximum frequency fmax of the frequency range of the diagnosis range selected by the operator (step S140). Furthermore, the extraction unit 17 judges whether the minimum frequency fmin extracted in step S140 is less than a first threshold value fref1 determined in advance (step S150). The first threshold value fref1 used in step S150 is set to a relatively small value (for example, about 50 Hz) so that low-frequency sounds are excluded from the diagnosis range. If the minimum frequency fmin is less than the first threshold value fref1 (step S150: YES), the extraction unit 17 judges whether the maximum frequency fmax extracted in step S140 is equal to or greater than a second threshold value fref2 determined in advance (step S160). The second threshold value fref2 used in step S160 is set to a relatively large value (for example, about 1000 Hz) that is sufficiently larger than the first threshold value fref1 and is used to judge whether the operator intentionally includes a relatively high frequency in the diagnosis range.

[0020] If the maximum frequency fmax is equal to or greater than the second threshold value fref2 (step S160: YES), the extraction unit 17 reads out vehicle state data within the diagnosis range (step S170), and determines whether the vehicle state data includes the rotation speed of the drive source of the vehicle V (step S180). The rotation speed of the drive source is the rotation speed of the engine if the vehicle V is a vehicle equipped with only an engine or a hybrid vehicle (HEV, PHEV), and is the rotation speed of the electric motor that generates power for traveling if the vehicle V is a battery electric vehicle (BEV) or a fuel cell vehicle (FCEV).

[0021] When the vehicle state data includes the rotation speed of the drive source of the vehicle V (step S180), the extraction unit 17 calculates the frequency f3 of the third-order component based on the maximum rotation speed of the drive source in the diagnosis range selected by the operator (step S190). When the vehicle V is a vehicle equipped with only an engine or a hybrid vehicle, in step S190, the frequency of the explosion third-order component (f3=(Nemax / 60)×2×3 in the case of a four-cylinder engine) is calculated based on the maximum rotation speed Nemax of the engine in the diagnosis range selected by the operator. In addition, when the vehicle V is a battery electric vehicle or a fuel cell vehicle, in step S190, the frequency of the rotation third-order component of the motor (f3=Nmmax / 60×3) is calculated based on the maximum rotation speed Nmmax of the motor in the diagnosis range selected by the operator. The frequency f3 is a value larger than the first threshold value fref1, regardless of the drive source of the vehicle V.

[0022] Furthermore, the extraction unit 17 modifies the diagnostic range selected by the operator so that the minimum frequency fmin becomes the frequency f3 calculated in step S190, and causes the display unit 11 to display the spectrogram so that only the modified diagnostic range is visible (step S200). As shown by the double line in Fig. 3(b), the frequency range of the modified diagnostic range is the range from the frequency f3 calculated in step S190 to the maximum frequency fmax selected by the operator.

[0023] After the process of step S200, the medical inquiry information acquisition unit 13 cooperates with the extraction unit 17 and the display control unit 18 to determine the medical inquiry information (step S210). In step S210, the extraction unit 17 extracts information to be provided to the server 20 as medical inquiry information from the vehicle condition data within the diagnosis range. Also in step S210, the display control unit 18 causes the display unit 11 to display a message instructing input of the medical inquiry information. After the operator completes input of the medical inquiry information, the medical inquiry information acquisition unit 13 determines the information extracted by the extraction unit 17 and the information input by the operator as the final medical inquiry information.

[0024] After the inquiry information is confirmed, when the worker taps an information transmission button displayed on the display unit 11, information required for diagnosing the abnormal sound is transmitted from the communication module 12 of the mobile terminal 10 to the server 20 (step S220). In this embodiment, the information transmitted from the mobile terminal 10 to the server 20 includes at least the time axis data of the sound, the inquiry information, the vehicle state data, and information defining the diagnosis range. When the information required for diagnosing the abnormal sound is transmitted from the mobile terminal 10 to the server 20, the abnormal sound diagnosis unit 21 of the server 20 diagnoses the cause of the abnormal sound generated in the vehicle V based on the information provided from the mobile terminal 10, and transmits the diagnosis result to the mobile terminal 10. The diagnosis result includes the cause of the abnormal sound generated in the vehicle V, the part that is the source of the abnormal sound, and the measures to eliminate the abnormal sound read from the storage device 22. When the diagnosis result from the server 20 is received by the mobile terminal 10, the diagnosis result is displayed on the display unit 11 (step S230), and a series of processes executed by the mobile terminal 10 for diagnosing the abnormal sound is completed. By executing the process shown in FIG. 2, the worker can accurately explain the diagnosis results from the server 20 to the user or the like of the vehicle V and promptly proceed with measures to deal with the abnormal noise.

[0025] 2, if it is determined in step S150 that the minimum frequency fmin is equal to or greater than the first threshold value fref1 (step S150: NO), if the maximum frequency fmax is less than the second threshold value fref2 (step S160: NO), or if the vehicle state data does not include the rotation speed of the drive source of the vehicle V (step S180: NO), the extraction unit 17 maintains the diagnosis range selected by the operator (step S205). In this case, after the process of step S205, the processes of steps S210-S230 are executed as shown in FIG. 2, and the series of processes executed by the mobile terminal 10 is terminated.

[0026] As described above, when the minimum frequency fmin of the diagnostic range of the spectrogram selected by the operator on the display unit 11 is equal to or greater than the first threshold value fref1 (step S150: NO), the extraction unit 17 as a diagnostic range adjustment unit of the abnormal sound diagnosis system 1 maintains the diagnostic range as it is (step S205). When the minimum frequency fmin of the selected range of the spectrogram is less than the first threshold value fref1 and the maximum frequency fmax of the selected range is equal to or greater than the second threshold value fref2 (steps S150: YES, S160: YES), the extraction unit 17 modifies the diagnostic range so that the minimum frequency fmin becomes the frequency f3 of the third-order component (explosion third-order component or rotation third-order component) calculated based on the maximum rotation speed (Nemax or Nmmax) of the drive source of the vehicle V in the diagnostic range acquired in synchronization with the sound data while maintaining the maximum frequency fmax (steps S190-S200).

[0027] As a result, even if an operator, for example, assumes that the frequency of an abnormal sound is relatively high and selects a high maximum frequency in the frequency range of the diagnosis range, and expands the frequency range of the diagnosis range to near 0 Hz for safety, the minimum frequency fmin of the diagnosis range can be raised to frequency f3 on condition that the minimum frequency fmin is less than the first threshold value fref1 and the maximum frequency fmax is equal to or greater than the second threshold value fref2. As a result, when an abnormal sound is diagnosed by the abnormality diagnosis unit 21 of the server 20, it is possible to prevent the abnormality diagnosis unit 21 from erroneously determining that a relatively low frequency sound that is not actually recognized as an abnormal sound is an abnormal sound. As a result, it is possible to optimize the abnormal sound diagnosis range in the abnormal sound diagnosis system 1, and to further improve the accuracy of abnormal sound diagnosis.

[0028] In step S190, for example, the frequency of the fourth-order rotation component of the motor may be calculated instead of the frequency f3 of the third-order component. That is, the frequency calculated in step S190 is not limited to the frequency f3 of the third-order component. In addition, the abnormal sound diagnosis support application installed in the mobile terminal 10 may be installed in a tablet terminal, a laptop or desktop personal computer, or the like, and the tablet terminal or the like may be used in place of the mobile terminal 10. Furthermore, the abnormal sound diagnosis system 1 may be configured as a single information processing device such as a personal computer. [Industrial Applicability]

[0029] The invention disclosed herein is extremely useful for diagnosing abnormal noises occurring in a vehicle. [Explanation of symbols]

[0030] 1 abnormal sound diagnosis system, 10 mobile terminal, 11 display unit, 16 calculation processing unit, 17 extraction unit (diagnosis range adjustment unit), 20 server, 21 abnormal sound diagnosis unit, V vehicle.

Claims

[Claim 1] An abnormal sound diagnosis system for diagnosing an abnormal sound generated in a vehicle based on time-series data of a sound emitted from the vehicle, comprising: A processor that acquires a spectrogram showing the relationship between time, frequency, and sound pressure from the time-series data of the sound; A display unit that displays the spectrogram; a diagnostic range adjustment unit that maintains the diagnostic range as it is when a minimum frequency of a diagnostic range of the spectrogram selected by a user on the display unit is equal to or greater than a predetermined threshold, and modifies the diagnostic range when the minimum frequency is less than the threshold so that the minimum frequency becomes a frequency of a predetermined order component calculated based on a maximum rotation speed of a drive source of the vehicle in the diagnostic range acquired in synchronization with the sound data; a diagnosis unit that diagnoses a cause of an abnormal sound generated in the vehicle based on the time-series data of the sound and the diagnosis range; An abnormal sound diagnosis system comprising:

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