Method and device for detecting abnormal engine noise

Abstract

To make a misfire determination based on a change of engine noise without acquiring an ignition pulse signal, etc. from a vehicle side.SOLUTION: Noise generated from an engine is acquired by a microphone to generate noise data (Step 1), and frequency data at individual time is generated through frequency analysis by using FFT (Step 2). On the basis of the frequency data, a fundamental frequency related to an engine speed is specified (Step 3), and on the basis of the fundamental frequency, data on harmonic of the fundamental frequency (time series frequency data) is extracted from the frequency data (Step 4). Regarding the harmonic, when a frequency decreases by a threshold value or larger (Step 5) and the frequency is restored for a short time (Step 6), misfire is determined (Step 7).SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to an abnormal noise detection technique for detecting phenomena such as misfires and abnormal combustion in engines such as vehicle engines based on the engine sounds. [Background technology]

[0002] For example, attempts have been made in the past to detect engine misfires from changes in sound or vibration. For example, Patent Document 1 discloses a technology in which the vibration sound of an engine is collected by a microphone, the sound within a certain time range after a predetermined time has elapsed since the generation of an ignition pulse is sampled, frequency analysis is performed, and the detected vibration waveform is compared with a normal vibration waveform for a specific frequency band to determine whether or not there is an abnormality. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 1-217218 Summary of the Invention [Problem to be solved by the invention]

[0004] The above technology requires that pulse signals indicating the ignition timing of each cylinder be acquired from the vehicle's engine controller in order to determine the sampling time frame, making it difficult to quickly and easily check for misfires and other abnormalities in automobile repair shops or in the finished vehicle inspection process on automobile production lines. [Means for solving the problem]

[0005] The engine abnormal noise detection method according to the present invention comprises: Acquire the sound produced by the engine to generate sound data, This sound data is converted according to frequency to generate frequency data, determining a fundamental frequency associated with the rotational speed of the engine based on the frequency data; Using this fundamental frequency, data on harmonics of the fundamental frequency is extracted from the frequency data. Based on the harmonic data, it is determined whether or not there is an abnormal sound contained in the sound data. The results of this determination are presented as information. In one embodiment, the presence or absence of abnormal noise is determined from the change over time in the frequency of at least one harmonic. In another embodiment, the presence or absence of abnormal noise is determined from the time variation of the frequency and sound pressure of at least one harmonic. In yet another embodiment, when the sound pressure of at least one harmonic tone decreases and returns to normal within a predetermined time range corresponding to a misfire, it is determined that the abnormal noise is due to a misfire. [Effects of the Invention]

[0006] According to this invention, by focusing on overtones of the fundamental frequency, changes in sound caused by engine misfires, abnormal combustion, etc. become more noticeable and are easier to detect as abnormal noises. Therefore, abnormal noises such as misfires can be detected without obtaining signals such as ignition timing from the vehicle side. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a functional block diagram of a first embodiment in which the present invention is applied to engine misfire detection. [Figure 2] 4 is a flowchart showing the flow of processing in the first embodiment. [Figure 3] FIG. 10 is an explanatory diagram of an example of a spectrogram displayed as frequency data. [Figure 4] 10 is a flowchart showing the flow of a process for identifying a fundamental frequency. [Figure 5] FIG. 10 is an explanatory diagram of a process for identifying a fundamental frequency. [Figure 6] FIG. 10 is a functional block diagram of a second embodiment. [Figure 7] 10 is a flowchart showing the flow of processing in a second embodiment. [Figure 8] FIG. 10 is a functional block diagram of a third embodiment. [Figure 9] 10 is a flowchart showing the flow of processing according to a third embodiment. [Figure 10] FIG. 10 is a functional block diagram of a fourth embodiment. [Figure 11]10 is a flowchart showing the flow of processing according to a fourth embodiment. [Figure 12] FIG. 11 is a functional block diagram of a fifth embodiment. [Figure 13] 13 is a flowchart showing the flow of processing according to the fifth embodiment. [Figure 14] FIG. 4 is an explanatory diagram showing a display example on a display unit. DETAILED DESCRIPTION OF THE INVENTION

[0008] An embodiment of the present invention applied to misfire detection in a vehicle engine is described below. This misfire detection embodiment is implemented, for example, as a misfire inspection to ensure that engine misfires do not occur during the final vehicle inspection process on an automobile production line. In general, during the final vehicle inspection process, an inspector test-drives the completed vehicle on a free roller, inspecting various components, including the engine, meters, and brakes. During the test run on the free roller, the engine is accelerated over an appropriate inspection time (e.g., 10 to 20 seconds) to gradually increase the engine speed from a low speed (e.g., 600 rpm) to a high speed (e.g., 6000 rpm). A misfire inspection is performed based on the engine sounds during this acceleration period. Hereinafter, the device of this embodiment is also referred to as a "misfire inspection device," and this misfire inspection device is configured as part of the inspection system used in the final vehicle inspection process.

[0009] The engine to be inspected may be either a gasoline engine (i.e., a spark-ignition internal combustion engine) or a diesel engine (i.e., a compression-ignition internal combustion engine), and may be either a four-stroke or two-stroke engine. In addition to in-line multi-cylinder engines, it may also be a V-type multi-cylinder engine.

[0010] 1 shows a functional block diagram of the misfire detection device of the first embodiment. The misfire detection device of the first embodiment is configured to include a sound measurement / acquisition unit 10, a frequency conversion unit 20, a fundamental frequency calculation unit 30, a harmonic identification unit 40, an abnormal sound detection unit 50, and a display unit 60.

[0011] The sound measurement and acquisition unit 10 includes a microphone that acquires engine sounds and converts them into electrical signals, i.e., sound data, and a recording unit that temporarily stores the sound data. The microphone is placed outside the vehicle so that it can collect engine sounds from the vehicle running on free rollers. The microphone's directivity and frequency characteristics are selected according to its position relative to the vehicle being measured and the frequency band of the engine sound. Typically, a microphone with directivity toward the vehicle is used. A microphone array or the like may be used to localize the sound source and obtain sound data from which noise has been removed. It is also possible to use an existing microphone, for example, one installed to collect horn sounds during the finished vehicle inspection process.

[0012] As described above, the misfire check is performed for, for example, 10 to 20 seconds while increasing the engine rotation speed, and therefore sound data having a time length of, for example, 10 to 20 seconds is acquired.

[0013] The frequency conversion unit 20 generates frequency data by converting the acquired sound data according to frequency using a frequency analysis method such as FFT (Fast Fourier Transform) or wavelet analysis. In particular, since it is necessary to ultimately determine the time change in frequency or sound pressure of a specific sound, frequency analysis is performed for each relatively short time domain to generate frequency data for each time.

[0014] When frequency data is expressed in two dimensions, for example, it is expressed as a frequency spectrum with frequency on the horizontal axis and power (or sound pressure) on the vertical axis.Furthermore, by superimposing the conversion results for each time in a time series, the frequency data may be handled as a so-called spectrogram, which is three-dimensional data including time.

[0015] Figure 3 shows an example of a spectrogram display of sound data obtained from a misfire inspection, processed using FFT. In accordance with the general spectrogram format, the horizontal axis represents time and the vertical axis represents frequency. The overall time length is approximately 15 seconds. Frequencies up to approximately 1000 Hz are included. The brightness and color of each point represent the power (dBA) (or sound pressure or amplitude) of a given frequency at a given time. While the attached image is black and white, an actual spectrogram display uses different colors, such as red for high power, yellow-green for medium power, and blue for low power, and the power is indicated by varying brightness within each color. The multiple lines in the spectrogram in Figure 3 represent frequencies that represent sound pressure peaks of several orders, including the fundamental frequency. In the illustrated example, the line in the lowest frequency band (designated by symbol F1) is the characteristic of the fundamental frequency, and several clearly visible lines (designated by symbols F2, F3, etc.) are the characteristic of frequencies corresponding to harmonics of the fundamental frequency. As described above, the misfire inspection in one embodiment is performed while the engine rotation speed is increasing, so the fundamental frequency and the frequencies of the harmonics tend to increase over time (i.e., as the engine rotation speed increases).

[0016] The graph shown below the spectrogram display in Figure 3 represents the original sound data with amplitude on the vertical axis.

[0017] The fundamental frequency calculation unit 30 determines the fundamental frequency associated with the engine rotation speed based on the frequency data obtained by the frequency conversion unit 20. Here, the fundamental frequency is determined from the engine sound, i.e., frequency data, without acquiring a signal indicating the engine rotation speed from the vehicle under test. In a multi-cylinder engine, sound is generated by intermittent combustion in each cylinder, which is the fundamental frequency. Here, the fundamental frequency is estimated from the sound pressure peaks included in the frequency data, while referring to information such as the number of strokes per cycle of the engine (i.e., whether it is a four-stroke or two-stroke engine), the number of cylinders, and the engine rotation speed range (for example, 600 to 6000 rpm that can be used during testing, or the range from the lower limit rotation speed during idling to the upper limit rotation speed limited by a limiter).

[0018] For example, in a four-stroke, four-cylinder engine, two explosions occur during one rotation of the crankshaft, so the possible fundamental frequency range corresponding to a rotational speed range of 600 to 6,000 rpm is 20 to 200 Hz. Therefore, the fundamental frequency is identified by searching for the frequency within this 20 to 200 Hz frequency range that shows the highest peak in sound pressure in the two-dimensional frequency spectrum or three-dimensional spectrogram described above. The identification of the fundamental frequency by the fundamental frequency calculation unit 30 will be described in further detail below.

[0019] The overtone identification unit 40 extracts data on harmonics of the fundamental frequency from the frequency data using the fundamental frequency identified by the fundamental frequency calculation unit 30. Theoretically, harmonics occur at frequencies that are integer multiples of the fundamental frequency. For example, if the fundamental frequency is 150 Hz, the peaks at integer multiples of that frequency, such as 300 Hz, 450 Hz, 600 Hz, and 750 Hz, are harmonics of the fundamental frequency. Therefore, the overtone identification unit 40 detects the presence or absence of peaks near frequencies that could be harmonics based on the frequency data and identifies the harmonics. In reality, harmonics are not measured as exact integer multiples of the fundamental frequency but exist within a certain range. Therefore, sound pressure peaks are searched for within a frequency range of, for example, approximately ±10% of the integer multiple frequency, and these peaks are identified as harmonics.

[0020] In the first embodiment, it is sufficient to identify one harmonic, so an appropriate harmonic order is extracted taking into consideration the characteristics of the engine, vehicle, etc. being inspected. In order to detect changes over time in frequency and sound pressure, as described below, it is preferable to extract a harmonic order of a relatively high order (e.g., 6th, 9th, etc.). Alternatively, multiple harmonic orders may be identified, and the sound pressures of each may be compared to select the harmonic with the highest peak sound pressure.

[0021] Harmonic data is time-series data including the frequency and sound pressure of a specific harmonic. As mentioned above, the harmonic data corresponds to the continuous line of high sound pressure points in the spectrogram display of Figure 3, for example. Figure 3 includes multiple harmonics, and the time-series data of each individual harmonic is extracted as harmonic data. Note that, since the first embodiment focuses on the time change of frequency as will be described later, the harmonic data may not include sound pressure information.

[0022] The abnormal sound detector 50 determines whether or not the approximately 15 seconds of sound data contains an abnormal sound caused by a misfire, based on the harmonic data extracted by the harmonic identification unit 40. Specifically, the abnormal sound detector 50 performs the abnormal sound determination based on whether or not the frequency of the specific harmonic being detected changes over time, or more specifically, whether or not the frequency of the specific harmonic has decreased and returned to within a predetermined time range corresponding to a misfire.

[0023] That is, in this embodiment, the abnormal sound detector 50 includes a frequency change amount calculator 51 and a frequency restoration time calculator 52. The frequency change amount calculator 51 calculates the amount of frequency change when the frequency temporarily drops in the time-series data of overtones. The frequency restoration time calculator 52 calculates the time it takes for the dropped frequency to restore. The abnormal sound detector 50 determines that an abnormal sound, or in other words a misfire, has occurred, for example, when there is a frequency drop of a certain threshold or more and the frequency restores within a certain time.

[0024] When a misfire occurs in a cylinder of an engine, the rotational speed of the crankshaft drops slightly, causing a change in the sound of the engine. However, if a misfire occurs under operating conditions with a fundamental frequency of 150 Hz, and the rotational speed drops by 2%, the frequency drop is only 3 Hz, and the rotational speed returns to normal when the next cylinder explodes, so the frequency drop is very short. Therefore, it is not only difficult for an inspector to determine the misfire by hearing, but also difficult to process data collected by a microphone, as noise and other factors can have a significant impact. Therefore, it is impossible to determine the misfire unless the timing is limited to a narrow range using an ignition pulse, as in Patent Document 1.

[0025] On the other hand, if the fundamental frequency drops by 3 Hz as in the example above, focusing on the fourth harmonic, for example, results in a frequency drop of 12 Hz, making the frequency drop during misfire more noticeable than the fundamental frequency. This makes it possible to detect misfire from engine sound alone, even without signals such as ignition timing. Furthermore, by determining whether the frequency has recovered in a short period of time in addition to the frequency drop, it is possible to distinguish between abnormal noise caused by misfire and sound changes or noise due to other factors.

[0026] In the spectrogram shown in Figure 3, which is an example, time-dependent changes in frequency corresponding to misfires can be seen at three locations indicated by arrows labeled "mf" (i.e., the frequency drops below the threshold and then recovers within a short period of time). Therefore, three misfires, labeled MF1, MF2, and MF3, were detected during the approximately 15-second test. As is clear from Figure 3, even with these misfires, there is almost no frequency change in the time-series data for the fundamental frequency (F1 in Figure 3).

[0027] The display unit 60 is a display means for displaying the judgment results to relevant parties such as workers in the vicinity of the inspection device, inspectors driving the vehicles, managers, and data scientists who utilize the data. For example, it may include a liquid crystal display or an organic EL display. If some kind of notification sound or voice is involved, it may also include a sound source, amplifier, speaker, etc. for generating and emitting the sound. The images, videos, sounds, voices, etc. displayed here are generated by image generation means and sound / voice generation means included in the display unit 60. Specific examples of what is displayed on the display will be described later.

[0028] FIG. 2 is a flowchart showing the processing flow of the misfire detection device of the first embodiment. First, the sound of the engine of a vehicle being test-run on a free roller is collected by the microphone of the sound measurement and acquisition unit 10 and acquired as sound data (Step 1). Next, the frequency conversion unit 20 converts the sound data according to frequency using a frequency analysis method such as FFT (Fast Fourier Transform) or wavelet analysis to generate frequency data. More specifically, frequency analysis is performed for each relatively short time domain to generate frequency data for each time (Step 2).

[0029] Next, as described above, the fundamental frequency is identified using the frequency data (step 3), and then appropriate harmonic time series data is extracted based on the fundamental frequency (step 4). Next, it is determined whether the frequency of this harmonic has dropped by more than a threshold (Step 5). For example, it is determined whether the frequency of the target harmonic has dropped by more than 10 Hz in one second. Note that "more than 10 Hz in one second" is just one example, and the appropriate threshold will vary depending on, for example, the order of the target harmonic. In other words, it is sufficient if the value is appropriate for detecting a sudden frequency drop caused by a misfire.

[0030] If the frequency drops by more than the threshold, it is further determined whether the frequency drop has recovered in a short time (step 6). For example, it is determined whether the original frequency has returned within 0.02 seconds. Note that "0.02 seconds" is merely an example, and will vary depending on the number of cylinders in the engine, etc. In other words, an appropriate value is set to identify whether the frequency drop is due to a misfire. As an example, "0.02 seconds" corresponds to the time interval (two crankshaft revolutions) between a misfire in a cylinder at 6000 rpm in a four-stroke engine and the next explosion in the same cylinder. Normally, the frequency recovers due to an explosion in another cylinder.

[0031] If the determinations in both steps 5 and 6 are YES, the process proceeds to step 7, where it is determined that a misfire has occurred and a message to that effect is displayed.

[0032] If the determination in either step 5 or step 6 is NO, the process proceeds to step 8, where the engine is determined to be normal (no misfire) and a message to that effect is displayed.

[0033] Finally, in step 9, it is determined whether or not retesting is necessary for some reason. If retesting is necessary, the process returns to step 1; if not, the test is terminated.

[0034] Next, the determination of the fundamental frequency in the fundamental frequency calculation unit 30 will be further described with reference to FIGS. 4 and 5. FIG. 4 is a flowchart showing the process flow for determining the fundamental frequency. First, engine specification data is acquired from a database included in the finished vehicle inspection device (step 101). This specification data includes at least information on the number of strokes per cycle of the engine (i.e., whether it is a four-stroke engine or a two-stroke engine), the number of cylinders, and the engine rotational speed range (for example, 600 to 6,000 rpm that can be used during inspection, or the range from the lower limit rotational speed during idle to the upper limit rotational speed limited by a limiter). Next, the frequency range of the fundamental frequency corresponding to the engine rotational speed range is calculated (step 102). For example, for a four-cylinder, four-stroke engine, the possible fundamental frequency range corresponding to a rotational speed range of 600 to 6,000 rpm is 20 to 200 Hz.

[0035] Next, proceeding to step 103, the sound data shown in FIG. 5(a) (original sound data having a time length of approximately 15 seconds) is subjected to frequency analysis using FFT for each relatively short time region (first, between T1 and T2) to generate frequency data. As a result, a two-dimensional frequency spectrum is obtained for the period between T1 and T2, as shown in FIG. 5(b). Then, all frequencies showing convex peaks in sound pressure or power in this frequency spectrum are extracted (step 104). Further, from among these multiple peak frequencies, a peak frequency that is within the above-mentioned possible fundamental frequency range (e.g., 20 to 200 Hz) and has the maximum sound pressure peak is extracted (step 105), and this is determined as the fundamental frequency for the period between T1 and T2 (step 106). FIG. 5(c) shows a portion of the spectrogram display described above, and as shown in FIG. 5(c), the fundamental frequency F1t1 for the period between T1 and T2 is determined.

[0036] After processing the sound data between T1 and T2 in this way, the target time domain is incremented in step 108, and steps 103 to 106 are performed for the next time period T2 to T3. That is, the sound data between T2 and T3 is frequency analyzed using FFT, multiple peak frequencies are extracted from the obtained frequency spectrum, and the peak frequency that is within the possible fundamental frequency range and has the maximum sound pressure peak is extracted and determined as the fundamental frequency between T2 and T3. As a result, the fundamental frequency F1t2 between times T2 and T3 is determined, as shown in Figure 5(c).

[0037] By repeating the processes of steps 103 to 106 for each time domain, the fundamental frequency is determined for each time domain. In step 107, it is determined whether the entire time range of the sound data has been processed. When all processing of the sound data having a time length of approximately 15 seconds has been completed, the identification of the fundamental frequency is completed. This results in the fundamental frequency being obtained in the form of time-series data.

[0038] The individual fundamental frequencies (F1t1, F1t2, F1t3, etc.) found for each time domain basically have continuous characteristics. If there are any discontinuous parts, the values in that section can be ignored and the values before and after can be made continuous. Alternatively, overtones can be identified from the values of parts that are generally continuous.

[0039] The fundamental frequency may be determined by other methods. For example, if there are multiple frequency peaks (convex mountain-shaped) in a two-dimensional frequency spectrum, the minimum peak frequency is determined as the fundamental frequency. Alternatively, if two or more peak frequencies are detected, the frequency difference between two adjacent peaks may be determined as the fundamental frequency.

[0040] As described above, the misfire detection device of the above embodiment can detect misfires without obtaining signals such as ignition timing signals from the vehicle by collecting engine sounds with a microphone located outside the vehicle and processing the signals. In particular, the device focuses on harmonics of the fundamental frequency, the range of change of which increases with misfire, and determines misfires based on the temporary drop and recovery of frequency associated with misfire. This ensures reliable misfire detection even in environments where other noises are present. Furthermore, misfire detection can be efficiently performed during test runs on free rollers during the completed vehicle inspection process.

[0041] Next, a misfire detection device according to a second embodiment will be described with reference to Figures 6 and 7. The following mainly focuses on the differences from the first embodiment. The misfire detection device according to the second embodiment determines whether an abnormal sound corresponding to a misfire is contained in sound data based on a drop in the sound pressure of the overtones, instead of a drop in the frequency of the overtones.

[0042] As shown in the block diagram of Fig. 6, the misfire detection device of the second embodiment, like the first embodiment, is configured to include a sound measurement / acquisition unit 10, a frequency conversion unit 20, a fundamental frequency calculation unit 30, a harmonic identification unit 40, an abnormal sound detection unit 50, and a display unit 60. Here, the abnormal sound detection unit 50 includes a sound pressure change amount calculation unit 53 and a sound pressure restoration time calculation unit 54, instead of the frequency change amount calculation unit 51 and the frequency restoration time calculation unit 52 of the first embodiment. Note that in the second embodiment, the harmonic data generated by the harmonic identification unit 40 includes at least sound pressure, and may not include time series information of frequency.

[0043] Sound pressure is represented by the brightness and color of each point in the spectrogram display of FIG. 3, for example, and by the height of a peak in a two-dimensional frequency spectrum. When a misfire occurs in an engine, a temporary drop in sound pressure occurs because the energy of the explosion is not added. However, this drop in sound pressure due to a misfire is impossible to detect in sound data that contains a mixture of many frequencies and noises. In contrast, as in this embodiment, by extracting data on specific harmonics (time-series data on the sound pressure of harmonics) and focusing on the time change in the sound pressure of these harmonics, it is possible to detect a change in sound pressure due to a misfire.

[0044] FIG. 7 is a flowchart showing the process flow for misfire inspection in the second embodiment. After extracting appropriate harmonic time-series data in steps 1 to 4, step 5A determines whether the sound pressure of the harmonic has dropped by more than a threshold value. For example, it determines whether the sound pressure of the target harmonic has dropped by more than 3 dB in one second. Note that "more than 3 dB in one second" is merely an example.

[0045] If the sound pressure has dropped by more than the threshold, it is further determined whether the sound pressure has recovered in a short time (step 6A). For example, it is determined whether the sound pressure has returned to its original level within 0.02 seconds. Note that "0.02 seconds" is merely an example.

[0046] If the determinations in both Steps 5A and 6A are YES, the process proceeds to Step 7, where it is determined that a misfire has occurred and a message to that effect is displayed.

[0047] If the determination in either step 5A or step 6A is NO, the process proceeds to step 8, where the engine is determined to be normal (no misfire) and a message to that effect is displayed.

[0048] Next, a misfire detection device according to a third embodiment will be described with reference to Figures 8 and 9. The misfire detection device according to the third embodiment determines whether an abnormal sound corresponding to a misfire is contained in sound data based on both a drop in the frequency of an overtone and a drop in the sound pressure of the overtone. Specifically, a misfire is determined to have occurred when a drop in the frequency of a certain overtone and a drop in the sound pressure occur substantially simultaneously.

[0049] As shown in the block diagram of Fig. 8, the misfire detection device of the third embodiment, like the first embodiment, is configured to include a sound measurement / acquisition unit 10, a frequency conversion unit 20, a fundamental frequency calculation unit 30, a harmonic identification unit 40, an abnormal sound detection unit 50, and a display unit 60. Here, the abnormal sound detection unit 50 includes a frequency change amount calculation unit 51 and a frequency restoration time calculation unit 52 as in the first embodiment, and a sound pressure change amount calculation unit 53 and a sound pressure restoration time calculation unit 54 as in the second embodiment. Note that in the third embodiment, the harmonic data generated by the harmonic identification unit 40 includes both frequency information and sound pressure information.

[0050] 9 is a flowchart showing the process flow for misfire detection in the third embodiment. After extracting appropriate harmonic time-series data in steps 1 to 4, step 5B determines whether the harmonic frequency and sound pressure have both dropped by more than a threshold. For example, it determines whether the frequency of the harmonic has dropped by more than 10 Hz in one second and whether the sound pressure has dropped by more than 3 dB in one second.

[0051] If there is a frequency drop or sound pressure drop that is equal to or greater than the threshold, it is further determined whether or not the frequency drop or sound pressure drop is restored in a short time (step 6B), for example, whether or not the original frequency and sound pressure are restored within 0.02 seconds.

[0052] If the determinations in both Steps 5B and 6B are YES, the process proceeds to Step 7, where it is determined that a misfire has occurred, and a message to that effect is displayed.

[0053] If the determination in either step 5B or step 6B is NO, the process proceeds to step 8, where the engine is determined to be normal (no misfire) and a message to that effect is displayed.

[0054] In this embodiment, by making both a decrease in frequency and a decrease in sound pressure the conditions, the misfire determination becomes stricter and false detection due to noise and the like is reduced.

[0055] Next, a misfire detection device of a fourth embodiment will be described with reference to Figures 10 and 11. The misfire detection device of the fourth embodiment determines whether an abnormal sound corresponding to a misfire exists based on a drop in frequency and a drop in sound pressure of multiple harmonics. Specifically, a misfire is determined when a drop in frequency and a drop in sound pressure occur substantially simultaneously for multiple harmonics.

[0056] As shown in the block diagram of FIG. 10 , the misfire detection device of the fourth embodiment, like the third embodiment, is configured to include a sound measurement / acquisition unit 10, a frequency conversion unit 20, a fundamental frequency calculation unit 30, a harmonic identification unit 40, an abnormal sound detection unit 50, and a display unit 60. Here, the harmonic identification unit 40 extracts harmonic data (time-series data of frequency and sound pressure) for all extractable harmonics. For example, if the fundamental frequency is 150 Hz, harmonics exist near integer multiples of 150 Hz, such as 300 Hz, 450 Hz, 600 Hz, 750 Hz, etc., and all of these harmonics are identified as much as possible and time-series data of their frequencies and sound pressures is generated.

[0057] As in the third embodiment, abnormal sound detection unit 50 includes a frequency change amount calculation unit 51, a frequency restoration time calculation unit 52, a sound pressure change amount calculation unit 53, and a sound pressure restoration time calculation unit 54, and further includes a harmonic number calculation unit 55 that calculates the number of harmonics in which time changes in frequency, etc. appear substantially simultaneously. The abnormal sound detection unit 50 determines that a misfire has occurred when the number of harmonics in which time changes in frequency, etc. appear substantially simultaneously is equal to or greater than a predetermined number.

[0058] FIG. 11 is a flowchart showing the processing flow for misfire detection in the fourth embodiment. After identifying the fundamental frequency in steps 1 to 3, time-series data for multiple (all extractable) harmonics is extracted in step 4. Then, in step 5C, it is determined whether these multiple harmonics have experienced a frequency drop of more than a threshold and a sound pressure drop of more than a threshold. For example, it is determined whether the frequency of the targeted harmonic has dropped by more than 10 Hz in one second and the sound pressure has dropped by more than 3 dB in one second. Note that the threshold may be different for each individual harmonic.

[0059] If there is a frequency drop or sound pressure drop that is equal to or greater than the threshold, it is further determined whether or not the frequency drop or sound pressure drop is restored in a short time (step 6C), for example, whether or not the original frequency and sound pressure are restored within 0.02 seconds.

[0060] If there is a phenomenon in which there is a frequency drop and sound pressure drop of more than the threshold value for at least one harmonic and the drop is restored within a predetermined time, then in step 10, it is determined whether such a phenomenon (a frequency drop and sound pressure drop and their restoration) occurred substantially simultaneously for a predetermined number or more of harmonic overtones, for example, two or more.

[0061] If the above phenomenon occurs simultaneously for two or more harmonics, proceed to step 7, determine that a misfire has occurred, and display a message to that effect.

[0062] If there is a drop in frequency and sound pressure equal to or greater than the threshold and no overtones are restored within the specified time, or if only one overtone is detected, the system proceeds to step 8, determines that the condition is normal (no misfire), and displays a message to that effect.

[0063] In this embodiment, by using the occurrence of time-dependent changes in frequency and sound pressure in multiple overtones as a weighting condition, misfire detection becomes stricter and false detection due to noise and the like is reduced.

[0064] In the above example, both the time change in frequency and the time change in sound pressure are detected for multiple harmonics, but it is also possible to determine only the time change in frequency as in the first embodiment, or only the time change in sound pressure as in the second embodiment.

[0065] Next, a misfire detection device according to a fifth embodiment will be described with reference to Figures 12 and 13. As with the fourth embodiment, the misfire detection device according to the fifth embodiment determines whether a change in frequency or the like has occurred over time in multiple harmonics, and checks whether the amount of frequency drop in each harmonics is correctly correlated with the order of the harmonic. Specifically, if the amount of frequency drop in multiple harmonics is correctly correlated with the order of each harmonic, it is determined to be a misfire. On the other hand, even if a drop in frequency and sound pressure has occurred in multiple harmonics, if the amount of frequency drop is not correctly correlated with the order, it is determined to be noise or the like, and is not a misfire.

[0066] For example, if the third harmonic has a frequency drop of 10 Hz, the sixth harmonic should have a frequency drop of 20 Hz. If this correlation is not maintained, it is possible that some kind of noise is present. Therefore, in the fifth embodiment, such a case is not determined to be a misfire.

[0067] 12, the misfire detection device of the fourth embodiment, like the third embodiment, is configured to include a sound measurement / acquisition unit 10, a frequency conversion unit 20, a fundamental frequency calculation unit 30, a harmonic identification unit 40, an abnormal sound determination unit 50, and a display unit 60. Here, like the fourth embodiment, the harmonic identification unit 40 extracts harmonic data (time series data of frequency and sound pressure) for all extractable harmonics.

[0068] As in the fourth embodiment, the abnormal sound detection unit 50 includes a frequency change amount calculation unit 51, a frequency restoration time calculation unit 52, a sound pressure change amount calculation unit 53, a sound pressure restoration time calculation unit 54, and a harmonic number calculation unit 55 that calculates the number of harmonics for which time changes in frequency, etc. appear substantially simultaneously. The abnormal sound detection unit 50 also includes a harmonic order / change amount calculation unit 56 that calculates the order and amount of frequency drop for each of multiple harmonics for which time changes in frequency, etc. appear substantially simultaneously. The abnormal sound detection unit 50 determines whether the amount of frequency drop for each of the multiple harmonics corresponds to the order of each harmonic and makes a final misfire detection.

[0069] FIG. 13 is a flowchart showing the process flow for misfire detection in the fifth embodiment. As in the fourth embodiment, after extracting time-series data for multiple (all extractable) harmonics in steps 1 to 4, step 5D determines whether these multiple harmonics have experienced a frequency drop of more than a threshold and a sound pressure drop of more than a threshold. For example, it determines whether the frequency of the target harmonic has dropped by more than 10 Hz in one second and whether the sound pressure has dropped by more than 3 dB in one second. Note that the threshold may be different for each individual harmonic.

[0070] If there is a frequency drop or sound pressure drop that is equal to or greater than the threshold, it is further determined whether or not the frequency drop or sound pressure drop is restored in a short time (step 6D), for example, within 0.02 seconds.

[0071] If there is a phenomenon in which there is a frequency drop and sound pressure drop of more than the threshold value for at least one harmonic and the drop is restored within a predetermined time, then in step 10, it is determined whether such a phenomenon (a frequency drop and sound pressure drop and their restoration) occurred substantially simultaneously for a predetermined number or more of harmonic overtones, for example, two or more.

[0072] If the above phenomenon occurs simultaneously for two or more harmonics, proceed to step 11 to determine whether the frequency downshift of each harmonic correlates correctly with the order of each harmonic. Since the actual frequency downshift is not strictly an integer multiple, it is desirable to add an appropriate tolerance when making this determination.

[0073] If the answer to step 11 is YES, that is, if there is a frequency drop and sound pressure drop equal to or greater than the threshold and the amount of each frequency drop is correctly correlated with the order of each harmonic for multiple harmonics that have been restored within the specified time, proceed to step 7, determine that a misfire has occurred, and display a message to that effect.

[0074] If there is a frequency drop and sound pressure drop that are greater than the threshold and no overtones are restored within the specified time, or if this is detected in only one overtone, or if the frequency drop in multiple overtones does not correspond to the order of the overtone, proceed to step 8, determine that the condition is normal (no misfire), and display a message to that effect.

[0075] In this embodiment, for multiple harmonics where a drop in frequency or the like has occurred, the weighting condition is that the amount of frequency drop in each harmonic corresponds to the order of the harmonic, thereby making misfire detection more stringent and reducing false detections due to noise or the like.

[0076] Next, an example of the display on the display unit 60 is shown in FIG. 14. In this example, to visually represent changes in fundamental frequency and harmonics, the spectrogram display described above is displayed on the right side of the screen (the area indicated by reference symbol A), and the original sound data is displayed as a two-dimensional graph below the spectrogram display. As mentioned above, the spectrogram is actually displayed in color. In this example, two misfires were detected during the approximately 15-second inspection period. To indicate the timing of each misfire, red inverted triangular arrows indicated by reference symbols B1 and B2 are displayed above the spectrogram display. This allows the user to understand the operating conditions under which the misfire occurred. In addition, in the illustrated example, the areas on the spectrogram where changes in the frequency and other parameters determined to be misfires over time are displayed are surrounded by red ellipses (indicated by reference symbols H1 and H2).

[0077] Additionally, in the upper left corner of the screen, as indicated by symbol C, a relatively large label indicates whether or not a misfire has occurred. In the illustrated example, a misfire has been detected, so "Misfire Present" is displayed in red. If no misfire has occurred, the display will read "No Misfire." Furthermore, if a misfire has occurred, the amount of sound pressure drop (indicated by symbol D), the amount of frequency drop (indicated by symbol E), and the number of misfires (indicated by symbol F) are displayed below part C.

[0078] Additionally, a button (indicated by the symbol G) representing a speaker is located at the bottom left side. When this speaker button G is pressed via a touch screen or the like, sound data edited over a relatively short period of time, including the misfire portion, is played back for the inspector to listen to. This allows the inspector to identify the engine sound accompanying the misfire.

[0079] The above display is one example, and the result of the abnormal noise determination could be simply displayed as OK / NG letters or as a circle or cross symbol. It is desirable to avoid excessive information volume on the screen so that it can be understood in a short time.

[0080] Furthermore, in the above display example, a spectrogram is used to visually represent the time changes of the fundamental frequency and overtones, but other displays that visualize sound, such as a mel spectrogram or keptogram, may also be used.

[0081] Next, as a different embodiment of abnormal sound detection of the present invention, the spectrogram display may be treated as image data and processed with a pre-created abnormal sound determination model. That is, as described above, a spectrogram display corresponding to sound data can be generated using frequency data obtained by frequency analyzing sound data, but an abnormal sound determination model can be generated in advance by using machine learning (or deep learning) to generate image data of a large number of spectrogram displays, including inspection examples with and without misfires, and the spectrogram display of the sound of the engine to be inspected can be processed as image data with the abnormal sound determination model, making it possible to determine the presence or absence of abnormal sounds from characteristic changes in the harmonic data on the image.

[0082] While the present invention has been described above as an embodiment in which it is applied to detecting engine misfires during the finished vehicle inspection process, it is not limited to this application and can be applied in a variety of other ways. For example, misfires can be easily detected during inspections and maintenance at dealerships or repair shops. Furthermore, the present invention can be applied to detecting not only misfires but also abnormal combustion in engines, abnormalities in mechanical components, and other abnormalities that are reflected in the engine sound.

[0083] Furthermore, in the above embodiment, misfire detection is performed while the engine speed is increased, but it is also possible to detect misfire while maintaining a constant engine speed.

[0084] The information presentation unit is not limited to the above-mentioned display unit that displays images, but may also be one that presents information only by voice. [Explanation of symbols]

[0085] 10...Sound measurement and acquisition section 20...Frequency conversion section 30...Fundamental frequency calculation section 40...Harmonic identification section 50...Abnormal noise detection section 60...Display section

Claims

1. Acquire the sound produced by the engine to generate sound data, This sound data is converted according to frequency to generate frequency data, determining a fundamental frequency associated with the rotational speed of the engine based on the frequency data; Using this fundamental frequency, data on harmonics of the fundamental frequency is extracted from the frequency data. Based on the harmonic data, it is determined whether or not there is an abnormal sound contained in the sound data. The results of this assessment will be presented. A method for detecting abnormal noise in an engine, comprising: determining whether or not an abnormal sound is present based on a time change in the frequency of at least one harmonic; How to detect abnormal engine noise.

2. Acquire the sound generated by the engine and generate sound data, This sound data is converted according to frequency to generate frequency data, determining a fundamental frequency associated with the rotational speed of the engine based on the frequency data; Using this fundamental frequency, data on harmonics of the fundamental frequency is extracted from the frequency data. Based on the harmonic data, it is determined whether or not there is an abnormal sound contained in the sound data. The results of this assessment will be presented. A method for detecting abnormal noise in an engine, comprising: An engine abnormal noise detection method that determines whether or not an abnormal noise is present based on time changes in frequency and sound pressure of at least one harmonic.

3. 3. The method for detecting abnormal noise from an engine according to claim 1, wherein a change in overtones caused by a misfire in the engine is determined to be the abnormal noise.

4. 2. The engine abnormal noise detection method according to claim 1, wherein when the frequency of the overtones drops and returns to normal within a predetermined time range corresponding to a misfire, it is determined that the abnormal noise is caused by a misfire.

5. Acquire sound generated from an engine and generate sound data; This sound data is converted according to frequency to generate frequency data, determining a fundamental frequency associated with the rotational speed of the engine based on the frequency data; Using this fundamental frequency, data on harmonics of the fundamental frequency is extracted from the frequency data. Based on the harmonic data, it is determined whether or not there is an abnormal sound contained in the sound data. The results of this assessment will be presented. A method for detecting abnormal noise in an engine, comprising: When the sound pressure of at least one harmonic has decreased and returned to normal within a predetermined time range corresponding to a misfire, it is determined that the abnormal noise is due to a misfire. How to detect abnormal engine noise.

6. 3. The engine abnormal noise detection method according to claim 2, wherein the occurrence of the abnormal noise is determined to be due to a misfire when the frequency and sound pressure of the overtones decrease and return to normal within a predetermined time range corresponding to a misfire.

7. 7. The method for detecting abnormal engine noise according to claim 1, wherein the sound of the engine mounted on the vehicle is collected by a microphone outside the vehicle.

8. The engine abnormal noise detection method according to any one of claims 1 to 7, wherein the fundamental frequency is identified by identifying, as the fundamental frequency, a frequency that indicates a peak in sound pressure within a possible fundamental frequency range based on the number of cylinders of the engine and the rotational speed range of the engine.

9. 9. The engine abnormal noise detection method according to claim 1, wherein frequencies that show sound pressure peaks in the vicinity of integer multiples of the fundamental frequency are identified as harmonics.

10. It determines whether multiple harmonics each experience a drop in frequency at the same time, Determine whether each frequency downshift corresponds to each order; 2. The method for detecting abnormal noise in an engine according to claim 1, wherein an abnormal noise is determined to exist when the noise corresponds to each of the orders.

11. generating a spectrogram representation corresponding to the sound data using the frequency data; A model for detecting abnormal noise is generated by machine learning using a large amount of spectrogram image data in advance.

11. The method for detecting abnormal noise from an engine according to claim 1, further comprising processing a spectrogram display of the sound of the engine to be inspected with the abnormal noise determination model to determine whether or not an abnormal noise is present.

12. a sound acquisition unit that acquires sounds generated from the engine and generates sound data; a frequency data generating unit that generates frequency data by converting the sound data according to the frequency; a fundamental frequency calculation unit that determines a fundamental frequency associated with the rotation speed of the engine based on the frequency data; a harmonic identification unit that uses the fundamental frequency to extract data on harmonics of the fundamental frequency from the frequency data; an abnormal sound determination unit that determines whether or not an abnormal sound is included in the sound data based on the harmonic data; an information presentation unit that presents the result of the determination; An engine abnormal noise detection device comprising: the abnormal sound determination unit determines the presence or absence of an abnormal sound from a time change in frequency of at least one harmonic. Engine noise detection device.

13. A sound acquisition unit that acquires sounds generated from an engine and generates sound data; a frequency data generating unit that generates frequency data by converting the sound data according to the frequency; a fundamental frequency calculation unit that determines a fundamental frequency associated with the rotation speed of the engine based on the frequency data; a harmonic identification unit that uses the fundamental frequency to extract data on harmonics of the fundamental frequency from the frequency data; an abnormal sound determination unit that determines whether or not an abnormal sound is included in the sound data based on the harmonic data; an information presentation unit that presents the result of the determination; An engine abnormal noise detection device comprising: the abnormal sound determination unit determines the presence or absence of an abnormal sound from time changes in frequency and sound pressure of at least one harmonic. Engine noise detection device.

14. A sound acquisition unit that acquires sounds generated from an engine and generates sound data; a frequency data generating unit that generates frequency data by converting the sound data according to the frequency; a fundamental frequency calculation unit that determines a fundamental frequency associated with the rotation speed of the engine based on the frequency data; a harmonic identification unit that uses the fundamental frequency to extract data on harmonics of the fundamental frequency from the frequency data; an abnormal sound determination unit that determines whether or not an abnormal sound is included in the sound data based on the harmonic data; an information presentation unit that presents the result of the determination; An engine abnormal noise detection device comprising: the abnormal noise determination unit determines that the abnormal noise is caused by a misfire when the sound pressure of at least one harmonic has decreased and returned to normal within a predetermined time range corresponding to a misfire. Engine noise detection device.

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