Vehicle inspection and display method and device

Abstract

To display the degree of reliability due to unevenness in operation together with other information when inspecting a component for abnormality by a sound, so as to prevent the component from being mistakenly determined as being abnormal by improper operation.SOLUTION: The present invention acquires a sound from a vehicle that is doing a trial run on free rollers (S1), and separates and extracts the sound of a horn which is the component to be inspected (S2). The frequency of the extracted sound of the horn is analyzed (S3), and compared with the frequency data of a normal component to calculate the degree of abnormality (S4). The length of time is calculated as the time characteristic of time-amplitude data of the extracted sound of the horn (S5), and it is compared with the length of time of a normal operation to calculate the degree of reliability (S6). A two-dimensional graph in matrix form is created on the basis of the calculated degree of abnormality and the degree of reliability, which is then shown on a display (S7). Inspection is finished when an inspector pushes a stop button after having repeated inspection appropriately (S8).SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a method and apparatus for displaying the results of an inspection of a vehicle such as an automobile to determine whether or not the sounds or vibrations emitted from various parts are abnormal. [Background technology]

[0002] For example, in the final stage of an automobile production line, in the finished vehicle inspection process, inspectors test drive the finished vehicle on free rollers and inspect numerous items, including the engine, meters, brakes, horn, and lights. Generally, during this inspection process, the presence or absence of abnormalities is determined by the inspector's sensory evaluation of various sounds and vibrations emitted from various parts of the vehicle. For example, in the case of a horn, the inspector sounds the horn and listens to it to confirm that it is normal. Sounds emitted from the transmission during acceleration and from the brakes during braking are also subject to sensory inspection.

[0003] Instead of such sensory testing, attempts have been made to detect abnormalities by acquiring sound or vibration with a microphone or sensor and analyzing the signals. For example, Patent Document 1 discloses a diagnostic device that acquires waveform data of sound or vibration emitted by a target device, performs time-frequency analysis on the waveform data to determine a time-frequency distribution, and determines whether an abnormality exists based on the time-frequency distribution in an extracted region that contains a fluctuation component. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 5783808 Summary of the Invention [Problem to be solved by the invention]

[0005] Depending on the type of inspection, the sound or vibration to be inspected may be generated by the inspector's operation. For example, in a horn inspection, the inspector sounds the horn by pressing the horn switch on the steering wheel, and in an acceleration inspection, the inspector depresses the accelerator pedal to a specified degree, and abnormal noises from the transmission, etc. are detected.

[0006] When inspecting parts or equipment that generate sound or vibration due to the inspector's operation, variations in operation can affect the sound or vibration that is generated. For example, in a horn inspection, if the horn switch is pressed for a short time, the correct sound may not be reproduced. Similarly, when accelerating, if the inspector does not increase the accelerator pedal opening properly, the sound produced will be different. Patent Document 1 does not take into account the effects of such variations in operation. Therefore, for example, a horn may be mistakenly determined to be abnormal even when it is normal. [Means for solving the problem]

[0007] The vehicle inspection and display method according to the present invention comprises: Acquire the sound or vibration emitted from the vehicle under inspection, From the acquired sounds or vibrations, the sounds or vibrations of the parts to be inspected that correspond to the inspector's operations are extracted to generate sound / vibration data. Generate time-amplitude data showing the time-amplitude distribution from the sound / vibration data of this part, The time characteristic of the amplitude in the time amplitude data is compared with a basic time characteristic corresponding to normal operation to calculate the reliability of the operation; The above sound / vibration data is analyzed, compared with the basic characteristics of normal parts, and the degree of abnormality of the part is calculated according to the difference from the basic characteristics. Both the information relating to the reliability and the information relating to the abnormality are displayed. [Effects of the Invention]

[0008] According to this invention, information regarding the reliability of the inspector's operation is displayed along with information regarding the degree of abnormality in the sound or vibration of the part being inspected, so that both can be taken into consideration when making a final abnormality determination for the target part, and if the reliability is low, re-inspection can be performed as necessary. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a functional block diagram of a first embodiment in which the present invention is applied to horn inspection in the finished vehicle inspection process. [Figure 2] 4 is a flowchart showing the flow of processing in the first embodiment. [Figure 3] FIG. 3 is an explanatory diagram showing a display example on a display unit according to the first embodiment. [Figure 4] FIG. 10 is a functional block diagram of a second embodiment. [Figure 5] 10 is a flowchart showing the flow of processing in a second embodiment. [Figure 6] FIG. 10 is an explanatory diagram showing a display example on a display unit according to a second embodiment. [Figure 7] FIG. 10 is a functional block diagram of a third embodiment. [Figure 8] 10 is a flowchart showing the flow of processing according to a third embodiment. [Figure 9] FIG. 11 is an explanatory diagram showing a display example on a display unit according to a third embodiment. [Figure 10] FIG. 10 is a characteristic diagram showing an example of time amplitude data. [Figure 11] FIG. 10 is a characteristic diagram showing an example of a frequency spectrum. DETAILED DESCRIPTION OF THE INVENTION

[0010] An embodiment of the present invention applied to the inspection of an automobile horn will be described below. This horn inspection is performed, for example, during the final stage of an automobile production line, known as the finished vehicle inspection process. Typically, during the finished vehicle inspection process, an inspector test-drives the finished vehicle on a free roller and inspects various components, including the engine, meters, and brakes. During this finished vehicle inspection, the inspector sounds the horn by pressing a horn switch on the steering wheel according to a predetermined inspection procedure, and the horn is inspected based on the sound produced. Since anomalies such as an incorrect horn pitch or tone can occur due to anomalies in the horn itself, an incorrect part (horn) model number, poor wiring contact, or anomalies in the horn switch, the inspection is based on the actual sound of the horn.

[0011] The horn inspection in the first embodiment is not a fully automated process that determines whether the horn is normal or abnormal, but rather is a type of inspection support system in which an inspector or other person makes the final determination of normality or abnormality by looking at the display shown on the screen (described later).

[0012] 1 shows a functional block diagram of the inspection display device of Example 1. The inspection display device of Example 1 includes a sound acquisition unit 10, a target sound extraction unit 20, a frequency characteristic analysis unit 30, a time characteristic analysis unit 40, a basic characteristic data storage unit 50, an abnormality degree calculation unit 60, a basic time characteristic data storage unit 70, a reliability calculation unit 80, a display control unit 90, and a display unit 100.

[0013] The sound acquisition unit 10 includes a microphone that acquires sounds generated from the vehicle under test 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 sounds from the vehicle, including the sound of the horn. The microphone's directivity and frequency characteristics are selected according to the measurement target. 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. Note that when vibrations from components are the target, a vibration detection sensor such as an acceleration pickup made of a piezoelectric element or the like is used instead of a microphone.

[0014] The target sound extraction unit 20 extracts the sound of the component being inspected, i.e., the horn, from the sound data acquired by the sound acquisition unit 10 and extracts it as component sound data. The sound acquired by the microphone may include other sounds, such as engine sounds, transmission operating sounds, brake operating sounds, and various other equipment operating sounds. The target sound extraction unit 20 separates and extracts the horn sound from the remaining sounds. For example, the horn sound can be identified using known techniques such as FFT (Fast Fourier Transform) and wavelet analysis. Separation is also possible using a filter that passes specific frequencies. Since the horn has a relatively high sound pressure, the horn sound can also be identified based on sound pressure. Since the horn switch is operated at a generally known timing according to a predetermined inspection sequence, separating and extracting the horn sound is easy. The unit then identifies the period during which the horn sound is at or above a predetermined intensity (amplitude), and extracts the data during that period as component sound data (i.e., horn sound data). More specifically, the timing a predetermined time (for example, about 0.1 seconds) before the occurrence of a horn sound of a predetermined intensity (amplitude) or greater is set as the "extraction start point," and the timing a predetermined time (for example, about 0.1 seconds) after the horn sound of a predetermined intensity (amplitude) or greater has ended is set as the "extraction end point," and the data is extracted and used as part sound data.

[0015] In addition, if the completed vehicle inspection process involves inspection of sounds from the engine, transmission, brakes, etc. in addition to the horn, the target sound extraction unit 20 may extract and extract data for each sound in parallel.

[0016] The frequency characteristic analysis unit 30 performs frequency analysis on the component sound data separated and extracted by the target sound extraction unit 20. For example, more detailed frequency data is generated by converting the data according to frequency using a frequency analysis method such as FFT or wavelet analysis.

[0017] 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. Note that the vertical axis of the frequency spectrum is not limited to power and sound pressure, and other parameters may be used, such as a value obtained by multiplying the A-characteristic function of hearing or loudness, which is a sensory quantity of hearing defined by ISO. Figure 11 shows an example of a frequency spectrum. 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.

[0018] The basic characteristic data storage unit 50 is composed of a database that stores the basic characteristics of the frequency data for normal parts. Specifically, it stores frequency data (such as frequency spectra and spectrograms) that have been determined to be operating normally in previous horn inspections. Each data item is accompanied by information such as the season, temperature, time, manufacturer name, inspection and shipping information, etc., at which the inspection was performed, allowing data filtering as needed. If the engine, transmission, brakes, etc. are inspected in addition to the horn during the finished vehicle inspection process, frequency data for these sounds is stored for each part.

[0019] The abnormality degree calculation unit 60 calculates the degree of abnormality by comparing the frequency data of the current inspection obtained in the frequency characteristic analysis unit 30 with the frequency data (in other words, basic characteristics) of a large number of normal parts stored in the basic characteristic data storage unit 50. In other words, by learning the normal state of the frequency data, the abnormality degree of the frequency data of the horn sound that is the subject of the current inspection is calculated using a so-called unsupervised learning method.

[0020] For example, the degree of abnormality α can be calculated by comparing the waveform of the frequency data of the inspection target with the waveform of the frequency data of a normal part using the following formula. That is, taking a frequency spectrum with frequency on the horizontal axis and power on the vertical axis as an example, for each waveform, the power value is sampled for each unit frequency (or it can be for each peak frequency) to create a data group. Then, for each frequency, if the normal value is Sb, the observed value is So, and the number of data is N, then: Abnormality α=(1 / N)×Σ((So-Sb) 2 ) This becomes:

[0021] In this example, the greater the degree of abnormality α, the greater the deviation from the characteristics of a normal part.

[0022] It is also possible to learn both the normal and abnormal states of the frequency data and calculate the degree of abnormality using a supervised learning method based on the spatial distance between them. For example, if there is reference data for abnormal values, a classification model for two classes, normal and abnormal, can be created and the degree of abnormality can be calculated from the distance between each.

[0023] Alternatively, the degree of abnormality can be calculated by any known appropriate statistical method, such as a method of calculating the degree of abnormality using the average value and standard deviation of data up to now, or a method of calculating the degree of abnormality based on a probability distribution.

[0024] In horn inspections, it is generally difficult to collect a large amount of data on abnormal sounds, and therefore it is preferable to calculate the degree of abnormality based on learning only the characteristics of normal parts. Depending on the target part, it is possible to learn the characteristics of both normal and abnormal parts.

[0025] The time characteristic analysis unit 40 converts the component sound data separated and extracted by the target sound extraction unit 20 into time-amplitude data showing a time-amplitude distribution with, for example, time on the horizontal axis and amplitude on the vertical axis, and then extracts the time characteristics. Note that if the component sound data output from the target sound extraction unit 20 is in the form of time-amplitude data showing a time-amplitude distribution with time on the horizontal axis and amplitude on the vertical axis, it may be used as is. Fig. 10 shows an example of time-amplitude data.

[0026] The appropriateness of the inspector's operation of the horn switch is reflected in the time characteristics of the time-amplitude data. In the first embodiment, the time characteristic is the time length of the time-amplitude data corresponding to the operation time of the horn switch. For example, the time length in the time-amplitude data where the amplitude (which may be intensity, power, etc.) is equal to or greater than a certain threshold is determined. If the sound of the target part is in a specific frequency band (such as in the case of a horn), it is possible to extract only the sound in that frequency band using a band-pass filter in advance, and then determine the period of time when the sound is louder than a certain threshold. Figure 10 shows an example of the time length Ta.

[0027] Similar to the basic characteristic data storage unit 50 described above, the basic time characteristic data storage unit 70 is a database that stores time characteristics (here, time lengths) when normal operation is performed. Specifically, data on the time lengths when the horn has been determined to be operating normally in previous horn inspections is stored. Each data item is accompanied by information such as the season, temperature, time, manufacturer name, inspection and shipping information, etc., when the inspection was performed, allowing data filtering as needed. If the engine, transmission, brakes, etc. are inspected in addition to the horn during the finished vehicle inspection process, time characteristic data for these sounds is stored for each part.

[0028] The reliability calculation unit 80 calculates the reliability by comparing the time characteristics (here, time length) of the current test obtained by the time characteristics analysis unit 40 with a large amount of normal time characteristic data (in other words, basic time characteristics) stored in the basic time characteristic data storage unit 70. In other words, by learning the normal state of the time characteristics, the reliability of the time characteristics of the horn sound that is the subject of the current test is calculated by a so-called unsupervised learning method.

[0029] For example, the parameter ρ corresponding to the squared deviation is calculated using the following formula. That is, assuming that the normal value is Tb and the observed value is To, ρ=(To-Tb) 2 Alternatively, ρ=(To-Tb) / Tb) may be used. Here, the smaller the parameter ρ, the higher the reliability.

[0030] The display control unit 90 creates display data for the calculated abnormality levels and reliability levels and displays them on the display unit 100. The display unit 100 is a display means for displaying inspection results to relevant parties, such as the inspector driving the vehicle, the production manager, and the data scientist who utilizes the data. For example, the display unit 100 may be configured with a liquid crystal display, an organic light-emitting diode (OLED) display, a head-mounted display (HMD), a smart watch, or the like. In addition, if audio is involved, the display unit 100 may also include a sound source for generating and emitting sound, an amplifier, a speaker, and the like. To provide appropriate displays, the display control unit 90 generates various displays, such as two-dimensional graphs, videos, and animations, and performs splitting, compositing, and switching between them. By controlling the display timing, it is also possible to display different results on multiple display units 100 at appropriate times.

[0031] FIG. 3 is an explanatory diagram showing an example of a display on the display unit 100 of the first embodiment. In this example, the display shows a matrix with the horizontal axis representing reliability and the vertical axis representing abnormality, and both the calculated reliability and abnormality are simultaneously displayed as one intersection on the matrix. In addition, in the example of FIG. 3, the results of multiple horn inspections performed on the same vehicle under inspection are also displayed as points. The current inspection result (shown as point P1, for example) is displayed as a relatively bright point, and other points representing past inspection results are displayed as relatively dark points.

[0032] Furthermore, in one embodiment, to facilitate judgment by inspectors, etc., a threshold value dividing the degree of abnormality into normal and abnormal is displayed as a straight line (shown as symbol L1) parallel to the horizontal axis. Points representing the test results are displayed in blue within the normal range below the threshold, and in red within the range above the threshold.

[0033] On the other hand, in terms of reliability, the left side of the display in Figure 3 has high reliability (small parameter ρ), while the right side has low reliability. Therefore, for example, the result at point P2 has a high degree of abnormality and is above the threshold L1, but because the reliability is low, it cannot be immediately determined that the horn is abnormal. In such a case, for example, it is possible to perform a horn inspection again. A test result with a high reliability, such as point P1, can be determined to be a relatively reliable result.

[0034] By displaying the results in a matrix as shown in Figure 3, inspectors can easily recognize the reliability of the inspector's operation as well as the degree of abnormality as inspection results. Also, by displaying the results of multiple inspections together as shown in Figure 3, inspectors can comprehensively evaluate the results of multiple inspections and ultimately determine whether there is any abnormality in the horn, horn switch, etc. of the vehicle being inspected.

[0035] Furthermore, for example, if multiple points showing the results of several inspections are biased toward the low reliability side (the right side of Figure 3), this means that the inspector's overall operating tendencies are inappropriate, making it easy for the inspector to take some kind of action himself (for example, making sure to press the horn switch for the specified period of time).

[0036] In addition to the display shown in FIG. 3, it is also possible to display the degree of abnormality and the degree of reliability as numerical values, or to switch between the degree of abnormality and the degree of reliability and display them sequentially.

[0037] 2 is a flowchart showing the processing flow of the inspection display device of the first embodiment. First, the microphone of the sound acquisition unit 10 collects the horn sound from a vehicle test-driving on a free roller and acquires it as sound data (Step 1). Next, the target sound extraction unit 20 separates and extracts the horn sound (Step 2).

[0038] In step 3, the extracted horn sound is subjected to frequency analysis. That is, the frequency characteristic analysis unit 30 converts the horn sound data according to frequency using a frequency analysis method such as FFT (Fast Fourier Transform) or wavelet analysis to generate frequency data such as a frequency spectrum or spectrogram. Next, the process proceeds to step 4, where the frequency data is compared with the frequency data of a large number of normal parts stored in the basic characteristic data storage unit 50 to calculate the degree of abnormality.

[0039] Meanwhile, in step 5, the time characteristic analysis unit 40 calculates the time characteristic of the time amplitude data of the horn sound extracted in step 2, which is the time length in this embodiment. Next, proceeding to step 6, the time length is compared with a large number of normal time lengths stored in the basic time characteristic data storage unit 70 to calculate the reliability.

[0040] Next, the process proceeds to step 7, where a matrix-like two-dimensional graph display such as that shown in FIG. 3 is created based on the degree of abnormality and the reliability calculated in steps 4 and 6, respectively, and this is displayed on the display serving as the display unit 100.

[0041] Finally, in step 8, it is determined whether the inspector has pressed the stop button to end the horn inspection. If the stop button has not been pressed after the inspection results are displayed on the display, the processes of steps 1 to 7, i.e., the horn inspection, are repeated. If the stop button is pressed, the inspection ends. When this inspection is completed, a message to that effect is displayed on the display that serves as the display unit 100. Along with the display, a buzzer or voice message indicating completion may be sounded.

[0042] Next, an inspection display device of a second embodiment will be described with reference to Figures 4 to 6. The following mainly describes the differences from the first embodiment. The inspection display device of the second embodiment differs from the first embodiment in three points: it determines whether a result is normal or abnormal and displays the result; it changes or modifies the threshold value of the degree of abnormality according to the reliability; and it uses the envelope of the waveform peak instead of the time length as the time characteristic that forms the basis for calculating the reliability.

[0043] 4 shows a functional block diagram of the inspection display device of Example 2. Like the first example, the inspection display device of Example 2 includes a sound acquisition unit 10, a target sound extraction unit 20, a frequency characteristic analysis unit 30, a time characteristic analysis unit 40, a basic characteristic data storage unit 50, an abnormality degree calculation unit 60, a basic time characteristic data storage unit 70, a reliability calculation unit 80, a display control unit 90, and a display unit 100, and further includes a normal / abnormal determination unit 110.

[0044] In the second embodiment, the time characteristic analysis unit 40 obtains the envelope of the waveform peak as the time characteristic of the time-amplitude data of the part sound under inspection. An example of the envelope is shown in FIG. 10. The envelope is generally obtained by eliminating t from ∂∂tf(x,y,t)=0, where f(x,y,t)=0 is the envelope equation for the group of curves f(x,y,t)=0. For example, as shown in FIG. 10, the slope θ of the envelope indicating the change in sound pressure or amplitude is calculated and used as a parameter indicating the operation of the inspector. When the envelope is expressed by the following linear equation, the coefficient C is equivalent to the slope θ, and so this value is used.

[0045] y=Ct+D (t=time, C and D are coefficients) The basic time characteristic data storage unit 70 stores a large amount of data on such time characteristics, that is, the gradient (θ, C) of the envelope curve, under normal conditions.

[0046] The reliability calculation unit 80 compares the gradient (θ, C) of the envelope calculated by the time characteristic analysis unit 40 with a large amount of normal time characteristic data stored in the basic time characteristic data storage unit 70, and calculates a parameter ρ indicating the reliability, as in the first embodiment described above.

[0047] The characteristics of the envelope are not limited to the above-mentioned simple linear approximation, but may be a comparison of the entire envelope. For example, when inspecting engine sound or transmission sound during acceleration, the slope of the envelope changes depending on the accelerator pedal depression (speed of opening increase), making it easy to estimate whether the operation is appropriate.

[0048] The normality / abnormality determination unit 110 performs normality / abnormality determination based on the degree of abnormality and the reliability calculated by the abnormality degree calculation unit 60 and the reliability calculation unit 80, respectively. First, a parameter ρ indicating the reliability is compared with a predetermined threshold A. If the parameter ρ is less than threshold A, it is determined that the operation is appropriate and the data is highly reliable, and to determine the degree of abnormality, the abnormality degree α is compared with threshold B. If the abnormality degree α is less than threshold B, it is determined to be normal, and if it is threshold B or greater, it is determined to be abnormal.

[0049] On the other hand, if the parameter ρ indicating the reliability is equal to or greater than the threshold A, it is determined that the reliability is low and is not at a level at which normal / abnormal determination can be made, and the final normal / abnormal determination is not made.

[0050] Here, the normality / abnormality determination unit 110 of the second embodiment includes a threshold value setting unit that variably sets the threshold value B for the abnormality level according to the reliability level, and the lower the reliability level, the lower the threshold value B. In other words, the range in which the abnormality level is determined to be normal becomes narrower.

[0051] 6 shows an example of a display displayed on the display unit 100 of the second embodiment. In this second embodiment, as in the first embodiment, both the reliability and the degree of abnormality, which are the test results, are simultaneously displayed as intersections on a matrix with the horizontal axis representing the reliability and the vertical axis representing the degree of abnormality. Threshold A for the reliability is represented by a straight line (L2) parallel to the vertical axis, and threshold B for the degree of abnormality is represented by a straight line (L1) along the horizontal axis. The line (L1) for threshold B is an inclined line as shown in the figure, because threshold B changes depending on the reliability.

[0052] Furthermore, the word "OK" indicating normality is displayed in the lower left area of ​​the figure where the reliability is high and the degree of abnormality is low, and the word "NG" indicating abnormality is displayed in the upper left area of ​​the figure where the reliability is high and the degree of abnormality is high. As with the first embodiment, points representing the results of the current test are displayed relatively bright, while points representing the results of previous tests are displayed relatively dark. Furthermore, points in the "OK" area are displayed in blue, points in the "NG" area are displayed in red, and points in the area on the right side of the figure where the reliability is low are displayed in gray to indicate that a judgment cannot be made.

[0053] As another example of the display, it is possible to display only the characters "normal", "abnormal", and "unable to determine", for example.

[0054] 5 is a flowchart showing the processing flow of the inspection display device of the second embodiment, in which step 1 acquires sound including component sound based on some operation, step 2 separates and extracts the sound of the target component (horn, engine, etc.), step 3 performs frequency analysis, and step 4 compares it with normal frequency data to calculate the degree of abnormality. Also, step 5 finds the envelope characteristic as the time characteristic, and step 6 compares this with the normal envelope characteristic to calculate the reliability.

[0055] Next, the process proceeds to step 11, where a threshold value B for the degree of abnormality is calculated according to the calculated reliability. In the next step 12, the calculated reliability (parameter ρ in this example) is compared with threshold value A, and if parameter ρ is less than threshold value A (high reliability), the process proceeds to step 13. In step 13, the calculated degree of abnormality α is compared with threshold value B. If parameter ρ indicating the reliability is equal to or greater than threshold value A, the process proceeds to step 7 without making the judgment in step 13.

[0056] If the abnormality level α is determined to be less than threshold B in step 13, the process proceeds to step 14, where the system determines the abnormality level to be "normal" and prepares to display that fact. If the abnormality level α is equal to or greater than threshold B, the process proceeds to step 15, where the system determines the abnormality level to be "abnormal" and prepares to display that fact.

[0057] In the next step 7, similar to the first embodiment, a matrix-like two-dimensional graph display such as that shown in Fig. 6 is created and displayed on the display that serves as the display unit 100. Finally, in step 8, it is determined whether the stop button has been pressed.

[0058] Next, an inspection display device of a third embodiment will be described with reference to Figures 7 to 9. The following mainly describes the differences from the second embodiment. The inspection display device of the third embodiment differs from the second embodiment in two points: the reliability threshold is changed or corrected depending on the degree of abnormality, and the balance between the positive and negative sides of the time amplitude data is used as the time characteristic that forms the basis for calculating the reliability instead of the envelope characteristic.

[0059] 7 shows a functional block diagram of the inspection display device of Example 3. Similar to Example 2, the inspection display device of Example 3 includes a sound acquisition unit 10, a target sound extraction unit 20, a frequency characteristic analysis unit 30, a time characteristic analysis unit 40, a basic characteristic data storage unit 50, an abnormality degree calculation unit 60, a basic time characteristic data storage unit 70, a reliability calculation unit 80, a display control unit 90, a display unit 100, and a normal / abnormal determination unit 110.

[0060] In the third embodiment, the time characteristic analysis unit 40 determines the balance between the positive and negative amplitudes in the time amplitude data as the time characteristic of the time amplitude data of the component sound being inspected. Fig. 10 shows an example of the balance between the positive side (Peak+) and the negative side (Peak-). This imbalance can occur in the sound of a simple harmonic component such as a horn, and is not due to a problem with the component itself, but rather occurs due to the operating state of the horn switch, which is one example of a noise that can be erroneously determined to be an abnormal noise.

[0061] The balance between the positive and negative amplitudes is, for example, simply expressed as the ratio between Peak+ and Peak-, and is calculated using the following formula:

[0062] ρ(B)=Peak+ / Peak- The balance between Peak+ and Peak- may be found at one point where the amplitude is maximum, or the balance between Peak+ and Peak- may be found at multiple points on the time axis.

[0063] The basic time characteristic data storage unit 70 stores a large amount of normal data on such time characteristics, that is, amplitude balance.

[0064] The reliability calculation unit 80 compares the balance ρ(B) calculated by the time characteristic analysis unit 40 with a large amount of normal data stored in the basic time characteristic data storage unit 70, and calculates a parameter ρ indicating the reliability, as in the first embodiment described above.

[0065] The normality / abnormality determination unit 110 performs normality / abnormality determination based on the degree of abnormality and the reliability calculated by the abnormality degree calculation unit 60 and the reliability calculation unit 80, respectively. That is, the parameter ρ indicating the reliability is compared with a predetermined threshold A to classify the parameter into two categories, normal and abnormal, and the abnormality degree α is compared with a threshold B to classify the parameter into two categories, normal and abnormal. Therefore, a total of four groups are classified by combining the two.

[0066] Here, the normality / abnormality determination unit 110 of the third embodiment includes a threshold setting unit that variably sets a threshold B for the degree of abnormality according to the reliability, and also includes a second threshold setting unit that variably sets a threshold A for the parameter ρ related to the reliability according to the degree of abnormality. As in the second embodiment, the lower the reliability, the lower the threshold B. Also, the higher the degree of abnormality, the lower the threshold A compared with the parameter ρ (the higher the reliability). In other words, the lower the reliability, the narrower the range in which the degree of abnormality is determined to be normal.

[0067] 9 shows an example of a display displayed on the display unit 100 of the third embodiment. In this third embodiment, as in the first and second embodiments, both the reliability and abnormality levels, which are the test results, are simultaneously displayed as intersections on a matrix with the horizontal axis representing reliability and the vertical axis representing abnormality levels. Threshold A for the reliability and threshold B for the abnormality level are represented by straight lines (L2, L1) inclined relative to the vertical and horizontal axes, respectively. These thresholds A and B divide the display area into four quadrants or regions.

[0068] In the lower left area of ​​the figure, where the reliability is high and the abnormality level is low, the word "OK" is displayed, indicating normality, and in the upper left area of ​​the figure, where the reliability is high and the abnormality level is high, the word "NG" is displayed, indicating abnormality. As in the first embodiment, points representing the results of the current test are displayed relatively bright, while points representing the results of previous tests are displayed relatively dark. In addition, points in the "OK" area are displayed in blue, and points in the "NG" area are displayed in red. Points in the lower right area of ​​the figure, where the reliability is low but the abnormality level is below threshold B, are displayed in green, indicating that they are likely to be normal. Points in the upper right area of ​​the figure, where the reliability is low and the abnormality level is above threshold B, are displayed in gray, indicating that they are likely to be abnormal.

[0069] Based on such a display, the inspector or other person will either make a final decision that the target part is normal and end the inspection, or make a final decision that the target part is faulty or abnormal and end the inspection, or repeat the inspection again.

[0070] 8 is a flowchart showing the processing flow of the inspection display device of the third embodiment, in which in step 1, sound including component sound based on some operation is acquired, in step 2, the sound of the target component (horn, etc.) is separated and extracted, in step 3, frequency analysis is performed, and in step 4, the degree of abnormality is calculated by comparing with normal frequency data. In addition, in step 5, the balance between the positive and negative sides of the amplitude is found as a time characteristic, and in step 6, this is compared with normal data to calculate the reliability.

[0071] Next, the process proceeds to step 21, where a threshold value A for the reliability is calculated according to the calculated degree of abnormality. In the next step 11, as in the second embodiment, a threshold value B for the degree of abnormality is calculated according to the calculated degree of reliability. In step 12, the calculated reliability (parameter ρ in this example) is compared with threshold value A, and if parameter ρ is less than threshold value A (high reliability), the process proceeds to step 13. In step 13, the calculated degree of abnormality α is compared with threshold value B.

[0072] If the abnormality level α is determined to be less than threshold B in step 13, the process proceeds to step 14, where the system determines the abnormality level to be "normal" and prepares to display that fact. If the abnormality level α is equal to or greater than threshold B, the process proceeds to step 15, where the system determines the abnormality level to be "abnormal" and prepares to display that fact.

[0073] In the next step 7, a two-dimensional graph display in a matrix form as shown in Fig. 9 is created and displayed on the display that serves as the display unit 100. Finally, in step 8, it is determined whether the stop button has been pressed.

[0074] Although one embodiment of the present invention has been described above, the present invention is not limited to the above embodiment and can be applied in various ways. For example, the present invention can be used for inspection and maintenance in a maintenance shop. In the above embodiment, the time characteristics of the amplitude in the time-amplitude data for determining the reliability of the operation are given as examples of the time length, envelope characteristics, and balance between the positive and negative sides. However, other elements may also be used as the time characteristics. Furthermore, the reliability may be calculated by combining a plurality of these elements. [Explanation of symbols]

[0075] 10...Sound acquisition section 20...Target sound extraction section 30...Frequency characteristic analysis section 40...Time characteristic analysis section 50...Basic characteristic data storage unit 60…Abnormality calculation unit 70...Basic time characteristic data storage unit 80...Reliability calculation unit 90...Display control unit 100...Display section 110 Normal / Abnormal Judgment Unit

Claims

1. Acquire the sound or vibration emitted from the vehicle under inspection, extracting the sound or vibration of the part to be inspected corresponding to the inspector's operation from the acquired sound or vibration to generate part sound / vibration data; Time amplitude data showing a time amplitude distribution is generated from this component sound / vibration data, The time characteristic of the amplitude in the time amplitude data is compared with a basic time characteristic corresponding to normal operation to calculate the reliability of the operation; The component sound / vibration data is analyzed, compared with the basic characteristics of normal components, and the degree of abnormality of the component is calculated according to the difference from the basic characteristics. displaying both the information about the reliability and the information about the abnormality; How to display vehicle inspections.

2. 2. The vehicle inspection display method according to claim 1, wherein the information about the reliability and the information about the abnormality level are simultaneously displayed on a display.

3. 3. The vehicle inspection display method according to claim 2, wherein the calculated reliability and abnormality degree are simultaneously displayed as intersections on a matrix having one axis representing reliability and the other axis representing abnormality degree.

4. 4. The vehicle inspection and display method according to claim 1, wherein the reliability is calculated by comparing with basic time characteristics corresponding to a large number of normal operations stored in a memory unit.

5. 5. The vehicle inspection and display method according to claim 1, wherein the reliability is calculated using a time length of the time amplitude data as at least one element as the time characteristic of the amplitude.

6. 6. The vehicle inspection and display method according to claim 1, wherein the reliability is calculated using an envelope curve connecting amplitude peaks of the time amplitude data as at least one element as the time characteristic of the amplitude.

7. 7. The vehicle inspection and display method according to claim 1, wherein the reliability is calculated using as at least one element a balance between the positive and negative sides of the time amplitude data as the time characteristic of the amplitude.

8. 8. The vehicle inspection and display method according to claim 1, wherein a threshold value for dividing the degree of abnormality into normal and abnormal is changed according to the reliability.

9. 9. The vehicle inspection and display method according to claim 1, wherein a threshold value for dividing the reliability into normal and abnormal states is changed according to the degree of abnormality.

10. a sound / vibration acquisition unit that acquires sound or vibration emitted from the vehicle under inspection; a part sound / vibration data generating unit that extracts the sound or vibration of the part to be inspected corresponding to the operation of the inspector from the acquired sound or vibration and generates part sound / vibration data; a time amplitude data generation unit that generates time amplitude data indicating a time amplitude distribution from the component sound / vibration data; a reliability calculation unit that calculates the reliability of the operation by comparing the time characteristic of the amplitude in the time amplitude data with a basic time characteristic corresponding to a normal operation; an abnormality degree calculation unit that analyzes the component sound / vibration data, compares it with basic characteristics of a normal component, and calculates the degree of abnormality of the component according to a difference from the basic characteristics; a display unit that displays both the information about the reliability and the information about the abnormality; A vehicle inspection and display device comprising:

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