Acoustic diagnostic device and acoustic diagnostic method

Abstract

The acoustic diagnostic device of the present invention includes an operation sound collection unit that collects operation sounds of equipment, an operation section operation sound extraction unit that collects images showing the operating state of the equipment, detects predetermined operation sections of the equipment using the collected images, and extracts operation sounds for each operation section detected from the operation sounds collected by the operation sound collection unit, a sound pressure level acquisition unit that acquires sound pressure levels for each frequency band predetermined for each operation section for the operation sounds for each operation section extracted by the operation section operation sound extraction unit, a deviation calculation unit that calculates a deviation from a sound pressure level during normal operation of the same equipment, operation section, and frequency band for the sound pressure levels for each operation section and each frequency band acquired by the sound pressure level acquisition unit, and an abnormal part identification unit that identifies an abnormal part of the equipment based on the deviation calculated by the deviation calculation unit.

Description

[Technical field]

[0001] The present invention relates to an acoustic diagnosis device and an acoustic diagnosis method for identifying an abnormal portion of equipment using an acoustic signal generated during operation of the equipment. [Background technology]

[0002] Patent Document 1 describes a diagnostic device that diagnoses an object to be diagnosed by comparing an observed waveform observed from the object to be diagnosed with a reference waveform. Patent Document 2 describes a state estimation device that estimates a change in the state of equipment based on waveforms and frequency information detected by multiple sensors installed in the equipment. Patent Document 3 describes a diagnostic device that detects chattering of rolling rolls using the frequency of an acoustic signal detected by an acoustic sensor installed in a rolling mill. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2008-164490 A [Patent Document 2] International Publication No. 2019 / 017345 [Patent Document 3] International Publication No. 2020 / 157818 Summary of the Invention [Problem to be solved by the invention]

[0004] The devices described in Patent Documents 1 to 3 may not be able to pinpoint the abnormal part of the equipment with high accuracy, especially when the equipment is composed of multiple parts. In more detail, when the equipment is composed of multiple parts such as bearings, reducers, cranks, dies, rolls, blocks, etc., the timing at which the operating sounds are generated differs depending on the parts. Therefore, unless the timing and frequency of the abnormal sound are taken into consideration, it is not possible to determine which part the abnormal sound is generated from, and it may not be possible to pinpoint the abnormal part of the equipment with high accuracy.

[0005] The present invention has been made to solve the above-mentioned problems, and an object of the present invention is to provide an acoustic diagnostic device and an acoustic diagnostic method that can accurately identify an abnormal portion of equipment. [Means for solving the problem]

[0006] The acoustic diagnostic device of the present invention is an acoustic diagnostic device that identifies an abnormal part of equipment using an acoustic signal generated during operation of the equipment, and includes an operation sound collection unit that collects operation sounds of the equipment, an operation section operation sound extraction unit that collects images showing the operating state of the equipment, detects predetermined operation sections of the equipment using the collected images, and extracts operation sounds for each operation section detected from the operation sounds collected by the operation sound collection unit, a sound pressure level acquisition unit that acquires sound pressure levels for each frequency band predetermined for each operation section for the operation sounds extracted by the operation section operation sound extraction unit, a deviation calculation unit that calculates a deviation from a sound pressure level during normal operation of the same equipment, operation section, and frequency band for the sound pressure levels for each operation section and each frequency band acquired by the sound pressure level acquisition unit, and an abnormal part identification unit that identifies an abnormal part of the equipment based on the deviation calculated by the deviation calculation unit.

[0007] The operation section operation sound extraction unit may input an image showing the operation state of the equipment acquired using an image sensor into an artificial intelligence model having an image showing the operation state of the equipment as input data and the operation state of the equipment shown by the image as output data, and detect a predetermined operation section of the equipment using the image showing the operation state of the equipment.

[0008] The sound pressure level acquisition unit classifies each operation sound based on predetermined operating conditions of the equipment and acquires the sound pressure level, and the deviation calculation unit calculates the degree of deviation from the sound pressure level during normal operation of the same equipment, operating section, frequency band, and operating condition, for the sound pressure level for each operating section, frequency band, and operating condition acquired by the sound pressure level acquisition unit.

[0009] The equipment may further include a database unit for storing conditions for extracting the operating sounds of the equipment and data required for identifying abnormal parts of the equipment, and the abnormal part identification unit may identify the abnormal parts of the equipment based on a pattern of deviation degrees that are determined based on the deviation degrees in each frequency band obtained for each operating section stored in the database unit.

[0010] It is preferable to include a normal state sound pressure level acquisition unit that acquires a sound pressure level for each frequency band predetermined for each operation section for the operation sound for each operation section extracted by the operation section operation sound extraction unit, and a normal distribution determination and recording unit that classifies the sound pressure levels acquired by the normal state sound pressure level acquisition unit for each equipment, each operation section, and each frequency band, and records them as sound pressure levels during normal operation of the equipment.

[0011] The normal distribution determination and recording unit may record, for each piece of equipment, a table including information indicating a relationship between a pattern of deviations of sound pressure levels from a sound pressure level during normal operation in each frequency band obtained for each operating section and an abnormal portion of the equipment.

[0012] The normal distribution determining and recording unit may record a table including information indicating the operation interval, the pattern of the deviation degree, and the relationship between the operating conditions of the equipment and the abnormal portion of the equipment.

[0013] The acoustic diagnosis method according to the present invention is an acoustic diagnosis method for identifying an abnormal part of equipment using an acoustic signal generated during operation of the equipment, and includes: an operation sound collection step of collecting operation sounds of the equipment; an operation section operation sound extraction step of collecting images showing the operating state of the equipment, detecting predetermined operation sections of the equipment using the collected images, and extracting operation sounds for each operation section detected from the operation sounds collected in the operation sound collection step; a sound pressure level acquisition step of acquiring sound pressure levels for each frequency band predetermined for each operation section for the operation sounds extracted in the operation section operation sound extraction step; a deviation calculation step of calculating a deviation from a sound pressure level during normal operation of the same equipment, operation section, and frequency band for the sound pressure levels for each operation section and each frequency band acquired in the sound pressure level acquisition step; and an abnormal part identification step of identifying an abnormal part of the equipment based on the deviation calculated in the deviation calculation step.

[0014] It is preferable that the method further includes a normal sound pressure level acquisition step of acquiring a sound pressure level for each predetermined frequency band for each operation interval for the operation sound extracted in the operation interval operation sound extraction step, and a normal distribution determination / recording step of classifying the sound pressure levels acquired in the normal sound pressure level acquisition step for each equipment, each operation interval, and each frequency band, and recording them as sound pressure levels during normal operation of the equipment.

[0015] The operation interval operation sound extraction step may include a step of inputting an image showing the operation state of the equipment acquired using an image sensor into an artificial intelligence model having an image showing the operation state of the equipment as input data and an operation state of the equipment shown by the image as output data, thereby detecting a predetermined operation interval of the equipment using the image showing the operation state of the equipment. Effect of the Invention

[0016] According to the acoustic diagnostic device and acoustic diagnostic method of the present invention, an abnormal part of the equipment is identified based on the sound pressure level for each operation section and each frequency band of the equipment, so that the abnormal part of the equipment can be identified with high accuracy. [Brief description of the drawings]

[0017] [Figure 1] FIG. 1 is a block diagram showing the configuration of an acoustic diagnostic apparatus according to one embodiment of the present invention. [Diagram 2] FIG. 2 is a flowchart showing the flow of data recording processing according to an embodiment of the present invention. [Diagram 3] FIG. 3 is a diagram for explaining the data recording process according to an embodiment of the present invention. [Figure 4] FIG. 4 is a diagram for explaining the data recording process according to an embodiment of the present invention. [Diagram 5] FIG. 5 is a diagram for explaining the data recording process according to an embodiment of the present invention. [Figure 6] FIG. 6 is a flowchart showing the flow of an abnormal portion identification process according to an embodiment of the present invention. [Figure 7] FIG. 7 is a diagram for explaining a specific example of the abnormal portion specifying process according to an embodiment of the present invention. [Figure 8] FIG. 8 is a diagram for explaining the abnormal portion specifying process of the embodiment. [Figure 9] FIG. 9 is a diagram showing frequency distribution of sound pressure levels when the operating conditions of the press equipment are limited and when they are not limited. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] An acoustic diagnostic apparatus and an acoustic diagnostic method according to an embodiment of the present invention will now be described with reference to the drawings.

[0019] 〔composition〕 Fig. 1 is a block diagram showing the configuration of an acoustic diagnostic device according to one embodiment of the present invention. As shown in Fig. 1, the acoustic diagnostic device 1 according to one embodiment of the present invention is a device that identifies an abnormal part of one or more predetermined diagnosis target facilities (hereinafter abbreviated as facilities) A ​​by using an acoustic signal generated during operation of the facilities A, and is configured by an information processing device such as a computer. Here, the facilities A are facilities that are composed of a plurality of parts, and examples of such facilities include steel-making facilities such as press facilities and rolling facilities.

[0020] The acoustic diagnostic device 1 includes an operation sound collection unit 11, an operation section operation sound extraction unit 12, a normal state sound pressure level acquisition unit 13, a normal distribution determination and recording unit 14, a sound pressure level acquisition unit 15, a deviation degree calculation unit 16, an abnormal part identification unit 17, and a database unit 18. The operation sound collection unit 11, the operation section operation sound extraction unit 12, the normal state sound pressure level acquisition unit 13, the normal distribution determination and recording unit 14, the sound pressure level acquisition unit 15, the deviation degree calculation unit 16, and the abnormal part identification unit 17 are functional blocks realized by a calculation processing unit such as a CPU inside the information processing device executing a computer program. The functions of each unit will be described later.

[0021] The database unit 18 is configured by a storage device such as a HDD that constitutes an information processing device, and includes a cutting condition database 18a, a frequency band database 18b, a normal distribution database 18c, an operating condition database 18d, and a deviation pattern database 18e. Details of each database will be described later. Note that the database unit 18 may be arranged outside the acoustic diagnostic device 1, and the acoustic diagnostic device 1 may acquire data from the database unit 18 via an electric communication line.

[0022] The acoustic diagnostic device 1 having such a configuration executes the data recording process and the abnormal part identification process described below to identify the abnormal part of the equipment A. The operation of the acoustic diagnostic device 1 when executing the data recording process and the abnormal part identification process will be described below.

[0023] [Data recording processing] Fig. 2 is a flowchart showing the flow of data recording processing according to one embodiment of the present invention. The flowchart shown in Fig. 2 starts when an execution command for data recording processing is input to the acoustic diagnostic device 1, and the data recording processing proceeds to step S1.

[0024] In the process of step S1, the operation sound collection unit 11 collects sound pressure signals of the operation sound of the equipment A during normal operation at a predetermined time interval using j types (j≧1) of acoustic sensors 20 such as microphones installed near the equipment A. In addition, the operation section operation sound extraction unit 12 collects images of the equipment A during normal operation using an image sensor 21 such as a camera installed near the equipment A. For example, in the example shown in FIG. 3(a), when a workpiece is transported to the equipment by a transport machine and processed, the operation sound collection unit 11 collects the operation sounds of the equipment and the transport machine, and the operation section operation sound extraction unit 12 collects images of the equipment including the workpiece and the transport machine. This completes the process of step S1, and the data recording process proceeds to the process of step S2.

[0025] In the process of step S2, the operation section operation sound extraction unit 12 detects the start time and end time of k types (k≧1) of operation sections of the equipment A determined in advance, using the images of the equipment A during normal operation collected in the process of step S1 (operation section detection process). For example, in the example shown in FIG. 3(b), the operation section operation sound extraction unit 12 detects the time when the workpiece entered the equipment, using the images collected by the image sensor 21. Details of this operation section detection process will be described later with reference to FIG. 4 and FIG. 5. Then, the operation section operation sound extraction unit 12 extracts a sound pressure signal for each operation section from the sound pressure signal collected in the process of step S1, based on the start time and end time of the detected operation section. For example, in the example shown in FIG. 3(c), the operation section operation sound extraction unit 12 extracts a sound pressure signal of operation section 1, which is a section for t1 seconds from the time when the workpiece entered the equipment, and operation section 2, which is a section for a predetermined time from the time when the workpiece left the equipment, based on the time when the workpiece entered the equipment and the time when the workpiece left the equipment. This completes the process of step S2, and the data recording process proceeds to step S3.

[0026] In the process of step S3, first, the normal sound pressure level acquisition unit 13 converts the sound pressure signal for each operation section cut out in the process of step S2 into a sound pressure level for a frequency using a Fourier transform, an octave band filter, or the like. Next, the normal sound pressure level acquisition unit 13 reads information (minimum and maximum values ​​of frequency, etc.) on l types (l≧1) of frequency bands used for abnormality detection predetermined for each operation section from the frequency band database 18b. Then, the normal sound pressure level acquisition unit 13 acquires the sound pressure level for each of l types of frequency bands (sound pressure level of normal operation sound) from the converted sound pressure level data using a band pass filter, or the like. For example, in the example shown in FIG. 3(d), the normal sound pressure level acquisition unit 13 acquires the sound pressure level of normal operation sound for each of the predetermined frequency bands 1 to 3. This completes the process of step S3, and the data recording process proceeds to the process of step S4.

[0027] In the process of step S4, the normal distribution determination and recording unit 14 classifies the sound pressure levels of the normal operation sounds acquired in the process of step S3 into operation sections and frequency bands, and records the sound pressure level distribution of the normal operation sounds and their characteristic quantities (average value, standard deviation, etc.) in the normal distribution database 18c. This completes the process of step S4, and the series of data recording processes ends.

[0028] [Motion section detection process] In the operation section detection process in the process of step S2, the operation section operation sound extraction unit 12 determines the start time and end time of k types of operation sections of the equipment A determined in advance, using an artificial intelligence model trained using images collected by the image sensor 21. Here, the artificial intelligence model is a learning model in which the images collected by the image sensor 21 are used as input data, and the operation state of the equipment A shown by the images is used as output data. The artificial intelligence model is generated by having the model learn a set of performance data of the images collected by the image sensor 21 and data on the operation state of the equipment A shown by the images as teacher data. The artificial intelligence model may be any model that can be used for image judgment such as deep learning or machine learning.

[0029] For example, when determining the time when a workpiece enters the equipment (workpiece entry detection) and the time when the workpiece leaves the equipment (workpiece exit detection) using images collected by the image sensor 21, an image in which the workpiece and the equipment are included in one image is selected as the image to be collected by the image sensor 21. Examples of images of workpiece entry detection (images 1 and 2) and images of workpiece exit detection (images 3 and 4) are shown in Figs. 4(a) and (b). As shown in Fig. 4(a), the moment when the workpiece enters the equipment and the state in which the workpiece has entered the equipment and moved forward are also used as images for learning the time when the workpiece entered the equipment. Also, as shown in Fig. 4(b), the moment when the workpiece leaves the equipment and the state in which the workpiece has left the equipment and moved forward are also used as images for learning the time when the workpiece left the equipment.

[0030] An example of the processing flow of the operation section operation sound extraction unit 12 from image acquisition of the equipment A to detection of workpiece entry and exit is shown in Fig. 5. As shown in Fig. 5, the operation section operation sound extraction unit 12 acquires an image using the image sensor 21, and inputs the acquired image data to the artificial intelligence model (step S21). Then, if the artificial intelligence model outputs an operation state in which a workpiece has entered the equipment (step S22: Yes), the operation section operation sound extraction unit 12 sets the trigger to the ON state (step S23). On the other hand, if the artificial intelligence model does not output an operation state in which a workpiece has entered the equipment (step S22: No), the operation section operation sound extraction unit 12 sets the trigger to the OFF state (step S24), and executes the processing of step S21 again.

[0031] If the trigger is turned on in the process of step S23, the operation section operation sound extraction unit 12 acquires an image using the image sensor 21 and inputs the acquired image data to the artificial intelligence model (step S25). Then, if the artificial intelligence model outputs an operation state in which a workpiece has been removed from the equipment (step S26: Yes), the operation section operation sound extraction unit 12 sets the trigger to an off state (step S27). This makes it possible to detect the time when the workpiece entered equipment A and the time when the workpiece was removed from equipment A based on the time when the trigger was turned on and off. On the other hand, if the artificial intelligence model does not output an operation state in which a workpiece has been removed from the equipment (step S26: No), the operation section operation sound extraction unit 12 executes the process of step S25 again.

[0032] [Abnormal part identification process] Fig. 6 is a flowchart showing the flow of an abnormal part identification process according to one embodiment of the present invention. The flowchart shown in Fig. 6 starts when an execution command for the abnormal part identification process is input to the acoustic diagnostic device 1, and the abnormal part identification process proceeds to step S11.

[0033] In the process of step S11, the operation sound collection unit 11 collects sound pressure signals of operation sounds of the equipment A during a predetermined period including k types (k≧1) of operation intervals of the equipment A classified in advance, at predetermined time intervals, using the acoustic sensor 20 installed near the equipment A. In addition, the operation interval operation sound extraction unit 12 collects images of the equipment A during normal operation, using the image sensor 21. This completes the process of step S11, and the abnormal part identification process proceeds to the process of step S12.

[0034] In the process of step S12, the operation section operation sound extraction unit 12 detects the start time and end time of k types (k≧1) of operation sections of the equipment A determined in advance, using the images of the equipment A during normal operation collected in the process of step S11. Then, the operation section operation sound extraction unit 12 extracts sound pressure signals for each of the k types of operation sections from the sound pressure signals collected in the process of step S11, based on the start time and end time of the detected operation sections. The process flow of this step S12 is the same as the process flow of step S2 described above, so a detailed description thereof will be omitted. This completes the process of step S12, and the abnormal part identification process proceeds to the process of step S13.

[0035] In the process of step S13, the sound pressure level acquisition unit 15 acquires sound pressure levels for k types of operation sections and l types of frequency bands from the sound pressure signal of the operation sound extracted in the process of step S12. The process flow of this step S13 is the same as the process flow of step S3 described above, so a detailed description thereof will be omitted. Note that the sound pressure level acquisition unit 15 may read information on m types of operation conditions that affect the operation of the equipment A from the operation condition database 18d, and acquire sound pressure levels for k types of operation sections, l types of frequency bands, and m types of operation conditions. This completes the process of step S13, and the abnormal part identification process proceeds to the process of step S14.

[0036] In the process of step S14, the deviation calculation unit 16 reads out information on the sound pressure level distribution during normal operation and its feature quantities from the normal distribution database 18c for each k types of operation intervals of the equipment A and for each l types of frequency bands. Next, using the read out information, the deviation calculation unit 16 calculates two or more deviations of the sound pressure level of the operation sound acquired in the process of step S13 relative to the sound pressure level during normal operation for each k types of operation intervals and for each l types of frequency bands. Then, using the calculated deviations, the deviation calculation unit 16 judges whether the equipment A is normal (0) or abnormal (1) for each deviation, and generates an array pattern vector of the judgment results (0,1) for each operation interval as a deviation pattern.

[0037] In the process of step S13, when m types of operating conditions are taken into consideration, the deviation calculation unit 16 calculates the deviation by referring to a data table such as that shown in Table 1 below. The data table shown in Table 1 below shows information on sound pressure level distribution and its feature quantities during normal operation for each of k types of operating sections, each of l types of frequency bands, and each of m types of operating conditions. The data table shown in Table 1 below corresponds to the contents of the extraction condition database 18a, the frequency band database 18b, the normal distribution database 18c, and the operating condition database 18d. This completes the process of step S14, and the abnormal part identification process proceeds to the process of step S15.

[0038] [Table 1]

[0039] In the process of step S15, the abnormal part identifying unit 17 identifies an abnormal part of the equipment A based on the deviation pattern y' generated in the process of step S14. Specifically, as shown in Figs. 7(a) and (b), the abnormal part identifying unit 17 identifies an abnormal part of the equipment A based on the operation section and deviation pattern y' registered in the deviation pattern database 18e in advance. k From the data table showing the relationship between the abnormal parts of equipment A and deviation pattern y', deviation pattern y similar to deviation pattern y k 7(b) is a data table in which deviation patterns obtained when an abnormality occurs in the equipment are recorded and organized in association with the abnormal part. This data table is created in advance and stored in the deviation pattern database 18e by the normal distribution determination and recording unit 14. The data table can be configured to identify the abnormal part based on a deviation pattern that takes into account m types of operating conditions in addition to classification for k types of operating sections and l types of frequency bands. Therefore, the abnormal part identifying unit 17 may identify the abnormal part of the equipment A by taking into account m types of operating conditions.

[0040] The abnormal part specifying unit 17 uses the Euclidean distance to determine the deviation pattern y kCalculate the similarity of deviation pattern y' to k The similarity shown in formula (1) is the degree of similarity between deviation pattern y' and deviation pattern y k The more similar the deviation pattern y is, the closer the value is to 1. k 7(a) and (b), since deviation pattern y' generated in the processing of step S14 matches deviation pattern y2, abnormal part identifying unit 17 identifies part B corresponding to deviation pattern y2 as the abnormal part of equipment A. This completes the processing of step S15, and the series of abnormal part identifying processes ends.

[0041]

number

[0042] As is clear from the above description, the acoustic diagnostic device 1 according to one embodiment of the present invention includes an operation sound collection unit 11 that collects operation sounds of equipment A, an operation section operation sound extraction unit 12 that collects images showing the operation state of equipment A using an image sensor 21, detects predetermined operation sections of equipment A using the collected images of equipment A, and extracts operation sounds for each operation section detected from the operation sounds collected by the operation sound collection unit 11, a sound pressure level acquisition unit 15 that acquires sound pressure levels for each frequency band predetermined for each operation section for the operation sounds for each operation section extracted by the operation section operation sound extraction unit 12, a deviation degree calculation unit 16 that calculates a deviation degree from a sound pressure level during normal operation of the same equipment, operation section, and frequency band for the sound pressure levels for each operation section and each frequency band acquired by the sound pressure level acquisition unit 15, and an abnormal part identification unit 17 that identifies an abnormal part of the equipment based on the deviation degree calculated by the deviation degree calculation unit 16. According to such a configuration, the abnormal part of the equipment A is identified based on the sound pressure level for each operating section and each frequency band of the equipment A, so that the abnormal part of the equipment A can be identified with high accuracy. Also, since the operating section of the equipment A is detected using the image sensor 21, the acoustic diagnostic device 1 can operate by itself. This also enables the abnormal part to be quickly adjusted, repaired, or replaced, so that deterioration or defective quality of the manufactured product can be suppressed. It can also contribute to improving the product yield.

[0043] Moreover, the acoustic diagnostic device 1 according to one embodiment of the present invention further includes a normal state sound pressure level acquisition unit 13 that acquires a sound pressure level for each predetermined frequency band for each operation section for the operation sound during normal operation of the equipment A extracted by the operation section operation sound extraction unit 12, and a normal distribution determination and recording unit 14 that classifies the sound pressure levels acquired by the normal state sound pressure level acquisition unit 13 for each equipment, each operation section, and each frequency band, records them as sound pressure levels during normal operation of the equipment A, and records a table in which deviation patterns obtained when an abnormality occurs in the equipment A for each frequency band are associated with abnormal parts for each operation section. According to this configuration, for the operation sound during normal operation of the equipment A, sound pressure levels for each equipment, each operation section, and each frequency band, and a table in which deviation patterns obtained when an abnormality occurs are associated with abnormal parts are recorded. As a result, even if a change occurs in the normal operation sound, new recording processing is performed on the normal operation sound, and the abnormal part of the equipment A is identified based on the sound pressure level for each operation section and each frequency band using the new normal operation sound data as a reference, so that it is possible to accurately identify the abnormal part of the equipment A. In addition, since the operation section of the equipment A is detected using the image sensor 21, the acoustic diagnostic device 1 can operate by itself.

[0044] Moreover, the acoustic diagnostic device 1 according to one embodiment of the present invention identifies an abnormal part of the equipment A while taking into account the operating conditions of the equipment A, and therefore can identify the abnormal part of the equipment A with high accuracy. That is, in general, in steel-making equipment such as press equipment and rolling equipment, even during normal operation, operating conditions such as the amount of rolling and materials change, and the loudness and frequency of the operation sound vary accordingly. For this reason, the normal range of the loudness and frequency of the operation sound becomes wider, and the loudness and frequency of the abnormal sound are buried within the normal range, and as a result, the equipment abnormality may not be accurately detected by the loudness and frequency of the operation sound alone. In contrast, the acoustic diagnostic device 1 according to one embodiment of the present invention identifies an abnormal part of the equipment A while taking into account the operating conditions of the equipment A, and therefore can identify the abnormal part of the equipment A with high accuracy. EXAMPLES

[0045] In this embodiment, an abnormal part of the press equipment was identified. By installing an acoustic sensor around the press equipment, the spectrum of the impact sound can be obtained at the moment when the press load occurs, and the operation sound of the conveyor can be obtained while the conveyor is operating after the press is completed. In addition, by installing cameras on the entry side and exit side of the press equipment, images of the slab entering the press equipment and the slab leaving the press equipment, as shown in Figs. 8(a) and (b), can be collected. In this embodiment, the images shown in Figs. 8(a) and (b) were input into an artificial intelligence model to detect the time when the work entered the press equipment and the time when the work left the press equipment. Then, the operating conditions of the press equipment were taken into account to narrow the range of the frequency distribution of the sound pressure level during normal operation of the press equipment, and the abnormal part of the press equipment was identified. Figure 9(a) shows the frequency distribution of sound pressure levels when the operating conditions of the press equipment are limited (average value 87.6 dB, standard deviation σ2.19), and Figure 9(b) shows the frequency distribution of sound pressure levels when the operating conditions of the press equipment are not limited (average value 85.7 dB, standard deviation σ1.6).

[0046] Next, assuming a die abnormality, the deviation of the press equipment was calculated when the sound pressure level was 91 dB. The following formula (2) was used to calculate the deviation of the press equipment. As a result, when the operating conditions were not limited, the deviation of the press equipment when the sound pressure level was 91 dB was calculated to be 1.5, and when the operating conditions were limited, the deviation of the press equipment when the sound pressure level was 91 dB was calculated to be 3.3. Therefore, if the deviation threshold for determining that an abnormality has occurred in the press equipment is set to 3, it is possible to accurately determine that an abnormality in the press equipment is the die. In addition, by searching for a deviation pattern similar to the calculated deviation pattern from deviation patterns registered in advance, it was possible to accurately determine that the abnormal part of the equipment is the die.

[0047]

number

[0048] Although the embodiments of the present invention have been described above, the present invention is not limited by the descriptions and drawings that form part of the disclosure of the present invention according to the present embodiments. In other words, other embodiments, examples, and operation techniques, etc., made by those skilled in the art based on the present embodiments are all included in the scope of the present invention. [Industrial Applicability]

[0049] According to the present invention, it is possible to provide an acoustic diagnosis device and an acoustic diagnosis method capable of identifying an abnormal portion of equipment with high accuracy. [Explanation of symbols]

[0050] 1 Acoustic diagnostic equipment 11 Operation sound collection unit 12 Operation section operation sound cut-out section 13 Normal sound pressure level acquisition section 14 Normal distribution determination and recording section 15 Sound pressure level acquisition section 16 Degree of deviation calculation section 17 Abnormal part identification part 18 Database Department 18a Extraction condition database 18b Frequency Band Database 18c Normal Distribution Database 18d Operating Conditions Database 18e Deviation Pattern Database 20 Acoustic Sensor A Equipment to be diagnosed

Claims

1. An acoustic diagnostic device that identifies an abnormal part of equipment using an acoustic signal generated during operation of the equipment, an operation sound collection unit that collects operation sounds of the equipment; an operation section operation sound extraction unit that collects images showing an operation state of the equipment, detects a predetermined operation section of the equipment using the collected images, and extracts an operation sound for each detected operation section from the operation sounds collected by the operation sound collection unit; a sound pressure level acquisition unit that acquires a sound pressure level for each frequency band that is predetermined for each operation section, for the operation sound for each operation section extracted by the operation section operation sound extraction unit; a deviation calculation unit that calculates a deviation of the sound pressure level for each of the operation sections and each of the frequency bands acquired by the sound pressure level acquisition unit from a sound pressure level during normal operation of the same equipment, operation section, and frequency band; an abnormality portion identifying unit that identifies an abnormal portion of the equipment based on the deviation degree calculated by the deviation degree calculating unit; An acoustic diagnostic device comprising:

2. 2. The acoustic diagnostic device according to claim 1, wherein the operation section operation sound extraction unit detects a predetermined operation section of the equipment using the image showing the operation state of the equipment by inputting an image showing the operation state of the equipment acquired using an image sensor to an artificial intelligence model having an image showing the operation state of the equipment as input data and an operation state of the equipment shown by the image as output data.

3. The sound pressure level acquisition unit classifies each operation sound based on a predetermined operating condition of the equipment and acquires a sound pressure level, The deviation degree calculation unit calculates a deviation degree from a sound pressure level during normal operation of the same equipment, operation section, frequency band, and operating condition for the sound pressure level for each operation section, frequency band, and operating condition acquired by the sound pressure level acquisition unit. The acoustic diagnostic device according to claim 1 .

4. a database unit for storing data necessary for extracting conditions for the operation sound of the equipment and for identifying an abnormal part of the equipment; 4. The acoustic diagnostic device according to claim 3, wherein the abnormal part identifying unit identifies an abnormal part of the equipment based on a pattern of deviation degrees that are elements of the deviation degrees in each frequency band obtained for each of the operation sections stored in the database unit.

5. a normal state sound pressure level acquisition unit that acquires a sound pressure level for each frequency band that is predetermined for each operation section for the operation sound for each operation section extracted by the operation section operation sound extraction unit; a normal distribution determination and recording unit that classifies the sound pressure levels acquired by the normal sound pressure level acquisition unit for each piece of equipment, each operating section, and each frequency band, and records the classified sound pressure levels as sound pressure levels during normal operation of the equipment; The acoustic diagnostic device according to any one of claims 1 to 4, comprising:

6. 6. The acoustic diagnostic device according to claim 5, wherein the normal distribution determination and recording unit records, for each piece of equipment, a table including information indicating a relationship between a pattern of deviations of sound pressure levels from a sound pressure level during normal operation in each frequency band obtained for each operation section and an abnormal portion of the equipment.

7. 7. The acoustic diagnostic device according to claim 6, wherein the normal distribution determining and recording unit records a table including information indicating the operation interval, a pattern of the deviation degree, and a relationship between an operating condition of the equipment and an abnormal portion of the equipment.

8. An acoustic diagnosis method for identifying an abnormal part of equipment using an acoustic signal generated during operation of the equipment, comprising: An operation sound collection step of collecting operation sounds of the equipment; an operation section operation sound extraction step of collecting images showing an operation state of the equipment, detecting a predetermined operation section of the equipment using the collected images, and extracting the operation sound for each detected operation section from the operation sound collected in the operation sound collection step; a sound pressure level acquisition step of acquiring a sound pressure level for each frequency band predetermined for each operation section for the operation sound extracted for each operation section in the operation section operation sound extraction step; a deviation calculation step of calculating a deviation of the sound pressure level for each of the operation sections and each of the frequency bands acquired in the sound pressure level acquisition step from a sound pressure level during normal operation of the same equipment, operation section, and frequency band; an abnormality portion identifying step of identifying an abnormal portion of the equipment based on the deviation degree calculated in the deviation degree calculating step; An acoustic diagnostic method comprising:

9. a normal state sound pressure level acquisition step of acquiring a sound pressure level for each frequency band predetermined for each operation section for the operation sound extracted for each operation section in the operation section operation sound extraction step; a normal distribution determination / recording step of classifying the sound pressure levels acquired in the normal sound pressure level acquisition step for each piece of equipment, each operation section, and each frequency band, and recording the classified sound pressure levels as sound pressure levels during normal operation of the equipment; The acoustic diagnostic method according to claim 8 , comprising:

10. 10. The acoustic diagnosis method according to claim 8 or 9, wherein the operation interval operation sound extraction step includes a step of inputting an image showing the operation state of the equipment, acquired by an image sensor, into an artificial intelligence model having an image showing the operation state of the equipment as input data and an operation state of the equipment shown by the image as output data, thereby detecting a predetermined operation interval of the equipment using the image showing the operation state of the equipment.

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