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 extracts operation sounds for each predetermined operation section of the equipment from the operation sounds collected by the operation sound collection unit, a sound pressure level acquisition unit that acquires sound pressure levels for each predetermined frequency band 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 of a sound signal 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 abnormal parts of equipment using acoustic signals 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 extracts operation sounds for each predetermined operation section of the equipment 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 abnormal parts of the equipment based on the deviation calculated by the deviation calculation unit.

[0007] 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.

[0008] The abnormal part identifying unit may identify an abnormal part of the equipment based on a pattern of deviation degrees that is determined for each of the operation sections and that has as its elements the deviation degrees in each of the frequency bands.

[0009] The system may further include a database unit for storing conditions for extracting the operation sounds of the equipment and data required to identify abnormal parts of the equipment, and the abnormal part identification unit may refer to a table containing information indicating the relationship between the operation interval and the deviation pattern stored in the database unit and the abnormal parts of the equipment.

[0010] The table includes information indicating the operating section, the pattern of the deviation degree, and the relationship between the operating conditions of the equipment and abnormal parts of the equipment, and the abnormal part identification unit refers to the table and identifies the abnormal part of the equipment by taking into consideration the operating section, the pattern of the deviation degree, and the operating conditions of the equipment.

[0011] 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.

[0012] 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.

[0013] 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.

[0014] The operation section may be determined in association with an operation time of the facility or an operation position of a component device of the facility.

[0015] The operating section may be determined based on specifying an assumed number of categories based on knowledge about equipment operation for a sensor signal installed in the equipment that can identify a change point in the operation of the equipment, and determining the position of the categories by extracting change points using a machine learning technique using data from the sensor signal or by extracting change points by data classification using clustering technology.

[0016] The acoustic diagnosis method of 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 for collecting operation sounds of the equipment; an operation section operation sound extraction step for extracting operation sounds for each predetermined operation section of the equipment from the operation sounds collected in the operation sound collection step; a sound pressure level acquisition step for acquiring sound pressure levels for each frequency band predetermined for each operation section for the operation sounds for each operation section extracted in the operation section operation sound extraction step; a deviation calculation step for 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 for identifying an abnormal part of the equipment based on the deviation calculated in the deviation calculation step.

[0017] 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. Effect of the Invention

[0018] 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]

[0019] [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 a method of extracting a sound pressure signal when the extraction condition of the sound pressure signal is specified using the crank angle. [Diagram 5] FIG. 5 is a diagram showing an example of change in sound pressure level of operation sound in one frequency band with respect to crank angle. [Figure 6] FIG. 6 is a diagram showing an example of classification of the operation sounds shown in FIG. [Figure 7] FIG. 7 is a flowchart showing the flow of the abnormal portion identification process according to one embodiment of the present invention. [Figure 8] FIG. 8 is a diagram for explaining a specific example of the abnormal portion specifying process according to an embodiment of the present invention. [Figure 9] FIG. 9 is a diagram for explaining the abnormal portion specifying process of the embodiment. [Figure 10] FIG. 10 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. [Figure 11] FIG. 11 is a diagram for explaining the abnormal portion specifying process of the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] 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.

[0021] 〔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.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] [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.

[0026] In the process of step S1, the operation sound collector 11 collects, at predetermined time intervals, sound pressure signals of operation sounds of the equipment A during a predetermined period during normal operation including operation intervals of k types (k≧1) of the equipment A classified in advance, using an acoustic sensor 20 such as a microphone installed near the equipment A. This completes the process of step S1, and the data recording process proceeds to the process of step S2.

[0027] In the process of step S2, the movement section operation sound extraction unit 12 extracts sound pressure signals for each of k types of movement sections from the sound pressure signals collected in the process of step S1. Specifically, the movement section operation sound extraction unit 12 reads out extraction conditions (extraction start time and extraction end time, etc.) for the sound pressure signals for each of k types of movement sections from the extraction condition database 18a. For example, in the example shown in FIG. 3(a), the movement section operation sound extraction unit 12 reads out information on the start time and end time of movement section 1, where the load rises and returns to a predetermined value, and the start time and end time of movement section 2, where the load maintains a predetermined value, as extraction conditions. Then, the movement section operation sound extraction unit 12 extracts sound pressure signals that satisfy the extraction conditions from the sound pressure signals collected in the process of step S1, thereby extracting sound pressure signals for each of k types of movement sections (see FIG. 3(b)).

[0028] The extraction conditions of the sound pressure signal are not limited to the method of designation using the start time and end time of the operation section, and may be designated based on the operation position of the target equipment. For example, in the case of a press equipment that processes a workpiece by pressing a die against the workpiece using a crank mechanism, the operation section of the press equipment can be designated by the magnitude of the crank angle at which the workpiece comes into contact. Specifically, if the crank angle when the workpiece first comes into contact with the die is α and the crank angle when the workpiece leaves the die is β, the press operation section can be designated by the crank angles α and β. In addition, if there is a process after the press operation (for example, the operation of a conveyor, etc.), the operation section of the process after the press can be designated by using a crank angle larger than the crank angle β. Therefore, in this case, the extraction conditions of the sound pressure signal can be designated using the crank angle. When the extraction conditions of the sound pressure signal are designated based on the operation position of the target equipment, information that associates the extraction conditions with the operation position of the equipment may be recorded and stored in the extraction condition database 18a.

[0029] FIG. 4 is a diagram for explaining a method of extracting a sound pressure signal when the extraction condition of the sound pressure signal is specified using a crank angle. The crank angle α when the workpiece first comes into contact with the die can be derived from the geometric relationship between the workpiece and the crank mechanism. In the example shown in FIG. 4, the press operation section in which the press load is applied to the workpiece is divided into a plurality of sections (subdivision sections 1 to 3), and a sound pressure signal is extracted in each subdivision section. When extracting the sound pressure signal, since the crank angle uniquely corresponds to the start time and end time of each subdivision section, it is sufficient to extract the sound pressure signal within the range of the crank angle.

[0030] As described above, the time and the operating position for specifying the operation section can be determined by considering the relationship between the manufacturing process of the product for each equipment and the operation of the equipment, but it is not easy to specify the section where the characteristic sound can be effectively extracted by the time and the operating position. In such a case, the operation section may be specified by using information on the change point of the sensor signal indicating the change of the equipment operation. For example, in the case of the above-mentioned press equipment, the start timing of the press operation can be detected by the change of the load of the press load, so the subdivision section may be specified by using the load cell signal that detects the press load. Specifically, in the example shown in FIG. 4, the load signal indicates the change of the load cell signal, and the subdivision section can be specified by including not only each rising and falling position of the load signal but also the section where the load signal is maximum. In this way, the position of each operation section can be specified by relating the time and the operating position such as the above-mentioned crank angle to the change point of the sensor signal such as the load cell that indicates the change of the equipment operation as described above.

[0031] In addition, the operation section can also be specified using a classification technique based on a combination of knowledge about equipment operations and data on the location of operation sounds associated with the equipment operations. A single equipment operation may generate multiple operation sounds, and even in such cases, diagnostic accuracy can be improved by classifying the generation patterns of operation sounds associated with the equipment operations. For example, in the case of press equipment, the operation from pressing the workpiece with a die to returning is considered to be one equipment operation, and the operation sounds can be classified by subdividing the process by crank angle, etc. Specifically, the operation section can be specified as follows based on prior knowledge of press operations.

[0032] (1) Section where the workpiece is transported: Operating noise is generated by the transport machine. (2) Section where the workpiece and die come into contact: Operating noise is generated by friction and collision vibrations when the workpiece and die come into contact. The operating noise is the sound of the workpiece and die coming into contact. (3) Section where the workpiece and die are separated: Operating noise is generated by the movement and vibration of the crank mechanism. The operating noise corresponds to the vibration and friction noise caused by the movement of the crank mechanism and die.

[0033] Although the frequency of the operation sound may differ in each of the above sections, the sound pressure level of the operation sound will differ greatly because the section includes a period in which the workpiece is pressed. Therefore, it is advisable to divide the operation section by focusing on the sound pressure level. FIG. 5 is a diagram showing an example of the change in the sound pressure level of the operation sound of one frequency band with respect to the crank angle. FIG. 5 shows the change in the sound pressure level in three press operations. As shown in FIG. 5, peaks in the sound pressure level can be confirmed near crank angles of 50°, 150°, and 250°. Therefore, since the peaks and the valleys of the peaks are the change points of the operation sound, in the example shown in FIG. 5, the operation sound is divided into three operation sounds. The change points of the operation sound can be obtained from the data, and a machine learning method can be used to detect the change points. In addition, for example, the K-means algorithm can be used as a method of classifying the operation sound. The K-means algorithm is a method of classifying data into a plurality of predetermined clusters. In the example shown in FIG. 5, there are three change points of the operation sound, so an example of classifying the operation sound into three is shown in FIG. 6(a) to (c).

[0034] In this way, when classifying operation sounds, the number of divisions for equipment operations is assumed based on prior knowledge and taking into account the equipment mechanism, and the change points of the sound pressure data are calculated based on the assumed number of divisions using machine learning techniques or clustering techniques, and the division positions are determined based on the calculated change points. When determining the division positions, data capable of expressing the characteristics or change point information of operations, such as load data (the above-mentioned load cell data), other than sound pressure data, may be used. In the above explanation, the sound pressure level of a specific frequency band is focused on to obtain an accurate division of the operation section, but the final operation section may be determined by selecting several candidate frequency bands of interest and performing the same process for each of them.

[0035] As described above, it is possible to accurately distinguish between operation sections with different characteristics based on sensor signals that can identify changing points in the operation of the equipment among sensor signals installed in the target equipment or its vicinity (sensor signals such as press load signals and sound pressure levels of acoustic signals in the case of press equipment). It is advisable to register in advance in the extraction condition database 18a conditions for distinguishing the operation sections thus determined based on the time period from the start time of the equipment or the operation position of the equipment (for example, the crank angle of the press equipment) as extraction conditions. This completes the process of step S2, and the data recording process proceeds to the process of step S3.

[0036] 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(c), the sound pressure level of normal operation sound is acquired 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.

[0037] 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 data on the sound pressure level distribution of the normal operation sounds and their feature quantities (average value, standard deviation, etc.) in the normal distribution database 18c. In addition, the normal distribution determination and recording unit 14 may further classify the signal levels of the normal operation signals into operation conditions, taking into account a plurality of operation conditions (e.g., m types of operation conditions). This completes the process of step S4, and the series of data recording processes ends.

[0038] In the data recording process, in addition to the process of recording data such as operating sounds during normal operation, a data table that organizes the relationship between deviation patterns from normal operation and abnormal parts (described later) is stored in advance in the deviation pattern database 18e. The deviation patterns and their registered contents will be explained after the definition of the deviation degree in the "Abnormal Part Identification Process" below.

[0039] [Abnormal part identification process] Fig. 7 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. 7 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.

[0040] In the process of step S11, the operation sound collector 11 collects, at predetermined time intervals, sound pressure signals of operation sounds of the equipment A during a predetermined period including operation intervals of k types (k≧1) of the equipment A classified in advance, using the acoustic sensor 20 installed near the equipment A. This completes the process of step S11, and the abnormal part identification process proceeds to the process of step S12.

[0041] In the process of step S12, the operation section operation sound extraction unit 12 extracts sound pressure signals for each of k types of operation sections from the sound pressure signals collected in the process of step S11. 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.

[0042] 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.

[0043] 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 and l types of frequency bands of the equipment A. Next, the deviation calculation unit 16 uses the read out information to calculate 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 l types of frequency bands. Then, the deviation calculation unit 16 uses the calculated deviations to determine whether the equipment A is normal (0) or abnormal (1) for each deviation, and generates an array pattern vector y' of the determination results (0,1) for each l types of frequency bands in each operation interval as a deviation pattern.

[0044] The deviation can be calculated, for example, by the following formula (1). Furthermore, the sound pressure level measurement value for the target operation section and target frequency band may be a statistical value such as the average value of the measurement values ​​within the operation section. After the deviation is calculated, if the deviation exceeds a predetermined threshold, it is determined to be abnormal (1), and otherwise it is determined to be normal (0). The threshold may be set, for example, at 3σ, which is about three times the standard deviation σ, but is not limited to this and may be adjusted and determined while observing the characteristics of the operational data.

[0045]

number

[0046] 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.

[0047] [Table 1]

[0048] 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. 8(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 The data table shown in FIG. 8(b) records deviation patterns obtained when an abnormality occurs in the equipment and organizes them in association with the abnormal parts. 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.

[0049] The abnormal part identifying unit 17 uses the Euclidean distance to identify the deviation pattern yk Calculate the similarity of deviation pattern y' to k The similarity shown in formula (2) is calculated by finding whether deviation pattern y' is larger than deviation pattern y k The more similar the deviation pattern y is, the closer the value is to 1. k For example, in the example shown in FIG. 8(a) and (b), deviation pattern y′ and deviation pattern y generated in the process of step S14 are read out. 2 Since these patterns match, the abnormal part identifying unit 17 identifies the deviation pattern y 2 The part B corresponding to the part A is identified as the abnormal part of the equipment A. This completes the process of step S15, and the series of abnormal part identification processes ends.

[0050]

number

[0051] 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 the equipment A, an operation section operation sound extraction unit 12 that extracts operation sounds for each predetermined operation section of the equipment A from the operation sounds collected by the operation sound collection unit 11, a sound pressure level acquisition unit 15 that acquires a sound pressure level for each frequency band that is 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 level 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 this configuration, an abnormal part of the equipment A is identified based on the sound pressure level for each operation section and each frequency band of the equipment A, so that the abnormal part of the equipment A can be identified with high accuracy. This also enables the rapid adjustment, repair or replacement of abnormal parts, making it possible to prevent deterioration or defects in the quality of manufactured products and contribute to improving product yields.

[0052] 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 there is a change in the normal operating sound, new recording processing is performed on the normal operating sound, and the abnormal parts of equipment A are identified based on the sound pressure levels for each operating section and each frequency band using the new normal operating sound data as a reference, so that the abnormal parts of equipment A can be identified with high accuracy.

[0053] 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

[0054] In this embodiment, the abnormal part of the press equipment shown in FIG. 9(a) was identified. By installing an acoustic sensor around the press equipment shown in FIG. 9(a), the spectrum of the impact sound can be obtained at the moment when the press load occurs, as shown in FIG. 9(b), and the operation sound of the conveyor can be obtained while the conveyor is operating after the press is completed. In this embodiment, the abnormal part of the press equipment was identified by narrowing the range of the frequency distribution of the sound pressure level during normal operation of the press equipment by taking into account the operating conditions of the press equipment. FIG. 10(a) shows the frequency distribution of the sound pressure level when the operating conditions of the press equipment are not limited (average value 87.6 dB, standard deviation σ2.19), and FIG. 10(b) shows the frequency distribution of the sound pressure level when the operating conditions of the press equipment are limited (average value 85.7 dB, standard deviation σ1.6).

[0055] Next, assuming a die abnormality, the deviation of the press equipment when the sound pressure level is 91 dB was calculated. The following formula (3) 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 the abnormality of the press equipment is the die. In addition, as shown in Figure 11, 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.

[0056]

number

[0057] 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]

[0058] 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]

[0059] 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 extracting unit that extracts operation sounds for each predetermined operation section of the equipment from the operation sounds collected by the operation sound collecting 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. 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 .

3. The acoustic diagnostic device according to claim 1 , 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 operation section.

4. a database unit for storing data necessary for identifying operating sound collection conditions and abnormal parts of the equipment; 4. The acoustic diagnostic device according to claim 3, wherein the abnormal part identifying unit identifies the abnormal part of the equipment by referring to a table including information indicating a relationship between the patterns of the operation sections and the deviation degrees stored in the database unit and the abnormal part of the equipment.

5. the table includes information indicating the operation section, a pattern of the deviation degree, and a relationship between an operating condition of the equipment and an abnormal portion of the equipment, the abnormality portion identifying unit identifies an abnormal portion of the equipment by referring to the table and taking into consideration the operation section, the pattern of the deviation degree, and an operating condition of the equipment. The acoustic diagnostic device according to claim 4.

6. 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 5, comprising:

7. 7. The acoustic diagnostic device according to claim 6, 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.

8. 8. The acoustic diagnostic device according to claim 7, 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.

9. 6. The acoustic diagnostic device according to claim 1, wherein the operation section is determined in association with an operation time of the facility or an operation position of a component device of the facility.

10. The acoustic diagnostic device according to any one of claims 1 to 5, wherein the operation section is determined based on designation of an assumed number of sections based on knowledge about equipment operation for a sensor signal installed in the equipment that can identify a change point in the operation of the equipment, and determination of section positions by extracting change points by a machine learning technique using data of the sensor signal or by extracting change points by data classification using clustering technology.

11. 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 extracting an operation sound for each predetermined operation section of the equipment 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:

12. 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 11 ,

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