Acoustic diagnostic device and acoustic diagnostic method

Abstract

The acoustic diagnostic device according to the present invention collects operation monitoring sensor signals for detecting operation information of the equipment, including operation sounds of the equipment and operation abnormalities, extracts operation sounds and operation monitoring sensor signals for each predetermined operation interval of the equipment from the collected operation sounds and operation monitoring sensor signals, obtains sound pressure levels for each frequency band predetermined for each operation interval for the operation sounds extracted for each operation interval, calculates the degree of deviation from the sound pressure level during normal operation of the same equipment, operation interval, and frequency band for the obtained sound pressure levels for each operation interval and each frequency band, detects operation information based on the operation monitoring sensor signals for each extracted operation interval, and determines an abnormal part of the equipment or an operation abnormality based on the degree of deviation and the operation information.

Description

[Technical Field]

[0001] The present invention relates to an acoustic diagnostic apparatus and an acoustic diagnostic method. [Background technology]

[0002] Equipment diagnostic techniques using acoustic sensors and other sensors have been known for some time. Specifically, 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 changes 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 mill rolls using the frequency of an acoustic signal detected by an acoustic sensor installed in the rolling mill. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2008-164490 [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 accurately identify the abnormal part of the equipment, especially for equipment composed of multiple parts. To address this issue, it is possible to use technology that detects abnormal sounds by focusing on the operating section of the equipment and identifies the part or parts from which the abnormal sound is generated. However, abnormal sounds include not only abnormal sounds caused by equipment abnormalities, but also abnormal sounds caused by operational abnormalities, such as improperly set operating conditions or abnormal sounds caused by the manufactured product, which occur even when no equipment abnormality is occurring. Furthermore, falsely detecting abnormal sounds caused by operational abnormalities as abnormal sounds caused by equipment abnormalities can lead to unnecessary inspections and equipment shutdowns.

[0005] However, even if the devices described in Patent Documents 1 to 3 detect an abnormality based on an abnormal sound, they may not be able to determine whether the abnormal sound is caused by an equipment abnormality or an operational abnormality. Furthermore, for equipment composed of multiple components, they may not be able to accurately identify the abnormal part of the equipment. Specifically, when equipment is composed of multiple components such as bearings, reducers, cranks, dies, rolls, and blocks, the timing at which operating sounds are generated varies depending on the components. Therefore, unless the timing and frequency of the abnormal sound are taken into consideration, it is not possible to determine which component is generating the abnormal sound, and it may not be possible to accurately identify the abnormal part of the equipment.

[0006] Furthermore, rolling equipment in the steel industry is an example of a manufacturing facility that is composed of multiple pieces of equipment, and hot rolling equipment, for example, is composed of various pieces of equipment such as a descaling device for sheet bars, which are the material to be rolled, before rolling, a crop shear that cuts crops from the head and tail ends of the sheet bars, a sheet bar heating device, multiple rolling mills installed after the rolling mills, numerous steel plate cooling equipment installed after the rolling mills, a coiler that winds up the rolled material, etc. With manufacturing facilities that are composed of multiple pieces of equipment like this, the devices described in Patent Documents 1 to 3 may not be able to accurately identify which piece of equipment is experiencing an abnormality.

[0007] The present invention has been made to solve the above-mentioned problems, and its purpose is to provide an acoustic diagnostic device and an acoustic diagnostic method that can prevent operational abnormalities from being mistakenly detected as equipment abnormalities and prevent operation and equipment shutdowns due to unnecessary inspections. [Means for solving the problem]

[0008] an operation interval sensor signal extracting unit that extracts an operation sound and an operation monitoring sensor signal for each predetermined operation interval of the equipment from the operation sound and the operation monitoring sensor signal collected by the sensor signal extracting unit; a sound pressure level acquiring unit that acquires a sound pressure level for each predetermined frequency band for each operation interval for the operation sound extracted by the operation interval sensor signal extracting unit; a deviation calculating unit that calculates a deviation from a sound pressure level during normal operation of the same equipment, operation interval, and frequency band for the sound pressure level for each operation interval and for each frequency band acquired by the sound pressure level acquiring unit; an operation information detecting unit that detects the operation information based on the operation monitoring sensor signal for each operation interval extracted by the operation interval sensor signal extracting unit; and an abnormality portion / operational abnormality identifying unit that determines an abnormal portion or an operational abnormality of the equipment based on the deviation and the operation information.

[0009] The sound pressure level acquisition unit classifies each operating 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.

[0010] The operation information detection unit preferably acquires, from a database for each operation abnormality, the operation monitoring sensor signal for calculating an operation abnormality index used to determine the operation abnormality and information regarding a method for processing the operation monitoring sensor signal, calculates an operation abnormality index for each operation index based on the acquired information, and determines the presence or absence of each operation abnormality using the calculated operation abnormality index.

[0011] The abnormal part / operational abnormality identifying unit may identify an abnormal part or an operational abnormality of the equipment based on a pattern of deviation degrees, which are elements of the deviation degrees in each frequency band obtained for each operating section, and on the presence or absence of the operational abnormality.

[0012] The system may include a database unit that stores data necessary for identifying conditions for collecting operating sounds of the equipment, abnormal parts of the equipment, and operational abnormalities, and the abnormal part / operation abnormality identification unit may identify the abnormal part or operational abnormality of the equipment by referring to a table stored in the database unit that contains information indicating the relationship between the operation section and the pattern of the deviation degree and the abnormal part of the equipment and the type of operational abnormality.

[0013] The table includes information indicating the relationship between the operation section, the deviation pattern, and the operating conditions of the equipment, and the abnormal part and operational abnormality of the equipment, and the abnormal part / operation abnormality identifying unit may refer to the table and identify the abnormal part or operational abnormality of the equipment, taking into consideration the operation section, the deviation pattern, and the operating conditions of the equipment.

[0014] The system may include a result output unit that outputs information relating to the identified abnormal portion or operational abnormality of the equipment, and the result output unit may output an alert based on the information relating to the abnormal portion or operational abnormality of the equipment, or output a command to stop the equipment to a control device of the equipment.

[0015] It is preferable to provide a normal state sound pressure level acquisition unit that acquires sound pressure levels for each predetermined frequency band for each operation section during normal operation of the equipment for the operation sound for each operation section extracted by the operation section sensor signal 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 into sound pressure levels for each equipment, each operation section, and each frequency band, and records them as sound pressure levels during normal operation of the equipment.

[0016] The normal distribution determination and recording unit may record, for each piece of equipment, a table including information indicating the relationship between a pattern of deviations of sound pressure levels from sound pressure levels during normal operation in each frequency band determined for each operating section and abnormal parts of the equipment, and information associating operational abnormalities of the equipment with the pattern of deviations.

[0017] The normal distribution determining and recording unit may record a table including information indicating the operation interval, the pattern of the deviation, and the relationship between the operating conditions of the equipment and the abnormal portion and operational abnormality of the equipment.

[0018] The acoustic diagnosis method according to the present invention includes a sensor signal collecting step of collecting operation monitoring sensor signals for detecting operation information of the equipment including operation sounds and operation abnormalities of the equipment; an operation section sensor signal extracting step of extracting operation sounds and operation monitoring sensor signals for each predetermined operation section of the equipment from the operation sounds and operation monitoring sensor signals collected in the sensor signal collecting step; a sound pressure level acquiring step of acquiring sound pressure levels for each predetermined frequency band for each operation section for the operation sounds extracted in the operation section sensor signal extracting 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 for each frequency band acquired in the sound pressure level acquiring step; an operation information detecting step of detecting the operation information based on the operation monitoring sensor signals for each operation section extracted in the operation section sensor signal extracting step; and an abnormal portion / operation abnormality identifying step of determining an abnormal portion or an operation abnormality of the equipment based on the deviation and the operation information.

[0019] The method may include a step of outputting an alert regarding the abnormal portion or operational abnormality of the equipment, or a command to stop the equipment to a control device of the equipment, based on the determination of the abnormal portion or operational abnormality of the equipment. [Effects of the Invention]

[0020] The acoustic diagnostic device and acoustic diagnostic method according to the present invention can prevent operational abnormalities from being mistakenly detected as equipment abnormalities, and can prevent unnecessary shutdowns of operations and equipment due to inspections. [Brief explanation of the drawings]

[0021] [Figure 1] FIG. 1 is a block diagram showing the configuration of an acoustic diagnostic apparatus according to one embodiment of the present invention. [Figure 2] FIG. 2 is a flowchart showing the flow of data recording processing according to one embodiment of the present invention. [Figure 3]FIG. 3 is a diagram for explaining the data recording process according to one embodiment of the present invention. [Figure 4] FIG. 4 is a flowchart showing the flow of the abnormal portion / operation abnormality identification process according to one embodiment of the present invention. [Figure 5] FIG. 5 is a diagram illustrating an example of the data table. [Figure 6] FIG. 6 is a diagram showing an example of an operational abnormality information table. [Figure 7] FIG. 7 is a diagram showing an example of searching for equipment abnormalities and operational abnormalities using a table of deviation patterns associated with equipment abnormalities and operational abnormalities. [Figure 8] FIG. 8 is a diagram showing frequency distributions of sound pressure levels when the operating conditions of the press equipment are not limited and when they are limited. [Figure 9] FIG. 9 is a diagram showing an example of a change in die adjustment abnormality before and after adjustment of the die projection allowance. [Figure 10] FIG. 10 is a diagram for explaining the abnormal portion / operation abnormality identification process using the deviation pattern in the embodiment. [Figure 11] FIG. 11 is a diagram for explaining the abnormal portion / operation abnormality identification process using the deviation pattern in the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0022] The configuration and operation of an acoustic diagnostic apparatus according to one embodiment of the present invention will be described below with reference to the drawings.

[0023] 〔composition〕 First, the configuration of an acoustic diagnostic apparatus according to one embodiment of the present invention will be described with reference to Fig. 1. Fig. 1 is a block diagram showing the configuration of an acoustic diagnostic apparatus according to one embodiment of the present invention.

[0024] As shown in FIG. 1 , an acoustic diagnostic device 1 according to one embodiment of the present invention identifies an abnormal portion of one or more predetermined pieces of equipment A to be diagnosed (hereinafter referred to as equipment) using acoustic signals detected by an acoustic sensor 30a during operation of the equipment. The acoustic diagnostic device 1 also determines the operational status of the equipment A using an operation monitoring sensor 30b, and identifies an abnormality caused by an operational abnormality of the equipment A based on the determination result. The acoustic diagnostic device 1 is configured with an information processing device such as a computer. The equipment A is composed of multiple parts, and examples of such equipment include steelmaking equipment such as press equipment and rolling equipment. The operation monitoring sensor 30b is a sensor other than an acoustic sensor that detects the status of the equipment A and equipment related to the equipment A, and is a sensor for detecting operational information of the equipment A, including operational abnormalities. Details of the operation monitoring sensor 30b will be described later.

[0025] The acoustic diagnostic device 1 comprises a sensor signal collecting unit 11, an operating section sensor signal extracting unit 12, a normal state sound pressure level acquiring unit 13, a normal distribution determining and recording unit 14, an abnormal operation information recording unit 15, a sound pressure level acquiring unit 16, a deviation calculating unit 17, an operation information detecting unit 18, an abnormal portion and abnormal operation identifying unit 19, and a result output unit 20. Each of these units is realized by an arithmetic processing unit such as a CPU in an information processing device constituting the acoustic diagnostic device 1 executing a computer program. The functions of each of these units will be described later.

[0026] The acoustic diagnostic device 1 includes a database unit 60. The database unit 60 is configured by a storage device such as an HDD that constitutes an information processing device, and includes a cutting condition database 60a, a frequency band database 60b, a normal distribution database 60c, an operating condition database 60d, a deviation pattern database 60e, and an operational abnormality database 60f. Details of each database will be described later. The database unit 60 may be located outside the acoustic diagnostic device 1, and the acoustic diagnostic device 1 may acquire data from the database unit 60 via a telecommunications line.

[0027] The acoustic diagnostic device 1 having such a configuration executes the data recording process and the abnormal part / operational abnormality identification process described below to identify the abnormal part or operational abnormality of the equipment A. The operation of the acoustic diagnostic device 1 when executing the data recording process and the abnormal part / operational abnormality identification process will be described below.

[0028] [Data recording processing] First, the operation of the acoustic diagnostic device 1 when executing data recording processing will be described with reference to Figures 2 and 3. Figure 2 is a flowchart showing the flow of data recording processing according to one embodiment of the present invention. Figure 3 is a diagram for explaining data recording processing according to one embodiment of the present invention. The flowchart shown in Figure 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.

[0029] In the processing of step S1, the sensor signal collector 11 uses an acoustic sensor 30a, such as a microphone, installed near equipment A to collect sound pressure signals of the operation sound of equipment A during a predetermined period of normal operation, including k (k≧1) operating sections of pre-classified equipment A, at predetermined time intervals. The sensor signal collector 11 also uses operation monitoring sensors 30b installed on equipment A and equipment related to equipment A to collect operation monitoring sensor signals during a predetermined period of normal operation, including k (k≧1) operating sections of pre-classified equipment A, at predetermined time intervals. The operation monitoring sensor signals are sensor signals other than acoustic sensor signals indicating the status of equipment A and equipment related to equipment A. For example, in the case of a press machine, these signals are load cell signals indicating the press load. Measurement values ​​of the temperature of the material being pressed may also be targeted. In the case of a conveying device, conveying speed signals and the like are also targeted. In the case of a rolling mill, sensor signals indicating the equipment and operational status, as well as measurement values ​​such as acceleration, roll reduction position, roll peripheral speed, and motor torque, are targeted. This completes the process of step S1, and the data recording process proceeds to step S2.

[0030] In the processing of step S2, the operation section sensor signal extraction unit 12 extracts sound pressure signals and operation monitoring sensor signals for each of k types of operation sections from the sound pressure signals and operation monitoring sensor signals collected in the processing of step S1. Specifically, the operation section sensor signal extraction unit 12 reads extraction conditions (extraction start time, extraction end time, etc.) for the sound pressure signals and operation monitoring sensor signals for each of k types of operation sections from the extraction condition database 60a. For example, in the example shown in FIGS. 3(a) to 3(d), the operation section sensor signal extraction unit 12 reads, as extraction conditions, information on the start and end times of operation section 1, in which the load rises and returns to a predetermined value, and the start and end times of operation section 2, in which the load maintains the predetermined value. The operation section sensor signal extraction unit 12 extracts signals that satisfy the extraction conditions from the sound pressure signals and operation monitoring sensor signals collected in the processing of step S1, thereby extracting sound pressure signals for each of k types of operation sections (see FIG. 3(b)). Furthermore, the operation monitoring sensor signal may also include the load signal (load cell signal) shown in Figure 3(a), and in the case of a slab sizing press facility, the load cell signals corresponding to the press loads on both sides of the slab under pressure may also be extracted as operation monitoring sensor signals. This completes the processing of step S2, and the data recording processing proceeds to the processing of step S3.

[0031] In the process of step S3, first, the normal state sound pressure level acquisition unit 13 converts the sound pressure signal for each operation section extracted in the process of step S2 into a sound pressure level for each frequency using a Fourier transform, an octave band filter, or the like. Next, the normal state sound pressure level acquisition unit 13 reads information (such as minimum and maximum frequency values) related to l types (l≧1) of frequency bands used for abnormality detection predetermined for each operation section from the frequency band database 60b. Then, the normal state 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 state sound pressure level acquisition unit 13 acquires the sound pressure level of the normal operation sound for each of 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.

[0032] 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 by operation section and by frequency band, 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 60c. Similarly, the normal distribution determination and recording unit 14 records data on feature quantities such as the average value and standard deviation of each operation monitoring sensor signal under normal conditions in the normal distribution database 60c. The normal distribution determination and recording unit 14 may further classify the sound pressure levels and operation monitoring sensor signals by operating condition, taking into account multiple operating conditions (e.g., m types of operating conditions). This completes the process of step S4, and the data recording process proceeds to the process of step S5.

[0033] In the processing of step S5, the normal distribution determination and recording unit 14 records in the deviation pattern database 60e a deviation pattern table that organizes in advance the relationship between deviation patterns from normal times, which will be described later, and abnormal parts and operation abnormalities of equipment A. In addition, the abnormal operation information recording unit 15 stores in advance in the abnormal operation database 60f an abnormal operation information table (see FIG. 6) that associates various operation abnormalities, abnormal operation indices that can evaluate various operation abnormalities, operation monitoring sensor signals and calculation formulas required to calculate each abnormal operation indices, and related equipment abnormalities. Details of the deviation patterns, deviation pattern table, and abnormal operation information table will be explained in the abnormal part and operation abnormality identification processing below. This completes the processing of step S5, and the series of data recording processes ends.

[0034] [Identification of abnormal parts and operational abnormalities] Next, the operation of the acoustic diagnostic device 1 when executing the abnormal part / operational abnormality identification process will be described with reference to Fig. 4 to Fig. 7. Fig. 4 is a flowchart showing the flow of the abnormal part / operational abnormality identification process according to one embodiment of the present invention. The flowchart shown in Fig. 4 starts when an execution command for the abnormal part / operational abnormality identification process is input to the acoustic diagnostic device 1, and the abnormal part / operational abnormality identification process proceeds to step S11.

[0035] In the processing of step S11, the sensor signal collector 11 uses an acoustic sensor 30a, such as a microphone, installed near the equipment A to collect sound pressure signals of the operation sound of the equipment A during a predetermined period of normal operation, including an operation interval of k types (k≧1) of the equipment A that have been classified in advance, at predetermined time intervals. In addition, the sensor signal collector 11 uses an operation monitoring sensor 30b installed on the equipment A and devices related to the equipment A to collect operation monitoring sensor signals of the equipment A during a predetermined period of normal operation, including an operation interval of k types (k≧1) of the equipment A that have been classified in advance, at predetermined time intervals. This completes the processing of step S11, and the abnormal portion / operation abnormality identification processing proceeds to the processing of step S12.

[0036] In the processing of step S12, the operation section sensor signal extraction unit 12 extracts sound pressure signals and operation monitoring sensor signals for each of k types of operation sections from the sound pressure signals and operation monitoring sensor signals collected in the processing of step S11. Specifically, the operation section sensor signal extraction unit 12 reads extraction conditions (extraction start time, extraction end time, etc.) for the sound pressure signals and operation monitoring sensor signals for each of the k types of operation sections from the extraction condition database 60a. Then, the operation section sensor signal extraction unit 12 extracts sound pressure signals and operation monitoring sensor signals that satisfy the extraction conditions from the sound pressure signals and operation monitoring sensor signals collected in the processing of step S11, thereby extracting sound pressure signals and operation monitoring sensor signals for each of the k types of operation sections. As for the extraction conditions for the sound pressure signals and operation monitoring sensor signals for each of the k types of operation sections, a method of determining the start position and end position of the operation section based on data indicating the operation position of the equipment may be used instead of a method of reading information regarding the start time and end time of each operation section. This completes the process of step S12, and the abnormal portion / operation abnormality identification process proceeds to step S13.

[0037] In the process of step S13, the sound pressure level acquisition unit 16 acquires sound pressure levels for each of k types of operating intervals 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. The sound pressure level acquisition unit 16 may read information on m types of operating conditions that affect the operation of equipment A from the operating condition database 60d, and acquire sound pressure levels for each of k types of operating intervals, l types of frequency bands, and m types of operating conditions. This completes the process of step S13, and the abnormal portion / operation abnormality identification process proceeds to the process of step S14.

[0038] In the processing of step S14, the deviation calculation unit 17 reads information on the sound pressure level distribution during normal operation and its feature quantities from the normal distribution database 60c for each of k types of operation intervals of equipment A and for each of l types of frequency bands. Next, using the read information, the deviation calculation unit 17 calculates the deviation of the sound pressure level of the operation sound acquired in the processing of step S13 relative to the sound pressure level during normal operation for each of k types of operation intervals and l types of frequency bands. Then, using the calculated deviation, the deviation calculation unit 17 determines whether 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 of l types of frequency bands for each operation interval as a deviation pattern. The deviation can be calculated, for example, using the following mathematical formula (1).

[0039]

number

[0040] In the process of step S13, when m types of operating conditions are taken into consideration, the deviation calculation unit 17 calculates the deviation by referring to a data table such as that shown in Fig. 5. The data table shown in Fig. 5 shows information on the sound pressure level distribution and its feature quantities during normal operation for each of k types of operating intervals, l types of frequency bands, and m types of operating conditions. The data table shown in Fig. 5 corresponds to the contents of the extraction condition database 60a, frequency band database 60b, normal distribution database 60c, and operating condition database 60d. This completes the process of step S14, and the abnormal portion / operational abnormality identification process proceeds to the process of step S15.

[0041] In step S15, the operation information detection unit 18 detects operation information for equipment A using the operation monitoring sensor signal extracted in step S12. The operation information is information about a specific operational abnormality, or information indicating normal operation if no operational abnormality is detected. A method for detecting operation information will now be described. Some deviation patterns of acoustic signals associated with specific abnormal locations are identical to deviation patterns of acoustic signals caused by operational abnormalities or operational setting changes. For example, in a sizing press facility that reduces the width of a slab in a steel manufacturing process, if the press load balance on both sides of the slab in the width direction is disrupted, the press noise from one side may become louder. In such a case, adjusting the protrusion margins of the press dies on both sides (OP side and DR side) that reduce the slab width restores the load balance and returns the sound distribution to normal.

[0042] Therefore, such abnormal sounds related to press equipment are abnormal sounds resulting from operational abnormalities, not from equipment abnormalities. However, as the press load increases, the sound pressure level of a specific frequency band of the press sound increases, and a specific deviation pattern is observed. In this case, the press load balance indicator (e.g., the difference or ratio between the two loads) from both sides of the slab is monitored, and if this press load balance indicator falls outside a predetermined range, it is recognized as an operational abnormality. In the case of an operational abnormality, the deviation pattern of the corresponding acoustic signal is treated as an operational abnormality, not an equipment abnormality. Furthermore, ideally, the slab temperature is above a specified value and the slab is soft. However, if the slab is not sufficiently heated and the slab temperature is low, the slab may be too hard to process, resulting in an operational abnormality and an abnormally loud sound. In such cases, the slab temperature is monitored, and if it falls below a predetermined temperature, it is recognized as an operational abnormality, but not an equipment abnormality.

[0043] Therefore, in this embodiment, various operational abnormalities and their respective operational abnormality indices for evaluating the operational abnormalities are defined for the operational information, and an operational abnormality information table that associates the operation monitoring sensor signals and calculation formulas required to calculate each operational abnormality index is stored in advance in the operational abnormality database 60f. Association with related equipment abnormalities is also performed. For example, as shown in FIG. 6, the type of operational abnormality, the operation monitoring sensor signals used to determine the operational abnormality, the operational abnormality index calculation formulas, the determination criteria, and related equipment abnormalities are tabulated and registered as an operational abnormality information table in the operational abnormality database 60f. This process is performed in step S5 of the data recording process, as described above.

[0044] In step S15, the operation information detection unit 18 first reads the operation abnormality information table from the operation abnormality database 60f and calculates, for each section of the extracted operation monitoring sensor signal, an operation abnormality index corresponding to each operation abnormality in the operation abnormality information table, using the operation monitoring sensor signal extracted in step S12. Next, based on the calculation results of the operation abnormality index, the operation information detection unit 18 determines whether the operation abnormality index is equal to or greater than a threshold value, indicating an abnormality, or whether the operation abnormality index is less than the threshold value, indicating a normal state. The operation information detection unit 18 then outputs and records the determination results as operation information for each section of the extracted operation monitoring sensor signal. While the operation information is generated and acquired within the acoustic diagnostic device 1, a separate operation abnormality determination device may be installed to acquire the operation information (presence or absence of various operation abnormalities). This completes step S15, and the abnormality location / operation abnormality identification process proceeds to step S16.

[0045] In the process of step S16, the abnormal part / operational abnormality identifying unit 19 combines the deviation pattern obtained in the process of step S14 with the operation information detected in the process of step S15, and identifies the operation abnormality if it is an operation abnormality, or identifies the abnormal part if it is an equipment abnormality rather than an operation abnormality. FIGS. 7(a) and (b) are diagrams showing an example of searching for equipment abnormalities and operation abnormalities using a table of deviation patterns associated with equipment abnormalities and operation abnormalities. The abnormal part / operational abnormality identifying unit 19 searches for deviation pattern y' similar to deviation pattern y' obtained in the process of step S14. k In the example shown in FIGS. 7(a) and 7(b), the deviation pattern y registered in advance as shown in FIG. k The table shown in FIG. 7(a) is a deviation pattern table, and deviation pattern y1 or deviation pattern y2 is a candidate for a pattern that matches deviation pattern y' shown in FIG. 7(a). If there is no perfectly matching deviation pattern, the similarity of the vectors of the deviation patterns may be evaluated and the most similar deviation pattern may be selected. The abnormal part / operation abnormality identifying unit 19 uses the Euclidean distance as shown in the following formula (2) to identify deviation pattern y'.k The similarity of the deviation pattern y is calculated based on the calculated similarity. k The similarity shown in formula (2) is calculated by the following formula: k The more similar it is to the deviation pattern y, the closer it is to 1. k Information about the abnormal part of equipment A corresponding to the above is read out.

[0046]

number

[0047] Next, the abnormal part / operational abnormality identifying unit 19 refers to the contents of the operation information obtained in the processing of step S15, and identifies the deviation pattern y k The abnormal part / operation abnormality identifying unit 19 determines whether the operation abnormality associated with the deviation pattern y is normal (0) or abnormal (1). k and the corresponding specific operational abnormality (0, 1), it is determined whether a specific equipment abnormality or operational abnormality has occurred, and the determination result is output. In the example shown in FIGS. 7(a) and (b), deviation patterns y1 and y2 are associated with operation information for operational abnormality A and operational abnormality B. In this case, because operational abnormality A is abnormal (1) and operational abnormality B is normal (0), deviation pattern y2 is selected from the candidate deviation patterns y1 and y2. For the selected deviation pattern y2, the determination result is operational abnormality A, so in this case, no abnormal part is output, and operational abnormality A is output as the result. For example, if operational abnormality A is normal (0), deviation pattern y1 is selected, and in that case, an abnormality in part A is identified, and the result is output. This completes the processing of step S16, and the abnormal part / operation abnormality identification processing proceeds to step S17.

[0048] In the process of step S17, the result output unit 50 outputs information about the equipment abnormality or operational abnormality identified in the process of step S16 by displaying it on a display device such as a display. The result output unit 50 may output an emergency stop command for equipment A (equipment to be diagnosed) to a control device of equipment A (equipment to be diagnosed) based on the information about the equipment abnormality or operational abnormality, in order to prevent the occurrence of a serious problem that could damage equipment A. This completes the process of step S17, and ends the series of abnormal part / operation abnormality identification processes.

[0049] As is clear from the above description, in the acoustic diagnostic device 1 according to one embodiment of the present invention, the sensor signal collection unit 11 collects operation monitoring sensor signals for detecting equipment operation information, including equipment operation sounds and operational abnormalities. The operation section sensor signal extraction unit 12 extracts operation sounds and operation monitoring sensor signals for each predetermined operation section of the equipment from the collected operation sounds and operation monitoring sensor signals. The sound pressure level acquisition unit 16 acquires sound pressure levels for each predetermined frequency band for each extracted operation section, and the deviation calculation unit 17 calculates the degree of deviation from the sound pressure level during normal operation for the same equipment, operation section, and frequency band for the acquired sound pressure levels for each operation section and each frequency band. The operation information detection unit 18 then detects operation information based on the operation monitoring sensor signals for each extracted operation section, and the abnormality location / operational abnormality identification unit 19 identifies the abnormal location or operational abnormality of the equipment based on the deviation and the operation information. This prevents operational abnormalities from being mistakenly detected as equipment abnormalities, and prevents unnecessary shutdowns of operations and equipment due to inspections.

[0050] [Example] In this example, the range of the sound pressure level distribution during normal operation of the press equipment was narrowed by limiting the operating conditions of the press equipment. Furthermore, by combining this with the press equipment's operating information, equipment abnormalities and operational abnormalities of the press equipment were distinguished. Figure 8(a) shows the frequency distribution of sound pressure levels (mean: 58.9 dB, standard deviation σ: 4.2) when the operating conditions of the press equipment were not limited. Figure 8(b) shows the frequency distribution of sound pressure levels (mean: 58.9 dB, standard deviation σ: 0.9) when the operating conditions of the press equipment were limited. In this example, the operating conditions were classified by steel type, and all steel types were divided into several groups based on steel types with similar material properties. In this example, the operating conditions were limited to one of these steel type groups.

[0051] Next, we calculated the deviation of the press equipment when the sound pressure level was 63 dB, assuming a die abnormality. Equation (1) above 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 0.2. When the operating conditions were limited, the deviation of the press equipment when the sound pressure level was 63 dB was calculated to be 5.1. Therefore, if the deviation threshold for determining whether an abnormality has occurred in the press equipment is set to 3, an abnormality will be detected when the operating conditions are limited. Next, we will show an example of an operational abnormality due to differences in the die projection adjustment status of the press equipment. Figure 9 shows the change in die adjustment abnormality before and after die projection adjustment. Due to a balance difference between the left and right sides of the die, the die adjustment abnormality value is 1, indicating an abnormality. As shown in Figure 9, when the die projection adjustment is adjusted on December 15, the die adjustment abnormality value becomes 0, and the die adjustment abnormality is no longer an operational abnormality. An example of an abnormality determination, including such an operational abnormality, is shown below.

[0052] Figures 10(a) and 10(b) show an example of a deviation pattern that is determined to be an operational abnormality. The acoustic signal portion of deviation pattern y' shown in Figure 10(a) is similar to the two deviation patterns y1 and y2. However, the values ​​for "die load abnormality" and "slab temperature abnormality" in the operation information for deviation pattern y' shown in Figure 10(a) are "1" and "0," respectively, which match the values ​​for deviation pattern y2 for "die adjustment abnormality." Therefore, the obtained deviation pattern y' is determined to be an operational abnormality, "die adjustment abnormality," rather than a die abnormality in the press equipment. Die load abnormality is an indicator that is determined to be an abnormality when the load balance difference between the left and right sides of the die is greater than or equal to a predetermined value. In contrast, in the example shown in Figures 11(a) and 11(b), the values ​​for "die abnormality" and "slab temperature abnormality" in the operation information for deviation pattern y' are "0." Therefore, in this example, the obtained deviation pattern y' matches deviation pattern y1 for "die abnormality," and is therefore determined to be a die abnormality in the press equipment, rather than a "die adjustment abnormality."

[0053] Although the present invention has been described above as an embodiment, the present invention is not limited to the description and drawings that form part of the disclosure of the present invention. In other words, other embodiments, examples, and operational techniques that can be made by those skilled in the art based on the present invention are all included in the scope of the present invention. [Industrial Applicability]

[0054] According to the present invention, it is possible to provide an acoustic diagnostic device and an acoustic diagnostic method that can prevent operational abnormalities from being mistakenly detected as equipment abnormalities and prevent operation and equipment shutdowns due to unnecessary inspections. [Explanation of symbols]

[0055] 1 Acoustic diagnostic equipment 11 Sensor signal collection unit 12. Motion section sensor signal extraction section 13 Normal sound pressure level acquisition unit 14 Normal Distribution Determination and Recording Section 15. Operational abnormality information recording section 16 Sound pressure level acquisition unit 17 Deviation calculation section 18 Operation information detection unit 19 Abnormal parts / operation abnormality identification parts 20 Result output section 30a Acoustic Sensor 30b Operation monitoring sensor 60 Database Department 60a Extraction condition database 60b frequency band database 60c Normal Distribution Database 60d Operating Conditions Database 60e Deviation Pattern Database 60f Operational Anomaly Database A. Equipment to be diagnosed

Claims

1. a sensor signal collecting unit that collects operation monitoring sensor signals for detecting operation information of the equipment, including operation sounds and abnormal operations of the equipment; an operation section sensor signal extracting unit that extracts operation sounds and operation monitoring sensor signals for each predetermined operation section of the equipment from the operation sounds and operation monitoring sensor signals collected by the sensor signal collecting unit; a sound pressure level acquisition unit that acquires a sound pressure level for each frequency band predetermined for each movement section for the movement sound extracted by the movement section sensor signal extraction unit; a deviation calculation unit that calculates a deviation of the sound pressure level for each operation section and each frequency band 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 operation information detection unit that detects the operation information based on the operation monitoring sensor signal for each operation section extracted by the operation section sensor signal extraction unit; an abnormality portion / operational abnormality identifying unit that determines an abnormal portion or an operational abnormality of the equipment based on the deviation degree and the operation information; An acoustic diagnostic device comprising:

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

3. 3. The acoustic diagnostic device according to claim 2, wherein the operation information detection unit acquires, for each operation abnormality, information on the operation monitoring sensor signals and a method for processing the operation monitoring sensor signals for calculating an operation abnormality index used to determine an operation abnormality, calculates an operation abnormality index for each operation index based on the acquired information, and determines the presence or absence of each operation abnormality using the calculated operation abnormality index.

4. 4. The acoustic diagnostic device according to claim 3, wherein the abnormal portion / operational abnormality identifying unit identifies an abnormal portion or an operational abnormality of the equipment based on a pattern of deviation degrees, the pattern being based on the deviation degrees in each frequency band obtained for each operation section, and on the presence or absence of the operational abnormality.

5. a database unit for storing data necessary for identifying conditions for collecting operation sounds of the equipment, abnormal parts of the equipment, and operational abnormalities; the abnormal portion / operational abnormality identification unit identifies the abnormal portion or the operational abnormality of the equipment by referring to a table stored in the database unit and including information indicating a relationship between the operation section and the deviation degree pattern and the abnormal portion of the equipment and the type of operational abnormality; The acoustic diagnostic device according to claim 4.

6. the table includes information indicating a relationship between the operation section, the pattern of the deviation degree, and the operating conditions of the equipment, and an abnormal portion and an operational abnormality of the equipment; the abnormal portion / operational abnormality identifying unit refers to the table and identifies an abnormal portion or an operational abnormality of the equipment in consideration of the operation section, the pattern of the deviation degree, and the operating conditions of the equipment. The acoustic diagnostic device according to claim 5 .

7. a result output unit that outputs information about the identified abnormal portion or operational abnormality of the equipment; 7. The acoustic diagnostic device according to claim 5, wherein the result output unit outputs an alert based on information relating to an abnormal portion or an operational abnormality of the equipment, or outputs a command to a control device of the equipment to stop the equipment.

8. a normal state sound pressure level acquisition unit that acquires a sound pressure level for each predetermined frequency band for each operation section during normal operation of the equipment, for the operation sound for each operation section extracted by the operation section sensor signal 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; 7. The acoustic diagnostic device according to claim 1, comprising:

9. 9. The acoustic diagnostic device according to claim 8, 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 sound pressure levels during normal operation in each frequency band obtained for each operating section and abnormal parts of the equipment, and information relating the pattern to operational abnormalities of the equipment.

10. 9. The acoustic diagnostic device according to claim 8, wherein the normal distribution determination and recording unit records a table including information indicating the operation section, the pattern of the deviation degree, and a relationship between the operating conditions of the equipment and an abnormal portion and an operational abnormality of the equipment.

11. a sensor signal collecting step of collecting operation monitoring sensor signals for detecting operation information of the equipment including operation sounds and operation abnormalities of the equipment; an operation section sensor signal extraction step of extracting operation sounds and operation monitoring sensor signals for each predetermined operation section of the equipment from the operation sounds and operation monitoring sensor signals collected in the sensor signal collection step; a sound pressure level acquiring step of acquiring a sound pressure level for each frequency band predetermined for each movement section for the movement sound extracted in the movement section sensor signal extracting step; a deviation calculation step of calculating a deviation of the sound pressure level for each operation section and each frequency band 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 operation information detection step of detecting the operation information based on the operation monitoring sensor signal for each operation section extracted in the operation section sensor signal extraction step; an abnormal portion / operational abnormality identifying step of determining an abnormal portion or an operational abnormality of the equipment based on the deviation degree and the operation information; An acoustic diagnostic method comprising:

12. 12. The acoustic diagnosis method according to claim 11, further comprising a step of outputting an alert regarding the abnormal portion or operational abnormality of the equipment, or outputting a command to a control device of the equipment to stop the equipment, based on the determination of the abnormal portion or operational abnormality of the equipment.

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