Acoustic diagnostic device and acoustic diagnostic method

Abstract

To provide an acoustic diagnostic device and acoustic diagnostic method capable of accurately identifying abnormalities in each piece of equipment constituting a group of equipment, as well as abnormalities in the components that make up each piece of equipment. [Solution] The acoustic diagnostic device according to the present invention comprises: an operating sound collection unit for collecting operating sounds of equipment; an equipment start time acquisition unit for identifying the start time of the equipment; an operating section operating sound extraction unit for extracting operating sounds for each operating section of the equipment from the operating sounds collected by the operating sound collection unit, based on the start time of the equipment identified by the equipment start time acquisition unit and predetermined operating sections of the equipment; a sound pressure level acquisition unit for acquiring the sound pressure level for each predetermined frequency band for each operating section of the equipment extracted by the operating section operating sound extraction unit; a deviation degree calculation unit for calculating the deviation degree of the sound pressure level for each operating section and each frequency band acquired by the sound pressure level acquisition unit; and an abnormality detection unit for detecting an abnormality of the equipment based on the deviation degree calculated by the deviation degree calculation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 describes a diagnostic apparatus that diagnoses a diagnostic target by comparing an observation waveform observed from the diagnostic target with a reference waveform. Patent Document 2 describes a state estimation apparatus that estimates a change in the state of equipment based on waveform and frequency information detected by a plurality of sensors provided in the equipment. Patent Document 3 describes a diagnostic apparatus that detects chatter of a rolling roll using the frequency of an acoustic signal detected by an acoustic sensor provided in a rolling mill.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the apparatuses described in Patent Documents 1 to 3, particularly for equipment composed of a plurality of parts, there is a possibility that the abnormal part of the equipment cannot be accurately specified. Specifically, when the equipment is composed of a plurality of parts such as bearings, reducers, cranks, molds, rolls, and blocks, the timing of the generation of operating sounds varies depending on the parts. Therefore, if the timing and frequency at which abnormal sounds occur are not considered, it is impossible to know from which part the abnormal sound is generated, and there is a possibility that the abnormal part of the equipment cannot be accurately specified.

[0005] Furthermore, a rolling mill in the steelmaking process is an example of a group of equipment composed of multiple pieces of equipment. For example, a hot rolling mill consists of various pieces of equipment, such as a device to remove scale from sheet bars before rolling, a crop shear to cut the crop at the leading and trailing ends of the sheet bars, a heating device for the sheet bars, multiple rolling mills, numerous steel sheet cooling devices installed downstream of the rolling mills, and a coiler to wind up the rolled steel sheets. In such a group of equipment composed of multiple pieces of equipment, the devices described in Patent Documents 1 to 3 may not be able to accurately identify which piece of equipment is malfunctioning, as described above.

[0006] The present invention was made to solve the above problems, and its objective is to provide an acoustic diagnostic device and acoustic diagnostic method that can accurately identify abnormalities in each piece of equipment constituting a group of equipment, as well as abnormalities in the components constituting each piece of equipment. [Means for solving the problem]

[0007] The acoustic diagnostic device according to the present invention is an acoustic diagnostic device that identifies abnormal parts of equipment for a plurality of pieces of equipment constituting a group of equipment using acoustic signals during the operation of the equipment and start signals indicating the start of the equipment, and comprises: an operation sound collection unit that collects the operation sounds of the equipment; an equipment start time acquisition unit that acquires the start signals of the equipment and identifies the start time of the equipment based on the acquired start signals; an operation section operation sound extraction unit that extracts the operation sounds of the equipment for each operation section from the operation sounds collected by the operation sound collection unit based on the start time of the equipment identified by the equipment start time acquisition unit and predetermined operation sections of the equipment; a sound pressure level acquisition unit that acquires the sound pressure level for each predetermined frequency band for each operation section of the equipment extracted by the operation section operation sound extraction unit; a deviation degree calculation unit that calculates the deviation degree of the sound pressure levels for each operation section and each frequency band acquired by the sound pressure level acquisition unit; and an abnormality detection unit that detects abnormalities in the equipment based on the deviation degree calculated by the deviation degree calculation unit.

[0008] The deviation calculation unit may calculate the degree of deviation of the sound pressure levels for each operating section and each frequency band acquired by the sound pressure level acquisition unit from the sound pressure levels for each operating section and each frequency band acquired during the normal operation of the equipment.

[0009] It is preferable to include an abnormal part identification unit that identifies the abnormal part of the equipment based on the deviation degree calculated by the deviation degree calculation unit.

[0010] The abnormal part identification unit may identify the abnormal part of the equipment based on a pattern of deviations, which is composed of the deviations in each frequency band determined for each operating section.

[0011] The system includes a database unit that stores data necessary for collecting operating sounds of the equipment and identifying abnormal parts of the equipment. The abnormal part identification unit identifies the abnormal parts of the equipment by referring to a table containing information that shows the relationship between the operating interval and deviation degree patterns stored in the database unit and the abnormal parts of the equipment.

[0012] The table includes information showing the relationship between the operating interval, the deviation pattern, and the operating conditions of the equipment and the abnormal part of the equipment. The abnormal part identification unit may refer to the table and identify the abnormal part of the equipment, taking into account the operating interval, the deviation pattern, and the operating conditions of the equipment.

[0013] The system may include: a normal sound pressure level acquisition unit that acquires the sound pressure level for each predetermined frequency band for each operating section of the operating sound extracted by the operating section sound extraction unit; and 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 them as the sound pressure levels during normal operation of the equipment.

[0014] The normal distribution determination and recording unit may record a table for each piece of equipment that includes information showing the relationship between the deviation pattern, which is determined by the degree of deviation of the sound pressure level from the normal sound pressure level in each frequency band obtained for each operating interval, and the abnormal part of the equipment.

[0015] If the equipment involves rotational movement, the abnormal part identification unit may identify the abnormal part of the equipment by classifying the sound pressure level by the rotational order obtained by normalizing the frequency by the rotational speed.

[0016] The aforementioned group of equipment is a hot rolling mill, and may include a rolling mill reduction device, rolling rolls, rolling roll motor, rolling work roll shift device, scale removal device, roll coolant device, and strip coolant device.

[0017] The acoustic diagnostic method according to the present invention is an acoustic diagnostic method for identifying abnormal parts of equipment for a plurality of equipment constituting a group of equipment, using acoustic signals during equipment operation and start signals indicating the start of equipment, and includes: an operation sound collection step for collecting the operating sounds of the equipment; an equipment start time acquisition step for acquiring the start signals of the equipment and identifying the start time of the equipment based on the acquired start signals; an operation section operation sound extraction step for extracting the operating sounds of the equipment for each operation section from the operation sounds collected in the operation sound collection step, based on the start time of the equipment identified in the equipment start time acquisition step and predetermined operating sections of the equipment; a sound pressure level acquisition step for acquiring the sound pressure level for each predetermined frequency band for each operating section of the equipment extracted in the operation section operation sound extraction step; a deviation degree calculation step for calculating the deviation degree of the sound pressure level for each operating section and each frequency band acquired in the sound pressure level acquisition step; and an abnormality detection step for detecting an abnormality of the equipment based on the deviation degree calculated in the deviation degree calculation step.

[0018] It is preferable to include an abnormal part specifying step of specifying an abnormal part of the facility based on the deviation degree calculated in the deviation degree calculation step.

[0019] For the operating sound of each operation section cut out in the operation section operating sound cut-out step, a normal sound pressure level acquisition step of acquiring the sound pressure level for each frequency band predetermined for each operation section, and classifying the sound pressure level acquired in the normal sound pressure level acquisition step for each facility, each operation section, and each frequency band, and recording it as the sound pressure level during normal operation of the facility, may be included.

Effect of the Invention

[0020] According to the acoustic diagnosis apparatus and the acoustic diagnosis method according to the present invention, it is possible to accurately specify an abnormality of each facility constituting a facility group and an abnormality of components constituting each facility.

Brief Description of the Drawings

[0021] [Figure 1] FIG. 1 is a block diagram showing the configuration of an acoustic diagnosis apparatus according to an embodiment of the present invention. [Figure 2] FIG. 2 is a flowchart showing the flow of data recording processing according to an embodiment of the present invention. [Figure 3] FIG. 3 is a flowchart showing the flow of abnormality detection / abnormal part specifying processing according to an embodiment of the present invention. [Figure 4] FIG. 4 is a flowchart showing the flow of time measurement processing according to an embodiment of the present invention. [Figure 5] FIG. 5 is a diagram showing an example of a data table. [Figure 6] FIG. 6 is a diagram for explaining abnormal part specifying processing using a deviation pattern. [Figure 7] FIG. 7 is a diagram showing the sound pressure level distribution when the operating conditions of a rolling mill are not limited and when they are limited. [Figure 8]Figure 8 shows the change in frequency and rotational order with respect to the rotational speed of a rotating body. [Figure 9] Figure 9 shows the sound pressure levels during normal and abnormal conditions in the embodiment. [Modes for carrying out the invention]

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

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

[0024] As shown in Figure 1, the acoustic diagnostic device 1, which is one embodiment of the present invention, is a device that identifies abnormal parts of each piece of equipment using acoustic signals during operation and sensor signals indicating the start of each piece of equipment that constitute the manufacturing equipment A to be diagnosed, and is composed of an information processing device such as a computer. Here, each piece of equipment that constitutes the manufacturing equipment A is composed of multiple parts, and examples of such manufacturing equipment A include a collection of equipment in the steelmaking process such as a hot rolling mill, a cold rolling mill, and a continuous annealing mill. Examples of equipment that constitute the hot rolling mill include the rolling mill's reduction device, rolling rolls, rolling roll motor, rolling work roll shift device, scale removal device, roll coolant device, and strip coolant device.

[0025] The acoustic diagnostic device 1 comprises an operating sound collection unit 1a, an equipment startup time acquisition unit 1b, an operating section operating sound extraction unit 1c, a normal sound pressure level acquisition unit 1d, a normal distribution determination and recording unit 1e, a sound pressure level acquisition unit 1f, a deviation degree calculation unit 1g, an abnormality detection unit 1h, and an abnormality location identification unit 1i. Each of these units is realized by a computing processing unit such as a CPU within the 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 1j. The database unit 1j is composed of a storage device such as an HDD that constitutes the information processing device, and includes a extraction condition database 1j1, a frequency band database 1j2, a normal distribution database 1j3, an operating condition database 1j4, and a deviation pattern database 1j5. Details of each database will be described later. Alternatively, the database unit 1j may be located outside the acoustic diagnostic device 1, and the acoustic diagnostic device 1 may be configured to acquire data from the database unit 1j via a telecommunications line.

[0027] The acoustic diagnostic device 1, having this configuration, detects abnormalities in equipment B, which constitutes manufacturing equipment A, and identifies the abnormal parts by performing the data recording process and abnormality detection / abnormal part identification process described below. The operation of the acoustic diagnostic device 1 when performing the data recording process and abnormality detection / abnormal part identification process will be explained below.

[0028] [Data recording processing] First, with reference to Figure 2, the operation of the acoustic diagnostic device 1 when performing data recording processing will be explained. Figure 2 is a flowchart showing the flow of 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 step S1, the sound collection unit 1a collects acoustic signals of the operating sounds of multiple pieces of equipment B that constitute the manufacturing equipment A at predetermined time intervals, using acoustic sensors C (C1, C2, C3, ..., CN), such as microphones, installed in their vicinity, during a predetermined period of normal operation that includes k types (k≧1) of operation intervals for each piece of equipment classified in advance. With this, the process of step S1 is completed, and the data recording process proceeds to step S2.

[0030] In step S2, the equipment start time acquisition unit 1b identifies the start time of each piece of equipment by using equipment signals or sensor signals that can determine the start time of each piece of equipment as trigger signals to know when each piece of equipment starts. Examples of signals that can determine the start time of each piece of equipment include, for a scale removal device using high-pressure water spray in a hot rolling mill, a spray start signal indicating the start of spray water discharge; for a rolling mill, a metal-in signal indicating the timing when the material to be rolled is loaded into the rolling mill; a work roll shift signal for shifting the work rolls of the rolling mill; a work roll peripheral speed or acceleration signal to know the acceleration timing of the work rolls; a roll coolant signal; a strip coolant signal, etc. The equipment start time acquisition unit 1b identifies the start time of each piece of equipment as the timing when these signals exceed predetermined values. With this, the process in step S2 is completed, and the data recording process proceeds to the process in step S3.

[0031] In step S3, the operating section sound extraction unit 1c extracts k types of acoustic signals for each operating section from the sound pressure signal collected in step S1. Specifically, the extraction condition database 1j1 records the association of each acoustic sensor C with the target equipment, and further records the operating section from which the acoustic signal is extracted for each acoustic sensor C. It also records the correspondence between each piece of equipment and the sensor signal or equipment signal used to determine the start time of each piece of equipment. The operating section sound extraction unit 1c refers to the extraction condition database 1j1 to determine the start time and extraction section for each acoustic signal from the start time of each piece of equipment identified in step S2, and extracts the acoustic signal based on the identified start time and extraction section. Note that the start time of the operating section does not necessarily have to coincide with the start time of each piece of equipment; the operating section may be the time before or after the start time of the equipment. Furthermore, if a fixed time width cannot be determined in advance for the operating interval, the sensor signal for determining the operating interval and its determination criteria are recorded in the extraction condition database 1j1, and the operating interval operating sound extraction unit 1c extracts the acoustic signal by referring to this information. With this, the processing in step S3 is completed, and the data recording process proceeds to the processing in step S4.

[0032] Note that the processes in steps S2 to S4 are executed sequentially each time sensor signals and acoustic signals related to equipment B are acquired, but they may be executed simultaneously for multiple pieces of equipment B if their start-up timings are simultaneous.

[0033] In step S4, the normal sound pressure level acquisition unit 1d first converts the acoustic signals for each operating section extracted in step S3 into sound pressure levels relative to their frequencies, using Fourier transforms, octave band filters, etc. Next, the normal sound pressure level acquisition unit 1d reads information (minimum and maximum frequencies, etc.) for l types (l≧1) of frequency bands used for anomaly detection, predetermined for each operating section, from the frequency band database 1j2. Then, the normal sound pressure level acquisition unit 1d uses a bandpass filter, etc., to obtain the sound pressure levels for each of the l types of frequency bands (sound pressure levels of normal operating sounds) from the converted sound pressure level data. With this, the process of step S4 is completed, and the data recording process proceeds to step S5.

[0034] In step S5, the normal distribution determination and recording unit 1e classifies the sound pressure levels of the normal operating sounds acquired in step S4 for each operating section and frequency band, and records the sound pressure level distribution of the normal operating sounds and its feature quantities (mean value, standard deviation, etc.) in the normal distribution database 1j3. With this, the process of step S5 is completed, and the series of data recording processes is finished.

[0035] In addition to recording data such as the sound pressure level of normal operation sounds during data recording, the data table summarizing the relationship between deviation patterns from normal operation and abnormal parts, as described later, is pre-stored in the deviation pattern database 1j5. The deviation patterns and their registration details will be explained in the abnormality detection and abnormal part identification process below.

[0036] [Anomaly detection and abnormal part identification processing] Next, with reference to Figures 3 to 6, the operation of the acoustic diagnostic device 1 when performing abnormality detection and abnormal location identification processing will be explained. Figure 3 is a flowchart showing the flow of abnormality detection and abnormal location identification processing, which is one embodiment of the present invention. The flowchart shown in Figure 3 starts when an execution command for abnormality detection and abnormal location identification processing is input to the acoustic diagnostic device 1, and the abnormality detection and abnormal location identification processing proceeds to step S11.

[0037] In step S11, the sound collection unit 1a collects acoustic signals of the operating sounds of multiple pieces of equipment B that constitute the manufacturing equipment A at predetermined time intervals, using acoustic sensors installed near each piece of equipment, within a predetermined period that includes the k types (k≧1) of operating intervals for each piece of equipment that have been classified in advance. With this, the process of step S11 is completed, and the abnormality detection and abnormality location identification process proceeds to step S12.

[0038] In step S12, the equipment start time acquisition unit 1b identifies the start time of each piece of equipment using equipment signals or sensor signals that can determine the start time of each piece of equipment. The flow of this step S12 is the same as the flow of step S2 described above, so a detailed explanation is omitted. With this, the process of step S12 is completed, and the abnormality detection and abnormality location identification process proceeds to step S13.

[0039] In step S13, the operating section sound extraction unit 1c extracts k types of acoustic signals for each operating section from the acoustic signals collected in step S11. In a hot rolling mill, multiple rolling mills are arranged in tandem, and the materials to be rolled are sequentially loaded into each rolling mill and rolled. The following describes a method for determining the time length, i.e., the extraction conditions, for extracting acoustic signals to capture the jamming sound of the materials to be rolled in adjacent rolling mills, with reference to Figure 4.

[0040] Figure 4 is a flowchart showing the flow of time measurement processing after metal-in at each rolling mill. In the time measurement processing shown in Figure 4, first, the detection of a metal-in signal at rolling mill F1 is used as the trigger detection (step S21: Yes), and the time T1 at which the metal-in signal is detected at rolling mill F1 is measured as the start time (step S22). Next, the detection of a metal-in signal at rolling mill F2 is used as the trigger detection (step S23: Yes), and the time T2 at which the metal-in signal is detected at rolling mill F2 is measured as the start time (step S24). Similarly, for all rolling mills, the time at which the metal-in signal is detected is measured as the start time. Once the time measurement processing shown in Figure 4 is completed, information regarding the operating interval (time length) Ts from when the operating sound (e.g., rolling sound, roll coolant sound, work roll peripheral speed acceleration sound, etc.) is generated until it disappears is obtained from the extraction condition database 1j1. Then, acoustic signals within the range of the operating interval Ts are extracted from the time T (=T1, T2,…) measured in the time measurement processing.

[0041] Furthermore, the start time of the operating interval does not necessarily have to coincide with the start time of each piece of equipment; the operating interval may be the time before and after the start time of the equipment. Also, the trigger signal may be set to capture a change in the state of the equipment (operating interval). For example, in hot rolling, the rolling speed may be accelerated midway through. In that case, the rotational speed of the rolling roll motor, such as the peripheral speed of the work roll, can be monitored, and its acceleration can be used as a trigger signal. By setting a predetermined threshold for the acceleration, the timing of the change in the acceleration state can be determined. In this case, a different operating interval can be set based on the acceleration sound of the rolling roll motor during acceleration and used for abnormality diagnosis. With this, the processing in step S13 is completed, and the abnormality detection and abnormality location identification process proceeds to the processing in step S14.

[0042] In step S14, the sound pressure level acquisition unit 1f acquires the sound pressure level for each of k operating sections and each of l frequency bands from the acoustic signal of the operating sound extracted in step S13. The flow of this step S14 is the same as the flow of step S4 described above, so a detailed explanation is omitted. Alternatively, the sound pressure level acquisition unit 1f may read information on m types of operating conditions that affect the operation of each piece of equipment from the operating condition database 1j4 and acquire the sound pressure level for each of k operating sections, each of l frequency bands, and each of m types of operating conditions. With this, the processing of step S14 is completed, and the abnormality detection and abnormality location identification process proceeds to step S15.

[0043] In step S15, the deviation calculation unit 1g reads information about the sound pressure level distribution and its features during normal operation from the normal distribution database for each of the k operating intervals and l frequency bands. Next, using the read information, the deviation calculation unit 1g calculates two or more deviations from the sound pressure level of the operating sound acquired in step S13 relative to the sound pressure level during normal operation, for each of the k operating intervals and l frequency bands. The deviation can be calculated, for example, by the formula (1) shown below. The formula for calculating the deviation is not limited to formula (1), and depending on the characteristics of the detected signal, for example, the denominator may be a power such as the square of the standard deviation, or other adjustment coefficients may be multiplied by the whole. In addition, the sound pressure level measurement values ​​for the target operating interval and target frequency band may be statistical values ​​such as the average value of the measurement values ​​within the operating interval.

[0044]

number

[0045] Furthermore, if m types of operating conditions are considered in the process of step S14, the deviation calculation unit 1g calculates the deviation by referring to a data table as shown in Figure 5. The data table shown in Figure 5 shows information on the sound pressure level distribution and its features during normal operation for each k types of operating intervals, each l type of frequency band, and each m type of operating condition. The data table shown in Figure 5 corresponds to the contents of the extraction condition database 1j1, the frequency band database 1j2, the normal distribution database 1j3, and the operating condition database 1j4. With this, the process of step S15 is completed, and the anomaly detection and anomaly location identification process proceeds to the process of step S16.

[0046] In step S16, the anomaly detection unit 1h calculates the degree of deviation and determines that if the degree of deviation exceeds a predetermined threshold, it is abnormal (1), and otherwise it is normal (0). The threshold can be set based on, for example, 3σ, which is about three times the standard deviation σ, but it is not limited to this and can be adjusted and determined while looking at the characteristics of the operational data. The anomaly detection unit 1h uses the calculated degree of deviation to determine whether the equipment is normal (0) or abnormal (1) for each degree of deviation, and generates an array pattern vector y' of the determination results (0,1) for each of the l types of frequency bands in each operating section as a deviation pattern. With this, the processing of step S16 is completed, and the anomaly location identification process proceeds to step S17.

[0047] In step S17, the abnormal location identification unit 1i identifies the abnormal location of each piece of equipment constituting manufacturing equipment A based on the deviation pattern y' generated in step S16. Specifically, as shown in Figures 6(a) and (b), the abnormal location identification unit 1i identifies the operating interval and deviation pattern y' which are registered in the deviation pattern database 1j5 in advance. k From the data table showing the relationship between the abnormal parts of each piece of equipment that make up manufacturing equipment A, deviation pattern y' is similar to deviation pattern y kThe system searches for abnormalities. The data table shown in Figure 6(b) records deviation patterns obtained when equipment malfunctions occur and organizes them in association with the abnormal location. This data table is created in advance and stored in the deviation pattern database 1j5 by the normal distribution determination and recording unit 1e. In addition, the data table can be configured to identify abnormal locations based on deviation patterns that take into account k types of operating intervals and l types of frequency bands, as well as m types of operating conditions. Therefore, the abnormal location identification unit 1i may also identify abnormal locations of each piece of equipment constituting manufacturing equipment A by taking m types of operating conditions into account.

[0048] Furthermore, the abnormal area identification unit 1i uses the Euclidean distance to identify the deviation pattern y, as shown in the following formula (2). k The similarity of the deviation pattern y' to is calculated, and similar deviation patterns y are identified based on the calculated similarity. k It is recommended to explore this. The similarity shown in formula (2) is given by the same result when deviation pattern y' is the same as deviation pattern y k The closer it is to 1, the more similar it is to the original. The abnormal site identification unit 1i then identifies the discovered deviation pattern y k Information regarding the abnormal part of the equipment corresponding to the deviation pattern is read out. For example, in the example shown in Figures 6(a) and (b), the deviation pattern y' and deviation pattern y2 generated in step S14 match, so the abnormal part identification unit 17 identifies part B, which corresponds to deviation pattern y2, as the abnormal part of the equipment. With this, the process in step S15 is completed, and the series of abnormality detection and abnormal part identification processes are finished.

[0049]

number

[0050] As is clear from the above explanation, in the acoustic diagnostic device 1, which is one embodiment of the present invention, first, the operating sound collection unit 1a collects the operating sound of equipment B. Next, the equipment start time acquisition unit 1b acquires the start signal of equipment B and identifies the start time of equipment B based on the acquired start signal. Next, the operating section operating sound extraction unit 1c extracts the operating sound for each operating section of equipment B from the operating sound collected by the operating sound collection unit 1a, based on the start time of equipment B identified by the equipment start time acquisition unit 1b and the predetermined operating sections of equipment B. Next, the sound pressure level acquisition unit 1f acquires the sound pressure level for each predetermined frequency band for each operating section of equipment B, for the operating sound extracted by the operating section operating sound extraction unit 1c. Next, the deviation degree calculation unit 1g calculates the deviation degree of the sound pressure level for each operating section and each frequency band acquired by the sound pressure level acquisition unit 1f. Then, the abnormality detection unit 1h detects an abnormality in equipment B based on the deviation degree calculated by the deviation degree calculation unit 1g. This allows for the accurate identification of abnormalities in each piece of equipment that makes up the equipment group, as well as abnormalities in the components that make up each piece of equipment.

[0051] [Variation] In rolling mills, there are operations where the rotational speed of the rolling rolls changes as the rolling process progresses. The rolling rolls are rotated by the rolling motor, and the frequency of the operating noise generated changes depending on the rotational speed. Therefore, if abnormality diagnosis of a rolling mill is performed by classifying conditions by frequency band, it becomes necessary to classify conditions finely according to the rotational speed, which increases the number of deviation patterns and may hinder abnormality diagnosis. Accordingly, when the equipment being diagnosed involves rotational movement, it is better to classify conditions by the rotational order (frequency / rotational speed), which is obtained by dividing the frequency by the rotational speed of the rolling motor (e.g., rpm), rather than by frequency. This normalizes the frequency change of the noise generated due to the rotational change, minimizes the number of classifications, and improves the accuracy of abnormality detection. Note that for equipment involving specific rotations, one rotational order classification may be used instead of one classification by frequency band.

[0052] [Example 1] In this embodiment, acoustic sensors were installed around the rolling mill, and the sound of steel plates getting caught in the work rolls was extracted based on the metal-in signal. The deviation pattern of the normal sound pressure distribution after frequency conversion was obtained. The sound extraction interval was defined as t1 seconds before and t2 seconds after the time the metal-in signal was turned on, and sound with a data length of t1 + t2 seconds was acquired. In addition, times t1 and t2 were adjusted so that the sounds of the metal plates getting caught from adjacent rolling mills were not mixed. Specifically, when extracting the sound of the metal plates getting caught from rolling mill F1, times t1 and t2 were adjusted to a time when the sound of the metal plates getting caught from rolling mill F2 was not included. The frequency band of the metal plates getting caught was set within the range of the lower and upper limits of the frequency at which the metal plates getting caught is present, centered on the frequency at which the sound pressure is maximum. Figure 7(a) shows the sound pressure level distribution when there are no limitations on the operating conditions of the rolling mill, and Figure 7(b) shows the sound pressure level distribution when the operating conditions of the rolling mill are limited. As shown in Figures 7(a) and (b), it can be seen that by limiting the operating conditions, the number of coils with the assumed abnormal sound pressure level (83 dB) can be clearly separated. The operating conditions were limited to one of the following categories: steel type p (=1,2,3,…) category × reduction ratio q (=1,2,3,…) category. In addition, the change in sound pressure level associated with changes in steel type material and reduction ratio was investigated, and the points where changes were observed were reflected as categories. However, the operating conditions may be divided into multiple categories. Then, work roll splitting was assumed by dividing into two deviation patterns, and the degree of deviation when the sound pressure level in a certain band reaches 83 dB was calculated. The following formula (3) was used to calculate the degree of deviation. As a result, the degree of deviation when there is no limitation of operating conditions was 1.7, and the degree of deviation when operating conditions are limited was 4.0, confirming that the accuracy of abnormality diagnosis is improved by limiting the operating conditions. Specifically, by setting the threshold for the degree of abnormality to be judged as abnormal to 4, abnormalities can be judged with high accuracy.

[0053]

number

[0054] [Example 2] In this embodiment, the frequency of the sound pressure level that changes in response to the rotational speed of the rotating body and the changes in the sound pressure level in each frequency band were organized by rotational order. The rotational order was defined as the first rotational component, where one rotation is considered one period, and the nth rotational component is the nth rotational component. Figures 8(a) and (b) show the changes in frequency and rotational order with respect to the rotational speed of the rotating body. In the example shown in Figure 8(a), the frequency changes according to the rotational speed, but as shown in Figure 8(b), by converting to the rotational order, the rotational order does not change even if the rotational speed changes. Therefore, by monitoring the sound pressure level by rotational order rather than the sound pressure level by frequency through rotational order conversion, equipment abnormalities can be detected accurately without increasing the number of abnormal patterns. Furthermore, by further classifying according to the rotational speed, even more accurate abnormality diagnosis can be performed.

[0055] [Example 3] In this embodiment, an abnormality was detected in a high-pressure spray water supply system for scale removal of sheet bars, installed on the entry side of the finishing rolling mill of a hot rolling mill. In a normally operating high-pressure spray water supply system, after high-pressure spray water is injected in conjunction with the insertion of the sheet bar, an acoustic signal with a high sound pressure level is obtained for a predetermined time in a frequency band dependent on the diameter of the spray nozzle. Therefore, in this embodiment, an acoustic signal was acquired using the signal for the start of high-pressure spray water injection in conjunction with the insertion of the sheet bar as a trigger signal, and sound pressure data for a predetermined time corresponding to the predetermined operating interval for each piece of equipment was acquired, and the sound pressure level for each frequency band was obtained. As a result, as shown in Figure 9, in the initial normal operation, the sound pressure level around 3 kHz was high, but in the time period of 0.5 seconds or more when a minute hole occurred in the spray water piping, the sound pressure level around 8 kHz was also high. In this case, if we divide the frequency band into five bands as follows: below 1.25Hz, between 1.25Hz and 2.5Hz, between 2.5Hz and 5Hz, between 5Hz and 10Hz, and above 10Hz, the sound pressure level vector under normal conditions will be

[0100] T (where T represents transposition), and the sound pressure level vector under abnormal conditions will be

[0110] T. Therefore, the deviation pattern will be

[0010] T, and we searched the deviation pattern database 1j5 to identify the abnormality corresponding to this deviation pattern as the generation of minute holes in the spray water piping of the scale removal device. The deviation pattern database 1j5 also contained data where the frequency band of the abnormal sound was changed according to the size of the minute holes.

[0056] Although embodiments applying the invention made by the present inventors have been described above, the present invention is not limited by the descriptions and drawings that constitute part of the disclosure of the present invention in this embodiment. That is, all other embodiments, examples, and operational techniques made by those skilled in the art based on this embodiment are included in the scope of the present invention. [Explanation of Symbols]

[0057] 1. Acoustic diagnostic equipment 1a Sound collection unit 1b Equipment Startup Time Acquisition Section 1c Operating section, sound extraction section 1d Normal sound pressure level acquisition unit 1e Normal Distribution Determination and Recording Section 1f Sound pressure level acquisition unit 1g Deviation calculation unit 1h Anomaly detection unit 1i Abnormal part identification part 1j Database Department 1j1 Extraction Condition Database 1j2 Frequency Band Database 1j3 Normal Distribution Database 1j4 ​​Operating Conditions Database 1j5 Deviation Pattern Database A Manufacturing equipment B,B1,B2,B3,BN Equipment C, C1, C2, C3, CN Acoustic Sensor

Claims

1. An acoustic diagnostic device that identifies abnormal parts of equipment by using acoustic signals during equipment operation and start signals indicating equipment startup for multiple pieces of equipment constituting a group of equipment, A sound collection unit for collecting the operating sounds of the aforementioned equipment, A device start time acquisition unit acquires the start signal of the aforementioned equipment and identifies the start time of the equipment based on the acquired start signal. Based on the start time of the equipment identified by the equipment start time acquisition unit and the predetermined operating interval of the equipment, the operating interval sound extraction unit extracts the operating sounds of the equipment for each operating interval from the operating sounds collected by the operating sound collection unit. A sound pressure level acquisition unit acquires the sound pressure level for each predetermined frequency band for each operating section of the operating sound of the equipment extracted by the operating section operating sound extraction unit, A deviation calculation unit calculates the degree of deviation of the sound pressure level acquired by the sound pressure level acquisition unit for each operating section and each frequency band, An abnormality detection unit detects an abnormality in the equipment based on the degree of deviation calculated by the deviation calculation unit, An acoustic diagnostic device equipped with the following features.

2. The acoustic diagnostic apparatus according to claim 1, wherein the deviation degree calculation unit calculates the degree of deviation of the sound pressure levels for each operating section and each frequency band acquired by the sound pressure level acquisition unit from the sound pressure levels for each operating section and each frequency band acquired during the normal operation of the equipment.

3. The acoustic diagnostic device according to claim 1, further comprising an abnormality location identification unit that identifies an abnormal part of the equipment based on the deviation degree calculated by the deviation degree calculation unit.

4. The acoustic diagnostic device according to claim 3, wherein the abnormality location identification unit identifies the abnormality location of the equipment based on a pattern of deviations, the deviations in each frequency band determined for each operating section.

5. The system includes a database unit that stores data necessary for collecting operating sounds from the equipment and identifying abnormal parts of the equipment. The acoustic diagnostic device according to claim 4, wherein the abnormal part identification unit identifies the abnormal part of the equipment by referring to a table containing information that shows the relationship between the operating interval and the deviation degree pattern stored in the database unit and the abnormal part of the equipment.

6. The table includes information showing the operating interval, the pattern of the degree of deviation, and the relationship between the operating conditions of the equipment and the abnormal part of the equipment. The abnormal part identification unit identifies the abnormal part of the equipment by referring to the table and considering the operating interval, the pattern of the degree of deviation, and the operating conditions of the equipment. The acoustic diagnostic device according to claim 5.

7. A normal sound pressure level acquisition unit acquires the sound pressure level for each predetermined frequency band for each operating section of the operating sound extracted by the operating section sound extraction unit, A normal distribution determination and recording unit 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 them as the sound pressure levels during normal operation of the equipment. The acoustic diagnostic device according to claim 1, comprising:

8. The acoustic diagnostic device according to claim 7, wherein the normal distribution determination and recording unit records a table for each piece of equipment that includes information showing the relationship between a pattern of deviations, which are elements of the degree of deviation of the sound pressure level from the normal sound pressure level in each frequency band determined for each operating interval, and the abnormal part of the equipment.

9. The acoustic diagnostic device according to claim 3, wherein, if the equipment is equipment that performs rotational motion, the abnormal part identification unit identifies the abnormal part of the equipment by classifying the sound pressure level by the rotational order obtained by normalizing the frequency by the rotational speed.

10. The acoustic diagnostic device according to claim 1, wherein the equipment group is a hot rolling mill, and the equipment includes a rolling mill reduction device, rolling rolls, rolling roll motor, rolling work roll shift device, scale removal device, roll coolant device, and strip coolant device.

11. An acoustic diagnostic method for identifying abnormal parts of equipment in a group of equipment, using acoustic signals during equipment operation and start signals indicating equipment startup, A sound collection step for collecting the operating sounds of the aforementioned equipment, A step to acquire the equipment start time, which involves acquiring the start signal of the equipment and determining the start time of the equipment based on the acquired start signal, Based on the start time of the equipment identified in the equipment start time acquisition step and the predetermined operating interval of the equipment, the operating interval sound extraction step extracts the operating sounds of the equipment for each operating interval from the operating sounds collected in the operating sound collection step. A sound pressure level acquisition step is performed to acquire the sound pressure level for each predetermined frequency band for each operating section of the operating sound of the equipment extracted in the operating section sound extraction step, A deviation calculation step which calculates the degree of deviation of the sound pressure level for each operating section and each frequency band acquired in the sound pressure level acquisition step, An abnormality detection step in which an abnormality of the equipment is detected based on the degree of deviation calculated in the degree of deviation calculation step, Acoustic diagnostic methods, including those mentioned above.

12. The acoustic diagnostic method according to claim 11, further comprising a step of identifying an abnormal part of the equipment based on the deviation calculated in the deviation calculation step.

13. The normal sound pressure level acquisition step involves acquiring the sound pressure level for each predetermined frequency band for each operating sound extracted in the operating sound extraction step for each operating sound in A normal distribution determination and recording step is performed, which classifies the sound pressure levels acquired in the normal sound pressure level acquisition step for each piece of equipment, each operating section, and each frequency band, and records them as the sound pressure levels when the equipment is operating normally. The acoustic diagnostic method according to claim 11 or 12, including the method described in claim 11 or 12.

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