WO2021250765A1

WO2021250765A1 - Abnormal sound observation system for metal material machining equipment

Info

Publication number: WO2021250765A1
Application number: PCT/JP2020/022625
Authority: WO
Inventors: 光彦佐野
Original assignee: 東芝三菱電機産業システム株式会社
Priority date: 2020-06-09
Filing date: 2020-06-09
Publication date: 2021-12-16
Also published as: JP7151910B2; JPWO2021250765A1; CN114502926A

Abstract

An abnormal sound observation system according to the present invention comprises: at least two microarrays which detect sound; and a processing device which performs sound signal processing in which sound signals that have been detected by the microarrays are processed. The at least two microarrays include first and second microarrays. In the sound signal processing, a first abnormal sound portion is extracted from the sound signal which has been detected by the first microarray, and a second abnormal sound portion is extracted from the sound signal which has been detected by the second microarray. Further, the direction of an abnormal sound source relative to the first microarray (first relative direction) is estimated on the basis of the first abnormal sound portion, and the direction relative to the second microarray (second relative direction) is estimated on the basis of the second abnormal sound portion. On the basis of the positions of the first and second microarrays on a reference coordinate plane and the first and second relative directions, the position of the abnormal sound source on the coordinate plane is estimated.

Description

Abnormal noise observation system for metal processing equipment

The present invention relates to a system for observing abnormal acoustic components (hereinafter, also referred to as "abnormal noise") generated in equipment for processing metal materials.

The processing process of the metal material typically includes a rolling process and an incidental process thereof. In the equipment where the processing process is performed (hereinafter, also referred to as "processing equipment"), various abnormal noises are generated during the operation. Abnormal noise is an acoustic component having a frequency component, frequency distribution, and volume that does not occur during normal operation.

Abnormal noise is caused by various causes. For example, if the metal material warps in the vertical direction or bends in the horizontal direction (that is, the working side or the driving side of the metal material transport line), the metal material comes into contact with surrounding mechanical equipment. The vertical warp of the metal material occurs when there is a difference between the temperature on the upper surface and the temperature on the lower surface of the metal material. If there is a difference between the surface condition of the upper rolling roll and that of the lower rolling roll, and a difference in rolling ratio and the resulting elongation ratio occurs on the upper and lower surfaces of the metal material, vertical warpage also occurs.

The bending of the metal material in the left-right direction occurs, for example, as follows. That is, if the temperature on the left and right of the metal material is different due to the variation in temperature rise in the processing furnace, the rolling load on the left and right will be different due to the difference in deformation resistance of the metal material. In addition, there is a laterality in the elastic deformation (mill extension) of the rolling mill. Then, on the exit side of the rolling mill, a plate thickness difference (wedge) occurs in the left-right direction of the metal material. When a wedge is formed, there is a difference between the elongation on the right side of the metal material and that on the left side. Therefore, the metal material bends in the left-right direction.

Another cause of abnormal noise is mill vibration. Mill vibration is caused by elastic deformation of the rolling mill and changes in the frictional state between the rolling roll and the metal material. Eccentricity due to damage to a part of a rotating body such as a motor, a drive shaft, and a roll is another cause of abnormal noise.

Leaving a state in which abnormal noise is generated during operation may lead to major troubles. Therefore, it is necessary to appropriately identify the cause of the abnormal noise and take appropriate measures. However, conventionally, the identification and countermeasures of abnormal noise generated in the processing equipment have been performed exclusively by humans. That is, when the worker recognizes the occurrence of abnormal noise in the driver's cab or the transport line, the cause of the abnormal noise is estimated based on the rough direction of arrival of the abnormal noise and past experience, and countermeasures are taken.

However, the direction of arrival of abnormal sounds that can be recognized by the human ear is not accurate. Therefore, for example, it is extremely difficult for the human ear to identify whether the abnormal noise is generated on the left side or the right side of the rolling mill. In addition, if the worker's cognitive ability and experience are low, the occurrence of abnormal noise may be overlooked. Further, the work performed by the worker is not limited to the recognition of abnormal noise. Therefore, even if the worker's recognition ability is high, it is impossible to deal with the abnormal noise when it occurs while always paying attention to the abnormal noise. Therefore, there was a possibility that the occurrence of abnormal noise was overlooked and no countermeasures were taken.

If a microphone or vibration sensor is installed in advance in a processing device that is expected to generate abnormal noise and the signals from these detection devices are analyzed, there is a possibility that the source of the abnormal noise can be automatically identified. However, the processing equipment includes various devices, and many devices are expected to generate abnormal noise. Therefore, it is economically limited to attach a detection device to all of these devices. In addition, in order to process the signal from the detection device, it is necessary to install a large number of long connection cables. Wireless connection is conceivable, but some devices in processing equipment have sources of electromagnetic noise such as motors and drive devices. Therefore, in reality, it is necessary to reduce the number of detection devices to some extent, and there is a possibility that the generation of abnormal noise itself cannot be detected for the devices in which the detection devices are not installed.

As a conventional technique for detecting the generation of abnormal noise, the techniques disclosed in Patent Documents 1 and 2 are exemplified. In these conventional techniques, a microphone device having a large number of directional microphones arranged in a matrix is used. Then, based on the information on the arrangement position of the microphone device, the position of the sound field collected by a certain directional microphone in the sound collecting area is specified. However, in order to increase the position resolution, it is necessary to arrange more microphones with stronger directivity. Therefore, in these conventional techniques, there is a limit to the improvement of the position resolution.

Patent Document 3 discloses a technique for attaching a device that transmits a position specifying signal to a monitored object. However, it is not realistic to attach a transmitter to a metal material to be worked, and it is extremely difficult to apply the transmitter to a metal material that is hot-rolled.

Patent Document 4 discloses a device for specifying the direction of acoustic waves using a single microphone array. However, in this device, although the direction from the microphone array to the source of the acoustic wave can be known, it is difficult to specify the distance from the microphone array to the source.

Japanese Patent Application Laid-Open No. 64-46672 Japanese Patent No. 4443247 Japanese Patent Application Laid-Open No. 10-132651 Japanese Patent Application Laid-Open No. 11-64089

The present invention has been made to solve the above-mentioned problems, and to provide a technique capable of automatically and highly accurately identifying the position of the source of abnormal noise generated in a metal material processing facility. The purpose.

The first invention is an abnormal noise observation system for observing abnormal noise generated in a metal material processing facility.
The abnormal noise observation system is
At least two microphone arrays that detect sound, and
A processing device that performs acoustic signal processing that processes acoustic signals detected by at least two microphone arrays, and
To prepare for.
The at least two microphone arrays
A first microphone array directed to the first space including the location of the processing equipment,
A second microphone array directed to a second space that shares a part of the space with the first space at the position of the processing equipment.
including.
The processing device is used in the acoustic signal processing.
The first abnormal sound portion is extracted from the acoustic signal detected by the first microphone array, and the first abnormal sound portion is extracted.
The second abnormal sound portion is extracted from the acoustic signal detected by the second microphone array, and the second abnormal sound portion is extracted.
Based on the first abnormal noise unit, the first relative orientation indicating the relative orientation of the abnormal noise source with respect to the first microphone array is estimated.
Based on the second abnormal sound portion, the second relative direction indicating the relative direction with respect to the second microphone array is estimated.
The position of the abnormal noise source on the coordinate plane is estimated based on the positions of the first and second microphone arrays on the reference coordinate plane and the first and second relative orientations.

The second invention further has the following features in the first invention.
The at least two microphone arrays further include a third microphone array directed at a third space that shares a portion of space with at least one of the first and second spaces at the location of the processing equipment.
In the acoustic signal processing, the processing device further
The third abnormal sound part is extracted from the acoustic signal detected by the third microphone array, and the third abnormal sound portion is extracted.
Based on the third abnormal sound portion, a third relative orientation indicating the relative orientation with respect to the third microphone array is estimated.
The processing device estimates the position of the abnormal noise source based on the combination of two microphone arrays of the first, second and third microphone arrays.

The third invention further has the following features in the first or second invention.
When the processing device further estimates the position of the abnormal sound source in the acoustic signal processing, the coordinates from any microphone array of the at least two microphone arrays to the abnormal sound source. Based on the distance on the surface and the time when the abnormal sound from the abnormal sound source is detected in the arbitrary microphone array, the time when the abnormal sound is generated at the abnormal sound source is estimated.

The fourth invention further has the following features in the third invention.
The abnormal noise observation system further includes a display device for displaying the operating status of the processing equipment.
The processing device further
Information acquisition processing to acquire information on the metal material processed in the processing equipment,
An association process of associating the abnormal noise generation information including the estimated position of the abnormal noise source and the estimated occurrence time of the abnormal noise at the abnormal noise source with the information of the metal material.
A display process for outputting information on the metal material associated with the abnormal noise generation information to the display device, and
I do.

The fifth invention further has the following features in the third or fourth invention.
The abnormal noise observation system further includes a storage device for recording the operating status of the processing equipment.
The processing device further
Information acquisition processing to acquire information on the metal material processed in the processing equipment,
An association process of associating the abnormal noise generation information including the estimated position of the abnormal noise source and the estimated occurrence time of the abnormal noise at the abnormal noise source with the information of the metal material.
A recording process for recording the information of the metal material associated with the abnormal noise generation information in the storage device, and
I do.

The sixth invention further has the following features in any one of the third to fifth inventions.
The abnormal noise observation system further includes a control device for controlling the processing device constituting the processing facility.
The processing device further
An emergency for urgently operating at least a part of the processing apparatus based on the abnormal noise generation information including the estimated position of the abnormal noise source and the estimated occurrence time of the abnormal noise at the abnormal noise source. An emergency control process for outputting a control command to the control device is performed.

According to the first invention, acoustic signal processing is performed. According to the acoustic signal processing, the first abnormal sound part is extracted from the acoustic signal detected by the first microphone array, and the first relative direction of the abnormal sound source with respect to the first microphone array is determined based on the first abnormal sound part. It is calculated. Further, the second abnormal sound portion is extracted from the acoustic signal detected by the second microphone array, and the second relative direction of the abnormal sound source with respect to the second microphone array is calculated based on the second abnormal sound portion. Then, the position of the abnormal sound source on the reference coordinate plane is estimated based on the positions of the first and second microphone arrays on the reference coordinate plane and the first and second relative directions. Therefore, according to the acoustic signal processing, it is possible to automatically and highly accurately identify the position of the abnormal noise source.

According to the second invention, it is possible to automatically and highly accurately identify the position of the abnormal noise source based on the combination of two microphone arrays of the first, second and third microphone arrays. It becomes.

According to the third invention, when the position of the abnormal noise source is estimated, the time of occurrence of the abnormal noise at the abnormal noise source is estimated. Therefore, it is possible to automatically and highly accurately identify the time when the abnormal noise is generated.

According to the fourth invention, the information of the metal material to which the abnormal noise generation information is attached is displayed on the display device. Therefore, it is possible to provide the operator who sees the display device with the information of the metal material to which the abnormal noise generation information is attached. This leads to a reduction in the burden on workers in processing equipment.

According to the fifth invention, the information of the metal material to which the abnormal noise generation information is attached is recorded in the storage device. Therefore, after processing the metal material, it is possible to trace back from the abnormal noise generation information and identify the metal material related to this. It is also possible to utilize the abnormal noise generation information attached to the metal material information as product quality information.

According to the sixth invention, an emergency control command is output to the control device based on the abnormal noise generation information. An emergency control command is a control command for urgently operating at least a part of a processing device. Therefore, it is possible to prevent the generation of abnormal noise from developing into a big trouble.

It is a top schematic diagram which shows the 1st application example to the processing equipment of the abnormal noise observation system which concerns on embodiment. It is a top schematic diagram which shows the 2nd application example to the processing equipment of the abnormal noise observation system which concerns on embodiment. It is a figure which shows the whole structure example of the abnormal noise observation system which concerns on embodiment. It is a schematic diagram which shows the structural example of the microphone array. It is a figure which shows the functional configuration example of a processing apparatus. It is a figure which shows an example of a relative direction. It is a figure explaining the installation condition (second installation condition). It is a figure which shows the example of the list of the metal material information associated with the abnormal noise generation information. It is a schematic diagram which shows the image example displayed on the display device. It is a schematic diagram which shows the image example displayed on the display device.

Hereinafter, the abnormal noise observation system according to the embodiment of the present invention will be described with reference to the drawings. The elements common to each figure are designated by the same reference numerals, and duplicate description will be omitted. Further, the present invention is not limited to the following embodiments.

1. 1. Application example of abnormal noise observation system 1-1. First Application Example FIG. 1 is a schematic top view showing a first application example of the abnormal noise observation system according to the embodiment to processing equipment. In FIG. 1, a side guide SG is drawn. The side guide SG is a device that stabilizes the transport direction DD of the metal material MTL processed in the processing equipment. The side guide SG is installed, for example, on the entry side and the exit side of the rough rolling mill. In another example, the side guide SG is installed between the stands of the finish rolling mill. In yet another example, the side guide SG is installed on the entry side of the winder. The side guide SG is an example of a "processing device" that constitutes a processing facility. Other examples of processing equipment include rough rolling mills, finish rolling mills, skin pass rolling mills and winders.

FIG. 1 also depicts

microphone arrays

10A and 10B. Configuration examples of these microphone arrays will be described later. These microphone arrays are installed on the same side (that is, the working side or the driving side) with respect to the metal MTL. These microphone arrays make up an abnormal noise observation system. The sound collecting surface of the microphone array 10A is directed to the space SPA. The spatial SPA includes at least the location of the processing equipment. The sound collecting surface of the microphone array 10B is directed to the space SPB. Spatial SPA also includes at least the location of the processing equipment. Part of the space SPA overlaps with part of the space SPB. In the example shown in FIG. 1, the overlapping space L12 is a part of the space of the transport line including the side guide SG.

1-2. Second Application Example FIG. 2 is a schematic top view showing a second application example to the processing equipment of the abnormal noise observation system according to the embodiment. In the second application example, the

microphone arrays

10A, 10B, 10C and 10D constitute an abnormal noise observation system. The installation locations of the

microphone arrays

10A and 10B are the same as those in the first application example. The microphone arrays 10C and 10D are installed on the same side (that is, the working side or the driving side) with respect to the metal material MTL. The sound collecting surface of the microphone array 10C is directed to the spatial SPC. The sound collecting surface of the microphone array 10D is directed to the spatial SPD. Spatial SPCs and SPDs also include at least the location of processing equipment. The space in which any two of the spaces SPA to SPD overlap includes a part of the space of the transport line including the side guide SG.

Hereinafter, these microphone arrays are collectively referred to as "microphone array 10" except when the microphone arrays 10A to 10D are distinguished.

2. 2. Configuration example of the abnormal noise observation system FIG. 3 is a diagram showing an overall configuration example of the abnormal noise observation system. In the example shown in FIG. 3, the abnormal noise observation system 100 includes a microphone array 10, a control device 20, a processing device 30, a processing device 40, a display device 50, and a storage device 60.

2-1. Microphone array The microphone array 10 detects the sound in the space to which the sound collecting surface is directed. At least two microphone arrays 10 are installed around the processing equipment. The installation example of the microphone array 10 has already been described. Each of the microphone arrays 10 is connected to the processing device 40 via a cable. Since the amount of signals from the microphone array 10 is large, it is necessary to perform high-speed transmission between the microphone array 10 and the processing device 40. However, it is generally difficult to use a long cable for high-speed transmission. Therefore, it is desirable to provide a sub-processing device having a part of the functions of the processing device 40 for each microphone array 10 and connect the sub-processing device and the microphone array 10 via a short cable. In this case, each of the sub-processing devices is connected to the processing device 40 via a long cable.

Each of the microphone arrays 10 is equipped with an amplifier and an analog-to-digital converter (neither is shown). The sampling period of the analog-to-digital conversion is set to be at least twice the upper limit frequency determined in consideration of the frequency component of the abnormal sound to be detected so that aliasing does not occur. The range of the "frequency component of the abnormal sound to be detected" is, for example, 10 Hz to 100 kHz. The recently developed MEMS microphone can capture an acoustic signal having a frequency exceeding 100 kHz, and is therefore preferably applicable to the microphone array 10.

FIG. 4 is a schematic diagram showing a configuration example of the microphone array 10. In the example shown in FIG. 4, the microphone array 10 includes five microphones 11 to 15 arranged in the horizontal direction. A plurality of microphone groups similar to the microphones 11 to 15 may be arranged in the vertical direction.

The distance d between two adjacent microphones in the microphone array 10 is not particularly limited. For example, the interval d is set to ½ or less of the wavelength λ at the above-mentioned upper limit frequency. If the interval d is longer than this wavelength, it may take a lot of time to scan the signal in the extraction process described later, or the estimation accuracy of the direction of the abnormal sound may decrease. The distance L between the two microphones at both ends in the horizontal direction (that is, the microphones 11 and 15) is also not particularly limited. However, the volume detected by the microphone array 10 is inversely proportional to the square of the distance L. Therefore, it is desirable that the distance L is set to be equal to or lower than the lower limit value determined in consideration of the signal noise ratio of abnormal noise, the microphone sensitivity, and the ambient sound.

2-2. Control device The control device 20 manages the operation in the processing equipment. The control device 20 is a computer including a processor, a memory, and an input / output interface. The control device 20 includes information on the operating status of the processing equipment (hereinafter, also referred to as “operating status information”), information on the metal material processed by the processing equipment (hereinafter, also referred to as “metal material information”), and. It has information on the processing conditions of the metal material (hereinafter, also referred to as "processing condition information").

As the operating status information, information indicating the operating status of the processing apparatus 30 is exemplified. Examples of the metal material information include information on the metal material identification number, material type classification, and dimension classification. As the metal material information, information on the position of the metal material on the transport line is also exemplified. This position information is acquired based on a signal from a detection device installed at a key point on the transport line and a signal from a drive device of various motors. As the processing condition information, the target plate thickness on the output side of various rolling mills and the speed condition of the metal material in the processing process are exemplified.

The control device 20 sends the operation status information and the metal material information to the processing device 40 and the storage device 60. The control device 20 also generates a control command for the processing device 30 based on the metal material information and the processing condition information. As the control command, information on the control amount input to various actuators of the processing apparatus 30 is exemplified. The control device 20 sends a control command to the processing device 30 and the storage device 60. When the control device 20 receives an emergency control command from the processing device 40, the control device 20 sends it to the processing device 30. The emergency control command is a control command generated in an emergency of the processing equipment.

2-3. Processing device The processing device 30 processes a metal material on a transport line. Examples of the processing apparatus 30 include the side guide SG, the rough rolling mill, the finish rolling mill, the skin pass rolling mill, and the winding machine described with reference to FIGS. 1 and 2.

2-4. Processing device The processing device 40 detects the generation of abnormal noise in the processing equipment. The processing device 40 is a computer including a processor, a memory, and an input / output interface, like the control device 20.

The processing device 40 determines whether or not it is necessary to detect the occurrence of abnormal noise based on the information received from the control device 20. When it is determined that it is necessary to detect the occurrence of abnormal noise, the processing device 40 detects the occurrence of abnormal noise based on the acoustic information detected by the microphone array 10. When the processing device 40 detects the occurrence of abnormal noise, the processing device 40 generates abnormal noise generation information. As the abnormal noise generation information, information on the estimated position of the abnormal noise generation source (Allophone source) and the estimated time of the abnormal noise generation is exemplified. The processing device 40 associates the generated abnormal noise generation information with the metal material information based on the metal material information received from the control device 20. Details of the series of processes will be described later.

The processing device 40 also performs processing (that is, display processing) for displaying the metal material information associated with the abnormal noise generation information on the display device 50. The processing device 40 further performs a process (that is, a recording process) for recording the metal material information associated with the abnormal noise generation information in the storage device 60. The processing device 40 further performs a process for urgently controlling the processing device 30 (that is, an urgent control process) based on the information of the estimated position of the abnormal noise source. Details of these processes will also be described later.

2-5. Display device The display device 50 is provided in, for example, a driver's cab in which a worker (field worker) is stationed. In another example, the display device 50 is provided in the management room where the manager (remote worker) is stationed. The operation status information is displayed on the display device 50. This display is performed based on the information stored in the storage device 60. The display device 50 also displays video data output from a camera installed at a key point of the processing equipment. The worker and the manager monitor the operating status of the processing equipment, the state of the movable portion of the processing device 30, and the like via the display device 50.

2-6. Storage device The storage device 60 stores information on the arrangement and outer shape of the processing device 30. The storage device 60 also stores information on the state of the movable portion of the processing device 30. Information on the state of the moving part is included in the operating status information. The storage device 60 also stores the acoustic information detected by the microphone array 10. The storage device 60 may receive this acoustic information from the processing device 40 or may receive it directly from the microphone array 10. The storage device 60 also stores metal material information with abnormal noise generation information.

3. 3. Configuration Example of Processing Device FIG. 5 is a diagram showing a functional configuration example of the processing device 40. In the example shown in FIG. 5, the processing device 40 includes an information acquisition unit 41, an acoustic signal processing unit 42, a display processing unit 43, a recording processing unit 44, and an emergency control processing unit 45. These functional units are realized by the processing device 40 processor executing various programs stored in the memory.

Some of these functional parts may be realized in the above-mentioned sub-processing device. For example, the information acquisition unit 41 and a part of the acoustic signal processing unit 42 may be realized in the sub processing device. Specifically, the processing related to the acoustic signal processing of the information acquisition unit 41 and the processing performed by the extraction processing unit 42A and the direction estimation processing unit 42B, which will be described later, may be performed in the sub-processing device.

3-1. Information acquisition unit The information acquisition unit 41 performs a process of acquiring operation status information and metal material information (information acquisition process). The information acquisition unit 41 also acquires the acoustic information detected by the microphone array 10. The information acquisition unit 41 determines whether or not it is necessary to detect the occurrence of abnormal noise based on the operation status information and the metal material information. For example, the information acquisition unit 41 determines that it is necessary to detect the occurrence of abnormal noise when the processing equipment is in operation. When the information acquisition unit 41 determines that it is necessary to detect the occurrence of abnormal noise, the information acquisition unit 41 sends the metal material information and the acoustic information to the acoustic signal processing unit 42.

3-2. Acoustic signal processing unit The acoustic signal processing unit 42 processes the acoustic signal included in the acoustic information. In the example shown in FIG. 5, the acoustic signal processing unit 42 includes an extraction processing unit 42A, an orientation estimation processing unit 42B, a position estimation processing unit 42C, a time estimation processing unit 42D, and an association processing unit 42E. ing.

3-2-1. Extraction processing unit The extraction processing unit 42A performs processing (extraction processing) for extracting an abnormal sound portion from an acoustic signal. Specifically, the extraction processing unit 42A first performs digital filter processing, short-time Fourier transform, or wavelet transform on the acoustic signal, and extracts (the signal waveform) of the frequency component (signal waveform) equal to or lower than the above-mentioned upper limit frequency. Here, the number of frequency components to be extracted is F (F ≧ 1).

In the digital filter processing, the characteristic frequency of the bandpass filter is selected so that the overlap of the frequency spectra of the frequency components becomes sufficiently small, and the parameters suitable for the characteristic frequency are set. In the short-time Fourier transform, a part of the acoustic signal is extracted by multiplying the acoustic signal by a window function represented by a Gaussian function or the like, and converted into a frequency spectrum by the Fourier transform. At this time, by scanning the window function in the time direction, the time change of the frequency spectrum is calculated, and a desired frequency component is extracted as a part of the frequency spectrum. The wavelet transform may be a continuous wavelet transform or a discrete wavelet transform. When extracting two or more frequency components, the wavelet scaling factor is set so that the overlap of frequency spectra becomes sufficiently small in the continuous wavelet transform.

The extraction processing unit 42A determines, for example, whether or not the signal strength (volume) deviates from the range during normal operation for each frequency component. In another example, the extraction processing unit 42A determines whether or not the ratio of signal strength between frequency components (that is, signal strength ratio or volume ratio) deviates from the range during normal operation. If there is a frequency component that deviates from the normal range, or if there is a combination of frequency components that deviate from the normal range, the extraction processing unit 42A determines this as an abnormal sound, cuts out the abnormal sound portion on the time axis, and makes an orientation. It is sent to the estimation processing unit 42B.

3-2-2. Direction estimation processing unit The direction estimation processing unit 42B performs processing (direction estimation processing) for estimating the relative orientations of the abnormal sound sources with respect to the microphone array 10 based on the information of the abnormal noise unit received from the orientation estimation processing unit 42B. conduct. Examples of the method for estimating the relative orientation include a method based on phase detection, a method based on the mutual correlation coefficient, and a method based on the eigenvalue analysis of the correlation matrix.

In the method based on phase detection, for example, the arrival direction of abnormal sound at that frequency is estimated based on the phase difference between the same frequency components of the abnormal sound portion detected by each microphone of the microphone array 10. Specifically, first, each frequency component of the abnormal sound part detected by each microphone is divided by the effective value of the amplitude (that is, the square root of the root mean square of the amplitude of each sampling point) to make the amplitude intensity uniform. .. Hereinafter, the frequency component having the same amplitude intensity is also referred to as a “normalized component”.

Focus on any two microphones arranged in the horizontal direction in one microphone array 10 (hereinafter, referred to as "microphone i" and "microphone j" for convenience of explanation). In the method based on phase detection, the normalized components of the same frequency in the abnormal sound part detected by these two microphones are multiplied (that is, the amplitude of the microphone i and that of the microphone j are multiplied for each sampling point. ), And the average value (DC component) x is calculated. The phase difference δ _ij is calculated by the following equation (1) using the inverse function of the cosine function from the addition theorem of the sine function.

The arrival direction θ is _{calculated by substituting the phase difference δ ij} into the following equation (2).

In equation (2), λ is a wavelength and is _{expressed using the speed of sound c and the frequency f when the phase difference δ ij} is calculated (that is, λ = c / f). d _ij is the distance between the microphone i and the microphone j. θ is an angle formed by a line (normal) NL that passes through the center of the microphone array 10 and is perpendicular to the sound collecting surface of the microphone array 10 and a line segment SL that connects the center and the abnormal sound source.

FIG. 6 shows an example of the arrival direction θ, the normal NL, and the line segment SL. In the example shown in FIG. 6, NL _10A and NL _10B as normals NL and SL _10A _{and SL 10B} as line segments SL are drawn. Further, as the arrival direction theta, and relative direction theta _10A of noise sources AS for the microphone array 10A, a relative direction theta _10B of noise sources AS, are depicted for the microphone array 10B. In FIG. 6, the coordinate point center position of the reference coordinate system of the microphone array 10A _(x _{a, y} a) is represented by, is represented by it coordinate points of the microphone array 10B _(x _{b, y} b) .. Further, the position of the abnormal noise source AS is represented by a _{coordinate point (x ab} , _yab).

The method based on the intercorrelation coefficient focuses on the same frequency component in the abnormal sound portion detected by the above-mentioned two microphones (that is, microphones i and j). _{In this method, the intercorrelation coefficient rij} when one of the frequency components is shifted by the time difference Δt in the time axis direction is calculated by the following equation (3).

In the formula (3), s _ij is the covariance of each frequency component of the abnormal sound portion detected by the microphones i and j. s _i is the standard deviation of each frequency component of the abnormal sound portion detected by the microphone i, and s _j is that of the microphone j. n is the total number of data (i, j), ik and jk are numerical values of each frequency component, and i ^- and j ^- are average values.

The intercorrelation coefficient _rij is calculated while changing the time difference Δt. _{The phase difference δ ij} is calculated from the following equation (4) showing the product of the time difference Δt when the mutual correlation coefficient r _{ij becomes maximum and the frequency f.} The arrival direction θ is calculated by the equation (5) modified by substituting the _{phase difference δ ij} into the equation (2) described above.

The method based on the eigenvalue analysis of the correlation matrix can estimate the arrival direction θ with high resolution. Examples of this method include the MUSIC method, the Rot MUSIC method, and the ESPRIT method. Hereinafter, the MUSIC method will be described.

In the MUSIC method, attention is paid to all combinations of the above-mentioned two microphones for one microphone array 10. In the MUSIC method, first, the intercorrelation coefficient _rij is calculated for each of these combinations, and the correlation matrix R = [ _rij _{] is created based on the intercorrelation coefficient rij} (i and j are Two microphones combined). Subsequently, the eigenvalue λ _i of the correlation matrix R, the eigenvector e _i , and its Hermitian conjugate e _i * are calculated.

When there are a plurality of incoming abnormal sounds, the total number of these abnormal sounds is set to n. In the MUSIC method, the eigenvalues λ _{i of} each microphone are arranged in descending order, and a new subscript m is added (λ _m : m = 1 to N). _{Further, the subscript p is assigned to n λ m} from the larger one (p = 1 to n), and the subscript q is assigned to the remaining λ _m (q = n + 1 to N). Counting a certain threshold or more lambda _m as effective abnormal noise, it may automatically determine the lambda _m shake subscript p.

The arrival direction θ is calculated as follows. First, the variable θ is scanned in the range of −π / 2 ≦ θ ≦ π / 2, and the relative phase vector ν (θ) expressed by the following equation (6) and the evaluation value y expressed by the equation (7). (Θ) and are calculated.

In equation (6), j is an imaginary unit. The variable θ when the evaluation value y (θ) indicates the minimum value (≈0) is recorded as _{θ p.} This θ _p represents the arrival direction θ of each signal.

The direction estimation processing unit 42B may perform weighting averaging processing in order to improve the estimation accuracy of the arrival direction θ. For example, when the amplitudes of the plurality of frequency components in the abnormal sound portion are significant signals (that is, in the case of signals exceeding the noise level), the above-mentioned estimation method is applied to each of these frequency components. Then, the arrival direction θ is obtained for each frequency component. In the weighted averaging process, a coefficient corresponding to the amplitude of the frequency component is multiplied by each arrival direction θ, and the average value θ _{ave of} these is calculated.

The direction estimation processing unit 42B sends the calculated arrival direction θ to the position estimation processing unit 42C. Here, it is desirable that the direction estimation processing unit 42B sends the arrival direction θ to the position estimation processing unit 42C when the incident angle condition and the installation condition are satisfied. Hereinafter, these two conditions will be described.

Regarding the incident angle condition, according to the configuration example of the microphone array 10 described with reference to FIG. 4, sound can be collected in the range of −π / 2 ≦ θ ≦ π / 2. That is, the arrival direction can be estimated in the range of −π / 2 ≦ θ ≦ π / 2. However, when θ exceeds a certain range, the estimation accuracy of the arrival direction drops sharply. If a decrease in estimation accuracy of about 10% is allowed, the range of θ is −70 ° ≦ θ ≦ 70 °. The incident angle condition means that the arrival direction θ is within this allowable range.

Regarding the installation conditions, according to the installation example of the microphone array 10 described with reference to FIGS. 1 and 2, any two microphone arrays 10 (hereinafter referred to as “microphone array p” and “microphone array q” for convenience of explanation). There is an overlapping part in the space where the sound collecting surface of is facing. However, if there is no overlapping portion, it is highly possible that the arrival direction θ of the abnormal sound having a different generation position has been calculated. The same is true if there are no overlapping parts on the transport line. The same can be said when the angle between the sound collecting surface of the microphone array p and that of the microphone array q is too large.

Therefore, the installation conditions include the following two individual conditions.
First installation condition: There is an overlapping portion in the space where the sound collecting surfaces of the microphone arrays p and q are facing, and the overlapping portion exists on the transport line. Second installation condition: Regarding the microphone arrays p and q. _{The angles φ p} and φ _q formed by each normal line NL and the reference line satisfy −45 ° ≦ | φ _p −φ _q | ≦ 45 °. The position of the microphone array 10 and the direction of the sound collecting surface are known. Therefore, the determination of whether or not the installation condition is satisfied may be a prerequisite for the calculation of the arrival direction θ. In this case, first, the two microphone arrays 10 are narrowed down based on the installation conditions. Subsequently, the arrival direction θ is calculated for the two microphone arrays 10 that have been narrowed down. Then, it is determined whether or not the incident angle condition is satisfied with respect to the calculated arrival direction θ.

FIG. 7 shows an example of the angle φ and the normal NL. In the example shown in FIG. 7, NL _10A and NL _10C as normals NL are drawn. The normal NL _10A is the same as that shown in FIG. Further, in FIG. 7, as the angle phi _p and phi _q, the angle phi _10A and phi _10C is depicted. The angle φ _10A is an angle formed by the _{reference line BL 10A} passing through the center of the microphone array 10A and parallel to the X axis and the normal line NL _10A. The angle φ _10C is an angle formed by the _{reference line BL 10C} passing through the center of the microphone array 10C and parallel to the X axis and the normal line NL _10C. In FIG. 7, the center position coordinate point of the microphone array 10A _(x _{a, y} a) is represented by, it is represented by it coordinate points of the microphone array _{_{10C (x c, y c)}} . Further, the position of the abnormal noise source AS is represented by a _{coordinate point (x ac} , y _ac).

3-2-3. Position estimation processing unit The position estimation processing unit 42C estimates the position of the abnormal noise source AS based on the information of the arrival direction θ received from the direction estimation processing unit 42B and the information of the position of the microphone array 10. (Position estimation processing) is performed. Specifically, the position estimation processing unit 42C first selects any two microphone arrays 10 from which the arrival direction θ has been calculated (that is, the microphone arrays p and q). In making this selection, the above-mentioned incident angle conditions and installation conditions are used.

When at least the first installation condition is satisfied for the microphone arrays p and q, the position estimation processing unit 42C uses the following equations (8) and (9) to position the abnormal noise source in the reference coordinate system (x _pq , y _pq ). To calculate.

In equations (8) and (9), (x _p ⁰ , y _p ⁰ ) and (x _q ⁰ , y _q ⁰ ) are the positions of the microphone arrays p and q in the frame of reference. Typically, (x _p ⁰ , y _p ⁰ ) is the position of the microphone in the center of the microphone array p, and (x _q ⁰ , y _q ⁰ ) is the position of that of the microphone array q.

_{The position estimation processing unit 42C calculates the position (x pq} , y _pq ) for all combinations of the two microphone arrays 10 in which the arrival direction θ has been calculated. For example, when the total number of microphone arrays 10 for which the arrival direction θ is calculated is u, there are u × (u-1) combinations of the two. The position estimation processing unit 42C calculates the weighted average of u × (u-1) positions (x _pq , y _pq ) by the following equations (10) and (11).

In equations (10) and (11), w _pq is a weighting factor.

u × (u-1) pieces of position _(x _{pq, y} pq) calculated position by weighted average of _(x _{est, y} est) corresponds to the estimated position of noise source AS. The position estimation processing unit 42C sends information on the estimated position ( _xest , _yest ) to the time estimation processing unit 42D, the display processing unit 43, the recording processing unit 44, and the emergency control processing unit 45. The information of the estimated position (x _est , y _est ) constitutes the abnormal noise generation information.

3-2-4. Time estimation processing unit The time estimation processing unit 42D includes _{information on the position (xest} , _yest ) received from the position estimation processing unit 42C, and any one microphone array 10 (for convenience of explanation, "microphone array p"). ) Performs processing (time estimation processing) to estimate the time when the abnormal noise occurred based on the information of the position. Specifically, the time estimation processing section 42D calculates the estimated time _{t est} of noise generated by the following equation (12).

In equation (12), L _p is the distance _{from the position (x est} , y _est ) to the position (x _p ⁰ , y _p ^0).

The time estimation processing unit 42D sends the estimated time _test information to the display processing unit 43, the recording processing unit 44, and the emergency control processing unit 45. The information at the time _test constitutes information on the occurrence of abnormal noise.

3-2-5. Processing unit 42E association processor association associates the estimated position received from the position estimation processing section 42C _(x _{est, y} est) and information, the information of the estimated time _{t est} received from the time estimation processing unit 42D, the. Further, the association processing unit 42E associates these abnormal noise generation information with the metal material information received from the information acquisition unit 41. By associating the abnormal noise generation information with the metal material information, the metal material existing at the estimated position (x _est , y _est _{) at the estimated time test is specified.} If the estimated position (x _est , y _est ) is the position of the processing device 30, it is specified that the processing device 30 is the abnormal noise source AS, and further, the metal material processed by the processing device 30. Is also identified.

FIG. 8 is a diagram showing an example of a list of metal material information associated with abnormal noise generation information. In the example shown in FIG. 8, the abnormal noise generation information is composed of the estimated position and the estimated time information. The information on the estimated position is represented by the coordinate points (x, y), and the information on the estimated time is represented by the time (X: Y). Information on the date on which the abnormal noise occurred may be added to the information on the estimated time. The metal material information is composed of information on an identification number, a material type classification, and a dimensional classification.

The association processing unit 42E sends the metal material information to which the abnormal noise generation information is attached to the display processing unit 43, the recording processing unit 44, and the emergency control processing unit 45. It is desirable that the metal material information be transmitted each time the association process is performed.

3-3. Display processing unit The display processing unit 43 performs processing for displaying the metal material information with the abnormal noise generation information received from the association processing unit 42E on the display device 50. In this display process, the external shape information of the processing device 30 around the _{estimated position (xest} , _yest ) is stored in the storage device 60 based on the information of the _{estimated position (xest} , _yest ) included in the abnormal noise generation information. Read from. Subsequently, an image is generated in which a schematic diagram showing the outer shape of the metal material and a figure (or symbol) showing the abnormal noise source AS are superimposed on the read outer shape of the processing apparatus 30. Then, the information of the generated image is sent to the display device 50.

Ancillary information may be added to the image information transmitted to the display device 50. As additional information, information on the estimated time _test , information on the metal material displayed on the display device 50, and information on the state of the movable portion of the processing device 30 existing at the _{estimated position (xest} , _{yest) are exemplified.} If a process for identifying the cause of the abnormal noise is separately performed based on the abnormal noise generation information, the operating status information, etc., the information on the cause is included in the attached information. If a process for diagnosing the severity of the occurrence of abnormal noise is performed separately, information on the result of the diagnosis (for example, caution required, suspension of operation, etc.) is included in the attached information.

9 and 10 are schematic views showing an example of an image displayed on the display device 50. In the example shown in FIG. 9, a two-dimensional image is displayed on the display device 50. In the example shown in FIG. 10, a three-dimensional image is displayed on the display device 50. In these examples, the metal material that was present at the estimated position ( _xest , _yest _{) at the estimated time test is placed in the center.} A figure indicating the source of abnormal noise is drawn at the estimated position (x _est , y _est). Also, in these examples, the identification number MTL_ID as a metal material information, as the information of the estimated time _{t est} time (X: Y) and, are displayed.

3-4. Recording processing unit The recording processing unit 44 performs processing for recording the metal material information with the abnormal noise generation information received from the association processing unit 42E in the storage device 60. In this recording process, ancillary information may be added to the metal material information. As the attached information, the same attached information as described in the display process is exemplified.

3-5. Emergency control unit emergency control processing unit 45, based on the estimated position received from the position estimation processing section 42C _(x _{est, y} est) and information, the estimated time _{t est} of information received from the position estimation processing section 42D, the And generate an emergency control command. Emergency control commands are generated to avoid the occurrence of malfunctions related to abnormal noise. As the emergency control command, a control command for urgently opening the side guide SG installed on the downstream side _{of the estimated position (xest} , _{est) in the transport line is exemplified.} As the emergency control command, a control command for urgently widening the roll gap of the rolling mill installed on the downstream side _{of the estimated position (xest} , _{yest) in the transport line is also exemplified.}

4. Effect According to the abnormal sound observation system according to the embodiment described above, the acoustic signal processing unit 42 performs extraction, direction estimation, and position estimation processing, so that _{information on the estimated position (xest} , _yest ) is automatically obtained. Moreover, it is possible to generate with high accuracy. Therefore, it is possible to automatically and highly accurately identify the position of the abnormal noise source AS.

Further, since the time estimation process is performed by the acoustic signal processing unit 42 (time estimation processing unit 42D), it is possible to automatically and accurately generate the _{estimated time test information.} Therefore, it is possible to automatically and highly accurately identify the time when the abnormal noise occurs.

Further, since the association processing is performed by the acoustic signal processing unit 42 (association processing unit 42E) _{, various measures based on the estimated position (xest} , _yest ), the estimated time _test, and the metal material information can be taken immediately. It will be possible. Further, according to the association processing, it is possible to trace back from the abnormal noise generation information after the processing and identify the metal material related to the abnormal noise generation information. It is also possible to utilize the abnormal noise generation information attached to the metal material information as product quality information.

Further, since the display process and the record process are performed together with this association process, it is possible to enhance the effect of executing the association process. Further, since the emergency control process is performed, it is possible to prevent the generation of abnormal noise from developing into a big trouble.

10, 10A, 10B, 10C,

10D Microphone array

11, 12, 13, 14, 15 Microphone 20 Control device 30 Processing device 40 Processing device 41 Information acquisition unit 42 Sound signal processing unit 42A Extraction processing unit 42B Direction estimation processing unit 42C Position Estimating processing unit 42D Time estimation processing unit 42E Association processing unit 43 Display processing unit 44 Recording processing unit 45 Emergency control processing unit 50 Display device 60 Storage device 100 Abnormal sound observation system MTL Metal material SG Side guide SPA, SPB, SPC, SPD space θ, θ _10A , θ _10B Arrival direction (relative direction)

Claims

It is an abnormal noise observation system for observing abnormal noise generated in metal material processing equipment.
At least two microphone arrays that detect sound, and
A processing device that performs acoustic signal processing that processes acoustic signals detected by at least two microphone arrays, and
Equipped with
The at least two microphone arrays
A first microphone array directed to the first space including the location of the processing equipment,
A second microphone array directed to a second space that shares a part of the space with the first space at the position of the processing equipment.
Including
The processing device in the acoustic signal processing
The first abnormal sound portion is extracted from the acoustic signal detected by the first microphone array, and the first abnormal sound portion is extracted.
The second abnormal sound portion is extracted from the acoustic signal detected by the second microphone array, and the second abnormal sound portion is extracted.
Based on the first abnormal noise unit, the first relative orientation indicating the relative orientation of the abnormal noise source with respect to the first microphone array is estimated.
Based on the second abnormal sound portion, the second relative direction indicating the relative direction with respect to the second microphone array is estimated.
It is characterized in that the position of the abnormal noise source on the coordinate plane is estimated based on the position of the first and second microphone arrays on the reference coordinate plane and the first and second relative orientations. Abnormal noise observation system.
The abnormal noise observation system according to claim 1.
The at least two microphone arrays further include a third microphone array directed to a third space that shares a portion of space with at least one of the first and second spaces at the location of the processing equipment.
In the acoustic signal processing, the processing device further
The third abnormal sound part is extracted from the acoustic signal detected by the third microphone array, and the third abnormal sound portion is extracted.
Based on the third abnormal sound portion, a third relative orientation indicating the relative orientation with respect to the third microphone array is estimated.
An abnormal noise observation system characterized in that the position of the abnormal noise source is estimated based on a combination of two microphone arrays among the first, second and third microphone arrays.
The abnormal noise observation system according to claim 1 or 2.
In the acoustic signal processing, the processing device further
When the position of the abnormal sound source is estimated, the distance on the coordinate plane from any microphone array of the at least two microphone arrays to the abnormal sound source and the abnormal sound source from the abnormal sound source. An abnormal sound observation system characterized in that an abnormal sound generation time is estimated at the abnormal sound generation source based on the time when the abnormal sound is detected in the arbitrary microphone array.
The abnormal noise observation system according to claim 3.
Further equipped with a display device for displaying the operating status of the processing equipment,
The processing device further
Information acquisition processing to acquire information on the metal material processed in the processing equipment,
An association process of associating the abnormal noise generation information including the estimated position of the abnormal noise source and the estimated occurrence time of the abnormal noise at the abnormal noise source with the information of the metal material.
A display process for outputting information on the metal material associated with the abnormal noise generation information to the display device, and
An abnormal noise observation system characterized by performing.
The abnormal noise observation system according to claim 3 or 4.
Further equipped with a storage device for recording the operating status of the processing equipment,
The processing device further
Information acquisition processing to acquire information on the metal material processed in the processing equipment,
An association process of associating the abnormal noise generation information including the estimated position of the abnormal noise source and the estimated occurrence time of the abnormal noise at the abnormal noise source with the information of the metal material.
A recording process for recording the information of the metal material associated with the abnormal noise generation information in the storage device, and
An abnormal noise observation system characterized by performing.
The abnormal noise observation system according to any one of claims 3 to 5.
Further equipped with a control device for controlling the processing device constituting the processing facility,
The processing device further
An emergency for urgently operating at least a part of the processing device based on the abnormal noise generation information including the estimated position of the abnormal noise source and the estimated occurrence time of the abnormal noise at the abnormal noise source. An abnormal noise observation system characterized by performing emergency control processing that outputs control commands to the control device.