ABNORMAL-SOUND DETECTION DEVICE AND ABNORMAL-SOUND DETECTION METHOD

Abstract

[Problem] To provide an abnormal-sound detection device in which incorrect detection is rare. [Solution] An abnormal-sound detection device has an imaging means, an operation range identification means, a sound collection means, an abnormal-sound detection means, an abnormal-sound generation position identification means, and an abnormal-sound source determination means. The imaging means captures an image of a diagnosis object. The operation range identification means identifies and stores the operation range of the diagnosis object on the basis of the image captured by the imaging means. The sound collection means collects sounds arriving from the diagnosis object and the vicinity thereof. The abnormal-sound detection means detects abnormalities in sounds included in the collected sounds. When an abnormality in a sound is detected by the abnormal-sound detection means, the abnormal-sound generation position identification means identifies the position at which the abnormality of the sound was generated. The abnormal-sound source determination means compares the operation range and the abnormal-sound generation position of the diagnosis object, and determines whether the abnormality of the sound is derived from an abnormality of the diagnosis object.

Claims

1. An abnormal-sound detection device comprising: at least one memory storing instructions; and at least one processor configured to access the at least one memory and execute the instructions to: identify an operation range of a diagnosis object, based on a video of the diagnosis object captured by a camera; sense an abnormality of a sound from among sounds collected by a microphone, the sounds arriving from the diagnosis object; identify, based on the collected sound, a generation position of the sound in which an abnormality is sensed; and determine, based on the operation range, and the generation position of the sound in which an abnormality is sensed, whether the abnormality of the sound is derived from the diagnosis object or derived from another element.

2. The abnormal-sound detection device according to claim 1, wherein the at least one processor is further configured to execute the instructions to: determine that the sound in which an abnormality is sensed is an abnormal sound of the diagnosis object, when the generation position of the sound in which an abnormality is sensed is in the operation range.

3. The abnormal-sound detection device according to claim 1, wherein the at least one processor is further configured to execute the instructions to: determine that the sound in which an abnormality is sensed is an abnormal sound derived from a factor other than the diagnosis object, when the generation position of the sound in which an abnormality is sensed is out of the operation range.

4. The abnormal-sound detection device according to claim 1, wherein the at least one processor is further configured to execute the instructions to: identify the generation position of the sound in which an abnormality is sensed, based on a difference of sounds each collected by each of microphones in a microphone array in which a plurality of microphones are arrayed.

5. The abnormal-sound detection device according to claim 1, wherein the at least one processor is further configured to execute the instructions to: perform a machine learning to generate an abnormal characteristic determination criterion for determining an abnormal characteristic of the sound, based on sounds collected from the diagnosis object and a vicinity thereof.

6. An abnormal-sound detection method comprising: capturing a video of a diagnosis object; identifying an operation range of the diagnosis object, based on the video; collecting sounds arriving from the diagnosis object; sensing an abnormality of a sound from among the sounds; identifying, based on the collected sounds, a generation position of the sound in which an abnormality is sensed; and determining, based on the operation range, and the generation position of the sound in which an abnormality is sensed, whether the abnormality of the sound is derived from the diagnosis object or derived from another element.

7. The abnormal-sound detection method according to claim 6, further comprising determining that the sound in which an abnormality is sensed is an abnormal sound of the diagnosis object, when the generation position of the sound in which an abnormality is sensed is within the operation range.

8. The abnormal-sound detection method according to claim 6, further comprising determining that the sound in which an abnormality is sensed is an abnormal sound derived from a factor other than the diagnosis object, when the generation position of the sound in which an abnormality is sensed is out of the operation range.

9. The abnormal-sound detection method according to claim 6, further comprising: collecting the sounds by use of a microphone array in which a plurality of microphones are arrayed; and identifying the generation position of the sound in which an abnormality is sensed, based on a difference of the sounds each collected by each of the microphones.

10. A non-transitory computer-readable recording medium recording an abnormal-sound detection program that causes a computer to execute: a step of identifying an operation range of a diagnosis object, based on a video of the diagnosis object captured by a camera; a step of sensing an abnormality of a sound from among sounds collected by a microphone, the sounds arriving from the diagnosis object; a step of identifying, based on the collected sounds, a generation position of the sound in which an abnormality is sensed; and a step of determining, based on the operation range, and the generation position of the sound in which an abnormality is sensed, whether the abnormality of the sound is derived from the diagnosis object or derived from another element.

Description

BRIEF DESCRIPTION OF DRAWINGS

[0013] FIG. 1 is a block diagram illustrating an abnormal-sound detection device according to a first example embodiment.

[0014] FIG. 2 is a block diagram illustrating an abnormal-sound detection device according to a second example embodiment.

[0015] FIG. 3 is a schematic diagram illustrating measurement of an abnormal sound of a diagnosis object according to the second example embodiment.

[0016] FIG. 4 is a schematic diagram illustrating measurement of an abnormal sound derived from an object other than a diagnosis object according to the second example embodiment.

[0017] FIG. 5 is a flowchart illustrating an operation of the abnormal-sound detection device according to the second example embodiment.

[0018] FIG. 6 is a block diagram illustrating a configuration example of an abnormal-sound detection device according to a third example embodiment.

[0019] FIG. 7 is a flowchart illustrating a learning operation of an abnormality determination criterion according to the third example embodiment.

EXAMPLE EMBODIMENT

[0020] Example embodiments of the present invention will be described below in detail with reference to the drawings. A technically preferable limitation is imposed on the example embodiments described below in order to implement the present invention, but does not limit the scope of the invention to the following. The same reference sign may be assigned to a similar component in each drawing, and description thereof may be omitted.

First Example Embodiment

[0021] FIG. 1 is a block diagram illustrating an abnormal-sound detection device according to the present example embodiment. The abnormal-sound detection device includes an imaging means 1, an operation range identification means 2, a sound collection means 3, an abnormal-sound sensing means 4, an abnormal-sound generation position identification means 5, and an abnormal-sound derivation determination means 6.

[0022] The imaging means 1 captures a video of a diagnosis object.

[0023] The operation range identification means 2 identifies and stores an operation range of the diagnosis object, based on the video captured by the imaging means 1.

[0024] The sound collection means 3 collects sounds arriving from the diagnosis object and a vicinity thereof.

[0025] The abnormal-sound sensing means 4 senses an abnormality of a sound included in the collected sounds.

[0026] When an abnormality of a sound is sensed by the abnormal-sound sensing means 4, the abnormal-sound generation position identification means 5 identifies a position at which the abnormality of the sound occurs.

[0027] The abnormal-sound derivation determination means 6 compares the operation range and the abnormal-sound generation position of the diagnosis object, and determines whether an abnormal sound is derived from an abnormality of the diagnosis object.

[0028] As described above, the abnormal-sound detection device according to the present example embodiment can select and detect an abnormality of a sound derived from an abnormality of a diagnosis object. Thus, an abnormal-sound detection device in which incorrect detection is rare can be provided.

Second Example Embodiment

[0029] FIG. 2 is a block diagram illustrating an abnormal-sound detection device 100 according to the present example embodiment. The abnormal-sound detection device 100 includes a camera 110 that optically captures a diagnosis object 200, and a microphone array 120 that collects a sound arriving from the diagnosis object. The abnormal-sound detection device 100 also includes an operation range identification unit 130, an A/D converter 140, an abnormal characteristic determination unit 150, an abnormal-sound generation position identification unit 160, an abnormal-sound derivation determination unit 170, and a database 180. Herein, A/D is an abbreviation of analog/digital. The database 180 includes an operation range storage unit 181 that stores an identified operation range, a diagnosis object abnormal-sound storage unit 182 that stores an abnormal sound derived from a diagnosis object, and an another-factor abnormal-sound storage unit 183 that stores an abnormal sound derived from a factor other than the diagnosis object.

[0030] The operation range identification unit 130 identifies an operation range of the diagnosis object 200 from a video captured by the camera 110. Although an identification method of an operation range may be any method, a feature point is extracted from the video, and a shape of a diagnosis object is recognized based on the feature point, for example. An operation range of the diagnosis object can be identified based on a movement range of the feature point in a predetermined period. The operation range is stored in the operation range storage unit 181. Although description is given herein by use of an example in which an imaging means is the camera 110, an imaging means may be a three-dimensional sensor (3D sensor). The three-dimensional sensor is, for example, a sensor which measures a distance to each point of an object by scanning with pulse laser. It is possible to form an image of the diagnosis object from data acquired by the three-dimensional sensor. Although description is given assuming that the number of cameras is one, the number of cameras may be a plural number. The database may be abbreviated as DB in the following description.

[0031] The A/D converter 140 converts a sound collected by the microphone array 120 into a digital signal. The microphone array 120 is a plurality of microphones arrayed by a predetermined rule. When this microphone array 120 is used, a direction of a sound source can be estimated from a difference in arrival time or a phase difference of sounds each arriving at each microphone from the same sound source.

[0032] The abnormal characteristic determination unit 150 determines an abnormal characteristic of a sound signal input from the A/D converter 140. Although a way of determination may be any way, determination can be performed by magnitude of a sound pressure level of a sound signal, or can be performed by sound quality, for example. A sound signal is frequency-analyzed, and sound quality can be represented by a ratio of each frequency component, for example. A change rate of a sound with time, such as a sound pressure level or a speed of variation in sound quality may be used.

[0033] With regard to a sound determined by the abnormal characteristic determination unit 150 to have an abnormal characteristic, the abnormal-sound generation position identification unit 160 identifies a position at which the abnormal sound is generated. Identification of a position can be performed based on a difference in arrival time at each microphone or a phase difference of sounds determined to be the same sound from a correlativity, for example.

[0034] The abnormal-sound derivation determination unit 170 compares an operation range stored in the operation range storage unit 181 with an abnormal-sound generation position, and identifies whether the generation position of the abnormal sound is within the operation range or out of the operation range. When the abnormal-sound generation position is within the operation range, the abnormal-sound derivation determination unit 170 determines that the abnormality of the sound is derived from the diagnosis object, and stores the abnormal sound in the diagnosis object abnormal-sound storage unit 182.

[0035] On the other hand, when the abnormal-sound generation position is out of the operation range, the abnormal-sound derivation determination unit 170 determines that the abnormality of the sound is derived from a factor other than the diagnosis object. The abnormal-sound derivation determination unit 170 stores such an abnormal sound unrelated to the diagnosis object in the another-factor abnormal-sound storage unit 183 as an another-factor abnormal sound.

[0036] FIG. 3 is a schematic diagram illustrating measurement status when an abnormal sound is generated in an operation range of a diagnosis object. The camera 110 captures a video of the diagnosis object 200, and the microphone array 120 collects sounds arriving from the diagnosis object 200 and a vicinity thereof. In this example, facilities 201, 202, 203, and 204 are included as elements of the diagnosis object 200. An operation range 201a of the facility 201, and an operation range 203a of the facility 203 are indicated by dotted lines. Since the facilities 202 and 204 are immobile, it is assumed that operation ranges thereof correspond to outer shapes of the facilities.

[0037] In the example of FIG. 3, presence of an abnormal-sound generation position 300 within the operation range 201a of the facility 201 is identified based on an output of the microphone array 120. In such a case, it is determined that the facility 201, i.e., a diagnosis object has an abnormality, and an abnormal sound is derived from the diagnosis object.

[0038] FIG. 4 is a schematic diagram illustrating a measurement example when an abnormality of a sound occurs out of an operation range of a diagnosis object. In this case, presence of the abnormal-sound generation position 300 out of an operation range of a facility being a diagnosis object is identified based on an output of the microphone array 120, and it is determined that the abnormality of the sound is derived from a factor other than the diagnosis object. As in this example, when a running state of a facility of a factory or the like is continuously monitored, entry of a person into a monitoring area, carrying of goods, or the like can be cited as an example of a cause for generation of an abnormal sound of another factor, for example.

[0039] FIG. 5 is a flowchart illustrating an operation of the abnormal-sound detection device 100 described above. First, a diagnosis object is captured (S1). Next, an operation range of the diagnosis object is identified based on a captured video (S2). Next, a sound of the diagnosis object is measured (S3). Whether the measured sound has an abnormal characteristic is determined (S4). Herein, when the sound has no abnormal characteristic (S4_No), a return is made to S3, and measurement of a sound, i.e., monitoring of a sound is repeated. On the other hand, when the sound has an abnormal characteristic, whether a generation position of the sound is within the operation range of the diagnosis object is determined (S5). When the generation position of the sound is within the operation range (S5_Yes), it is determined that the sound is an abnormal sound of the diagnosis object (S6), and is stored in the database as the abnormal sound together with information on the sound generation position (S7). Further, an alarm indicating that the abnormal sound is detected may be reported (S8).

[0040] On the other hand, when the generation position of the sound having an abnormal characteristic is out of the operation range of the diagnosis object, it is determined that the sound is not an abnormal sound of the diagnosis object (S9), and is stored in the database as an another-factor abnormal sound (S10). A return is made to S3, and monitoring of a sound is continued.

[0041] As described above, according to the present example embodiment, incorrect detection that detects, as an abnormal sound of a diagnosis object, an abnormal sound derived from a factor other than the diagnosis object is eliminated, and abnormal-sound detection accuracy can be improved.

Third Example Embodiment

[0042] FIG. 6 is a block diagram illustrating an abnormal-sound detection device according to the present example embodiment. An abnormal-sound detection device 101 has nearly the same configuration as the abnormal-sound detection device 100 according to the second example embodiment, but an abnormal characteristic determination unit 150 is equipped with an abnormal characteristic determination criterion learning unit 151.

[0043] The abnormal characteristic determination criterion learning unit 151 machine-learns an abnormal characteristic determination criterion. Specifically, for example, the abnormal characteristic determination criterion learning unit 151 machine-learns, for a predetermined period or a predetermined number of times, a sound measured in a period in which a diagnosis object does not generate an abnormal sound, and forms an abnormal characteristic determination criterion such as a criterion value of a normal sound or an allowable limit value. Although a method of machine learning may be any method, a self-organizing map method, a k-nearest neighbor method, or the like can be used, for example. The abnormal characteristic determination criterion learning unit 151 stores the learned abnormal characteristic determination criterion in an abnormal characteristic determination criterion storage unit 184.

[0044] Next, an operation of the abnormal characteristic determination criterion learning unit 151 is described. FIG. 7 is a flowchart illustrating the operation of the abnormal characteristic determination criterion learning unit 151. First, a measurement period or the number of measurements for performing learning is set (S101). For example, when a running state of a facility of a factory or the like is continuously monitored, setting may be performed with the number of times of predetermined operations because a facility or the like repeatedly performs the same operation. Thus, herein, description is given assuming that setting is performed with the number of times. Setting may be performed with a predetermined period. At start of machine learning, a number of measurements N is first reset. In other words, N=0 is set (S102).

[0045] Next, a sound of a diagnosis object is measured in order to collect data for learning (S103). Then, the measured sound is recorded (S104). After recording, N=N+1 is set by adding 1 to the number of measurements N (S105). Herein, when the updated N is less than a predetermined number of times K (S106_Yes), a return is made to S103, and measurement of a sound is repeated. On the other hand, when N=K (S106_No), machine learning for statistically processing data on a recorded sound is performed, and an abnormal characteristic determination criterion is formed (S107). The formed abnormal characteristic determination criterion is stored in the abnormal characteristic determination criterion storage unit 184 (S108).

[0046] When an abnormal characteristic determination criterion formed by machine learning as above is used, an abnormal sound generated from a diagnosis object can be detected as a sound being far from a normal value even when the abnormal sound is an unknown sound.

[0047] As described above, according to the present example embodiment, an abnormal-sound detection device being capable of detecting an unknown abnormal sound can be configured.

[0048] A program causing a computer to execute the processing according to the first to third example embodiments described above, and a recording medium storing the program also fall within the scope of the present invention. For example, a magnetic disk, a magnetic tape, an optical disk, a magnet-optical disk, a semiconductor memory, or the like can be used as the recording medium.

[0049] While the invention has been particularly shown and described with reference to exemplary embodiments thereof, the invention is not limited to these embodiments. It will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present invention as defined by the claims.

[0050] This application is based upon and claims the benefit of priority from Japanese Patent Application No. 2018-026170, filed on Feb. 16, 2018, the disclosure of which is incorporated herein in its entirety by reference.

REFERENCE SIGNS LIST

[0051] 1 Imaging means [0052] 2 Operation range identification means [0053] 3 Sound collection means [0054] 4 Abnormal-sound sensing means [0055] 5 Abnormal-sound generation position identification means [0056] 6 Abnormal-sound derivation determination means [0057] 100, 101 Abnormal-sound detection device [0058] 110 Camera [0059] 120 Microphone array [0060] 130 Operation range identification unit [0061] 140 A/D converter [0062] 150 Abnormal characteristic determination unit [0063] 151 Abnormal characteristic determination criterion learning unit [0064] 160 Abnormal-sound generation position identification unit [0065] 170 Abnormal-sound derivation determination unit [0066] 180 Database [0067] 181 Operation range storage unit [0068] 182 Diagnosis object abnormal-sound storage unit [0069] 183 Another-factor abnormal-sound storage unit [0070] 184 Abnormal characteristic determination criterion storage unit [0071] 200 Diagnosis object [0072] 300 Abnormal-sound generation position