Abnormality determination device of rotary wing part and method thereof

Abstract

To provide a technique capable of properly detecting an abnormality of a rotary wing part.SOLUTION: An abnormality determination device comprises: an acquisition unit 101 which acquires audio data containing sounds generated by the rotation of a rotary wing part with a plurality of blades arranged in a circumferential direction, and a rotational period of the rotary wing part; a signal processing unit 102 which generates power time-series data of the audio data; a period calculation unit 103 which calculates a peak period, which is a period from a peak to the next peak when a power peak periodically appears, on the basis of the generated power time-series data; and an abnormality determination unit 104 which determines that an abnormality exists in the blade when the peak period matches the rotational period.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present disclosure relates to a technique for determining an abnormality in a rotor blade portion. [Background technology]

[0002] Patent Document 1 discloses a technique for determining an abnormality in a blade by detecting a Doppler shift component of an acoustic signal generated by the blade of a rotating wind power generation turbine. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2018-105872 A Summary of the Invention [Problem to be solved by the invention]

[0004] The Doppler shift component of the acoustic signal generated by the rotating blade may not be clearly detectable when the blade moves away from the microphone, and therefore the conventional technology described in Patent Document 1 may not be able to properly detect an abnormality in the blade.

[0005] In view of the above problems, an object of the present disclosure is to provide a technique capable of appropriately detecting an abnormality in a rotor section. [Means for solving the problem]

[0006] In order to solve the above problems, an abnormality judgment device according to one aspect of the present disclosure includes an acquisition unit that acquires audio data including sound generated by rotation of a rotor section having a plurality of blades arranged in a circumferential direction and the rotation period of the rotor section, a signal processing unit that generates time series data of the power of the audio data, a peak calculation unit that calculates a peak period, which is the period from a peak to the next peak when power peaks appear periodically, based on the generated time series data of power, and an abnormality judgment unit that judges that there is an abnormality in the blade if the peak period and the rotation period match.

[0007] An abnormality determination method according to another aspect of the present disclosure includes the steps of acquiring audio data including sound generated by rotation of a rotor section having a plurality of blades arranged in a circumferential direction and a rotation period of the rotor section, generating time series data of the power of the audio data, calculating a peak period, which is the period from a peak to the next peak when power peaks appear periodically, based on the generated time series data of power, and determining that there is an abnormality in the blade if the peak period and the rotation period match.

[0008] Any combination of the above components, and conversion of the present disclosure into a method, device, system, recording medium, computer program, etc. are also valid aspects of the present disclosure. Effect of the Invention

[0009] According to the present disclosure, it is possible to provide a technique for appropriately detecting an abnormality in a rotor blade portion. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a front view illustrating a schematic configuration of a wind power generation wind turbine according to a first embodiment. [Diagram 2] FIG. 1 is a side view illustrating a schematic configuration of a wind power generation wind turbine according to a first embodiment. [Diagram 3]FIG. 1 is a diagram showing an example of an image of an acoustic spectrum in which a Doppler shift component appears. [Figure 4] 1 is a diagram showing an abnormality determination system for a rotor blade portion according to a first embodiment; [Diagram 5] FIG. 2 is a functional block diagram of a determination-side processing device according to the first embodiment. [Figure 6] 1 is a flowchart showing an abnormality determination process executed by a determination-side processing device according to a first embodiment; [Figure 7] 11 is a diagram illustrating an example of time-series data of the power of an audio signal picked up by a microphone when there is no abnormality in the blade. FIG. [Figure 8] 11 is a diagram illustrating an example of time-series data of the power of an audio signal picked up by a microphone when an abnormality occurs in a blade. FIG. [Figure 9] FIG. 13 is a diagram illustrating a correlogram of time series data of power. [Figure 10] FIG. 13 is a diagram illustrating an abnormality determination system according to a first modified example. [Figure 11] FIG. 2 is a side view of a wind turbine according to a first modified example. [Figure 12] FIG. 11 is a side view of a wind turbine according to a second modified example. [Figure 13] 13 is a flowchart showing an abnormality determination process executed by a determination-side processing device of the second embodiment. [Figure 14] 8 is a diagram illustrating time-series data of power in the 9 kHz band of FIG. 7. FIG. [Figure 15] 9 is a diagram illustrating time-series data of power in the 12 kHz band of FIG. 8. FIG. [Figure 16] 13 is a flowchart showing an abnormality determination process executed by a determination-side processing device of the third embodiment. [Figure 17] FIG. 13 is a diagram showing an example of a correlogram in which the peak period and the rotation period coincide in continuous frequency bands. [Figure 18] FIG. 13 is a functional block diagram of a determination-side processing device according to the fourth embodiment. [Figure 19]13 is a flowchart showing an abnormality determination process executed by a determination-side processing device of the fourth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] First embodiment FIG. 1 is a front view that illustrates a schematic configuration of a wind power generation wind turbine 10 according to the first embodiment. FIG. 2 is a side view that illustrates a schematic configuration of the wind power generation wind turbine 10 according to the first embodiment. The wind power generation wind turbine 10 includes a rotor section 11, a nacelle 12, a support 13, and a microphone 14. The rotor section 11 and the nacelle 12 are installed on the upper part of the support 13. In the first embodiment, the X direction in the figure will be described as the left-right direction or width direction of the wind power generation wind turbine 10, the Y direction as the up-down direction of the wind power generation wind turbine 10, and the Z direction as the front-rear direction of the wind power generation wind turbine 10.

[0012] The rotor section 11 has a head 15 and three blades 16a to 16c arranged at equal angular intervals in the circumferential direction on the head 15. When the blades 16a to 16c are not particularly distinguished from one another, they may be collectively referred to as blade 16. The rotation axis CL of the rotor section 11 is arranged parallel to the Z direction. Each blade 16 of the rotor section 11 rotates about the rotation axis CL.

[0013] Inside the nacelle 12, a rotation sensor 17 for detecting the rotation period Tr of the blades 16, a generator 18, and the like are provided. The rotation period Tr is the time it takes for one blade 16 to rotate once around the rotation axis CL. The wind received by the blades 16 causes each blade 16 to rotate about the rotation axis CL, and accordingly, the head 15 also rotates about the rotation axis CL. In the wind power generating wind turbine 10, the generator 18 is operated by the rotational force of the head 15, and the generator 18 converts the rotational energy of the head 15 into electrical energy to generate electricity.

[0014] The microphone 14 collects sounds generated by the rotation of the rotor part 11. For example, the microphone 14 collects blade sounds generated by the blades 16 of the rotor part 11 during rotation. The microphone 14 of the first embodiment is installed below the blades 16 on the support 13, and mainly collects sounds from above. The dashed line in FIG. 2 illustrates the directivity of the microphone 14. As shown in FIG. 2, the directivity of the microphone 14 of the first embodiment covers the entire blades 16a to 16c of the rotor part 11. The microphone 14 of the first embodiment has a communication function for voice data based on the collected sound, and periodically transmits the voice data to the abnormality determination device 50 described later via a network NW described later. The microphone 14 may transmit the voice data to the abnormality determination device 50 using the communication function of the wind turbine side communication device 21 described later.

[0015] For example, the microphone 14 may constantly transmit voice data to the abnormality determination device 50 while the wind power generation wind turbine 10 is in operation. The microphone 14 may also transmit voice data for a predetermined period, such as 10 minutes per day or 1 minute per hour, to the abnormality determination device 50. The microphone 14 may also transmit voice data for a specified period to the abnormality determination device 50 in response to a request based on an operation on the abnormality determination device 50. In the following embodiments, an arbitrary period in the voice data transmitted as described above is referred to.

[0016] Here, the blades 16 for wind power generation may be damaged by lightning strikes, bird strikes, etc. Due to the nature of power generation equipment, there is a demand to detect abnormalities in the blades 16 while continuing operation as long as possible, that is, without stopping the rotation of the blades 16 or the power generation.

[0017] In contrast, the technology described in Patent Document 1 detects abnormalities in the blades by detecting Doppler shift components in which sharp frequency components shift over time by analyzing bright lines in an image of the acoustic spectrum of the acoustic signal generated by the blades. However, since image analysis generally requires a large amount of calculations, the technology described in Patent Document 1 has the problem of requiring a GPU or the like, which leads to larger equipment and increased costs for using cloud servers, etc.

[0018] In addition, the technology described in Patent Document 1 has a problem that, if the Doppler shift component does not appear clearly in the acoustic spectrum, an abnormality in the blade cannot be detected. FIG. 3 shows an example of an image of an acoustic spectrum in which a Doppler shift component appears. In FIG. 3 and FIG. 7 and FIG. 8 described later, the vertical axis is frequency and the horizontal axis is time, and the power distribution is shown by making the color lighter as the power increases. The distance between the blade and the microphone moves away or closer as the blade rotates. As shown by the dotted line in FIG. 3, when the distance is closer, the Doppler shift component appears as a prominent bright line Ds in the spectrogram. In FIG. 3, the bright line Ds is formed by a high-power sound component that extends horizontally within the area surrounded by the dotted line around 8200 [Hz]. On the other hand, when the distance increases, the Doppler shift component may not appear as an emission line, for example, at the point P between the adjacent ends of two Doppler shift emission lines Ds, where the Doppler shift emission line Ds is interrupted at around 8200 [Hz] in Figure 3. Since the emission line Ds appears only slightly within a very short period when the distance decreases, the frequency transition may not be fully observed, making it difficult to determine whether there is an abnormality in the blade. Furthermore, the shape of the emission line Ds when the distance decreases is not uniform due to the influence of wind direction, etc., making it difficult to improve detection accuracy.

[0019] Furthermore, the technology described in Patent Document 1 has a problem in that the installation position of the microphone is limited to below the wind turbine. The Doppler shift component occurs when the distance between the microphone and the blade changes over time. Therefore, it is necessary to install the microphone in a place where the distance between the microphone and the blade changes significantly, that is, below the wind turbine. This requires that a microphone be installed for each wind turbine to collect sound, which may increase installation costs and require the effort of moving the microphone.

[0020] Taking the above problems into consideration, the first embodiment will be described in detail. Fig. 4 shows an abnormality determination system 1 for a rotor blade section 11 according to the first embodiment. The abnormality determination system 1 of the first embodiment includes a wind power generation wind turbine 10 and an abnormality determination device 50. The wind power generation wind turbine 10 and the abnormality determination device 50 are connected to each other via a network NW so as to be able to communicate with each other.

[0021] The nacelle 12 of the wind power generating wind turbine 10 of the first embodiment includes a wind turbine side communication device 21, a wind turbine side processing device 22, and a wind turbine side storage device .

[0022] The wind turbine-side communication device 21 communicates with other devices such as the abnormality determination device 50. The wind turbine-side communication device 21 communicates by any communication method, such as wired or wireless. The wind turbine-side communication device 21 communicates with the determination-side communication device 51 via the network NW.

[0023] The wind turbine-side storage device 23 stores programs, data, etc. used by the wind turbine-side processing device 22. The wind turbine-side storage device 23 may be a semiconductor memory, a hard disk, etc. The wind turbine-side storage device 23 stores the rotation period Tr of the blades 16 detected by the rotation sensor 17. The wind turbine-side storage device 23 of the first embodiment constantly stores the rotation period Tr. However, this is not limited to this, and the wind turbine-side storage device 23 may also store the rotation period Tr intermittently, for example, for one minute every hour.

[0024] The wind turbine-side processing device 22 executes various processes in the wind power generation wind turbine 10. The wind turbine-side processing device 22 in the first embodiment periodically reads out the rotation period Tr from the wind turbine-side storage device 23 and transmits the read rotation period Tr via the wind turbine-side communication device 21 to an acquisition unit 101 (see FIG. 5) of a determination-side processing device 52 described below. For example, the wind turbine-side processing device 22 transmits the average value of the rotation periods obtained during the period from a predetermined time before the current time (e.g., 20 seconds before) to the current time as the rotation period Tr to the acquisition unit 101 (see FIG. 5) described below.

[0025] The abnormality determination device 50 is placed on the Internet or on a cloud, for example. The abnormality determination device 50 includes a determination-side communication device 51, a determination-side processing device 52, and a determination-side storage device 53.

[0026] The determination-side communication device 51 communicates with other devices such as the wind power generation turbine 10. The determination-side communication device 51 communicates by any communication method, such as wired or wireless. The determination-side communication device 51 communicates with a wind turbine-side communication device 21 (described later) of the wind power generation turbine 10 via the network NW.

[0027] The decision-making storage device 53 stores programs, data, and the like used by the decision-making processing device 52. The decision-making storage device 53 may be a semiconductor memory, a hard disk, or the like.

[0028] The judgment side processing device 52 is, for example, an arithmetic processing device. FIG. 5 is a functional block diagram of the judgment side processing device 52 of the first embodiment. Each component shown in the functional blocks of FIG. 5 and FIG. 18 described later is realized in hardware by a CPU, memory, other LSI, etc. of an arbitrary computer, and in software by a program loaded in a memory, etc., but here, a functional block realized by their cooperation is drawn. Therefore, it is understood by those skilled in the art that these functional blocks can be realized in various forms, such as hardware only or a combination of hardware and software. The judgment side processing device 52 includes an acquisition unit 101, a signal processing unit 102, a period calculation unit 103, an abnormality judgment unit 104, and a notification unit 105.

[0029] The acquisition unit 101 acquires the voice data of the blade sound picked up by the microphone 14 and the rotation period Tr of the rotor portion 11 detected by the rotation sensor 17. For example, the acquisition unit 101 periodically receives the voice data and the rotation period Tr via the determination-side communication device 51, thereby acquiring the voice data and the rotation period Tr.

[0030] The signal processing unit 102 executes various signal processing on the acquired voice data. The signal processing unit 102 generates time series data of the power of the acquired voice data. For example, the signal processing unit 102 of the first embodiment executes frequency axis conversion processing such as Fourier transform for a predetermined time window on the acquired voice data, and shifts the time window by a predetermined amount on the time axis and executes frequency axis conversion processing repeatedly to generate time series data of the power indicating the power distribution on the frequency axis and the time axis.

[0031] The period calculation unit 103 calculates a peak period Tp of the generated power time series data. The method of calculating the peak period Tp by the period calculation unit 103 will be described later.

[0032] The abnormality determination unit 104 determines the presence or absence of an abnormality in the blade 16 based on the rotation period Tr and the calculated peak period Tp. The method of determining an abnormality in the blade 16 by the abnormality determination unit 104 will be described later.

[0033] The notification unit 105 notifies the result of the determination as to whether or not there is an abnormality in the blades 16. For example, the notification unit 105 notifies by displaying the result of the determination as to whether or not there is an abnormality in the blades 16 on a security guard terminal device (not shown) used by a security guard who performs a security inspection of the wind power generation wind turbine 10. Also, for example, the notification unit 105 may notify by generating an alarm sound from a speaker of the security guard terminal device, or, if the abnormality determination device is equipped with a display, may display the determination result on the display.

[0034] FIG. 6 is a flowchart showing the abnormality determination process S100 executed by the determination side processing device 52 of the first embodiment.

[0035] In step S101, the acquisition unit 101 acquires audio data and a rotation period Tr including sound generated by rotation of the rotor 11. For example, the acquisition unit 101 acquires the audio data and the rotation period Tr by receiving the audio data and the rotation period Tr periodically transmitted from the microphone 14 and the wind turbine-side processing device 22, respectively.

[0036] In step S102, the signal processing unit 102 generates time-series data of power based on the acquired voice data.

[0037] In step S103, the period calculation unit 103 calculates a peak period Tp based on the generated power time series data. The peak period Tp here refers to the period from one peak to the next peak when power peaks appear periodically in the power time series data. The peak here refers to a peak having power that exceeds a predetermined power threshold, for example. The next peak refers to a peak having the same power as the previous peak. The "peaks having the same power" also includes cases where two peaks have a difference within a predetermined range of the degree of error.

[0038] A method for calculating the peak period Tp will be described. The rotor 11 of the wind power generation turbine 10 rotates at approximately the same period over a short period (for example, about one minute). For this reason, if the wind power generation turbine 10 has three blades 16, the microphone 14 picks up a loud blade sound every time the three blades 16 approach the microphone 14, that is, every 1 / 3 of the rotation period Tr of the wind power generation turbine 10. If there is no abnormality in the blades 16, there is no difference between the three blades 16, so the blade sound of approximately the same volume is picked up regardless of which blade 16 passes.

[0039] FIG. 7 illustrates time series data of the power of the audio signal collected by the microphone 14 when there is no abnormality in the blade 16. In FIG. 7, times t1 and t4 indicate the times when the blade 16a is closest to the microphone 14, times t2 and t5 indicate the times when the blade 16b is closest to the microphone 14, and times t3 and t6 indicate the times when the blade 16c is closest to the microphone 14. As shown in FIG. 7, when there is no abnormality in the blade 16, power peaks P1 to P6 appear every time the blades 16a to 16c approach the microphone 14 due to rotation. Therefore, the peak period Tp is 1 / 3 of the rotation period Tr of the wind power generation wind turbine 10. In other words, when Tr=Tp×3, it can be said that there is no abnormality in the blade 16.

[0040] FIG. 8 illustrates time series data of the power of the audio signal collected by the microphone 14 when the blade 16 has an abnormality. When the blade 16 has an abnormality such as damage, an abnormal sound such as a whistling sound (a whistling sound) is generated from the abnormal blade 16. This whistling sound is generated all the time while the rotor section 11 is rotating. Here, the distance between the blade 16 and the microphone 14 repeatedly approaches and moves away in accordance with the rotation period Tr. Therefore, in the example of FIG. 8, the whistling sound component S appears so that the frequency and power are periodically increased and decreased in the 8000 to 12000 [Hz] band. For example, the frequency and power of the whistling sound component S reach a peak value at 12000 [Hz] at time t7, the frequency and power of the whistling sound component S decrease immediately after time t7, the frequency and power of the whistling sound component S reach a peak value again at time t8, and the frequency and power reach the next peak value in the same period. That is, the time between time t7 when the power of the whistling sound component S reaches a peak in a particular frequency band and time t8 when the power reaches a peak again in that frequency band corresponds to the peak period Tp of the whistling sound power. Because the whistling sound is being generated by one of the three blades 16 that is damaged, the peak period Tp of the whistling sound power is not 1 / 3 the rotation period Tr but is the same period as the rotation period Tr. In other words, when Tr=Tp, it can be said that there is an abnormality in the blade 16.

[0041] In the first embodiment, the period calculation unit 103 calculates the peak period Tp by creating a correlogram of the created power time series data. FIG. 9 illustrates a correlogram of the power time series data. The correlogram is a graph in which the shift amount (lag) from the original data is taken on the horizontal axis and the correlation coefficient between the original data and the shifted data is taken on the vertical axis. The period calculation unit 103 generates data shifted by a predetermined amount in the time axis direction from the power time series data, and calculates the correlation coefficient of the autocorrelation between the original data and the data shifted by the predetermined amount. The shift amount in the time axis direction is called a lag. The period calculation unit 103 increases the lag little by little, calculates the correlation coefficient of the autocorrelation at each lag, and integrates the correlation coefficient of the autocorrelation calculated at each lag to create the correlogram. Referring to the correlogram in FIG. 9, the peaks (local maximum values) of the correlation coefficient appear at a predetermined interval of about 2500 lags. The peak (local maximum value) here refers to a peak when the correlation coefficient is greater than a predetermined threshold value (for example, about 0.4 in this embodiment). The period calculation unit 103 calculates the peak period Tp based on the lag between the peaks (local maximum values) of the correlation coefficient in the created correlogram. This method of calculating the peak period Tp using the correlogram requires less calculation than the determination method using an image as exemplified in Patent Document 1, so that it is possible to miniaturize the device and reduce cloud usage fees.

[0042] Returning to FIG. 6, in step S104, the abnormality determination unit 104 determines whether the rotation period Tr and the peak period Tp match. Matching here is not limited to, for example, the rotation period Tr and the peak period Tp being completely matched, and also includes a case in which there is a difference between the rotation period Tr and the peak period Tp within a predetermined range, such as the degree of error. If they match (Yes in step S104), the abnormality determination process S100 proceeds to step S105. If they do not match (No in step S104), the abnormality determination process S100 proceeds to step S106.

[0043] In step S105, the abnormality determination unit 104 determines that the blade 16 has an abnormality.

[0044] In step S106, the abnormality determination unit 104 determines that the blade 16 has no abnormality.

[0045] In step S107, the notification unit 105 notifies the abnormality determination unit 104 of the result of the determination as to whether or not there is an abnormality in the blade 16. After step S107, the abnormality determination process S100 ends.

[0046] The abnormality determination device 50 of the first embodiment includes an acquisition unit 101 that acquires voice data including a sound generated by the rotation of the rotor unit 11 having a plurality of blades 16 arranged in the circumferential direction and a rotation period Tr of the rotor unit 11, a signal processing unit 102 that generates time series data of the power of the voice data, a period calculation unit 103 that calculates a peak period Tp, which is a period from a peak to the next peak when power peaks appear periodically in an arbitrary frequency band, based on the generated time series data of the power, and an abnormality determination unit 104 that determines that there is an abnormality in the blade 16 when the peak period Tp and the rotation period Tr match. Here, the Doppler shift has various slopes and shapes of frequency transition, and may not appear clearly in an image. Therefore, it is not easy to improve the accuracy in detecting it by image processing as described in Patent Document 1. On the other hand, according to the first embodiment, the presence or absence of an abnormality in the blade 16 can be determined by comparing the peak period Tp and the rotation period Tr calculated based on the time series data of the power, regardless of the shape and slope of the Doppler shift. Therefore, compared to the method of performing image analysis of Doppler shift as described in Patent Document 1, it is possible to detect abnormalities in the blades 16 with higher accuracy.

[0047] Furthermore, according to the first embodiment, since an abnormality in the blade 16 can be determined only by calculating the data string of the time series data of the power, the amount of calculation can be reduced compared to the method of performing image analysis of the Doppler shift as described in Patent Document 1. This makes it possible to miniaturize the device and reduce the cloud usage fee.

[0048] A modification of the first embodiment will now be described.

[0049] In the first embodiment, the abnormality determination device 50 is arranged on the Internet or on the cloud, but is not limited to this and may be provided in the terminal device used by the security personnel or in the nacelle 12 described above.

[0050] In the first embodiment, the voice data and the rotation period Tr are periodically transmitted to the abnormality determination device 50, but this is not limiting. For example, the abnormality determination device 50 may transmit a request to the wind turbine 10, and the wind turbine 10 may respond to the request by transmitting the voice data and the rotation period Tr to the abnormality determination device 50. Furthermore, the voice data and the rotation period Tr may be constantly transmitted to the abnormality determination device 50. In this case, the abnormality determination device 50 may constantly execute the abnormality determination process for the blades 16.

[0051] In the first embodiment, the rotor section 11 has three blades 16a to 16c, but is not limited to this and may have two or more blades 16.

[0052] In the first embodiment, the signal processor 102 generates the power time series data indicating the power distribution on the frequency axis and the time axis by executing a frequency axis conversion process such as a Fourier transform, but is not limited to this. For example, the signal processor 102 may generate the power time series data by calculating the square of the waveform amplitude for the acquired voice data.

[0053] In the first embodiment, the rotation period Tr is acquired from the rotation sensor 17, but is not limited thereto. The rotation period Tr may be acquired from, for example, the passing sound of the blade 16. The passing sound of the blade 16 has a wide band and appears as a vertically long line as shown in FIG. 7, and can be observed in a plurality of frequency bands. When this characteristic is observed, it is regarded as a blade passing sound, and the value three times the period from the vertically long line to the next vertically long line is set as the rotation period Tr. At this time, it is preferable to determine whether the rotation period Tr is within a reasonable range based on the rotation speed of the rotor 11. Since the wind power generation wind turbine 10 generally has an upper limit on the rotation speed per hour, and abnormal sounds of the blade 16 can only be heard at a certain rotation speed (for example, 15 RPM (rotation per minute)) or more, the range of a reasonable rotation period is determined. For example, if the rotation speed of the rotor 11 is 15 to 30 RPM, the reasonable range of the rotation period Tr is 2 to 4 seconds.

[0054] FIG. 10 shows the anomaly determination system 1 according to the first modification. FIG. 11 is a side view of the wind power generation wind turbine 10 according to the first modification. In the first embodiment, the microphone 14 is installed below the rotor 11, but this is not limited to the above. As shown in FIG. 10 and FIG. 11, for example, the microphone 14 may be installed in or on the nacelle 12. In addition, the directivity of the microphone 14 in the first modification covers only the upper half of the rotation area of ​​the blades 16 of the wind power generation wind turbine 10, and the sound of the blades 16 passing through the upper half can be appropriately picked up by the microphone 14. In the first modification, the wind turbine side storage device 23 stores the voice data picked up by the microphone 14. The wind turbine side processing device 22 reads out the voice data from the wind turbine side storage device 23 and transmits the voice data to the anomaly determination device 50 via the wind turbine side communication device 21. In this way, when the microphone 14 is installed in or on the nacelle 12, it is not necessary to provide a separate communication function for voice data in the microphone 14, and therefore costs can be reduced. Note that when the microphone 14 is installed in or on the nacelle 12, the distance between the blade 16 and the microphone 14 is unlikely to change even when the blade 16 rotates, and frequency changes due to Doppler shift are unlikely to occur. Therefore, while the method described in Patent Document 1 does not allow the microphone 14 to be installed in or on the nacelle 12, the method disclosed herein allows an abnormality to be detected based on the presence or absence of periodicity even when the microphone 14 is installed in or on the nacelle 12.

[0055] FIG. 12 is a side view of a wind power generation windmill 10 according to a second modification. The microphone 14 may be installed at a location away from the rotor part 11 to monitor the rotor part 11 of multiple wind power generation windmills 10. The distant location may be, for example, a location away from the rotor part 11 by a distance equal to or greater than the height of the wind power generation windmill 10. The directivity of the microphone 14 in the second modification covers only the lower half of the rotation area of ​​the blades 16 of the wind power generation windmill 10, and the microphone 14 can appropriately pick up the sound of the blades 16 passing through the lower half. For example, by using a microphone 14 whose directivity can be dynamically changed such as a microphone array, an anomaly detection can be performed on multiple wind power generation windmills 10 using one (one set) microphone 14. This configuration makes it possible to reduce the installation cost and movement cost of the microphone 14. Incidentally, when the microphone 14 is sufficiently far away, no frequency change due to Doppler shift occurs, so the method described in Patent Document 1 does not allow the microphone 14 to be installed sufficiently far away. However, the method disclosed herein can detect anomalies based on the presence or absence of periodicity even when the microphone 14 is sufficiently far away.

[0056] In the first embodiment, the rotor section 11 of the wind power generation wind turbine 10 is illustrated, but the present invention is not limited to this. For example, the rotor section 11 may be a large fan or a propeller.

[0057] Second embodiment A second embodiment of the present disclosure will be described below. In the drawings and description of the second embodiment, components that are the same as or equivalent to those in the first embodiment are given the same reference numerals. Explanations that overlap with the first embodiment will be omitted as appropriate, and the description will focus on configurations that differ from the first embodiment.

[0058] Fig. 13 is a flowchart showing the abnormality determination process S200 executed by the determination side processing device 52 of the second embodiment. Steps S201, and S205 to S207 in Fig. 13 are basically similar to steps S101, and S105 to S107 in Fig. 6, and therefore description thereof will be omitted.

[0059] In step S202, the signal processing unit 102 generates power time series data for each of a plurality of frequency bands from the power time series data based on the acquired voice data. For example, the signal processing unit 102 executes STFT (Short Time Fourier Transform) processing for a predetermined time window on the acquired voice data, shifts the time window by a predetermined amount on the time axis, and repeats frequency axis conversion processing to generate power time series data, thereby obtaining power time series data for each of a plurality of frequency bands. Note that, for example, the signal processing unit 102 may execute BPF (Band Pass Filter) processing to narrow down the acquired voice data to voice data related to a plurality of different frequency bands, and execute the processing described above in the first embodiment on the voice data of each frequency band that has been subjected to the BPF processing, thereby generating power time series data for each of a plurality of frequency bands.

[0060] In step S203, the period calculation unit 103 calculates a peak period Tp for each of the generated multiple pieces of power time-series data.

[0061] In step S204, the abnormality determination unit 104 determines whether the peak period Tp and the rotation period Tr match for each frequency band. If the peak period Tp and the rotation period Tr match in any frequency band (Yes in step S204), the abnormality determination process S200 proceeds to step S205. If the peak period Tp and the rotation period Tr do not match in any frequency band (No in step S204), the abnormality determination process S200 proceeds to step S206.

[0062] FIG. 14 illustrates the time series data of power in the 9 kHz band of FIG. 7. FIG. 15 illustrates the time series data of power in the 12 kHz band of FIG. 8. In FIG. 14, a power peak due to the blade sound of the blade 16a appears at time t9, a power peak due to the blade sound of the blade 16b appears at time t10, a power peak due to the blade sound of the blade 16c appears at time t11, and a power peak due to the blade sound of the blade 16a appears again at time t12. In FIG. 15, a power peak in the 12 kHz band due to the blade sound of the abnormal blade 16 appears at time t13, and a power peak in the 12 kHz band due to the blade sound of the abnormal blade 16 appears again at time t14. As shown in FIG. 14 and FIG. 15, by generating the power time series data for each frequency band, it is possible to obtain power time series data with reduced noise, and therefore it is possible to calculate the peak period Tp with higher accuracy.

[0063] In the second embodiment, the signal processing unit 102 generates power time series data for a plurality of frequency bands based on the acquired voice data. The period calculation unit 103 calculates a peak period Tp for each of the calculated plurality of power time series data. The abnormality determination unit 104 determines that there is an abnormality in the blade 16 when at least one of the calculated plurality of peak periods Tp matches the rotation period Tr. According to the second embodiment, it is possible to improve the accuracy of abnormality determination for the blade 16.

[0064] Third embodiment The third embodiment of the present disclosure will be described below. In the drawings and description of the third embodiment, the same or equivalent components as those in the second embodiment are denoted by the same reference numerals. Explanations that overlap with the second embodiment will be omitted as appropriate, and the configurations that differ from the second embodiment will be described in detail.

[0065] Fig. 16 is a flowchart showing the abnormality determination process S300 executed by the determination side processing device 52 of the third embodiment. Steps S301 to S303 and S305 to S307 in Fig. 16 are basically similar to steps S201 to S203 and S205 to S207 in Fig. 13, and therefore description thereof will be omitted.

[0066] In step S304, the abnormality determination unit 104 determines whether or not the peak periods Tp in consecutive frequency bands, among the peak periods Tp calculated for each of the plurality of frequency bands, match with the rotation period Tr.

[0067] FIG. 17 shows an example of a correlogram in which the peak period Tp and the rotation period Tr in consecutive frequency bands match. In the frequency band of 8727 [Hz] to 12363 [Hz] surrounded by the dotted line Q in FIG. 17, the peak period Tp and the rotation period Tr match. Here, the "consecutive frequency band" refers to a certain frequency band and the next frequency band. For example, in FIG. 17, the frequency band of 8727 [Hz] and the next frequency band of 9454 [Hz] correspond to consecutive frequency bands. In the example of FIG. 17, for example, the peak period Tp and the rotation period Tr match in both the frequency band of 8727 [Hz] and the frequency band of 9454 [Hz]. Therefore, the abnormality determination unit 104 determines that the peak period Tp and the rotation period Tr in consecutive frequency bands match.

[0068] If the peak periods Tp and the rotation periods Tr match in the consecutive frequency bands (Yes in step S304), the abnormality determination process S300 proceeds to step S305. If the peak periods Tp and the rotation periods Tr do not match in any consecutive frequency bands (No in step S304), the abnormality determination process S300 proceeds to step S306.

[0069] In the third embodiment, when a plurality of peak periods Tp in consecutive frequency bands among the calculated plurality of peak periods Tp match the rotation period Tr, the abnormality determination unit 104 determines that there is an abnormality in the blade 16. According to the third embodiment, it is possible to suppress erroneous detection of an abnormality due to the influence of noise, and therefore it is possible to determine an abnormality in the blade 16 with higher accuracy.

[0070] Fourth embodiment Hereinafter, a fourth embodiment of the present disclosure will be described. In the drawings and description of the fourth embodiment, components that are the same as or equivalent to those in the second embodiment are given the same reference numerals. Explanations that overlap with the second embodiment will be omitted as appropriate, and the description will focus on configurations that differ from the second embodiment.

[0071] 18 is a functional block diagram of the determination-side processing device 52 of the fourth embodiment. The determination-side processing device 52 of the fourth embodiment further includes a position estimation unit 106. When it is determined that there is an abnormality in the blade 16, the position estimation unit 106 estimates the abnormality occurrence position in the blade 16 based on the frequency band in which the peak period Tp and the rotation period Tr coincide with each other.

[0072] Fig. 19 is a flowchart showing the abnormality determination process S400 executed by the determination side processing device 52 of the fourth embodiment. Steps S401 to S406 in Fig. 19 are basically the same as steps S201 to S206 in Fig. 13, and therefore description thereof will be omitted.

[0073] In step S407, the position estimation unit 106 estimates the position where the abnormality has occurred in the blade 16. The following formula (1) is a calculation formula for estimating the position where the abnormality has occurred in the blade 16 when the blade 16 has an abnormality. L=Tr×Vs(Fh-Fl) / 4πFh Equation (1) Here, L is the distance from the rotation axis CL to the abnormality occurrence position of the blade 16, Vs is the sound speed, and Fh and Fl respectively indicate the maximum frequency band and the minimum frequency band of the whistling sound. The maximum frequency band Fh and the minimum frequency band Fl of the whistling sound are the maximum frequency band and the minimum frequency band of the frequency band in which the peak period Tp and the rotation period Tr coincide with each other. In the example of FIG. 17, the maximum frequency band Fh is the 12363 [Hz] band, and the minimum frequency band Fl is the 8727 [Hz] band. If the rotation period Tr is 2.63 [sec] and the sound speed Vs is 330.036 [m / sec], in the example of FIG. 17, the position estimation unit 106 estimates that an abnormality occurs in the blade 16 at a position 20.33 [m] from the rotation axis CL based on the formula (1).

[0074] In step S408, the notification unit 105 notifies the user that an abnormality has occurred in the blade 16 and the location where the abnormality has occurred. For example, the notification unit 105 causes a security guard terminal device (not shown) to display the information that an abnormality has occurred in the blade 16 and the estimated location where the abnormality has occurred (in this embodiment, a position 20.33 [m] from the rotation axis CL).

[0075] After step S406, in step S409, the notification unit 105 notifies that no abnormality has occurred in the blade 16. For example, the notification unit 105 causes a security guard terminal device (not shown) to display that no abnormality has occurred in the blade 16.

[0076] After steps S408 and S409, the abnormality determination process S400 ends.

[0077] In the fourth embodiment, when it is determined that there is an abnormality in the blade 16, the position estimation unit 106 estimates the position where the abnormality has occurred in the blade 16 based on the frequency band in which the peak period Tp and the rotation period Tr coincide. According to the fourth embodiment, since it is possible to grasp the position where the abnormality has occurred in the blade 16, maintenance is facilitated.

[0078] The present disclosure has been described above based on the embodiments. The embodiments are merely examples, and it will be understood by those skilled in the art that various modifications are possible in the combination of each component and each processing step, and that such modifications are also within the scope of the present disclosure. [Explanation of symbols]

[0079] 1 Anomaly judgment system, 10 Wind power generation turbine, 11 Rotating blade section, 12 Nacelle, 13 Support, 14 Microphone, 15 Head, 16 Blade, 17 Rotation sensor, 18 Generator, 21 Wind turbine side communication device, 22 Wind turbine side processing device, 23 Wind turbine side memory device, 50 Anomaly judgment device, 51 Judgment side communication device, 52 Judgment side processing device, 53 Judgment side memory device, 101 Acquisition unit, 102 Signal processing unit, 103 Period calculation unit, 104 Anomaly judgment unit, 105 Notification unit, 106 Position estimation unit.

Claims

1. an acquisition unit that acquires audio data including a sound generated by rotation of a rotor unit having a plurality of blades arranged in a circumferential direction and a rotation period of the rotor unit; a signal processing unit that generates time series data of the power of the voice data; a period calculation unit that calculates a peak period, which is a period from one peak to the next peak when the power peaks appear periodically, based on the generated time series data of the power; an abnormality determination unit that determines that an abnormality exists in the blade when the peak period and the rotation period match; An abnormality determination device comprising:

2. The signal processing unit generates time series data of the power for a plurality of frequency bands based on the acquired voice data, the period calculation unit calculates the peak period for each of the generated time series data of the power in a plurality of frequency bands; the abnormality determination unit determines that an abnormality exists in the blade when at least one of the calculated multiple peak periods matches the rotation period. The abnormality determination device according to claim 1 .

3. the abnormality determination unit determines that there is an abnormality in the blade when the peak periods in consecutive frequency bands among the calculated peak periods in a plurality of frequency bands match the rotation period. The abnormality determination device according to claim 2 .

4. a position estimating unit configured to estimate a position where an abnormality has occurred on the blade based on the frequency band in which the peak period and the rotation period coincide when it is determined that an abnormality has occurred on the blade, The abnormality determination device according to claim 2 or 3.

5. Acquiring audio data including a sound generated by rotation of a rotor unit having a plurality of blades arranged in a circumferential direction and a rotation period of the rotor unit; generating time series data of the power of the voice data; calculating a peak period, which is a period from one peak to the next peak when the power peaks appear periodically, based on the generated time series data of the power; determining that there is an abnormality in the blade if the peak period and the rotation period match; The abnormality determination method includes:

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