Device, method, and program

Abstract

To estimate at least one of a liquid volume or dryness level of a fluid.SOLUTION: An estimation device includes: an acquisition unit that acquires state data containing intensity data for at least one frequency band related to a detection signal detected by a vortex flowmeter for a fluid; a state determination estimation unit that estimates a state of the fluid on the basis of the acquired state data; a fluid quality estimation unit that estimates at least one of a liquid volume or dryness level of the fluid from the intensity data according to the estimated state of the fluid; and an output unit that outputs at least one of the estimated liquid volume or dryness level.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an apparatus, a method, and a program. [Background technology]

[0002] Patent Document 1 describes a "wetness measurement method in which the flow rate of wet steam flowing through a pipe is measured using one of a vortex flowmeter or an ultrasonic flowmeter and an orifice flowmeter, and the wetness in the pipe is obtained using the flow rate measurement value." Patent Document 2 describes that "The eddy current measurement device can measure the flow velocity of the first phase of a two-phase or more medium that typically flows during use using the Karman vortex frequency recorded by the eddy current sensor 200, and can also quickly (i.e., online) detect the occurrence of at least a second phase in the form of a dispersed particle flow and / or droplet flow." Patent Document 3 describes that "During use of the eddy current measurement device, the mass flow rate of the wall flow of the second phase is quantitatively measured in a simple manner based on the standard deviation or kurtosis determined from the measurement signal and the vortex frequency, and by referring to a correlation." [Prior art document] [Patent documents] Patent Document 1: JP 2013-185915 A Patent Document 2: Patent No. 5443514 Patent Document 3: Patent No. 5355724 Summary of the Invention

[0003] In a first aspect of the present invention, an estimation device is provided that includes an acquisition unit that acquires state data including intensity data of at least one frequency band related to a detection signal detected by a vortex flowmeter for a fluid, a state determination estimation unit that estimates the state of the fluid based on the acquired state data, a fluid quality estimation unit that estimates at least one of a liquid amount or a dryness fraction of the fluid from the intensity data depending on the estimated fluid state, and an output unit that outputs at least one of the estimated liquid amount or dryness fraction.

[0004] In the above estimation device, the acquisition unit may acquire, from the vortex flowmeter, a digital signal indicating intensity data of at least one frequency band.

[0005] In any of the above estimation devices, the acquisition unit may acquire state data including at least one of the flow rate, temperature, pressure, viscosity, concentration, or density measured for the fluid, and the fluid quality estimation unit may estimate at least one of the liquid amount or dryness fraction for the fluid from the acquired intensity data and the state data including at least one of the flow rate, temperature, pressure, viscosity, concentration, or density measured for the fluid, depending on the estimated state of the fluid.

[0006] In the above estimation device, the vortex flowmeter outputs a pulse signal indicating the vortex frequency of the detection signal generated based on intensity data of at least one frequency band to the calculation device, and the acquisition unit may acquire status data including at least one of the pressure, flow velocity, volumetric flow rate, density, or mass flow rate of the fluid calculated by the calculation device based on the pulse signal.

[0007] In any of the above estimation devices, the acquisition unit may acquire status data including intensity data of multiple frequency bands related to the detection signal of the fluid detected by the vortex flowmeter, and the fluid quality estimation unit may estimate at least one of the liquid amount or dryness fraction of the fluid from the intensity data of the multiple frequency bands.

[0008] In the above estimation device, the fluid quality estimation unit may estimate at least one of the liquid amount and dryness fraction of the fluid from the maximum value of the intensity data of the plurality of frequency bands or a combined value of the intensity data of the plurality of frequency bands.

[0009] In any of the above estimation devices, the acquisition unit may acquire normalized intensity data, and the fluid quality estimation unit may estimate at least one of the liquid amount or dryness fraction of the fluid from the normalized intensity data.

[0010] Any of the above estimation devices may further include a selection unit that selects from a plurality of calculations an operation corresponding to the estimated fluid state, and the fluid quality estimation unit may estimate at least one of the liquid amount or dryness fraction of the fluid from the acquired strength data using the selected calculation.

[0011] In any of the above estimation devices, the state determination / estimation unit may estimate whether the state of the fluid is a stratified flow, a stratified wavy flow, or an annular mist flow, based on the acquired state data.

[0012] In the above-mentioned estimation device, the state determination / estimation unit may determine whether the state of the fluid is wet steam based on the acquired state data, and if it is determined that the state of the fluid is wet steam, estimate whether the state of the fluid is a stratified flow, a stratified wavy flow, or annular mist flow based on the acquired state data.

[0013] In any of the above estimation devices, the state determination / estimation unit may estimate the state of the fluid based on the acquired state data, using a first model that estimates the state of the fluid from the state data.

[0014] In the above estimation device, the selection unit may select a second model corresponding to the estimated state of the fluid from a plurality of second models that estimate at least one of the liquid amount and dryness fraction of the fluid from intensity data of at least one frequency band related to the detection signal of the fluid, depending on the state of the fluid, and the fluid quality estimation unit may estimate at least one of the liquid amount and dryness fraction of the fluid from the intensity data using the second model selected by the selection unit.

[0015] The above estimation device may further include a learning processing unit that generates a second model for each state of the fluid.

[0016] In a second aspect of the present invention, a learning device is provided that includes a learning processing unit that generates a second model for each fluid state, which estimates at least one of the liquid amount and dryness fraction of the fluid from intensity data of at least one frequency band related to a detection signal detected by a vortex flowmeter for the fluid.

[0017] In a third aspect of the present invention, there is provided a method comprising the steps of: a computer acquiring status data including intensity data of at least one frequency band relating to a detection signal detected by a vortex flowmeter for a fluid; a computer estimating a status of the fluid based on the acquired status data; a computer estimating at least one of a liquid amount or a dryness fraction for the fluid from the intensity data in accordance with the estimated status of the fluid; and a computer outputting at least one of the estimated liquid amount or dryness fraction.

[0018] In a fourth aspect of the present invention, there is provided a learning method including a step in which a computer generates, for each state of the fluid, a second model that estimates at least one of the liquid amount and dryness fraction of the fluid from intensity data of at least one frequency band related to a detection signal detected by a vortex flowmeter for the fluid.

[0019] In a fifth aspect of the present invention, a program is provided for causing a computer to function as an acquisition unit that acquires state data including intensity data of at least one frequency band related to a detection signal detected by a vortex flowmeter for a fluid, a state determination estimation unit that estimates the state of the fluid based on the acquired state data, a fluid quality estimation unit that estimates at least one of the liquid amount or dryness fraction of the fluid from the intensity data depending on the estimated fluid state, and an output unit that outputs at least one of the estimated liquid amount or dryness fraction.

[0020] In a sixth aspect of the present invention, a learning program is provided for causing a computer to function as a learning processing unit that generates, for each state of a fluid, a second model that estimates at least one of the liquid amount and dryness fraction of the fluid from intensity data of at least one frequency band related to a detection signal detected by a vortex flowmeter for the fluid.

[0021] The above summary of the invention does not list all of the necessary features of the present invention, and subcombinations of these features may also constitute inventions. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is an example of a block diagram illustrating a system 10 according to an embodiment of the present invention. [Figure 2] 4 is an explanatory diagram for explaining the flow pattern of a fluid inside a pipe 40. FIG. [Figure 3] 4 is a flowchart showing an example of the operation of the vortex flowmeter 100 in the measurement device 20 according to the present embodiment. [Figure 4] FIG. 10 is an explanatory diagram for explaining intensity data. [Figure 5] 4 is a flowchart showing an example of the operation of the estimation device 400 in the measurement device 20 according to the present embodiment. FIG. [Figure 6] FIG. 2 is an explanatory diagram illustrating an example of a first model. [Figure 7] FIG. 10 is an explanatory diagram illustrating an example of a second model. [Figure 8] 22 illustrates an example computer 2200 in which aspects of the present invention may be embodied, in whole or in part. DETAILED DESCRIPTION OF THE INVENTION

[0023] The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the scope of the invention according to the claims. Furthermore, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0024] FIG. 1 is an example of a block diagram showing a system 10 according to this embodiment. Note that these blocks are functionally separated functional blocks and may not necessarily correspond to the actual device configuration. In other words, just because one block is shown in this diagram does not necessarily mean that it is composed of one device. Also, just because separate blocks are shown in this diagram does not necessarily mean that they are composed of separate devices. The same applies to the subsequent block diagrams.

[0025] The system 10 collects data from devices installed in facilities such as plants. Examples of plants include industrial plants such as chemical or biotech plants, plants that manage and control wellheads or surrounding areas of gas or oil fields, plants that manage and control power generation such as hydroelectric, thermal, and nuclear power, plants that manage and control environmental power generation such as solar or wind power, and plants that manage and control water supply and sewage systems or dams. The system 10 includes a measurement device 20 and an external device 30.

[0026] The measurement device 20 measures the state of a fluid (also referred to as a measured fluid) flowing inside a pipe 40. The measurement device 20 includes a vortex flowmeter 100, a sensor 200, a calculation device 300, and an estimation device 400.

[0027] The vortex flowmeter 100 detects vortices generated in a fluid in order to measure the flow rate and other characteristics of the fluid flowing through a pipe 40. The vortex flowmeter 100 includes a vortex generator 110, a detection unit 120, a charge converter 130, an AD converter 140, a frequency analysis unit 150, a pulse conversion unit 160, and a communication unit 170.

[0028] The vortex shedder 110 is provided in a pipe 40 through which a fluid to be measured flows. The detector 120 detects at least one detection signal corresponding to vortices (Karman vortices) generated by the vortex shedder 110 when the fluid to be measured flowing in the pipe 40 collides with the vortex shedder 110. The detector 120 may output a first detection signal and a second detection signal detected by two piezoelectric elements provided at two different locations on the vortex shedder 110. Each piezoelectric element outputs, as a detection signal, an electric charge corresponding to the stress at the location on the vortex shedder 110 where the piezoelectric element is provided.

[0029] The charge converter 130 is connected to the detection unit 120. The charge converter 130 converts the charge of at least one detection signal into a voltage signal, thereby converting the at least one detection signal into a voltage format. The charge converter 130 may convert each of the first detection signal and the second detection signal into a voltage signal. Note that if the detection unit 120 outputs a detection signal in a voltage format or a current format, the charge converter 130 is not necessary.

[0030] The AD converter 140 is connected to the charge converter 130. The AD converter 140 converts at least one detection signal in a voltage format into at least one detection signal in a digital format. The AD converter 140 may convert each of the first detection signal and the second detection signal into a digital signal.

[0031] The frequency analysis unit 150 is connected to the AD converter 140. The frequency analysis unit 150 performs frequency analysis on the digital detection signal input from the AD converter 140 and performs noise removal on the detection signal based on the frequency analysis. The frequency analysis unit 150 may divide the detection signal into multiple frequency bands and perform frequency analysis based on intensity data indicating the signal strength of each frequency band (for example, the maximum amplitude or average amplitude of the signal in each frequency band). The frequency analysis unit 150 may determine, through the frequency analysis, a frequency band including the vortex frequency of the detection signal and a coefficient λ for noise removal.

[0032] The pulse converter 160 is connected to the frequency analyzer 150. The pulse converter 160 may generate a pulse signal that indicates the vortex frequency of the detection signal from which noise has been removed by the frequency analyzer 150.

[0033] The communication unit 170 is connected to the frequency analysis unit 150 and the pulse conversion unit 160, and is connected to the calculation device 300 and the estimation device 400 via a wired or wireless connection. The communication unit 170 is capable of communicating with at least one of the calculation device 300 and the estimation device 400 using a communication protocol defined by HART (registered trademark), BRAIN, Foundation Fieldbus (registered trademark), ISA100.11a, etc. The communication unit 170 may transmit status data including intensity data of at least one frequency band acquired by the frequency analysis unit 150 to the estimation device 400. The communication unit 170 may transmit a pulse signal generated by the pulse conversion unit 160 to the calculation device 300.

[0034] The sensor 200 is connected to the computing device 300 by wire or wirelessly. The sensor 200 may be, for example, at least one of a pressure sensor, a flow meter, a temperature sensor, a density meter, a viscometer, or a concentration meter. The sensor 200 may transmit status data, which is at least one of the flow rate, temperature, pressure, viscosity, concentration, or density measured for the fluid to be measured flowing in the pipe 40, to the computing device 300. The sensor 200 may be located inside or outside the detection unit 120 of the vortex flowmeter 100, and may transmit the status data to the computing device 300 via the communication unit 170.

[0035] The arithmetic device 300 is connected to the estimation device 400. The arithmetic device 300 may be a computer such as a personal computer (PC), a tablet computer, a smartphone, a workstation, a server computer, or a general-purpose computer, or may be a computer system in which multiple computers are connected. Such a computer system is also a computer in the broad sense. The arithmetic device 300 may also be implemented by one or more virtual computer environments executable within a computer. The arithmetic device 300 may also be an internal computer of the vortex flowmeter 100.

[0036] The arithmetic device 300 calculates one or more status data from data received from the sensor 200 or the vortex flowmeter 100. The arithmetic device 300 may calculate the status data of the fluid from a pulse signal received from the vortex flowmeter 100. The arithmetic device 300 includes a pulse counting unit 310 and a calculation unit 320.

[0037] The pulse counting unit 310 is connected to the calculation unit 320. The pulse counting unit 310 counts the pulses of the pulse signal received from the vortex flowmeter 100 to measure the number of pulses per unit time of the pulse signal (i.e., the vortex frequency).

[0038] The calculation unit 320 calculates at least one of the pressure, flow velocity, volumetric flow rate, density, and mass flow rate of the fluid from the vortex frequency measured by the pulse counting unit 310. The calculation unit 300 outputs state data including at least one of the pressure, flow velocity, volumetric flow rate, density, and mass flow rate calculated by the calculation unit 320 to the estimation device 400. The calculation unit 300 may also transmit state data including at least one of the flow velocity, temperature, pressure, viscosity, concentration, and density received from the sensor 200 to the estimation device 400.

[0039] The estimating device 400 is connected to the external device 30. The estimating device 400 may be a computer such as a PC, a tablet computer, a smartphone, a workstation, a server computer, or a general-purpose computer, or may be a computer system in which multiple computers are connected. Such a computer system is also considered a computer in a broad sense. The estimating device 400 may also be implemented by one or more virtual computer environments executable within a computer. The estimating device 400 may also be an internal computer of the vortex flowmeter 100.

[0040] The estimation device 400 estimates at least one of the liquid amount and the dryness fraction of the fluid based on the state data related to the fluid detected by the vortex flowmeter 100. The estimation device 400 includes an acquisition unit 410, a state determination / estimation unit 420, a selection unit 430, a fluid quality estimation unit 440, a learning processing unit 450, and an output unit 460.

[0041] Here, the fluid in this embodiment may include at least one of a gas (steam) from which water or the like has evaporated and a liquid from which the gas has condensed. The estimated liquid amount indicates, for example, the mass of the condensed liquid in the steam. The estimated dryness fraction indicates, for example, the mass ratio of the gas-steam to the mass of the steam.

[0042] The acquisition unit 410 is connected to the communication unit 170 and the calculation device 300. The acquisition unit 410 acquires, from the communication unit 170, status data including intensity data of at least one frequency band related to a detection signal detected by the vortex flowmeter 100 for the fluid. The acquisition unit 410 may acquire, from the calculation device 300, status data including at least one of the flow velocity, temperature, pressure, viscosity, concentration, and density measured for the fluid. The acquisition unit 410 may acquire at least one of the status data of the pressure, flow velocity, volumetric flow rate, density, and mass flow rate of the fluid calculated by the calculation device 300 based on the pulse signal.

[0043] The state determination estimation unit 420 is connected to the acquisition unit 410. The state determination estimation unit 420 estimates the state of the fluid based on the state data acquired by the acquisition unit 410. The state determination estimation unit 420 may estimate the flow pattern of the fluid in the pipe 40 as the state of the fluid based on the state data acquired by the acquisition unit 410. The flow pattern will be described later with reference to FIG. 2. The state determination estimation unit 420 may estimate the state of the fluid based on the state data acquired by the acquisition unit 410 using a first model that estimates the state of the fluid from the state data.

[0044] The selection unit 430 is connected to the state determination / estimation unit 420. The selection unit 430 selects a calculation corresponding to the estimated state of the fluid from a plurality of calculations. The selection unit 430 may select a different calculation depending on the flow pattern of the fluid. The selection unit 430 may select a second model corresponding to the state of the fluid estimated by the state determination / estimation unit 420 from a plurality of second models that estimate at least one of the liquid amount and dryness fraction of the fluid from intensity data of at least one frequency band related to the detection signal of the fluid, depending on the state of the fluid.

[0045] The fluid quality estimation unit 440 is connected to the selection unit 430. The fluid quality estimation unit 440 estimates at least one of the liquid amount or dryness fraction of the fluid from the intensity data acquired by the acquisition unit 410, depending on the state of the fluid estimated by the state determination / estimation unit 420. The fluid quality estimation unit 440 may estimate at least one of the liquid amount or dryness fraction of the fluid from the intensity data acquired by the acquisition unit 410 by a calculation selected by the selection unit 430. The fluid quality estimation unit 440 may estimate at least one of the liquid amount or dryness fraction of the fluid from the intensity data acquired by the acquisition unit 410, using a second model selected by the selection unit 430.

[0046] The learning processing unit 450 is connected to the state determination / estimation unit 420, the selection unit 430, and the fluid quality estimation unit 440. The learning processing unit 450 performs a learning process for a plurality of models used in the estimation device 400. The learning processing unit 450 may generate a first model that estimates the state of the fluid from intensity data of at least one frequency band related to the fluid detection signal. The learning processing unit 450 may generate a second model for each state of the fluid.

[0047] The output unit 460 is connected to the fluid quality estimation unit 440 and the external device 30. The output unit 460 outputs at least one of the liquid amount or dryness fraction estimated by the fluid quality estimation unit 440 to the external device 30.

[0048] The external device 30 may be a display device that displays the estimation result of the estimation device 400, or a PC that stores the estimation result of the estimation device 400, or the like.

[0049] Fig. 2 is an explanatory diagram for explaining the flow patterns of a fluid inside the pipe 40. Fig. 2 shows XZ cross sections and YZ cross sections inside the pipe 40 for the flow patterns of laminar flow, laminar wavy flow, and annular mist flow. In Fig. 2, the fluid flows in the Y-axis direction inside the pipe 40.

[0050] For example, a fluid containing evaporated water is in the wet steam state, where liquid water is mixed with gaseous vapor, and has three flow states (flow patterns). In a stratified flow, the liquid flows gently along the bottom of the tube 40. In a stratified wavy flow, the liquid flows in a wavy manner along the bottom of the tube 40. In an annular mist flow, the liquid flows in contact with the entire inner wall of the tube 40, resulting in many droplets mixing with the vapor. These flow patterns vary depending on the gas flow rate and wetness of the fluid. The three flow patterns result in different Karman vortex states and vortex detection signals in measurements by the vortex flowmeter 100, and also have different effects on fluid measurements. These fluid states are similar for fluids other than evaporated water.

[0051] Therefore, in this embodiment, the liquid amount or dryness fraction of the fluid, which is an index of steam quality, is estimated by calculation according to the state of the fluid.

[0052] 3 is a flow diagram showing an example of the operation of the vortex flowmeter 100 according to this embodiment. In step S300, when the fluid to be measured flows through the pipe 40 and reaches a certain flow velocity or higher, vortices called Karman vortices are generated downstream of the vortex shedder 110. The Karman vortices are generated alternately on the left and right (or up and down) around the axis of the vortex shedder 110, and the generation of vortices changes the pressure in the fluid. The two piezoelectric elements of the detection unit 120 output charges corresponding to the pressure at their installation locations as a first detection signal and a second detection signal, respectively.

[0053] In step S310, the charge converter 130 converts the first and second detection signals, which are charge signals, into voltage signals using a charge amplifier. The charge converter 130 may then amplify the signal levels of the first and second detection signals in voltage format using a gain amplifier.

[0054] In step S320, the AD converter 140 converts the first and second detection signals, which are in analog format and have been converted into voltage format by the charge converter 130, into digital signals.

[0055] In step S330, the frequency analysis unit 150 may divide the first and second detection signals, each in digital format, into multiple frequency bands and acquire intensity data indicating the signal strength of each frequency band. The frequency analysis unit 150 may divide the detection signal into multiple frequency bands determined in advance depending on the inner diameter of the pipe 40 through which the fluid to be measured flows or the type of fluid to be measured. For example, the frequency analysis unit 150 may decompose the first detection signal into multiple frequency bands (e.g., octave bands) obtained by dividing the entire frequency band (e.g., 0 to 15 kHz) in which the vortex frequency is to be measured, and acquire intensity data indicating the signal strength of each frequency band. The frequency analysis unit 150 may decompose the second detection signal into multiple frequency bands, similar to the first detection signal, and acquire intensity data indicating the signal strength of each frequency band.

[0056] For example, the frequency analysis unit 150 may divide the detection signal into multiple frequency bands using a band-pass filter. As an example, the frequency analysis unit 150 may have multiple band-pass filters that correspond one-to-one to multiple frequency bands obtained by dividing the entire frequency band targeted by the vortex flowmeter 100, and each band-pass filter may pass signal components in the detection signal within its assigned pass frequency band and reduce or remove signal components outside the band. Alternatively, the frequency analysis unit 150 may convert the detection signal into a frequency domain signal by Fourier transforming it, thereby calculating the signal strength of the detection signal in each frequency band.

[0057] The frequency analysis unit 150 may output intensity data of one of the divided frequency bands to the communication unit 170. Furthermore, the frequency analysis unit 150 may transmit the intensity data of the divided frequency bands, for example, the intensity data of all the frequency bands, to the estimation device 400 via the communication unit 170. The frequency analysis unit 150 may transmit data indicating the frequency band corresponding to the intensity data to the estimation device 400 via the communication unit 170, together with the intensity data.

[0058] In step S340, the frequency analysis unit 150 removes noise from the detection signal using the intensity data in multiple frequency bands to obtain a vortex signal (a signal that produces a frequency proportional to the flow velocity generated by a Karman vortex).

[0059] First, the frequency analysis unit 150 identifies a frequency band that includes the vortex frequency of the vortex signal (also referred to as the vortex frequency band). The frequency analysis unit 150 may identify a frequency band that includes intensity data with the maximum signal intensity within the entire frequency band as the vortex frequency band.

[0060] The frequency analysis unit 150 may also identify a frequency band containing the intensity data with the maximum signal intensity within the entire frequency band. In the frequency band containing the identified maximum intensity data, the frequency analysis unit 150 may calculate a signal ratio between the maximum intensity data and the intensity data of the other detection signal (i.e., the ratio between the signal intensity of the first detection signal and the signal intensity of the second detection signal in the identified frequency band). If the calculated signal ratio is within a predetermined range, the frequency analysis unit 150 may identify the frequency band as a vortex frequency band. If the calculated signal ratio is not within the predetermined range, the frequency analysis unit 150 may similarly determine whether the calculated signal ratio is within the predetermined range for the frequency band containing the next (second largest) intensity data. The predetermined range may be set according to at least one of the frequency band assumed to be the occurrence frequency of Karman vortices calculated from the flow velocity of the fluid under measurement, the type of fluid under measurement, or the inner diameter of the pipe 40 through which the fluid under measurement flows.

[0061] Next, the frequency analysis unit 150 determines the coefficient λ for removing noise from the detection signal using intensity data of frequency bands other than the identified vortex frequency band. The frequency analysis unit 150 may determine the coefficient λ for removing noise using intensity data of frequency bands other than the identified vortex frequency band and at least one frequency band adjacent to the identified vortex frequency band.

[0062] The frequency analysis unit 150 may determine, as the coefficient λ, a signal ratio equal to or greater than a predetermined minimum signal ratio among signal ratios (ratios between the signal strength of the first detection signal and the signal strength of the second detection signal) in frequency bands other than the vortex frequency band that include signal strengths equal to or greater than a predetermined threshold. In this case, the frequency analysis unit 150 may determine the coefficient λ using a signal ratio in a frequency band in which the signal strength of the same detection signal as the detection signal with the maximum signal strength in the entire frequency band, among the first detection signal and the second detection signal, is equal to or greater than a predetermined threshold. If there is no signal that satisfies the above condition, the frequency analysis unit 150 may omit noise removal and output the first detection signal in the vortex frequency band to the pulse conversion unit 160. The predetermined threshold may be set according to at least one of the type of fluid to be measured and the inner diameter of the pipe 40 through which the fluid to be measured flows. The predetermined minimum signal ratio may be set according to the inner diameter of the pipe 40 through which the fluid to be measured flows.

[0063] Next, the frequency analysis unit 150 removes noise from the detection signal using the determined coefficient λ. The frequency analysis unit 150 may remove noise by linearly combining the first detection signal A and the second detection signal B output from the AD converter 140 using the coefficient λ. This allows the frequency analysis unit 150 to generate a combined signal C (vortex flow signal) containing a component of the vortex signal (C=A-λB).

[0064] Next, the frequency analysis unit 150 may extract signal components in the vortex frequency band identified in step S340 from the combined signal to generate a signal having a vortex frequency. The frequency analysis unit 150 may use, for example, a band-pass filter to pass signal components within the vortex frequency band of the combined signal and reduce or remove signal components outside the vortex frequency band. The frequency analysis unit 150 outputs the extracted combined signal in the vortex frequency band to the pulse conversion unit 160. For example, the extracted combined signal in the vortex frequency band is a signal having an approximately sinusoidal waveform.

[0065] The frequency analysis unit 150 may transmit strength data indicating the signal strength of at least one frequency band of the combined signal as status data to the estimation device 400 via the communication unit 170. For example, the frequency analysis unit 150 may use multiple band-pass filters with different pass frequency bands to divide the combined signal into multiple frequency bands that are the same as the multiple frequency bands divided in step S330. The frequency analysis unit 150 may output at least one of the strength data indicating the signal strength of each of the multiple frequency bands obtained by dividing the combined signal as status data to the communication unit 170. The frequency analysis unit 150 may transmit data indicating the frequency band corresponding to the strength data of the combined signal together with the strength data to the estimation device 400 via the communication unit 170.

[0066] In step S350 , the pulse converting section 160 may generate a pulse signal having the same frequency as the combined signal received from the frequency analyzing section 150 .

[0067] In step S360, the communication unit 170 outputs a pulse signal indicating the vortex frequency of the detection signal, which is generated based on the intensity data of at least one frequency band, to the calculation device 300. The communication unit 170 may transmit the pulse signal converted into a pulse by the pulse conversion unit 160 to the calculation device 300. This enables the calculation device 300 to perform pulse counting of the received pulse signal and calculate a frequency corresponding to the number of Karman vortices generated per unit time.

[0068] Figure 4 shows the signal strength of the detection signal of the vortex flowmeter 100 in multiple frequency bands. Figure 4 shows intensity data indicating the signal strength of each frequency band obtained by the frequency analysis unit 150 decomposing the first and second digital detection signals into multiple frequency bands in step S330 shown in Figure 3. In Figure 4, the vertical axis represents signal strength and the horizontal axis represents frequency. In Figure 4, A0 to A8 represent the signal strength of the first detection signal in the frequency bands 0 to f0, f0 to f1, f1 to f2, f2 to f3, f3 to f4, f4 to f5, f5 to f6, f6 to f7, and f7 to f8, respectively, and B0 to B8 represent the signal strength of the second detection signal in the frequency bands 0 to f0, f0 to f1, f1 to f2, f2 to f3, f3 to f4, f4 to f5, f5 to f6, f6 to f7, and f7 to f8, respectively. The values ​​of the frequencies f0 to f8 are each set in accordance with at least one of the inner diameter of the pipe 40 through which the fluid to be measured flows and the type of the fluid to be measured.

[0069] The frequency analysis unit 150 determines whether the frequency band f3 to f4 including the intensity data A4 with the maximum signal intensity within all frequency bands f0 to f8 is a vortex frequency band. The frequency analysis unit 150 calculates the signal ratio (A4 / B4), and if the calculated signal ratio is within a predetermined range, identifies the frequency band f3 to f4 as a vortex frequency band.

[0070] The frequency analysis unit 150 determines the coefficient λ for noise removal using intensity data of frequency bands excluding frequency bands f3 to f4 and frequency bands f2 to f3 and f4 to f5 before and after frequency bands f3 to f4. When the signal ratio A8 / B8 in frequency bands f7 to f8 including signal intensity A8 equal to or greater than a predetermined threshold, among frequency bands other than frequency bands f3 to f4, f2 to f3, and f4 to f5, is equal to or greater than a predetermined minimum signal ratio, the frequency analysis unit 150 may determine the signal ratio A8 / B8 as the coefficient λ.

[0071] The frequency analysis unit 150 uses the determined coefficient λ to linearly combine the first detection signal A and the second detection signal B output from the AD converter 140 with the coefficient λ to generate a combined signal C (C=A−λB). Next, the frequency analysis unit 150 uses a plurality of bandpass filters having different pass frequency bands to decompose the combined signal C into a plurality of frequency bands 0 to f0, f0 to f1, f1 to f2, f2 to f3, f3 to f4, f4 to f5, f5 to f6, f6 to f7, and f7 to f8, and acquires a combined signal C0 of the frequency bands 0 to f0, a combined signal C1 of the frequency bands f0 to f1, a combined signal C2 of the frequency bands f1 to f2, a combined signal C3 of the frequency bands f2 to f3, a combined signal C4 of the frequency bands f3 to f4, a combined signal C5 of the frequency bands f4 to f5, a combined signal C6 of the frequency bands f5 to f6, a combined signal C7 of the frequency bands f6 to f7, and a combined signal C8 of the frequency bands f7 to f8.

[0072] The frequency analysis unit 150 outputs the combined signal C4 to the pulse conversion unit 160. The pulse conversion unit 160 generates a pulse signal having the same frequency as the combined signal C4 and transmits it to the calculation device 300 via the communication unit 170. The calculation device 300 calculates a frequency corresponding to the number of Karman vortices generated per unit time from the received pulse signal.

[0073] The frequency analysis unit 150 transmits at least one of the intensity data indicating the signal intensity of the combined signals C0 to C8 in the plurality of frequency bands (particularly, the intensity data indicating the signal intensity of the combined signal C4) to the estimation device 400 via the communication unit 170. The frequency analysis unit 150 may transmit data indicating the corresponding frequency band together with the intensity data to the estimation device 400 via the communication unit 170.

[0074] 5 is a flow chart showing an example of the operation of the estimation device 400 according to this embodiment. The estimation device 400 may start the estimation operation when it receives status data from the vortex flowmeter 100 and the arithmetic device 300.

[0075] In step S500, the acquisition unit 410 acquires status data about the fluid to be measured. The acquisition unit 410 acquires, as status data, a digital signal indicating intensity data of at least one frequency band transmitted from the communication unit 170 of the vortex flowmeter 100. The acquisition unit 410 may acquire status data including intensity data of multiple frequency bands related to the detection signal of the fluid detected by the vortex flowmeter 100. The acquisition unit 410 may further acquire data indicating the corresponding frequency bands along with the intensity data. The acquisition unit 410 may acquire status data including at least one of flow velocity, temperature, pressure, viscosity, concentration, density, or vortex frequency from the calculation device 300. By using the status data from the calculation device 300 together with the intensity data, the estimation device 400 can improve the accuracy of the state estimation about the fluid and the accuracy of the estimation of the liquid volume or dryness fraction.

[0076] The acquisition unit 410 may periodically receive status data. The acquisition unit 410 may associate multiple status data acquired simultaneously or at the same interval from the vortex flowmeter 100 and the calculation device 300 and output the associated data to the status determination / estimation unit 420 and the fluid quality estimation unit 440. In the following steps, the status determination / estimation unit 420 and the fluid quality estimation unit 440 may perform estimation for each associated set of status data acquired simultaneously or at the same interval by the acquisition unit 410 from the vortex flowmeter 100 and the calculation device 300.

[0077] The acquisition unit 410 may perform preprocessing on the acquired intensity data and output the preprocessed intensity data to the state determination / estimation unit 420 and the fluid quality estimation unit 440. When the acquisition unit 410 acquires intensity data for multiple frequency bands, it may calculate a value that combines the multiple intensity data. For example, the acquisition unit 410 may calculate the sum or average of all or some of the multiple intensity data and output the sum or average of the intensity data to the state determination / estimation unit 420 and the fluid quality estimation unit 440. The acquisition unit 410 may acquire, as preprocessed intensity data, the sum, difference, ratio, or multiplication value of the maximum intensity data among the multiple intensity data and the intensity data in a frequency band adjacent to the frequency band corresponding to the maximum intensity data.

[0078] The acquiring unit 410 may also acquire intensity data normalized by preprocessing. The acquiring unit 410 may normalize the intensity data based on reference intensity data. For example, the acquiring unit 410 may calculate the difference or ratio between the acquired intensity data and the reference intensity data and acquire the calculated value as normalized intensity data. The acquiring unit 410 may pre-store reference intensity data corresponding to multiple frequency bands and use the reference intensity data corresponding to the frequency band of the intensity data to be preprocessed. The acquiring unit 410 may use pre-stored intensity data in the normal state of the vortex flowmeter 100 (e.g., the state at the start of operation) or intensity data acquired before the intensity data to be preprocessed was acquired as the reference intensity data. By normalizing the intensity data, the estimation device 400 can prevent a decrease in estimation accuracy due to changes in the detection signal caused by individual differences in the vortex flowmeter 100 or part replacement, etc.

[0079] The acquisition unit 410 may output at least one of preprocessed intensity data (preprocessed intensity data may also be simply referred to as intensity data) and non-preprocessed intensity data to the state determination / estimation unit 420.

[0080] In step S505, the state determination / estimation unit 420 determines whether the state of the fluid is superheated steam based on the state data of the fluid acquired by the acquisition unit 410. The state determination / estimation unit 420 may determine whether the state of the fluid is superheated steam by comparing at least one of the state data acquired by the acquisition unit 410 with a threshold. For example, the state determination / estimation unit 420 may determine that the state of the fluid is superheated steam (i.e., not wet steam) when the measured value of the temperature of the fluid exceeds the boiling point of the fluid (the boiling point of the liquid contained in the fluid) based on the measured value of the pressure of the fluid acquired by the acquisition unit 410. As an example, the state determination / estimation unit 420 may acquire the boiling point corresponding to the measured value of the pressure of the fluid from the vapor curve of the liquid in the fluid, and determine that the fluid is superheated steam when the measured value of the temperature of the fluid exceeds the boiling point. If the measured temperature of the fluid exceeds the boiling point of the fluid (Yes in step S505), the estimation device 400 may proceed to step S530, and if the measured temperature of the fluid is below the boiling point of the fluid (No in step S505), the estimation device 400 may proceed to step S510.

[0081] Furthermore, the state determination / estimation unit 420 may determine that the state of the fluid is superheated steam when the signal strength indicated by the intensity data is within a predetermined range. The predetermined range may be a range of signal strength for superheated steam previously obtained through experiments or the like.

[0082] In step S510, the state determination / estimation unit 420 determines whether the state of the fluid is wet steam based on the state data of the fluid acquired by the acquisition unit 410. The state determination / estimation unit 420 may determine whether the fluid is wet steam based on at least one of the measured values ​​of the acquired state data using a wet steam determination model that estimates whether the fluid is wet steam from at least one of the measured values ​​of the state data. The wet steam determination model may be a model that outputs a determination result such as logistic regression, a neural network, a support vector machine, a classification tree, change point detection, a k-nearest neighbor method, or a k-means method.

[0083] Furthermore, the state determination / estimation unit 420 may determine that the state of the fluid is wet steam (i.e., not superheated steam) when the measured temperature value of the fluid included in the state data is lower than the boiling point of the fluid. In this case, the state determination / estimation unit 420 may determine the state of the fluid using a steam curve.

[0084] In steps S505 and S510, the state determination / estimation unit 420 determines whether the state of the fluid is wet steam using the state data acquired by the acquisition unit 410. If the estimation device 400 determines that the fluid is not superheated steam in step S505 and that the fluid is wet steam in step S510 (Yes in S510), the process may proceed to step S520, and if the estimation device 400 determines that the fluid is not wet steam (No in S510), the process may proceed to step S530.

[0085] In step S520, the state determination / estimation unit 420 estimates whether the state of the fluid is a laminar flow, a laminar wavy flow, or an annular mist flow, based on the state data acquired by the acquisition unit 410. The state determination / estimation unit 420 may estimate the flow pattern of the fluid using a first model that estimates the state of the fluid from the state data related to the fluid. The first model may be a classification model, such as a logistic regression, a neural network, a support vector machine, a classification tree, a change-point detection, a k-nearest neighbor method, or a k-means method.

[0086] The state determination / estimation unit 420 may estimate the state of the fluid based on whether the acquired intensity data falls within a range corresponding to a laminar flow, a range corresponding to a laminar wavy flow, or a range corresponding to annular mist flow intensity data. The range corresponding to a laminar flow, a range corresponding to a laminar wavy flow, and a range corresponding to annular mist flow may be signal intensity ranges previously obtained by experiments or the like for each state.

[0087] In step S525, the selection unit 430 selects a calculation corresponding to the fluid state estimated by the state determination / estimation unit 420 from a plurality of calculations for estimating at least one of the liquid amount and the dryness fraction. The selection unit 430 may select a second model corresponding to the fluid flow pattern estimated by the state determination / estimation unit 420 from a second model corresponding to a stratified flow, a second model corresponding to a stratified wavy flow, or a second model corresponding to annular mist flow. The plurality of second models may be generated by a learning process for each corresponding flow pattern, and may be different from each other. The selection unit 430 may use a plurality of second models generated in advance by the learning processing unit 450.

[0088] The second model may be a regression model, such as multiple regression analysis, neural network, support vector regression, Gaussian process regression, regression tree, autoregressive model, logistic regression, classification tree, change point detection, k-nearest neighbor method, or k-means method. The neural network may include a convolutional neural network, a recurrent neural network, or a long-short-term memory neural network. The second model may be generated by a learning process using intensity data or preprocessed intensity data indicating the signal intensity of the piezoelectric element measured by the vortex flowmeter 100 as training data and dryness fraction or liquid volume corresponding to the intensity data as teacher data.

[0089] The second model may be a regression model that outputs a dryness fraction or a liquid volume in response to input of intensity data in at least one frequency band. As an example, the second model can output a dryness fraction or a liquid volume of the fluid to be measured using intensity data in a frequency band including the vortex frequency as input. As the fluid (steam) becomes wetter, the signal strength of the detection signal from the piezoelectric element decreases due to the moisture contained in the steam. Therefore, the signal strength of the piezoelectric element detected by the vortex flowmeter 100 has a correlation (e.g., a linear relationship) with the dryness fraction or liquid volume of the fluid to be measured. Therefore, the second model may be a model that outputs a larger dryness fraction or a smaller liquid volume in response to input of intensity data with a larger signal strength.

[0090] In step S530, the fluid quality estimation unit 440 estimates at least one of the liquid amount and the dryness fraction of the fluid from the intensity data. The fluid quality estimation unit 440 may estimate at least one of the liquid amount and the dryness fraction of the fluid from the intensity data of multiple frequency bands. The fluid quality estimation unit 440 may estimate at least one of the liquid amount and the dryness fraction of the fluid from the maximum value of the intensity data of multiple frequency bands or a value obtained by combining the intensity data of multiple frequency bands through preprocessing by the acquisition unit 410. The fluid quality estimation unit 440 may estimate at least one of the liquid amount and the dryness fraction of the fluid from the intensity data normalized through preprocessing by the acquisition unit 410.

[0091] The fluid quality estimation unit 440 may obtain, as an estimation result, at least one of the liquid amount and the dryness fraction output in response to inputting the intensity data acquired by the acquisition unit 410 into the second model selected by the selection unit 430. The fluid quality estimation unit 440 may obtain, as an estimation result, at least one of the liquid amount and the dryness fraction output in response to inputting intensity data of a predetermined frequency band (for example, a frequency band including an eddy frequency) from among a plurality of frequency bands into the second model. The fluid quality estimation unit 440 may input data indicating the corresponding frequency band together with the intensity data into the second model.

[0092] The fluid quality estimating unit 440 may estimate at least one of the liquid amount and the dryness fraction of the fluid from the acquired intensity data and state data including at least one of the flow rate, temperature, pressure, viscosity, concentration, and density measured for the fluid, depending on the estimated state of the fluid. The fluid quality estimating unit 440 may input all types of state data acquired by the acquiring unit 410 into the second model, or may input state data indicating predetermined types of measured values ​​from the flow rate, temperature, pressure, viscosity, concentration, and density acquired by the acquiring unit 410 together with the intensity data into the second model. The fluid quality estimating unit 440 may estimate one of the liquid amount and the dryness fraction, and calculate the other of the liquid amount and the dryness fraction from the estimated one using the vapor flow rate of the fluid.

[0093] If the state determination / estimation unit 420 determines in step S505 or S510 that the state of the fluid is not wet steam, the fluid quality estimation unit 440 may estimate that the liquid amount for the fluid is 0 or the dryness fraction is 100%. Since the state determination / estimation unit 420 determines that the fluid is superheated steam or is not wet steam, the fluid quality estimation unit 440 may use the liquid amount of 0 or the dryness fraction of 100% as the estimation result. In this case, the fluid quality estimation unit 440 may calculate the enthalpy or heat quantity of the superheated steam from the state data as the estimation result for the fluid.

[0094] In step S535, the output unit 460 outputs the estimation result of the fluid quality estimation unit 440 to the external device 30. The output unit 460 may output display data for displaying the estimation result on the external device 30. The output unit 460 may also output data for notifying a user of the estimation result by an alarm or the like of the external device 30. As an example, the output unit 460 may output data for notifying a user of the estimation result by an alarm of the external device 30 when the liquid amount or dryness fraction, which is the estimation result, exceeds a predetermined threshold or is equal to or less than a predetermined threshold. The predetermined threshold may be set in advance by a user or the like. The output unit 460 may also output control data for controlling the plant in accordance with the estimation result to the external device 30, and the external device 30 may control the plant in accordance with the control data.

[0095] FIG. 6 is an explanatory diagram illustrating an example of the first model. In the example of FIG. 6, the first model is a neural network model, and each node is represented by a circle. In the first model, state data (vortex frequency, flow velocity, intensity data, pressure, and temperature) related to the fluid acquired by the acquisition unit 410 from the vortex flowmeter 100 and the computing device 300 is input to each node in the input layer, and an estimated result of the flow pattern of the fluid is output to a node in the output layer. The three nodes in the output layer are a node that outputs a calculation result for a stratified flow (e.g., the probability that the fluid is a stratified flow), a node that outputs a calculation result for a laminar wavy flow (e.g., the probability that the fluid is a laminar wavy flow), and a node that outputs a calculation result for an annular mist flow (e.g., the probability that the fluid is an annular mist flow).

[0096] The number of layers and the number of neurons in the hidden layer of the first model may be set to any number. The activation function of the first model may be a general nonlinear function such as a ramp function, a sigmoid function, or a hyperbolic tangent function. From the viewpoint of the backpropagation method, a softmax function may be provided in the output layer when performing multi-value classification.

[0097] In the first model, in response to state data being input to the input layer, calculation results according to weights assigned to nodes in the intermediate layer are output to nodes in the output layer, and the state determination and estimation unit 420 may output the most probable flow pattern as the state of the fluid. Note that the state determination and estimation unit 420 may normalize each value of the state data to a predetermined range and input the normalized value to each node in the input layer. The learning processing unit 450 may perform a learning process using a data group of multiple measurement values ​​of the state data as training data and flow patterns related to the measurement values ​​as teacher data, thereby optimizing the weights, to generate the first model.

[0098] The state data for each flow regime used in the learning process may be data previously obtained through experiments, simulations, plant operations, or the like. As an example, the state data may be obtained by controlling the flow regime in the pipe 40 through an experiment, and the state data for each flow regime may be obtained via a user or automatically from an experimental device. Alternatively, the state data may be obtained for each flow regime determined by a user by observing the state of the fluid during an experiment or plant operation. Alternatively, the state data may be obtained for each flow regime identified by a user from the results of a simulation of the fluid in the pipe 40, or may be obtained for each flow regime automatically identified from the state of the system being simulated.

[0099] The first model has at least one node in an input layer, and all or some of the multiple measurement values ​​of the status data acquired by the acquisition unit 410 may be input to the node in the input layer. The moist steam determination model may also be a model similar to the first model, and may be subjected to the same learning process as the first model.

[0100] FIG. 7 is an explanatory diagram illustrating an example of the second model. In the example of FIG. 7, the second model is a neural network model, and each node is represented by a circle. In the second model, state data (vortex frequency, flow velocity, intensity data, pressure, and temperature) acquired by the acquisition unit 410 from the vortex flowmeter 100 and the arithmetic device 300 is input to each node in the input layer, and the liquid volume or dryness fraction of the fluid is output to each node in the output layer. The intermediate layer of the second model may have any number of layers and any number of neurons. The activation function of the second model may be a general nonlinear function such as a ramp function, a sigmoid function, or a hyperbolic tangent function. From the perspective of the backpropagation method, a softmax function may be provided in the output layer when performing multi-value classification.

[0101] In the second model, in response to state data being input to the input layer, calculation results (liquid volume or dryness fraction) according to weights assigned to nodes in the intermediate layer are output to nodes in the output layer, and the fluid quality estimating unit 440 may output the output liquid volume or dryness fraction as an estimation result. The fluid quality estimating unit 440 may normalize each value of the state data within a predetermined range and input the normalized value to each node in the input layer. The learning processing unit 450 may perform a learning process using a data set of multiple types of state data obtained for each flow pattern as training data and optimize the weights to generate the second model. The learning processing unit 450 may generate a second model corresponding to the stratified flow using multiple types of state data for a stratified flow as training data. The learning processing unit 450 may generate a second model corresponding to the stratified wavy flow using multiple types of state data for a stratified wavy flow as training data. The learning processing unit 450 may generate a second model corresponding to the annular mist flow by using, as training data, a plurality of types of state data when the fluid is an annular mist flow.

[0102] The state data for each flow pattern used in the learning process may be data obtained in advance by an experiment, a simulation, plant operation, etc. The state data may be obtained in the same manner as the state data used in the learning process of the first model.

[0103] The second model may have at least one node in the input layer, and all or part of the multiple types of state data acquired by the acquisition unit 410 may be input to the node in the input layer.

[0104] The estimator 400 of this embodiment can estimate the liquid amount or dryness fraction using a model corresponding to the state of the fluid, thereby achieving high estimation accuracy. Furthermore, since the estimator 400 uses the digital intensity data generated by the vortex flowmeter 100 for its estimation operation, the estimator 400 does not need to have an additional component for processing analog signals, and can have a simple configuration for processing digital values ​​in its estimation operation.

[0105] The fluid quality estimator 440 may estimate the liquid amount or dryness fraction using a function or table that defines the relationship between the intensity data and the liquid amount or dryness fraction. The acquirer 410 may not preprocess the intensity data. In this case, the vortex flowmeter 100 may preprocess the intensity data in the same manner as in step S500 and transmit the preprocessed intensity data to the estimator 400. This allows the estimator 400 to use the preprocessed intensity data in its estimation operation.

[0106] Furthermore, the estimation device 400 does not need to include the learning processing unit 450, and may store in advance a first model generated by an external learning device and a second model generated by an external learning device. In this case, the state determination / estimation unit 420 may estimate the state of the fluid using the stored first model, and the fluid quality estimation unit 440 may estimate at least one of the liquid volume and the dryness fraction using the stored second model. The learning device may include at least the learning processing unit 450 of the above-described embodiment. The learning device may be included in a computer such as a PC or an external device such as a cloud.

[0107] Various embodiments of the present invention may also be described with reference to flowcharts and block diagrams, where the blocks may represent (1) stages of a process in which operations are performed or (2) sections of an apparatus responsible for performing the operations. Particular stages and sections may be implemented by dedicated circuitry, programmable circuitry provided with computer-readable instructions stored on a computer-readable medium, and / or a processor provided with computer-readable instructions stored on a computer-readable medium. Dedicated circuitry may include digital and / or analog hardware circuitry, and may include integrated circuits (ICs) and / or discrete circuits. Programmable circuitry may include reconfigurable hardware circuitry, including logical AND, OR, XOR, NAND, NOR, and other logic operations, flip-flops, registers, memory elements such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), and the like.

[0108] A computer-readable medium may include any tangible device capable of storing instructions that are executed by an appropriate device, such that the computer-readable medium having instructions stored thereon comprises an article of manufacture containing instructions that can be executed to create means for performing the operations specified in the flowcharts or block diagrams. Examples of computer-readable media may include electronic, magnetic, optical, electromagnetic, and semiconductor storage media. More specific examples of computer-readable media may include floppy disks, diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disc (DVD), Blu-ray (RTM) disc, memory stick, integrated circuit card, and the like.

[0109] The computer readable instructions may include either assembler instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, JAVA®, C++, etc., and conventional procedural programming languages ​​such as the “C” programming language or similar programming languages.

[0110] The computer-readable instructions may be provided to a processor or programmable circuitry of a general-purpose computer, special-purpose computer, or other programmable processing device locally or over a wide-area network (WAN) such as a local area network (LAN), the Internet, etc., which executes the computer-readable instructions to create means for performing the operations specified in the flowcharts or block diagrams. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

[0111] 8 illustrates an example of a computer 2200 in which aspects of the present invention may be embodied, in whole or in part. Programs installed on the computer 2200 may cause the computer 2200 to function as or perform operations associated with an apparatus or one or more sections of the apparatus according to embodiments of the present invention, and / or to perform a process or steps of a process according to embodiments of the present invention. Such programs may be executed by the CPU 2212 to cause the computer 2200 to perform specific operations associated with some or all of the blocks in the flowcharts and block diagrams described herein.

[0112] A computer 2200 according to this embodiment includes a CPU 2212, a RAM 2214, a graphics controller 2216, and a display device 2218, which are interconnected by a host controller 2210. The computer 2200 also includes input / output units such as a communication interface 2222, a hard disk drive 2224, a DVD-ROM drive 2226, and an IC card drive, which are connected to the host controller 2210 via an input / output controller 2220. The computer also includes legacy input / output units such as a ROM 2230 and a keyboard 2242, which are connected to the input / output controller 2220 via an input / output chip 2240.

[0113] The CPU 2212 operates according to programs stored in the ROM 2230 and RAM 2214, thereby controlling each unit. The graphics controller 2216 acquires image data generated by the CPU 2212 into a frame buffer or the like provided in the RAM 2214 or into the graphics controller 2216 itself, and causes the image data to be displayed on the display device 2218.

[0114] The communication interface 2222 communicates with other electronic devices via a network. The hard disk drive 2224 stores programs and data used by the CPU 2212 in the computer 2200. The DVD-ROM drive 2226 reads programs or data from the DVD-ROM 2201 and provides the programs or data to the hard disk drive 2224 via the RAM 2214. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0115] The ROM 2230 stores therein a boot program or the like that is executed by the computer 2200 upon activation, and / or programs that depend on the hardware of the computer 2200. The input / output chip 2240 may also connect various input / output units to the input / output controller 2220 via a parallel port, a serial port, a keyboard port, a mouse port, etc.

[0116] The programs are provided by a computer-readable medium such as a DVD-ROM 2201 or an IC card. The programs are read from the computer-readable medium, installed in the hard disk drive 2224, RAM 2214, or ROM 2230, which are also examples of computer-readable media, and executed by the CPU 2212. Information processing described in these programs is read by the computer 2200, and brings about cooperation between the programs and the various types of hardware resources described above. An apparatus or method may be configured by realizing information manipulation or processing in accordance with the use of the computer 2200.

[0117] For example, when communication is performed between the computer 2200 and an external device, the CPU 2212 may execute a communication program loaded into the RAM 2214 and instruct the communication interface 2222 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 2212, the communication interface 2222 reads transmission data stored in a transmission buffer processing area provided in the RAM 2214, the hard disk drive 2224, the DVD-ROM 2201, or a recording medium such as an IC card, and transmits the read transmission data to the network, or writes reception data received from the network to a reception buffer processing area or the like provided on the recording medium.

[0118] The CPU 2212 may also cause all or a necessary portion of a file or database stored on an external recording medium such as the hard disk drive 2224, the DVD-ROM drive 2226 (DVD-ROM 2201), an IC card, etc. to be read into the RAM 2214, and perform various types of processing on the data on the RAM 2214. The CPU 2212 then writes back the processed data to the external recording medium.

[0119] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and may undergo information processing. The CPU 2212 may perform various types of processing on data read from the RAM 2214, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search / replacement, etc., as described throughout this disclosure and specified by the instruction sequences of the programs, and write the results back to the RAM 2214. The CPU 2212 may also search for information in a file, database, etc. on the recording medium. For example, if multiple entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored on the recording medium, the CPU 2212 may search for an entry that matches a condition specified by the attribute value of the first attribute from among the multiple entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0120] The above-described programs or software modules may be stored in a computer-readable medium on or near the computer 2200. A recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can also be used as a computer-readable medium, thereby providing the programs to the computer 2200 via the network.

[0121] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the claims that such modifications and improvements can also be included within the technical scope of the present invention.

[0122] It should be noted that the execution order of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before," "prior to," etc., and that the processes can be performed in any order unless the output of a previous process is used in a subsequent process. Even if the operational flow in the claims, specifications, and drawings is described using "first," "next," etc. for convenience, this does not mean that the processes must be performed in this order. [Explanation of symbols]

[0123] 10 Systems 20 Measuring Equipment 30 External device 40 tubes 100 Vortex Flowmeter 110 Vortex generator 120 Detector 130 Charge Converter 140 AD converter 150 Frequency analysis section 160 Pulse conversion unit 170 Communications Department 200 sensors 300 Arithmetic equipment 310 Pulse count unit 320 Arithmetic unit 400 Estimation device 410 Acquisition Department 420 State Judgment and Estimation Unit 430 Selection Section 440 Fluid quality estimation section 450 Learning processing unit 460 Output Section 2200 Computer 2201 DVD-ROM 2210 host controller 2212 CPU 2214 RAM 2216 Graphics Controller 2218 Display Device 2220 Input / Output Controller 2222 communication interface 2224 hard disk drive 2226 DVD-ROM drive 2230 ROM 2240 I / O chip 2242 keyboard

Claims

1. an acquisition unit that acquires status data including intensity data of at least one frequency band related to a detection signal detected by the vortex flowmeter for the fluid; a state determination / estimation unit that estimates a state of the fluid based on the acquired state data; a fluid quality estimation unit that estimates at least one of a liquid amount and a dryness fraction of the fluid from the strength data according to the estimated fluid state; an output unit that outputs at least one of the estimated liquid amount or dryness rate; Estimation device.

2. The acquisition unit acquires a digital signal representing intensity data of the at least one frequency band from the vortex flowmeter. The estimation device according to claim 1 .

3. the acquiring unit acquires the state data including at least one of a flow rate, a temperature, a pressure, a viscosity, a concentration, or a density measured for the fluid; The fluid quality estimation unit estimates at least one of a liquid amount and a dryness fraction of the fluid based on the acquired strength data and the state data including at least one of a flow velocity, a temperature, a pressure, a viscosity, a concentration, and a density measured for the fluid, in accordance with the estimated state of the fluid. The estimation device according to claim 1 .

4. the vortex flowmeter outputs a pulse signal indicating a vortex frequency of the detection signal, which is generated based on intensity data of the at least one frequency band, to a computing device; The acquisition unit acquires the state data including at least one of the pressure, flow velocity, volumetric flow rate, density, and mass flow rate of the fluid calculated by the arithmetic device based on the pulse signal. The estimation device according to claim 3 .

5. the acquisition unit acquires the status data including intensity data of a plurality of frequency bands related to a detection signal of the fluid detected by the vortex flowmeter; The fluid quality estimation unit estimates at least one of a liquid amount and a dryness fraction of the fluid from the intensity data of the plurality of frequency bands. The estimation device according to claim 1 .

6. The fluid quality estimation unit estimates at least one of a liquid amount and a dryness fraction of the fluid from a maximum value of the intensity data of the plurality of frequency bands or a combined value of the intensity data of the plurality of frequency bands. The estimation device according to claim 5 .

7. The acquisition unit acquires the normalized intensity data, The fluid quality estimator estimates at least one of a liquid amount and a dryness fraction of the fluid from the normalized intensity data. The estimation device according to claim 1 .

8. a selection unit that selects a calculation corresponding to the estimated fluid state from a plurality of calculations; The fluid quality estimation unit estimates at least one of a liquid amount and a dryness fraction of the fluid from the acquired intensity data by the selected calculation. The estimation device according to claim 1 .

9. The state determination / estimation unit estimates whether the state of the fluid is a laminar flow, a laminar wavy flow, or an annular mist flow based on the acquired state data. The estimation device according to claim 1 .

10. The state determination and estimation unit determining whether the state of the fluid is wet steam based on the acquired state data; When the state of the fluid is determined to be wet steam, the state of the fluid is estimated to be either a laminar flow, a laminar wavy flow, or an annular mist flow based on the acquired state data. The estimation device according to claim 9 .

11. The state determination / estimation unit estimates the state of the fluid based on the acquired state data using a first model that estimates the state of the fluid from the state data. The estimation device according to claim 1 .

12. the selection unit selects, in accordance with the state of the fluid, a second model corresponding to the estimated state of the fluid from a plurality of second models that estimate at least one of a liquid amount and a dryness fraction of the fluid from intensity data of at least one frequency band related to the detection signal of the fluid; The fluid quality estimation unit estimates at least one of a liquid amount and a dryness fraction of the fluid from the intensity data using the second model selected by the selection unit. The estimation device according to claim 8 .

13. a learning processing unit that generates the second model for each state of the fluid. The estimation device according to claim 12.

14. and a learning processing unit that generates a second model for each state of the fluid, the second model estimating at least one of the liquid amount and the dryness fraction of the fluid from intensity data of at least one frequency band related to a detection signal detected by the vortex flowmeter for the fluid. Learning device.

15. acquiring status data including intensity data of at least one frequency band relating to a detection signal detected by the vortex flowmeter for the fluid by a computer; the computer estimating a state of the fluid based on the acquired state data; the computer estimating at least one of a liquid amount and a dryness fraction of the fluid from the intensity data in response to the estimated fluid state; and wherein the computer outputs at least one of the estimated liquid amount or dryness fraction. method.

16. and a step in which the computer generates a second model for each state of the fluid, the second model estimating at least one of the liquid amount and the dryness fraction of the fluid from intensity data of at least one frequency band related to a detection signal detected by the vortex flowmeter for the fluid. How to learn.

17. Computer, an acquisition unit that acquires status data including intensity data of at least one frequency band related to a detection signal detected by the vortex flowmeter for the fluid; a state determination / estimation unit that estimates a state of the fluid based on the acquired state data; a fluid quality estimation unit that estimates at least one of a liquid amount and a dryness fraction of the fluid from the strength data according to the estimated fluid state; an output unit that outputs at least one of the estimated liquid amount and the dryness fraction; A program to function as a

18. Computer, a learning processing unit that generates a second model for each state of a fluid, the second model estimating at least one of a liquid amount and a dryness fraction of the fluid from intensity data of at least one frequency band related to a detection signal detected by the vortex flowmeter for the fluid; A learning program to function as a

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