Diagnostic device, measuring device, diagnostic method, and computer-readable medium

Abstract

The present invention provides a diagnostic device, a measuring device, a diagnostic method, and a computer-readable medium having a diagnostic program recorded thereon. The diagnostic device includes a diagnostic unit that diagnoses the state of the vortex flowmeter using the magnitude of the signal component of at least one target detection signal among at least one detection signal detected by the vortex flowmeter having a vortex generator and a detection unit that detects at least one detection signal corresponding to the vortex generated by the vortex generator, or the judgment result of the magnitude of at least one signal component of a combined signal obtained by linearly combining two or more of the at least one detection signal.

Description

Technical Field

[0001] The present invention relates to a diagnostic device, a measuring device, a diagnostic method, and a computer-readable medium having a diagnostic program recorded thereon. Background Art

[0002] Patent Document 1 discloses a vortex flowmeter. The vortex flowmeter of Patent Document 1 uses piezoelectric elements 81 and 82 to detect the alternating micro-deformations of vortex generator 7 caused by Karman vortices generated by the collision of the measured fluid flowing in pipe 6 with vortex generator 7. Because the number of Karman vortices generated per unit time is proportional to the flow velocity, the detected micro-deformations are used to measure the flow rate of the measured fluid (paragraphs 0002 to 0007, etc.).

[0003] Patent document 2 describes "a vortex flowmeter that uses first and second vortex signals output from two detection units that detect vortex signals generated by a vortex generating body, and outputs a signal ratio of the first and second vortex signals together with a vortex flow rate signal," and "the signal ratio is collected by the equipment management tool to perform predictive diagnosis of blockage of the vortex flowmeter" (Claim 1).

[0004] Patent Document 3 describes a vortex flowmeter that "digitizes a vortex signal, performs a fast Fourier transform on it to convert it into a frequency domain, and determines the presence or absence of noise using a determination unit set in the frequency domain" (paragraph 0027).

[0005] Prior art literature

[0006] Patent Document 1: Japanese Patent Application Laid-Open No. 2003-4497

[0007] Patent Document 2: Japanese Patent Application Laid-Open No. 2008-70292

[0008] Patent Document 3: Japanese Patent Application Laid-Open No. 5-79870 Summary of the Invention

[0009] In a first aspect of the present invention, a diagnostic device is provided. The diagnostic device may include a diagnostic unit for diagnosing the condition of a vortex flowmeter, the vortex flowmeter including a vortex generator and a detection unit for detecting at least one detection signal corresponding to a vortex generated by the vortex generator. The diagnostic unit diagnoses the condition of the vortex flowmeter using a determination result of the magnitude of each signal component of at least one target detection signal in the at least one detection signal detected by the vortex flowmeter, or the magnitude of at least one signal component of a combined signal obtained by linearly combining two or more of the at least one detection signal.

[0010] The diagnostic unit can diagnose the state of the vortex flowmeter based on the judgment result obtained by comparing the magnitude of the signal component of the combined signal with a threshold value.

[0011] The diagnostic device can include a normalization unit that normalizes the magnitude of the signal component of the combined signal using the vortex frequency.

[0012] Based on the judgment result that the magnitude of the signal component of the combined signal after normalization is below the first threshold value, the diagnostic unit can diagnose that the vortex flowmeter is abnormal.

[0013] Based on the judgment result that the magnitude of the signal component of the combined signal after normalization exceeds the first threshold value and is below the second threshold value, the diagnostic unit can predict that the vortex flowmeter will become abnormal.

[0014] The diagnostic device can further include a learning processing unit that generates the first threshold value through learning using the history of the magnitude of the signal component of the combined signal after normalization.

[0015] The diagnostic device can include a time series data storage unit that stores the time series data of the magnitude of the signal component of the combined signal after normalization. The diagnostic device can include a display processing unit that performs display processing for displaying the time series data.

[0016] Based on the judgment result of the change in the magnitude of the signal component of at least one object detection signal, the diagnostic unit can diagnose the state of the vortex flowmeter.

[0017] Based on the judgment result of the change in the magnitude of the signal component corresponding to the vortex frequency of at least one object detection signal, the diagnostic unit can diagnose the state of the vortex flowmeter.

[0018] The diagnostic device can further include a history storage unit that stores history record data associating the magnitude of the signal component corresponding to the vortex frequency of at least one past object detection signal with the vortex frequency. Based on the judgment result of the difference between the magnitude of the signal component associated with the vortex frequency equivalent to the vortex frequency of at least one object detection signal to be diagnosed and the magnitude of the signal component of at least one object detection signal to be diagnosed included in the history record data, the diagnostic unit can diagnose the state of the vortex flowmeter.

[0019] The diagnostic device can include a normalization unit that normalizes the magnitude of the signal component of at least one object detection signal using the vortex frequency.

[0020] Based on the judgment result that the magnitude of the signal component of at least one object detection signal after normalization is below the first threshold value corresponding to at least one object detection signal, the diagnostic unit can diagnose that the vortex flowmeter is abnormal.

[0021] According to a second aspect of the present invention, there is provided a measuring device including a diagnostic device and a vortex flowmeter.

[0022] According to a third aspect of the present invention, there is provided a diagnostic method. The diagnostic method may include obtaining the magnitude of each signal component of at least one target detection signal among at least one detection signal detected by a vortex flowmeter having a vortex generator and a detection unit that detects at least one detection signal corresponding to the vortex generated by the vortex generator, or the magnitude of at least one signal component of a combined signal obtained by linearly combining two or more of the at least one detection signal. The diagnostic method may include diagnosing the state of the vortex flowmeter using the determination result of the magnitude of the signal component.

[0023] According to a fourth aspect of the present invention, there is provided a computer-readable medium recording a diagnostic program executed by a computer. The computer can function as a diagnostic unit by executing the diagnostic program, and the diagnostic unit diagnoses the state of the vortex flowmeter using the determination result of the magnitude of each signal component of at least one target detection signal among at least one detection signal detected by a vortex flowmeter having a vortex generator and a detection unit that detects at least one detection signal corresponding to the vortex generated by the vortex generator, or the magnitude of at least one signal component of a combined signal obtained by linearly combining two or more of the at least one detection signal.

[0024] In addition, the above summary of the invention does not list all features of the present invention. Moreover, sub-combinations of these feature groups can also form an invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Shows the configuration of the measuring device 5 of the present embodiment.

[0026] Figure 2 Shows an example of a structure for detecting vibration caused by a vortex in the vortex flowmeter 10 of the present embodiment and a stress distribution generated in the vortex generator 20.

[0027] Figure 3 Shows the operation flow of the vortex flowmeter 10 of the present embodiment.

[0028] Figure 4 Shows the configuration of the diagnostic device 190 of the present embodiment.

[0029] Figure 5 Shows the diagnostic flow of the diagnostic device 190 for the vortex flowmeter 10 of the present embodiment.

[0030] Figure 6 Is a graph showing a diagnostic example of the diagnostic device 190 for the vortex flowmeter 10 of the present embodiment.

[0031] Figure 7 It is a graph showing a prediction example of the future state of the vortex flowmeter 10 by the diagnostic device 190 of the present embodiment.

[0032] Figure 8 It shows the learning processing flow of the diagnostic device 190 of the present embodiment.

[0033] Figure 9 It shows the configuration of the diagnostic device 990 according to a modified example of the present embodiment.

[0034] Figure 10 It shows the diagnostic process of the diagnostic device 990 according to the modified example for the vortex flowmeter 10 in the present embodiment.

[0035] Figure 11 It shows an example of the computer 2200 in which various aspects of the present invention can be implemented in whole or in part.

[0036] Explanation of reference numerals

[0037] 5 Measuring device, 10 Vortex flowmeter, 16 Pipeline, 20 Vortex generator, 24 Gap, 30 Detection unit, 34a - b Piezoelectric element, 100a - b Charge converter, 110a - b A / D converter, 120 Gate array, 125a - b Spectrum analyzer, 130 Adder, 135 Spectrum analyzer, 140 Band - pass filter, 145 Schmitt trigger, 150 Counter, 160 CPU, 170 Output circuit, 180 Display unit, 190 Diagnostic device, 400 Acquisition unit, 410 Measurement data storage unit, 420 Normalization unit, 430 Diagnostic unit, 440 Time - series data storage unit, 450 Diagnostic result output unit, 460 Display processing unit, 470 Display device, 480 Learning processing unit, 900 Acquisition unit, 910 Measurement data storage unit, 930 Diagnostic unit, 940 Time - series data storage unit, 950 Diagnostic data output unit, 960 Display processing unit, 970 Display device, 980 Learning processing unit, 990 Diagnostic device, 2200 Computer, 2201 DVD - ROM, 2210 Main 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 Input / output chip, 2242 Keyboard. Detailed description of the embodiments

[0038] Hereinafter, the present invention will be described by way of embodiments of the invention. However, the following embodiments do not limit the invention described in the claims. In addition, not all combinations of the features described in the embodiments are essential for the solution means of the invention.

[0039] Figure 1 Shows the configuration of the measuring device 5 of this embodiment. The measuring device 5 includes a vortex flowmeter 10 and a diagnostic device 190. In this embodiment, the vortex flowmeter 10 may adopt the same or similar configuration as the vortex flowmeter described in Patent Document 1 or 2. The vortex flowmeter 10 has: a vortex generator 20, a detection unit 30, one or more charge converters 100a - b (also denoted as "charge converter 100"), one or more A / D converters 110a - b (also denoted as "A / D converter 110"), a gate array 120, a CPU 160, an output circuit 170, and a display unit 180.

[0040] The vortex generator 20 is disposed in the pipeline through which the fluid to be measured flows. The detection unit 30 detects at least one detection signal corresponding to the vortex (Kármán vortex) generated by the vortex generator 20 due to the collision between the fluid to be measured flowing in the pipeline and the vortex generator 20. In this embodiment, as will be described later, the detection unit 30 outputs a first detection signal and a second detection signal detected by two piezoelectric elements provided at different two positions of the vortex generator 20. In this embodiment, each piezoelectric element outputs the charge corresponding to the stress in the position of the vortex generator 20 where each piezoelectric element is provided as a detection signal. Figure 2 As will be described later, the detection unit 30 outputs a first detection signal and a second detection signal detected by two piezoelectric elements provided at different two positions of the vortex generator 20. In this embodiment, each piezoelectric element outputs the charge corresponding to the stress in the position of the vortex generator 20 where each piezoelectric element is provided as a detection signal.

[0041] One or more charge converters 100 are connected to the detection unit 30, and by converting the charge of at least one detection signal into a voltage signal, at least one detection signal in voltage form is obtained. In this embodiment, the charge converter 100a converts the first detection signal into a voltage signal, and the charge converter 100b converts the second detection signal into a voltage signal. Additionally, when the detection unit 30 outputs a detection signal in voltage form or current form, the charge converters 100a - b are not required.

[0042] One or more A / D converters 110 are respectively connected to one or more charge converters 100, and convert at least one detection signal in voltage form into at least one detection signal in digital form. In this embodiment, the A / D converter 110a converts the first detection signal into a digital signal, and the A / D converter 110b converts the second detection signal into a digital signal.

[0043] The gate array 120 is connected to one or more A / D converters 110. The gate array 120 includes: one or more spectrum analyzers 125a - b (also denoted as "spectrum analyzer 125"), an adder 130, a spectrum analyzer 135, a band - pass filter 140, a Schmitt trigger 145, and a counter 150. Additionally, in the present embodiment, these circuits are installed in one gate array 120, but at least one of these respective circuits may also be installed in another IC or the like, and at least a part of them may also be implemented by executing a program in a CPU 160 or the like.

[0044] One or more spectrum analyzers 125 are connected to one or more A / D converters 110. One or more spectrum analyzers 125 perform spectrum analysis of the digital - form detection signals respectively input from one or more A / D converters 110. Spectrum analyzer 125a decomposes the first detection signal into a plurality of frequency bands (e.g., octave bands) obtained by dividing the entire frequency band (e.g., 0 - 15 KHz) that will be the measurement object of the vortex frequency, and outputs the signal intensity of each frequency band. Spectrum analyzer 125b decomposes the second detection signal into a plurality of frequency bands and outputs the signal intensity of each frequency band.

[0045] The adder 130 is connected to one or more A / D converters 110 and the CPU 160. The adder 130 linearly combines two or more of at least one detection signal using the noise ratio set from the CPU 160 and outputs the combined signal. Thus, the adder 130 can cancel and remove the noise components included in at least one detection signal and output a combined signal (also denoted as "vortex flow signal SQ") that includes the component of the vortex signal generated by the vortex.

[0046] The spectrum analyzer 135 is connected to the adder 130. The spectrum analyzer 135 performs spectrum analysis of the vortex flow signal SQ. The spectrum analyzer 135 decomposes the vortex flow signal SQ into a plurality of frequency bands and outputs the signal intensity of each frequency band. And the spectrum analyzer 135 sets the frequency band with the highest signal intensity among the plurality of frequency bands, based on the sensitivity curve proportional to the square of the frequency, as the pass - band of the band - pass filter 140 to the band - pass filter 140. Here, since the magnitude of the vibration caused by the Karman vortex is proportional to the square of the flow velocity (i.e., proportional to the square of the vortex frequency), the spectrum analyzer 135 determines the frequency band with the maximum signal intensity based on the sensitivity curve proportional to the square of the frequency as the frequency band including the vortex signal generated by the Karman vortex. Additionally, the spectrum analyzer 135 may also use the method described in Patent Document 2 to determine the frequency band including the vortex signal generated by the Karman vortex.

[0047] The band-pass filter 140 is connected to the adder 130 and the spectrum analyzer 135. The band-pass filter 140 allows the signal components within the passband set by the spectrum analyzer 135 in the vortex flow signal SQ to pass through, and reduces or removes the signal components outside the passband. Thereby, the band-pass filter 140 outputs a band-pass signal in the vortex flow signal SQ that has the highest signal intensity based on the sensitivity curve proportional to the square of the frequency and has a frequency corresponding to the number of Karman vortices generated per unit time. As an example, as shown in (a) in the figure, this band-pass signal is a signal having a substantially sinusoidal wave shape. Thus, the spectrum analyzer 135 and the band-pass filter 140 function as an extraction unit that extracts, from the combined signal (vortex flow signal SQ) obtained by linearly combining two or more of at least one detection signal, the frequency component having the highest signal intensity based on the sensitivity curve proportional to the square of the frequency (i.e., the frequency component corresponding to the vortex frequency).

[0048] The Schmitt trigger 145 is connected to the band-pass filter 140. The Schmitt trigger 145 converts the vortex flow signal SQ that has passed through the band-pass filter 140 into a pulse signal having the same frequency. The counter 150 is connected to the Schmitt trigger 145. The counter 150 measures the number of pulses per unit time (i.e., the frequency) by counting the pulse signal output from the Schmitt trigger 145. Thus, the Schmitt trigger 145 and the counter 150 function as a frequency measurement unit that measures the frequency of the frequency component corresponding to the vortex frequency in the combined signal extracted by the spectrum analyzer 135 and the band-pass filter 140.

[0049] The CPU 160 is connected to one or more spectrum analyzers 125, the spectrum analyzer 135, and the counter 150. The CPU 160 functions as a noise ratio calculation unit that receives the spectrum analysis results of the respective detection signals from the respective spectrum analyzers 125 and calculates the noise ratio based on the spectrum analysis results. The CPU 160 sets the calculated noise ratio in the adder 130.

[0050] In addition, the CPU 160 receives the number of pulses per unit time of the vortex flow signal SQ that has passed through the band-pass filter 140 from the counter 150. The CPU 160 functions as a flow rate calculation unit or a flow velocity calculation unit and calculates the flow rate or the flow velocity based on the vortex frequency represented by the number of pulses per unit time of the vortex flow signal SQ. The CPU 160 outputs the calculated flow rate or flow velocity to the output circuit 170 and the display unit 180. In addition, the CPU 160 receives the spectrum analysis result of the spectrum analyzer 135 from the spectrum analyzer 135.

[0051] The output circuit 170 is connected to the CPU 160. The output circuit 170 receives the measured flow rate or flow velocity from the CPU 160, and transmits the measured flow rate or flow velocity to the upper-level control device or human-machine interface device, for example, using communication protocols specified by HART (registered trademark), BRAIN, Foundation Fieldbus (registered trademark), ISA100.11a, etc. The display unit 180 receives the measured flow rate or flow velocity from the CPU 160 and displays it.

[0052] The diagnostic device 190 is connected to the vortex flowmeter 10. The diagnostic device 190 can be implemented by dedicated hardware or a dedicated computer having the diagnostic function of the vortex flowmeter 10. Instead, the diagnostic device 190 can also be implemented by a computer such as a PC (personal computer), tablet computer, smartphone, workstation, server computer, or general-purpose computer. In addition, the diagnostic device 190 can be implemented by a cloud computing system that is connected to the vortex flowmeter 10 via a network such as the Internet and provides cloud services such as diagnosis of the vortex flowmeter 10 and measurement of the flow rate or flow velocity of the vortex flowmeter 10, analysis of the measurement results, or control of the device based on the measurement results. When the diagnostic device 190 is implemented by a computer, the diagnostic device 190 can provide various functions of the diagnostic device 190 by executing a diagnostic program for the diagnostic device 190 on the computer.

[0053] The diagnostic device 190 receives the magnitude of each signal component of at least one object detection signal among at least one detection signal, the magnitude of at least one signal component of a combined signal obtained by linearly combining two or more of the at least one detection signal, that is, the amplitude or signal intensity of the signal component, etc., to make a judgment, and uses the judgment result to diagnose the state of the vortex flowmeter 10.

[0054] In addition, in the present embodiment, the diagnostic device 190 is a device separate from the vortex flowmeter 10 and connected to the vortex flowmeter 10. Instead, the diagnostic device 190 can be integrated with the vortex flowmeter 10 to be implemented as the measuring device 5. In addition, the diagnostic device 190 can also have at least a part of the functions of, for example, the gate array 120 or the CPU 160 in the vortex flowmeter 10 repeatedly, corresponding to the components for generating data required for diagnosis.

[0055] Figure 2Shows an example of (A) the structure for detecting vibrations caused by vortices and (B) the stress distribution generated in the vortex generator 20 in the vortex flowmeter 10 of the present embodiment. In the present embodiment, the vortex generator 20 is arranged perpendicular to the pipeline 16. When the fluid to be measured flowing in the pipeline 16 collides with the vortex generator 20, von Karman vortices are generated, and thus an alternating lift force is applied to the vortex generator 20, causing it to deform slightly.

[0056] The detection unit 30 includes one or more piezoelectric elements 34a - b (also denoted as "piezoelectric element 34") provided on the outside of the pipeline 16 and arranged on the vortex generator 20. In the present embodiment, the plurality of piezoelectric elements 34a - b are arranged by being buried at different positions in the extending direction of the vortex generator 20 (the direction perpendicular to the pipeline 16). The first piezoelectric element 34a is arranged at a position farther from the pipeline 16 than the second piezoelectric element 34b. Each piezoelectric element 34 detects the slight deformation of the vortex generator 20 at the portion where each piezoelectric element 34 is provided and outputs it as a charge signal. The number of von Karman vortices generated per unit time is proportional to the flow velocity, so the vortex flowmeter 10 can measure the flow rate of the fluid to be measured using the detection signal of the detection unit 30.

[0057] In addition, instead of the piezoelectric element 34, the detection unit 30 may have a sensor that detects the slight deformation or vibration of the vortex generator 20 by other methods. Furthermore, the piezoelectric element 34 may be arranged downstream of the vortex generator 20 instead of being arranged on the vortex generator 20 to receive the vibration caused by the von Karman vortices.

[0058] Here, each piezoelectric element 34 detects not only the signal generated by the von Karman vortices but also the noise generated by the vibration of the pipeline 16 and the like. Figure 2 of (B) represents Figure 2 An example of the distribution of stress generated by von Karman vortices and stress generated by noise such as vibration (stress distribution) at each position in the extending direction of the vortex generator 20 shown in (A) of. In the figure, S represents the stress distribution of the von Karman vortices, and N represents the stress distribution of the noise. As shown, these stress distributions are quite different.

[0059] The first piezoelectric element 34a outputs a first detection signal obtained by adding the vortex signal S1 corresponding to the stress generated by the von Karman vortices at the position where the first piezoelectric element 34a is provided and the noise N1 corresponding to the stress generated by the noise. In addition, the second piezoelectric element 34b outputs a second detection signal obtained by adding the vortex signal S2 corresponding to the stress generated by the von Karman vortices at the position where the second piezoelectric element 34b is provided and the noise N2 corresponding to the stress generated by the noise.

[0060] Here, if the first detection signal is set as Q1 and the second detection signal is set as Q2, these signals can be represented by the following equations (1) and (2).

[0061] Q1 = S1 + N1 (1)

[0062] Q2 = S2 + N2 (2)

[0063] Here, S1 and S2 are vortex signal components, and N1 and N2 are noise components. The ratio of the vortex signal components S1 / S2 and the ratio of the noise components N1 / N2 in the first detection signal and the second detection signal are different as shown in (B) of Figure 2 . Therefore, by multiplying equation (2) by the noise ratio (N1 / N2) and subtracting it from equation (1), the noise components can be canceled out, and the vortex flow rate signal SQ shown in the following equation (3) can be obtained.

[0064] SQ = Q1 - (N1 / N2) × Q2

[0065] = S1 + N1 - (N1 / N2)(S2 + N2)

[0066] = S1 - (N1 / N2) × S2

[0067] = S1 - γ × S2 (3)

[0068] Here, if the noise ratio γ = N1 / N2 is a known constant, the vortex flow rate signal SQ does not contain noise components but only contains the vortex signal components S1 and S2. The CPU 160 of the present embodiment calculates the noise ratio γ based on the spectral analysis results of the respective detection signals received from each spectral analyzer 125.

[0069] Figure 3 The operation flow of the vortex flowmeter 10 of the present embodiment is shown. In step 300 (S300), the vortex flowmeter 10 uses the detection unit 30, each charge converter 100, and each A / D converter 110 to obtain at least one detection signal.

[0070] In S310, each spectrum analyzer 125 performs spectrum analysis of the detection signal corresponding to that spectrum analyzer 125. Each spectrum analyzer 125 may be configured as follows: similar to the band amplifiers 23 and 24 described in Patent Document 2, it has a plurality of amplifiers AMP1 to n that correspond one-to-one to a plurality of bands (bands) into which the entire frequency band targeted by the vortex flowmeter 10 is divided. Each amplifier AMP amplifies the signal components within the allocated band in the detection signal and reduces or removes the signal components outside the band. Thus, the spectrum analyzer 125 can divide the detection signal by each band. And the spectrum analyzer 125 calculates the magnitude of the signal components of each band of the detection signal. Instead of this, each spectrum analyzer 125 may also perform Fourier transform on the detection signal to transform it into a signal in the frequency domain, and thus calculate the magnitude of the detection signal of each band.

[0071] In S320, the CPU 160 calculates the noise ratio γ based on the spectrum analysis results of each spectrum analyzer 125. The CPU 160 can calculate the noise ratio γ in the same way as the arithmetic unit 26 described in Patent Document 2.

[0072] The CPU 160 in the present embodiment regards the band that includes the second-highest signal component except for the signal component with the highest signal intensity based on the sensitivity curve proportional to the square of the frequency as the band with noise superimposed in the signal components of each band of the detection signal from the piezoelectric element 34a that is the farthest from the pipeline 16 among one or more piezoelectric elements 34. And the CPU 160 calculates the ratio of the first detection signal to the second detection signal in the band with noise superimposed as the noise ratio γ. Instead of this, the CPU 160 may also regard any other band except for the signal component with the highest signal intensity based on the sensitivity curve proportional to the square of the frequency as the band with noise superimposed to calculate the noise ratio γ.

[0073] The CPU 160 sets the calculated noise ratio γ to the adder 130. In addition, in order to determine the band with noise superimposed, the CPU 160 may also compare the magnitudes of the values obtained by dividing the magnitude of the signal components of each band of the detection signal by the square of the center frequency of that band, thereby determining the band with noise superimposed. Furthermore, the CPU 160 may update the noise ratio γ not every time a new detection signal is acquired, but, for example, update the noise ratio γ at regular intervals.

[0074] In S330, the adder 130 linearly combines two or more of the plurality of detection signals by taking a weighted sum using the noise ratio γ, thereby generating a vortex flow signal SQ that includes the components of the vortex signal. In the present embodiment, as shown in Equation (3), the adder 130 generates the vortex flow signal SQ by adding the first detection signal Q1 after multiplying the second detection signal Q2 by -γ times.

[0075] In S340, the spectrum analyzer 135 performs spectrum analysis of the vortex flow rate signal SQ, and calculates the signal intensity of each frequency band obtained by decomposing the vortex flow rate signal SQ into a plurality of frequency bands. Further, the spectrum analyzer 135 sets, as the pass band of the band-pass filter 140, the frequency band having the highest signal intensity among the plurality of frequency bands based on the sensitivity curve proportional to the square of the frequency. Here, the spectrum analyzer 135 may also set, as the pass band of the band-pass filter 140, the frequency band for which the value obtained by dividing the signal intensity of each frequency band by the square of the center frequency of that frequency band is the highest.

[0076] In S350, the band-pass filter 140 allows the signal components within the pass band set by the spectrum analyzer 135 in the vortex flow rate signal SQ to pass through, and reduces or removes the signal components outside the frequency band. In S360, the Schmitt trigger 145 and the counter 150 measure the frequency (vortex frequency) of the vortex flow rate signal SQ that has passed through the band-pass filter 140.

[0077] In S370, the CPU 160 calculates the flow rate or the flow velocity based on the frequency of the vortex flow rate signal SQ measured by the Schmitt trigger 145 and the counter 150. In S380, the output circuit 170 and the display unit 180 output and display the calculated flow rate or flow velocity.

[0078] The vortex flowmeter 10 shown above generates the vortex flow rate signal SQ by linearly combining the two detection signals Q1 and Q2 using the noise ratio γ. Instead of this, the vortex flowmeter 10 may also cancel out the noise components by linearly combining three or more detection signals to generate the vortex flow rate signal SQ.

[0079] In addition, the vortex flowmeter 10 may also measure the flow rate or the flow velocity using the detection signal from one piezoelectric element 34. In this case, the vortex flowmeter 10 includes the piezoelectric element 34, the charge converter 100, and the A / D converter 110 as a set, and may not include the spectrum analyzer 125 and the adder 130. The spectrum analyzer 135 and the band-pass filter 140 receive one detection signal, and output a band-pass signal having the highest signal intensity among the detection signals based on the sensitivity curve proportional to the square of the frequency and having a frequency corresponding to the number of von Kármán vortices generated per unit time. The Schmitt trigger 145 and the counter 150 measure the frequency of the detection signal that has passed through the band-pass filter 140. In this configuration, the vortex flowmeter 10 does not cancel out the noise components of the detection signal. However, if the detection signal includes a vortex signal component that is sufficiently large compared to the noise components, the vortex flowmeter 10 can measure the flow rate or the flow velocity of the fluid to be measured using a configuration having only one piezoelectric element 34.

[0080] Figure 4 Shows the configuration of the diagnostic device 190 of the present embodiment. The diagnostic device 190 includes: an acquisition unit 400, a measurement data storage unit 410, a normalization unit 420, a diagnosis unit 430, a time series data storage unit 440, a diagnosis result output unit 450, a display device 470, and a learning processing unit 480.

[0081] The acquisition unit 400 is connected to the CPU 160 of the vortex flowmeter 10. The acquisition unit 400 acquires measurement data from the vortex flowmeter 10, and the measurement data includes the magnitude of the signal component of the combined signal (vortex flow signal SQ) and the vortex frequency measured by the vortex flowmeter 10. Here, the diagnostic device 190 can acquire the spectral analysis result of the vortex flow signal SQ by the spectral analyzer 135 via the CPU 160, and thereby acquire the signal intensity of the frequency band including the vortex flow signal SQ.

[0082] The measurement data storage unit 410 is connected to the acquisition unit 400 and stores the measurement data acquired by the acquisition unit 400. The normalization unit 420 is connected to the measurement data storage unit 410. The normalization unit 420 normalizes the magnitude of the signal component of the vortex flow signal SQ included in the measurement data stored in the measurement data storage unit 410 using the vortex frequency. Here, as described above, since the magnitude of the vibration caused by the Karman vortex is proportional to the square of the flow velocity, the magnitude of the signal component of the vortex flow signal SQ is proportional to the square of the vortex frequency. Therefore, the normalization unit 420 divides the magnitude of the signal component of the vortex flow signal SQ by the square of the vortex frequency, thereby converting it into the magnitude of the signal component independent of the vortex frequency.

[0083] The diagnosis unit 430 is connected to the normalization unit 420. The diagnosis unit 430 diagnoses the state of the vortex flowmeter 10 based on the judgment result obtained by comparing the magnitude of the signal component of the vortex flow signal SQ after normalization with a threshold value. In addition, the diagnosis unit 430 acquires the change in the magnitude of the signal component of the vortex flow signal SQ after normalization from the time series data storage unit 440, and uses this change to predict the future state of the vortex flowmeter 10.

[0084] The time series data storage unit 440 is connected to the diagnosis unit 430. The time series data storage unit 440 stores the time series data of the magnitude of the signal component of the vortex flow signal SQ after normalization and the diagnosis result of the state of the vortex flowmeter 10.

[0085] The diagnostic result output unit 450 is connected to the time series data storage unit 440. The diagnostic result output unit 450 outputs the time series data of the diagnostic result including the state of the vortex flowmeter 10 stored in the time series data storage unit 440. The diagnostic result output unit 450 can output the time series data of the diagnostic result including the state of the vortex flowmeter 10 to a host device that controls or manages, for example, a factory where the vortex flowmeter 10 is configured. In addition, the diagnostic result output unit 450 may include a display processing unit 460. The display processing unit 460 performs processing (such as generation of a display screen or a web page) for causing the display device 470 to display the time series data of the diagnostic result including the state of the vortex flowmeter 10. The display device 470 is connected to the diagnostic result output unit 450 and displays the display screen generated by the display processing unit 460. The display device 470 may be provided outside the diagnostic device 190 (for example, at a location far from the diagnostic device 190), receive the display screen generated by the display processing unit 460 via a network, and display it.

[0086] The learning processing unit 480 is connected to the time series data storage unit 440. The learning processing unit 480 generates a judgment criterion including thresholds used by the diagnostic unit 430, etc. through learning using the history of the normalized magnitudes of the signal components of the vortex flow signal SQ stored in the time series data storage unit 440. In the present embodiment, the learning processing unit 480 receives at least one input during a period when the vortex flowmeter 10 is normal (normal period) or during a period when it is abnormal (abnormal period), and attaches a normal or abnormal annotation to the data at each time point in the time series data stored in the time series data storage unit 440. Then, the learning processing unit 480 uses the time series data with the attached annotation to generate a judgment criterion through learning.

[0087] Figure 5 Shows the diagnostic process of the diagnostic device 190 for the vortex flowmeter 10 in the present embodiment. In S500, the acquisition unit 400 acquires measurement data from the vortex flowmeter 10 and stores it in the measurement data storage unit 410. The measurement data includes the magnitude of the signal component of the vortex flow signal SQ and the vortex frequency measured by the vortex flowmeter 10. The acquisition unit 400 can sequentially acquire measurement data from the vortex flowmeter 10 in real time, or can acquire the measurement data stored in the vortex flowmeter 10 for a certain past period at once.

[0088] In S510, the normalization unit 420 calculates an index value obtained by normalizing the magnitude of the signal component of the vortex flow rate signal SQ in the measurement data stored in the measurement data storage unit 410 using the vortex frequency. This index value represents the sensitivity of the vortex flowmeter 10 that is independent of the vortex frequency in the vortex flow rate signal SQ. In addition, the magnitude of the signal component of the vortex flow rate signal SQ also varies according to the density of the fluid to be measured. Therefore, when the density of the fluid to be measured changes, the normalization unit 420 can further normalize the magnitude of the signal component of the vortex flow rate signal SQ according to the density of the fluid to be measured.

[0089] In S520, the diagnosis unit 430 determines whether the index value representing the normalized magnitude of the signal component of the vortex flow rate signal SQ is equal to or less than a first threshold Th1. And based on the determination result that the index value is equal to or less than the first threshold ( "Yes" in S520), the diagnosis unit 430 diagnoses that the vortex flowmeter 10 is abnormal in S530. The diagnosis result output unit 450 outputs the diagnosis result that the vortex flowmeter 10 is abnormal.

[0090] In S540, the diagnosis unit 430 determines whether the index value representing the normalized magnitude of the signal component of the vortex flow rate signal SQ exceeds the first threshold Th1 and is equal to or less than a second threshold Th2. And based on the determination result that the index value exceeds the first threshold Th1 and is equal to or less than the second threshold Th2 ( "Yes" in S540), in S550, although the vortex flowmeter 10 is not diagnosed as abnormal at the current time, it is predicted that the vortex flowmeter 10 will become abnormal later. This state indicates that although the vortex flowmeter 10 is not abnormal at the current time, it is recommended to perform a maintenance check on the vortex flowmeter 10. The diagnosis result output unit 450 outputs the prediction result that the vortex flowmeter 10 will become abnormal hereafter.

[0091] When the index value exceeds the second threshold Th2 ( "No" in S540), the diagnosis unit 430 diagnoses that the vortex flowmeter 10 is normal in S560. The diagnosis result output unit 450 outputs the diagnosis result that the vortex flowmeter 10 is normal.

[0092] In S570, the display processing unit 460 uses the time series data of the index value stored in the time series data storage unit 440 to predict the future state of the vortex flowmeter 10. The prediction method will be described later in association with Figure 7 this.

[0093] Figure 6This is a graph showing a diagnostic example of the vortex flowmeter 10 by the diagnostic device 190 of the present embodiment. This figure shows that when the horizontal axis represents the passage of time and the vertical axis represents the index value of the normalized magnitude of the signal component indicating the vortex flow signal SQ, the index value changes according to the passage of time. In order to diagnose the vortex flowmeter 10, the display processing unit 460 can use the curve shown in this figure to display the change of the index value.

[0094] In the present embodiment, as Figure 2 shown, a gap 24 is provided between the vortex generator 20 and the pipeline 16. If a foreign object blocks the gap 24, the vibration of the vortex generator 20 is suppressed, so that the amplitude of each detection signal becomes smaller. Eventually, the amplitude of the vortex flow signal SQ output from the band-pass filter 140 is smaller than the amplitude that can be detected by the Schmitt trigger 145, and thus the vortex flowmeter 10 cannot measure the vortex frequency.

[0095] As shown in this figure, the index value exceeds the second threshold Th2 when the vortex flowmeter 10 is normal, but gradually becomes smaller as the blockage progresses, and becomes below the second threshold Th2 at time t1. If the blockage progresses further, the index value further decreases and becomes below the first threshold Th1 at time t2.

[0096] In the present embodiment, the boundary of the index value at which the vortex flowmeter 10 can measure the vortex frequency is set as the first threshold Th1. In addition, in order to prevent the situation where it cannot be diagnosed as abnormal even though the vortex frequency cannot be measured, the first threshold Th1 can be a value with a certain margin. Thus, when the vortex flowmeter 10 cannot measure the flow rate or flow velocity due to foreign object blockage of the gap 24 or the like, the diagnostic device 190 can diagnose that the state of the vortex flowmeter 10 is abnormal.

[0097] In addition, in the present embodiment, the diagnostic unit 430 uses a second threshold Th2 that is larger than the first threshold Th1. Even when the index value exceeds the first threshold Th1, but when it becomes below the second threshold Th2, it predicts the occurrence of an abnormality in the future. Thus, the diagnostic device 190 can perform a diagnosis for recommending a maintenance inspection of the vortex flowmeter 10 before the vortex flowmeter 10 cannot measure the flow rate or flow velocity, thereby promoting the formulation of a maintenance inspection plan.

[0098] In addition, in Patent Document 2, instead of using a linear combination of the vortex signal component S1 in the first detection signal and the vortex signal component S2 in the second detection signal, the result of judging the magnitude of the signal ratio SR (= S1 / S2) is used to predict clogging (paragraphs 0017 to 0018, 0039 to 0044, etc.). As described above, since the vibration of the vortex generating body 20 is suppressed due to foreign matter clogging the gap 24 or the like, the amplitudes of the respective detection signals become smaller. Therefore, the amplitudes of both the vortex signal components S1 and S2 become smaller due to clogging. Accordingly, for the signal ratio SR obtained by dividing the vortex signal component S1 by the vortex signal component S2, since the reduction in the amplitudes of the vortex signal components S1 and S2 cancels out to some extent, the sensitivity to clogging is duller compared to the index value of the present embodiment.

[0099] In contrast, in the present embodiment, the size of the vortex flow rate signal SQ obtained by linearly combining the vortex signal components S1 and S2 is normalized using the vortex frequency, and thus the state of the vortex flowmeter 10 can be diagnosed with better sensitivity.

[0100] In addition, in the present embodiment, the diagnostic unit 430 compares the size of the signal component of the vortex flow rate signal SQ normalized using the vortex frequency with a threshold value. Instead of this, the diagnostic unit 430 may also compare the unnormalized signal component of the vortex flow rate signal SQ with a threshold value corresponding to the vortex frequency, that is, for example, a threshold value obtained by multiplying the first threshold value or the second threshold value by the square of the vortex frequency, and substantially compare the size of the signal component of the vortex flow rate signal SQ normalized using the vortex frequency with the first threshold value or the second threshold value.

[0101] Figure 7 It is a graph showing a prediction example of the future state of the vortex flowmeter 10 by the diagnostic device 190 of the present embodiment. This figure shows that when the horizontal axis represents the passage of time and the vertical axis represents the index value indicating the size of the signal component of the vortex flow rate signal SQ normalized, the index value changes according to the passage of time.

[0102] The diagnostic unit 430 is at Figure 5In the S570, using the time-series data of the index values stored in the time-series data storage unit 440 up to the current time t1, predict at least one of the time point t2 when the index value drops to the second threshold Th2 or the time point t3 when the index value drops to the first threshold Th1. As an example, the diagnosis unit 430 extends the trend of the index values within a predetermined period (for example, one week, one month, etc.) ending at the current time t1, and calculates the time point t2 or the time point t3 when the extended trend reaches the second threshold Th2 or the first threshold Th1. Here, the diagnosis unit 430 can approximate the trend of the index values using a linear function of the passage of time. Thus, the diagnosis unit 430 can perform a prediction suitable for the case where a substantially fixed amount of foreign matter accumulates per unit time as time passes. Instead of this, the diagnosis unit 430 can also use the result of fitting the change of the index values within a predetermined period to other functions to predict the trend of the index values.

[0103] The diagnosis result output unit 450 outputs at least one of the predicted time point t2 for recommending maintenance inspection of the vortex flowmeter 10 or the predicted time point t3 for diagnosing the vortex flowmeter 10 as abnormal, or the length of the period up to these predicted time points. The display processing unit 460 can output the prediction results of the time point t2 and the time point t3 by displaying the trend chart Figure 7 shown on the display device 470.

[0104] According to the diagnostic device 190 shown above, it is possible to predict the future time point t2 for recommending maintenance inspection or the future time point t3 for diagnosing the vortex flowmeter 10 as abnormal, and notify the user of the diagnostic device 190 of the prediction result sufficiently in advance compared to the occurrence of these events. Thus, the diagnostic device 190 can arrange a more optimal timing for the maintenance inspection of the vortex flowmeter 10, and can improve the usability of the vortex flowmeter 10.

[0105] Figure 8 Shows the learning processing flow of the diagnostic device 190 of the present embodiment. In S800, the learning processing unit 480 acquires the annotation of the time-series data stored in the time-series data storage unit 440. This annotation indicates whether the index value at the time point of at least a part of the time-series data stored in the time-series data storage unit 440 is normal or abnormal. For use in the learning of the first threshold Th1, the learning processing unit 480 of the present embodiment can input at least one of the normal period (index value > the first threshold Th1) or the abnormal period (index value ≤ the first threshold Th1) of the vortex flowmeter 10, attach a label indicating normal to the index value at each time point in the normal period, and attach a label indicating abnormal to the index value at each time point in the abnormal period.

[0106] For example, in the case where multiple inspections are carried out through regular inspections or ad-hoc inspections of the vortex flowmeter 10, if the vortex flowmeter 10 is normal at a certain inspection point and also normal at the next inspection point, it can be considered that the vortex flowmeter 10 is normal during the period between these inspections. Therefore, the learning processing unit 480 can receive the input of an annotation indicating that the vortex flowmeter 10 is normal during the period between these inspections.

[0107] In addition, in the case where the vortex flowmeter 10 is normal at a certain inspection point but abnormal at the next inspection point, the vortex flowmeter 10 changes from normal to abnormal at a certain point between the previous and subsequent inspections. Therefore, it can be considered that the vortex flowmeter 10 is abnormal during the period until the subsequent inspection. Therefore, the learning processing unit 480 can receive the input of an annotation indicating that the vortex flowmeter 10 is abnormal during this period. Here, the learning processing unit 480 can determine the point that becomes the boundary between normal and abnormal as the point that divides the period between the previous and subsequent inspections at a predetermined ratio, or can determine it as the point before a predetermined period based on the subsequent inspection.

[0108] Similarly, the learning processing unit 480 can input at least one of the normal period (index value > second threshold Th2) of the vortex flowmeter 10 or the period (index value ≤ second threshold Th2) for at least recommending the maintenance inspection of the vortex flowmeter 10, and attach the label used in the learning of the second threshold Th2 to the index value at each time point.

[0109] In S810, the learning processing unit 480 uses the measurement data with annotations attached to learn the judgment criteria for determining the normality or abnormality of the vortex flowmeter 10. The learning processing unit 480 collects at least one index value with a normal label attached and the index value with an abnormal label attached as learning data, and uses this learning data to generate through learning Figure 6 the first threshold Th1 and the second threshold Th2 shown.

[0110] As an example, the learning processing unit 480 can use a support vector machine (SVM) to generate through learning the first threshold Th1 that becomes the boundary for classifying the index value as normal or abnormal. Instead of this, the learning processing unit 480 can also generate through learning a neural network that takes the index value as input and outputs the classification or its probability of normal or abnormal, and sample the distribution of the output of the neural network corresponding to the index value to determine the first threshold Th1 that becomes the boundary. The learning processing unit 480 can also use various other machine learning methods to generate the first threshold Th1 through learning. Similarly, the learning processing unit 480 can use a support vector machine (SVM) or other machine learning methods to generate through learning the second threshold Th2 that becomes the boundary for classifying the index value as normal or recommending a maintenance inspection.

[0111] In S820, the learning processing unit 480 sets the determination criteria (the first threshold Th1 and the second threshold Th2) learned in S810 in the diagnosis unit 430. In response to receiving this setting, the diagnosis unit 430 uses the first threshold Th1 and the second threshold Th2 newly set by the learning processing unit 480 to perform diagnosis of the vortex flowmeter 10.

[0112] According to the diagnostic device 190 shown above, it is possible to learn the determination criteria for the state of the vortex flowmeter 10 using the measurement data obtained from the vortex flowmeter 10. Thus, the diagnostic device 190 can adjust the determination conditions according to the structure of the pipeline 16 and the vortex generator 20 in the part where the vortex flowmeter 10 is installed, the density of the fluid to be measured, and other measurement environments. In addition, the diagnostic device 190 can also adopt a structure without the learning processing unit 480. In this case, the diagnostic device 190 can use the pre-set first threshold Th1 and second threshold Th2 to perform diagnosis of the state of the vortex flowmeter 10.

[0113] Figure 9 Shows the configuration of the diagnostic device 990 which is a modification example of the present embodiment. The diagnostic device 990 is Figure 4 a modification example of the diagnostic device 190 shown, and uses the determination result of the magnitude of the signal component of at least one of one or more detection signals as the object detection signal to diagnose the state of the vortex flowmeter 10, instead of using the determination result of the magnitude of the signal component of the combined signal obtained by linearly combining the first detection signal and the second detection signal to diagnose the state of the vortex flowmeter 10. The diagnostic device 990 of this modification example is similar to Figure 4 the diagnostic device 190 shown, so the description is omitted except for the following differences.

[0114] The diagnostic device 990 includes: an acquisition unit 900, a measurement data storage unit 910, a diagnosis unit 930, a time series data storage unit 940, a diagnostic data output unit 950, a display device 970, and a learning processing unit 980. The acquisition unit 900 is connected to the vortex flowmeter 10. The acquisition unit 900 acquires measurement data from the vortex flowmeter 10, and the measurement data includes the magnitude of the signal component of at least one object detection signal that is the object of the diagnostic process performed by the diagnostic device 990 among at least one detection signal detected by the vortex flowmeter 10 and the vortex frequency measured by the vortex flowmeter 10.

[0115] The measurement data storage unit 910 is connected to the acquisition unit 900. The measurement data storage unit 910 is Figure 4 the same as the measurement data storage unit 410 shown, and stores the measurement data acquired by the acquisition unit 900.

[0116] The diagnostic unit 930 is connected to the measurement data storage unit 910. The diagnostic unit 930 determines the change in the magnitude of the signal component of each of at least one object detection signal. The diagnostic unit 930 diagnoses the state of the vortex flowmeter 10 based on the determination result of the change. The time series data storage unit 940 is connected to the diagnostic unit 930 and stores the time series data of the magnitude of the signal component of each of at least one object detection signal and the diagnosis result of the state of the vortex flowmeter 10. Here, the time series data storage unit 940 functions as a history record storage unit, and stores, as history record data, the time series data in which the magnitude of the signal component corresponding to the vortex frequency of each of the at least one past object detection signals is associated with the vortex frequency in the history record storage unit.

[0117] The diagnostic data output unit 950 is connected to the time series data storage unit 940. The diagnostic data output unit 950 outputs the time series data including the diagnosis result of the state of the vortex flowmeter 10 stored in the time series data storage unit 940. The diagnostic data output unit 950 may include a display processing unit 960. The display processing unit 960 performs processing for causing the display device 970 to display the time series data of the diagnosis result including the state of the vortex flowmeter 10. The display device 970 is connected to the diagnostic data output unit 950 and displays a display screen and the like generated by the display processing unit 960.

[0118] The learning processing unit 980 is connected to the time series data storage unit 940. The learning processing unit 980 uses the history of the magnitude of the signal component of each of at least one object detection signal stored in the time series data storage unit 940 to generate a judgment criterion for the diagnostic unit 930 through learning. Here, the learning processing unit 980 generates a judgment criterion for the magnitude of the signal component of each of at least one object detection signal through learning instead of combining the magnitudes of the signal components, and except for this point, can perform the same learning processing as Figure 4 the learning processing unit 480 shown.

[0119] Figure 10 The diagnostic process of the diagnostic device 990 for the vortex flowmeter 10 according to this modification is shown. In this diagnostic process, the diagnostic unit 930 in the diagnostic device 990 diagnoses the state of the vortex flowmeter 10 based on the determination result of the change in the magnitude of the signal component of each of at least one object detection signal. Here, the diagnostic unit 930 may use one object detection signal as the diagnostic object, or may use two or more object detection signals as the diagnostic object. Hereinafter, for the sake of convenience of explanation, the case where the diagnostic unit 930 uses one object detection signal as the diagnostic object will be described.

[0120] In S1000, the acquisition unit 900 acquires measurement data including the magnitude of the signal component of the object detection signal and the vortex frequency measured by the vortex flowmeter 10 from the vortex flowmeter 10. In the present embodiment, as an example, the acquisition unit 900 may acquire the magnitude of the signal component corresponding to the vortex frequency of the first detection signal from the CPU 160.

[0121] Here, the vortex flowmeter 10 uses the signal intensity of the band including the vortex frequency among the signal intensities of the respective bands of the first detection signal output from the spectrum analyzer 125a to the CPU 160 as the magnitude of the signal component corresponding to the vortex frequency of the first detection signal. Thereby, the diagnostic device 190 can acquire the magnitude S1 of the vortex signal component in the first detection signal. The acquisition unit 900 stores the acquired measurement data in the measurement data storage unit 910.

[0122] In S1010, the diagnostic unit 930 calculates the change in the magnitude of the signal component of the object detection signal to be diagnosed. The diagnostic unit 930 reads out the magnitude of the signal component of the past object detection signal from the time series data storage unit 940 and calculates the change in the magnitude of the signal component of the newly acquired object detection signal with respect to the past value.

[0123] Here, the magnitude of the vortex signal component of the object detection signal changes according to the vortex frequency. Therefore, the diagnostic unit 930 calculates the change using the magnitude of the signal component associated with the vortex frequency corresponding to the vortex frequency of the object detection signal included in the history data stored in the time series data storage unit 940 and the magnitude of the signal component of the object detection signal acquired by the acquisition unit 900. The diagnostic unit 930 may use the most recent one among the magnitudes of the signal components associated with the frequency corresponding to the vortex frequency of the object detection signal included in the history data stored in the time series data storage unit 940, or may use the one before a predetermined period (one week, one month, etc.), or may use the one with the maximum magnitude within the range of a predetermined period. In addition, the diagnostic unit 930 may also use the magnitude of the signal component associated with the vortex frequency near the vortex frequency corresponding to the object detection signal. For example, the diagnostic unit 930 may use the magnitude of the signal component associated with the vortex frequency included in the range of a predetermined frequency width before and after the vortex frequency of the object detection signal acquired by the acquisition unit 900 in the history data. In addition, the diagnostic unit 930 may also use the magnitude of the signal component associated with the vortex frequency included in the same frequency band as the vortex frequency of the object detection signal acquired by the acquisition unit 900 in the history data.

[0124] In addition, in the present embodiment, as an index of the change in the magnitude of the signal component of the object detection signal, the diagnostic unit 930 uses the amount of change that is the difference between the past value and the current value. This amount of change may be the amount of decrease from the past value to the current value (i.e., the value obtained by subtracting the current value from the past value). Instead of this, as an index of the change in the magnitude of the signal component of the object detection signal, the diagnostic unit 930 may also use other indices such as the rate of change.

[0125] In S1020, the diagnostic unit 930 determines whether the amount of change calculated in S1010 is equal to or greater than the third threshold Th3, and diagnoses the state of the vortex flowmeter 10 based on the determination result. When the amount of change is equal to or greater than the third threshold Th3 (Yes in S1020), the magnitude of the signal component of the newly acquired object detection signal is significantly lower than the magnitude of the past signal component associated with the same vortex frequency in the object detection signal. Therefore, the diagnostic unit 930 diagnoses in S1030 that the vortex flowmeter 10 is abnormal. Accordingly, the time-series data storage unit 940 stores the diagnostic result in association with the measurement data, and the diagnostic data output unit 950 outputs the abnormal diagnostic result.

[0126] When the amount of change is less than the third threshold Th3 (No in S1020), the magnitude of the signal component of the newly acquired object detection signal is hardly reduced compared to the magnitude of the past signal component associated with the same vortex frequency in the object detection signal. Therefore, the diagnostic unit 930 diagnoses in S1040 that the vortex flowmeter 10 is normal. Accordingly, the time-series data storage unit 940 stores the diagnostic result in association with the measurement data, and the diagnostic data output unit 950 outputs the normal diagnostic result.

[0127] In the diagnostic process shown above, the diagnostic device 990 diagnoses the vortex flowmeter 10 using one object detection signal. Instead of this, the diagnostic device 990 may also use two or more object detection signals to diagnose the vortex flowmeter 10. That is, the acquisition unit 900 acquires measurement data including the magnitudes of the signal components of two or more object detection signals from the vortex flowmeter 10, and the diagnostic unit 930 diagnoses the state of the vortex flowmeter 10 based on the determination results of the changes in the magnitudes of the signal components corresponding to the vortex frequency for each of the two or more object detection signals.

[0128] Here, in S1020, when the change amount of at least one object detection signal among two or more object detection signals is equal to or greater than a third threshold Th3 corresponding to the object detection signal, the diagnosis unit 930 diagnoses that the vortex flowmeter 10 is abnormal. Instead, in S1020, it is also possible that for all of the two or more object detection signals, when the change amount is equal to or greater than the third threshold Th3 corresponding to each object detection signal, the diagnosis unit 930 diagnoses that the vortex flowmeter 10 is abnormal. In this case, the third threshold Th3 may take different values for each object detection signal.

[0129] According to the diagnostic device 990 shown above, it is possible to diagnose the state of the vortex flowmeter 10 based on the judgment result of the change in the magnitude of the signal component of the object detection signal. Therefore, it is also possible not to use the combined signal obtained by linearly combining two or more detection signals. Therefore, the diagnostic device 990 can diagnose the vortex flowmeter 10 that does not generate a combined signal to measure the flow rate or flow velocity or the vortex flowmeter 10 that does not have the function of outputting a combined signal to the outside for diagnosis.

[0130] In addition, according to the diagnostic device 990 shown above, the diagnosis is performed using the magnitude of the signal component associated with the vortex frequency equivalent to the object detection signal included in the history data. Instead, the diagnostic device 990 may also Figure 4 be the same as the diagnostic device 190 shown above, which normalizes the magnitude of the signal component of the vortex flow signal SQ using the vortex frequency. The newly obtained object detection signal and the magnitude of the signal component of the object detection signal included in the history data are normalized using the vortex frequency and used for diagnosis.

[0131] In this case, the diagnostic device 990 may include a normalization unit similar to the Figure 4 normalization unit 420 shown above, which normalizes the magnitude of the signal component of at least one object detection signal using the vortex frequency. And the diagnosis unit 930 may Figure 4 be the same as the diagnosis unit 430 shown above, which judges the vortex flow signal SQ. Based on the judgment result that the magnitude of the signal component of each of at least one object detection signal after normalization is equal to or less than the first threshold corresponding to each of the at least one object detection signals, it is diagnosed that the vortex flowmeter is abnormal. This diagnosis unit 930 Figure 4 is the same as the diagnosis unit 430 shown above and can predict that the vortex flowmeter 10 will become abnormal. In this case, the diagnostic device 990 can perform diagnosis using the magnitude of the signal component associated with a vortex frequency different from the vortex frequency of the newly obtained object detection signal included in the history data.

[0132] In addition, the diagnostic device 990 may diagnose the state of the vortex flowmeter 10 based on the determination result of the change in the magnitude of the signal component of the vortex flow signal SQ instead of the determination result of the change in the magnitude of the signal component of the object detection signal. In this case, the diagnostic device 990 may not normalize the vortex flow signal SQ. Similar to the method shown in Figure 9 and Figure 10 , the magnitude of the signal component corresponding to the vortex frequency in the vortex flow signal SQ acquired by the acquisition unit 900 and the magnitude of the signal component associated with the vortex frequency in the past vortex flow signal SQ recorded in the history data are used to calculate the change in the magnitude of the signal component.

[0133] Various embodiments of the present invention may be described with reference to flowcharts and block diagrams. Here, a module may represent (1) a stage of a process of performing an operation or (2) a part of a device having the function of performing an operation. A specific stage and part may be implemented by a dedicated circuit, a programmable circuit supplied with computer-readable instructions stored on a computer-readable medium, and / or a processor supplied with computer-readable instructions stored on a computer-readable medium. The dedicated circuit may include digital and / or analog hardware circuits, and may also include an integrated circuit (IC) and / or discrete circuits. The programmable circuit may include a reconfigurable hardware circuit, which includes memory elements such as logical AND, logical OR, logical XOR, logical NAND, logical NOR, and other logical operations, flip-flops, registers, field-programmable gate arrays (FPGAs), programmable logic arrays (PLAs), etc.

[0134] The computer-readable medium may include any tangible device capable of storing instructions executable by an appropriate device. As a result, a computer-readable medium having instructions stored therein includes a product containing instructions capable of being executed to fabricate means for performing the operations specified by the flowchart or block diagram. Examples of the computer-readable medium may include: electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, etc. More specific examples of the computer-readable medium may include: floppy (registered trademark) disks, magnetic disks, 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 (registered trademark) disc, memory stick, integrated circuit card, etc.

[0135] Computer-readable instructions include any one of source code and object code described by any combination of one or more programming languages including assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, status setting data, or object-oriented programming languages such as Smalltalk (registered trademark), JAVA (registered trademark), C++, and existing procedural programming languages such as the "C" programming language or the same programming language.

[0136] The computer-readable instructions can be provided to a processor or a programmable circuit of a programmable data processing device such as a general-purpose computer, a special-purpose computer, or other computers via a local area network (LAN) or a wide area network (WAN) such as the Internet, and the computer-readable instructions are executed to fabricate means for performing the operations specified by a flowchart or a block diagram. Examples of the processor include: a computer processor, a processing unit, a microprocessor, a digital signal processor, a controller, a microcontroller, etc.

[0137] Figure 11 Examples of the computer 2200 that can implement various aspects of the present invention, either wholly or in part. Through the program installed in the computer 2200, the computer 2200 can function as an operation associated with the device of the embodiment of the present invention or one or more parts of the device, or execute the operation or the one or more parts, and / or the computer 2200 can execute the process of the embodiment of the present invention or a stage of the process. In order to cause the computer 2200 to execute specific operations associated with several or all of the modules of the flowcharts and block diagrams described in this specification, such a program can be executed by the CPU 2212.

[0138] The computer 2200 of the present embodiment includes a CPU 2212, a RAM 2214, a graphics controller 2216, and a display device 2218, which are interconnected through a main controller 2210. The computer 2200 further 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 main controller 2210 via an input / output controller 2220. The computer also includes conventional 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.

[0139] The CPU 2212 operates in accordance with programs stored in the ROM 2230 and the RAM 2214, thereby controlling each unit. The graphics controller 2216 acquires image data generated by the CPU 2212 in a frame buffer or the like provided in the RAM 2214 or in itself, and displays the image data on the display device 2218.

[0140] The communication interface 2222 can communicate with other electronic devices via a network. The hard disk drive 2224 stores programs and data used by the CPU 2212 within 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.

[0141] The ROM 2230 stores therein a boot program executed by the computer 2200 at activation and / or programs dependent on the hardware of the computer 2200. The input / output chip 2240 can 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.

[0142] Programs are provided by a computer-readable medium such as the DVD-ROM 2201 or an IC card. The programs are read from the computer-readable medium and installed in the hard disk drive 2224, the RAM 2214, or the ROM 2230, which are also examples of computer-readable media, and are executed by the CPU 2212. The information processing described within these programs is read into the computer 2200, thereby bringing about cooperation between the programs and the above various types of hardware resources. The device or method can be configured to implement the operation or processing of information by accompanying the use of the computer 2200.

[0143] For example, in the case of performing communication between the computer 2200 and an external device, the CPU 2212 can execute a communication program loaded in 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 within a recording medium such as the RAM 2214, the hard disk drive 2224, the DVD-ROM 2201, or an IC card, transmits the read transmission data to the network, or writes the received data received from the network to a reception buffer processing area provided on the recording medium, etc.

[0144] In addition, the CPU 2212 can read all or a necessary part of a file or database stored in an external recording medium such as the hard disk drive 2224, the DVD-ROM drive 2226 (DVD-ROM 2201), an IC card, etc. into the RAM 2214 and perform various types of processing on the data on the RAM 2214. Then, the CPU 2212 writes the processed data back to the external recording medium.

[0145] Various types of information, such as various types of programs, data, tables, and databases, can be stored in a recording medium and undergo information processing. The CPU 2212 performs various types of processing described throughout this disclosure on the data read from the RAM 2214 and writes the results back to the RAM 2214. The various types of processing include various types of operations, information processing, conditional judgment, conditional branch, unconditional branch, retrieval / replacement of information, etc. specified by the instruction sequence of the program. In addition, the CPU 2212 can retrieve information in files, databases, etc. within the recording medium. For example, in the case where a plurality of entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in the recording medium, the CPU 2212 can retrieve an entry that matches the condition specifying the attribute value of the first attribute from the plurality of entries, and read the attribute value of the second attribute stored in the entry, thereby obtaining the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0146] The programs or software modules described above can be stored in a computer-readable medium on or near the computer 2200. In addition, a recording medium such as a hard disk or RAM provided within a server system connected to a dedicated communication network or the Internet can be used as a computer-readable medium, whereby the program is provided to the computer 2200 via the network.

[0147] As described above, the present invention has been described using embodiments, but the technical scope of the present invention is not limited to the scope described in the above embodiments. It is obvious to those skilled in the art that various changes or improvements can be made to the above embodiments. According to the description in the claims, the ways in which such changes or improvements are made can also be included in the technical scope of the present invention.

[0148] In the claims, the description, and the drawings, the execution order of each process such as actions, processes, steps, and stages in the device, system, program, and method is not particularly specified as "earlier", "before", etc. In addition, it should be noted that as long as the output of the previous process is not used in the subsequent process, it can be implemented in any order. Regarding the action flow in the claims, the description, and the drawings, even if it is described using "first," "next," etc. for the sake of convenience, it does not mean that it must be implemented in that order.

Claims

1. A diagnostic device, characterized in that, Comprising a diagnostic unit for diagnosing the state of a vortex flowmeter, the vortex flowmeter having a vortex generator and a detection unit for detecting a plurality of detection signals corresponding to vortices generated by the vortex generator, the diagnostic unit diagnosing the state of the vortex flowmeter using the judgment results of the combined signal components of at least two target detection signals among the plurality of detection signals detected by the vortex flowmeter. The diagnostic unit calculates the changes corresponding to the passage of time of the combined signal components of the at least two target detection signals. The diagnostic unit further predicts the future time point when the vortex flowmeter will become abnormal based on the prediction judgment result that the magnitudes of the combined signal components of the at least two target detection signals after normalization exceed a first threshold and are below a second threshold.

2. The diagnostic device according to claim 1, characterized in that, The diagnostic unit diagnoses the state of the vortex flowmeter based on the judgment result obtained by comparing the magnitudes of the combined signal components of the at least two target detection signals with the first threshold and the second threshold.

3. The diagnostic device according to claim 2, characterized in that Comprising a normalization unit that normalizes the magnitudes of the combined signal components of the at least two target detection signals using the vortex frequency.

4. The diagnostic device according to claim 3, characterized in that, The diagnostic unit diagnoses that the vortex flowmeter is abnormal based on the judgment result that the magnitudes of the combined signal components of the at least two target detection signals after normalization are below the first threshold.

5. The diagnostic device according to claim 4, characterized in that, Further comprising a learning processing unit that generates the first threshold through learning using the history of the magnitudes of the combined signal components of the at least two target detection signals after normalization.

6. The diagnostic device according to any one of claims 3 to 5, characterized in that Further comprising: a time series data storage unit that stores time series data of the magnitudes of the combined signal components of the at least two target detection signals after normalization; and a display processing unit that performs display processing for displaying the time series data.

7. The diagnostic device according to claim 1, characterized in that The diagnostic unit diagnoses the state of the vortex flowmeter based on the judgment result of the magnitude change of the combined signal components of the at least two target detection signals.

8. The diagnostic device according to claim 7, wherein The diagnostic unit diagnoses the state of the vortex flowmeter based on the judgment result of the magnitude change of the combined signal components of the at least two target detection signals corresponding to the vortex frequency.

9. The diagnostic device according to claim 8, wherein: further comprising a history record storage unit that stores history record data associating the magnitudes of the combined signal components of the at least two target detection signals corresponding to the vortex frequency in the past with the vortex frequency in the history record storage unit. The diagnostic unit diagnoses the state of the vortex flowmeter based on the judgment result of the difference between the magnitude of the signal component associated with the vortex frequency equivalent to the vortex frequency of the at least two target detection signals to be diagnosed in the history record data and the magnitude of the signal components of the at least two target detection signals to be diagnosed.

10. The diagnostic device according to claim 1, wherein Comprising a normalization unit that normalizes the magnitudes of the combined signal components of the at least two target detection signals using the vortex frequency.

11. The diagnostic device according to claim 10, characterized in that, Based on the determination result that the magnitude of each of the combined signal components of the at least two object detection signals after normalization is below a first threshold corresponding to each of the at least two object detection signals, the diagnostic unit diagnoses that the vortex flowmeter is abnormal.

12. A measuring device, characterized in that Comprising: The diagnostic device according to any one of claims 1 to 11; And The vortex flowmeter.

13. A diagnostic method, characterized in that Comprising: Obtaining the combined signal component of each of at least two object detection signals among the plurality of detection signals detected by a vortex flowmeter having a vortex generator and a detection unit that detects a plurality of detection signals corresponding to the vortices generated by the vortex generator; And Using the determination result of the combined signal component to diagnose the state of the vortex flowmeter. Further comprising: Calculating the change corresponding to the passage of time of the combined signal component of each of the at least two object detection signals. Based on the prediction determination result that the magnitude of each of the combined signal components of the at least two object detection signals after normalization exceeds a first threshold and is below a second threshold, predicting the future time point when the vortex flowmeter will become abnormal.

14. A computer-readable medium having a diagnostic program recorded thereon, characterized in that The computer functions as a diagnostic unit by executing the diagnostic program. The diagnostic unit uses the determination result of the combined signal component of each of at least two object detection signals among the plurality of detection signals detected by a vortex flowmeter having a vortex generator and a detection unit that detects a plurality of detection signals corresponding to the vortices generated by the vortex generator to diagnose the state of the vortex flowmeter. Causing the computer to calculate the change corresponding to the passage of time of the combined signal component of each of the at least two object detection signals. Causing the computer to predict the future time point when the vortex flowmeter will become abnormal based on the prediction determination result that the magnitude of each of the combined signal components of the at least two object detection signals after normalization exceeds a first threshold and is below a second threshold.

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