CN114637960B - Signal processing method for time-frequency domain digital turbine flow sensor - Google Patents
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
- G06F17/142—Fast Fourier transforms, e.g. using a Cooley-Tukey type algorithm
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
- G01F1/05—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by using mechanical effects
- G01F1/20—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by using mechanical effects by detection of dynamic effects of the flow
- G01F1/32—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by using mechanical effects by detection of dynamic effects of the flow using swirl flowmeters
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- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
- H03H17/00—Networks using digital techniques
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- H03H17/00—Networks using digital techniques
- H03H17/02—Frequency selective networks
- H03H17/0202—Two or more dimensional filters; Filters for complex signals
- H03H2017/0207—Median filters
Abstract
The invention relates to a signal processing method of a time-frequency domain digital turbine flow sensor. The analog turbine flow sensor directly shapes the sensor signal into pulse signal for output and metering, and has good real-time performance. However, when the device works in an environment with strong noise interference, the output signal of the turbine flow sensor is easy to generate distortion, so that the pulse output is inaccurate and the accuracy is reduced. The digital turbine flow sensor can acquire sensor signals in real time, and calculate flow frequency through spectrum analysis, so that the digital turbine flow sensor has good anti-interference performance, but in order to ensure measurement accuracy, higher frequency resolution is required. When the caliber of the flowmeter is small and the measured flow is large, the signal frequency is high, the data volume participating in calculation is large, the operation time is long, and the instantaneity is poor. Therefore, the patent provides a novel signal processing method of the digital turbine flow sensor, and the signal processing method combining time and frequency domains can improve anti-interference performance, ensure measurement accuracy, reduce operation amount and ensure real-time performance.
Description
Technical Field
The invention belongs to the technical field of flow detection, and particularly relates to a signal processing method of a time-frequency domain digital turbine flow sensor.
Background
An impeller type turbine flow sensor (abbreviated as a "turbine flow sensor") is a speed type flow meter that operates based on a torque balance principle, and performs flow measurement using a linear relationship between a fluid flow rate and a frequency of a turbine flow sensor signal. The turbine flow sensor has the characteristics of simple and portable structure, high measurement accuracy, good reproducibility, convenient installation and use and the like, and is widely applied to industries such as petroleum, chemical industry, water supply, medical treatment and the like.
The prior literature, "research and design of turbine flowmeter real-time detection method" (Wang Weiwei, guizhou university, 2015) uses a signal amplification shaping circuit and a zero-crossing detection circuit to shape a sensor signal into a square wave signal, "low power consumption two-wire turbine flowmeter design" (Shao Hailong, shenming, shanghai university photoelectric information and computer engineering institute, 2016) uses an amplification filter circuit and a schmitt shaping circuit to shape the sensor signal into an electric pulse signal, "APP-based flow remote monitoring system research and design" (Liao Tongtong, changan university, 2020) uses a filter amplification circuit and a saturation circuit to shape the sensor signal into a pulse signal. All the above documents adopt an analog circuit to shape a sensor signal into a pulse or square wave signal for outputting and metering, and then the pulse number or frequency of the pulse signal is used for calculating a flow parameter, but if noise exists in the turbine flow sensor signal, the pulse signal is distorted, and the accuracy of flow measurement is reduced.
Prior document "A Zero Crossing Algorithm for the Estimation of the Frequency of a Single Sinusoid in White Noise" (Friedman V, IEEE Transactions on Signal Processing,1994, volume 42, 6) proposes an algorithm for determining the frequency of a sinusoidal signal affected by additive gaussian white noise by calculating zero crossing intervals, "An efficient approach to zero crossing detection based on opto-coupler" (Ankita Gupta, rajeev Thakur, sacin Murarka, international Journal of Engineering Research and Applications (ijeara), 2013, volume 3, 5) implements filtering and zero crossing detection of a signal slightly fluctuating around a fixed frequency based on hardware circuitry, "a new time-domain method for frequency Measurement of sinusoidal signals in critical noise conditions" (Angrisani L, D' Apuzzo M, grilo D et al, measurent, 2014, volume 49) calculates successive zero crossing time intervals of a sinusoidal signal affected by additive gaussian white noise, draws a signal half-cycle histogram, implements estimation of the signal cycle, and thus solves the signal frequency. The above-mentioned documents about zero-crossing detection method are mainly to study single-frequency signals, and the proposed method is difficult to realize in a single chip microcomputer.
The frequency of the calculated sensor signal in a digital turbine flow sensor generally has two ways: firstly, the FFT is used for directly calculating the signal frequency, but when the caliber of a sensor is small and the measured flow is large, the signal frequency is higher, higher frequency resolution is ensured, the data volume participating in FFT calculation is large, the operation time is long, and the real-time performance is poor. Secondly, the frequency of the sensor signal is indirectly calculated based on the signal period, when the signal period is calculated by adopting a zero-crossing detection method, if noise interference exists in the signal, the accuracy of the calculated signal data zero point position is difficult to ensure, and the calculated signal period has a larger phase difference with the actual signal period.
The invention provides a signal processing method of a time-frequency domain digital turbine flow sensor, which comprises the following steps: in the frequency domain, after removing the direct current component of the signal, firstly using digital low-pass filtering to process the signal, then estimating the frequency of the signal based on FFT, and then determining the parameters of the band-pass filter according to a table look-up method, thereby reducing the noise component in the signal, improving the anti-interference performance of the turbine flow sensor and ensuring the measurement precision; in the time domain, the zero point position in the signal data is calculated based on a linear interpolation method, the signal period is calculated based on a zero-crossing detection method, and then the signal frequency is solved, so that the operand is reduced, and the instantaneity is ensured.
Disclosure of Invention
The invention provides a signal processing method of a time-frequency domain digital turbine flow sensor, which comprises the following steps: firstly, removing direct current components in a turbine flow sensor signal, and filtering the signal by adopting a low-pass filter; secondly, estimating the frequency of a sensor signal by using FFT, determining parameters of a band-pass filter according to a table look-up method according to the estimated frequency, and carrying out band-pass filtering on the signal; and finally, calculating a signal zero point position based on a linear interpolation method, calculating a signal period based on a zero-crossing detection method, and then solving the signal frequency.
In the frequency domain, the specific technical process for processing the digital turbine flow sensor signal is as follows:
the method comprises the steps of filtering signals by using a Butterworth second-order digital low-pass filter, wherein the transfer function of the low-pass filter is as follows:
using N-point FFT (fast Fourier transform) to find frequency corresponding to maximum peak value in amplitude spectrum, and taking the frequency as estimated frequency f of signal e 。
The signal is filtered by adopting the Butterworth second-order band-pass filter, and the transfer function of the band-pass filter is as follows:
the parameters of the band-pass filter are based on the estimated frequency f e And (5) determining.
In the time domain, the specific technical process for processing the digital turbine flow sensor signal is as follows:
the method comprises the step of calculating a signal zero point coordinate by using a linear interpolation method. Find two adjacent points (x 1 ,y 1 )、(x 2 ,y 2 ) Calculating the zero point position x using a two-point interpolation method n The method comprises the following steps:
y 1 y 2 ≤0
the signal period is calculated based on a zero-crossing detection method. Number of half-period points T of nth sensor signal n The calculation formula of (2) is as follows:
T n ＝x n+1 -x n
screening the number of half-period points of the signal to obtain T n '. Signal frequency and f corresponding to number of signal half-period points e The phase difference cannot exceed the frequency resolution of FFT, and T is eliminated n The number of half-cycles which do not meet the condition is counted to obtain T n ' then T n ' satisfy the following formula:
in the formula, f s For signal sample rate, Δf is FFTFrequency resolution of (c):
and (5) performing median filtering. For the number T of half period points of the signal after screening n ' sorting, taking the average value of the number of half-period points of the signal of the middle 60% as the actual number of half-period points T of the sensor signal 0 Solving the frequency f of the sensor signal 0 ：
The beneficial effects of the invention are as follows: in the frequency domain, the noise component in the signal is reduced by combining a digital filtering method and FFT, the anti-interference performance of the turbine flow sensor is improved, and the measurement precision is ensured; in the time domain, the zero position of the signal is calculated based on a linear interpolation method, the signal period is calculated based on a zero-crossing detection method, and then the signal frequency is solved, so that the operand can be reduced, and the instantaneity is ensured.
Drawings
FIG. 1 is a block diagram of a forward input signal conditioning circuit;
FIG. 2 is a flow chart of a digital turbine flow sensor signal processing method;
FIG. 3 is a flow rate of 0.6m 3 At/h, the turbine flow sensor signal removes the direct current component and is compared with the low-pass filtered signal and the band-pass filtered signal;
FIG. 4 is a flow rate of 0.6m 3 And (h) at the time of/h, a signal spectrogram after low-pass filtering;
FIG. 5 is a flow rate of 0.6m 3 /h to 1.2m 3 And/h, testing the response time of the turbine flow sensor.
Detailed Description
The objects, technical solutions and advantages of the present invention will become more apparent by describing the present invention in further detail with reference to the accompanying drawings.
The specific implementation mode of the invention patent is introduced by taking a turbine flow sensor with the caliber of DN15 and the accuracy grade of 0.5 as a research object, and the effectiveness of the signal processing method is proved. The effectiveness of the signal processing method.
The flow range of the turbine flow sensor is 0.6m 3 /h～6.0m 3 And/h, the frequency range is about 136 Hz-1360 Hz, and the signal sampling rate is required to meet f according to the shannon sampling theorem s Not less than 2720Hz, f in this embodiment s ＝10000Hz。
Before the turbine flow sensor signal enters the transmitter, the turbine flow sensor signal passes through a forward input signal conditioning circuit, and a circuit schematic diagram is shown in fig. 1. The flow chart of the signal processing method of the digital turbine flow sensor is shown in fig. 2, and the specific technical process is as follows:
flow rate of 0.6m 3 And at/h, acquiring turbine flow sensor signals.
The signal is filtered by the Butterworth second-order digital low-pass filter, and the parameters of the second-order digital low-pass filter are as follows:
[a 0 ,a 1 ,a 2 ]＝[1.0,0.369527,0.195816]
[b 0 ,b 1 ,b 2 ]＝[0.391336,0.182672,0.391336]
the equation of difference is:
Y[n]＝0.391336*X[n]+0.782672*X[n-1]+0.391336*X[n-2]-0.369527*Y[n-1]-0.195816*Y[n-2]
after low-pass filtering the sensor signal, calculating the estimated frequency f of the sensor signal using a 1024-point FFT e 136.7188Hz.
At this time, the frequency resolution of the FFT is:
according to the method, a band-pass filter bank is designed according to the frequency range of the turbine flow sensor, the cut-off frequency range of the Butterworth second-order digital band-pass filter bank is 90 Hz-1440 Hz, the bandwidth is fixed to be 60Hz, the pass band ranges of two adjacent sets of filters have the same interval of 50%, and 44 sets of digital filter parameters are included in the band-pass range, as shown in table 1.
Table 1 specific passband ranges of each bandpass filter
| Sequence number | Passband (Hz) | Sequence number | Passband (Hz) | Sequence number | Passband (Hz) |
| 1 | [90,150] | 16 | [540,600] | 31 | [990,1050] |
| 2 | [120,180] | 17 | [570,630] | 32 | [1020,1080] |
| 3 | [150,210] | 18 | [600,660] | 33 | [1050,1110] |
| 4 | [180,240] | 19 | [630,690] | 34 | [1080,1140] |
| 5 | [210,270] | 20 | [660,720] | 35 | [1110,1170] |
| 6 | [240,300] | 21 | [690,750] | 36 | [1140,1200] |
| 7 | [270,330] | 22 | [720,780] | 37 | [1170,1230] |
| 8 | [300,360] | 23 | [750,810] | 38 | [1200,1260] |
| 9 | [330,390] | 24 | [780,840] | 39 | [1230,1290] |
| 10 | [360,420] | 25 | [810,870] | 40 | [1260,1320] |
| 11 | [390,450] | 26 | [840,900] | 41 | [1290,1350] |
| 12 | [420,480] | 27 | [870,930] | 42 | [1320,1380] |
| 13 | [450,510] | 28 | [900,960] | 43 | [1350,1410] |
| 14 | [480,540] | 29 | [930,990] | 44 | [1380,1440] |
| 15 | [510,570] | 30 | [960,1020] |
The transmitter selects the center frequency and the estimated frequency f of the sensor signal according to a table look-up method e The center frequency of the bandpass filter with the smallest phase difference is the frequency at the center of the passband of the filter. Thus, the bandpass filter passband is selected to be 120 Hz-180 Hz. The parameters of the second-order digital band-pass filter are:
α 0 ＝0.018503，β 1 ＝-1.954629，β 2 ＝0.962994
the equation of difference is:
Y'[n]＝0.018503*Y[n]-0.018503*Y[n-2]+1.954629*Y'[n-1]-0.962994*Y'[n-2]
to verify the flow point 0.6m 3 The 1200-point signal sampling data at the time of/h is taken as an example to illustrate the effectiveness of the signal processing method of the turbine flow sensor.
The turbine flow sensor signal is low pass filtered and band pass filtered to a signal pair such as that shown in fig. 3. The spectrum of the filtered signal is shown in FIG. 4, and the 1024-point FFT calculates the estimated frequency f of the signal e Is 136.7188Hz. As shown in fig. 3, in the time domain, the flow signal after the band-pass filtering becomes smoother, and the burr noise in the signal is effectively suppressed.
Linear interpolation method-based signal finding methodZero point position in the number data. In the signal data, if there are two adjacent sampling points (x 1 ,y 1 )、(x 2 ,y 2 ) The method comprises the following steps:
y 1 y 2 ≤0
calculation of x using linear interpolation 1 And x 2 Zero point position x in (a) n The method comprises the following steps:
and fifthly, calculating a signal period based on a zero-crossing detection method. The latter zero position x n+1 Subtracting the previous zero position x n Calculating the number T of sampling points contained in half period of signal n ：
T n ＝x n+1 -x n
Sixth step of screening signal half period point number T n Obtaining T' n 。T’ n Corresponding signal frequency and f e The difference is not more than Δf, T' n The following should be satisfied:
68.2667≤2T’ n ≤78.7692
and (2) counting T 'the number of half periods of the signals after screening' n Sequencing from small to large, taking the average value of the number of signal half-period points data of 60% in the middle as the actual number of half-period points T of the sensor signal 0 For 37.4195, the frequency f of the sensor signal is recalculated 0 ：
In order to actually verify the effectiveness of the signal processing method of the time-frequency domain digital turbine flow sensor provided by the invention, a transmitter based on the signal processing method of the digital turbine flow sensor is matched with a primary instrument of a 0.5-level DN15 turbine flow sensor, and a water flow calibration experiment is carried out on a water flow calibration device. The uncertainty of the calibration device is 0.05%, and the calibration is performed by adopting a volumetric method. Table 2 shows the water flow calibration test results.
Table 2 water flow calibration test results
The experimental results show that the flow rate range is 0.6m 3 /h～6.0m 3 Within/h, the digital turbine flow sensor using the signal processing method provided by the invention has the maximum value of 0.3565% of indication error value, the maximum value of repeatability of 0.0722% and the accuracy grade of 0.5.
In order to examine the response speed of the signal processing method of the time-frequency domain digital turbine flow sensor, the valve opening of the water flow calibration device is regulated to obtain different flow points, and a variable flow response speed experiment is carried out. The experimental procedure was as follows: the calibration device measures the initial state of the pipeline valve to be in a closed state, then adjusts the valve to increase the flow to v a About 0.6m 3 And/h, stabilizing for a period of time, and regulating the valve to quickly increase the flow to a larger flow v b Where v b Respectively 2.4m 3 /h、4.8m 3 /h、6.0m 3 And/h. Collecting variable flow signals and sensor output results in real time, and calculating flow in v a To v b Time t at which transmitter output begins to change during process 1 As shown in FIG. 5 (b) to time t at which stabilization is initiated 2 The time interval between (as shown in fig. 5 (c)) such that the variable flow response time of the digital turbine flow sensor can be obtained. The response time test results are shown in table 3.
TABLE 3 response speed test results
In table 3, the digital turbine flow sensor using the signal processing method proposed by the present invention has a variable flow response time of less than 1s.
In conclusion, the accuracy level of the digital turbine flow sensor developed based on the signal processing method provided by the invention reaches 0.5 level, and the high-precision measurement of water flow is realized; the response time of the variable flow is less than 1s, and the quick response during the flow change is realized.
Claims (1)
1. A time-frequency domain digital turbine flow sensor signal processing method is characterized in that: in the frequency domain, after removing the direct current component in the sensor signal, firstly adopting a low-pass filter to filter the signal, then using FFT to estimate the frequency of the sensor signal, then determining the parameters of a band-pass filter according to the estimated frequency by a table look-up method, carrying out band-pass filtering on the signal, improving the anti-interference performance of the turbine flow sensor and ensuring the measurement accuracy; in the time domain, calculating zero coordinates in signal data based on a linear interpolation method, calculating a signal period based on a zero-crossing detection method, and then solving the signal frequency to reduce the operand and ensure the instantaneity;
the processing procedure of the sensor signal in the frequency domain is as follows: removing a direct current component of a signal, and performing low-pass filtering on the signal; the signal frequency f is estimated by using N-point FFT e The method comprises the steps of carrying out a first treatment on the surface of the From the estimated frequency f e Determining parameters of a band-pass filter, and carrying out band-pass filtering on the signals;
the process of calculating the signal frequency in the time domain is:
calculating a signal zero point coordinate by using a linear interpolation method: find two adjacent points (x 1 ,y 1 )、(x 2 ,y 2 ) Zero point coordinate x is calculated using linear interpolation n The method comprises the following steps:
y 1 y 2 ≤0
the number of half-period points of the signal is calculated based on a zero-crossing detection method: the latter zero point coordinate x n+1 Subtracting the previous zero coordinate x n Is recorded as the number T of the nth half period of the sensor signal n ：
T n ＝x n+1 -x n
Screening the number of half-period points of the signal to obtain T' n : actual frequency of sensor signal f e The difference of the frequency resolution of FFT is not more than the difference of T n The number of half-cycles which do not meet the condition is counted to obtain T' n T 'then' n Satisfies the following formula:
wherein f s For signal sample rate, Δf is the frequency resolution of the FFT:
and (3) median filtering: for the number T 'of the half period of the signal after screening' n Sequencing, taking the average value of the number of half-period points of the signal of the middle 60% as the actual number of half-period points T of the sensor signal 0 The actual frequency f of the sensor signal is recalculated 0 ：
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| WO2017143649A1 (en) * | 2016-02-23 | 2017-08-31 | 合肥工业大学 | Kalman filter-based vortex flowmeter anti-transient shock interference signal processing method and system |
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| CN101644590A (en) * | 2009-09-08 | 2010-02-10 | 合肥工业大学 | Anti-strong interference vortex street flowmeter digital signal processing system based on single sensor |
| WO2017143649A1 (en) * | 2016-02-23 | 2017-08-31 | 合肥工业大学 | Kalman filter-based vortex flowmeter anti-transient shock interference signal processing method and system |
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