CN114637960A - Time-frequency domain digital turbine flow sensor signal processing method - Google Patents
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Abstract
The invention relates to a time-frequency domain digital turbine flow sensor signal processing method. The analog turbine flow sensor directly shapes the sensor signal into a pulse signal for output and measurement, and has good real-time performance. However, when the turbine flow sensor works in an environment with strong noise interference, the output signal of the turbine flow sensor is easy to distort, so that the pulse output is inaccurate and the precision is reduced. The digital turbine flow sensor can acquire sensor signals in real time, performs spectrum analysis to calculate flow frequency, has good anti-interference performance, and needs high frequency resolution in order to ensure measurement accuracy. 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 calculation time is long, and the real-time performance is poor. Therefore, the patent provides a new signal processing method for the digital turbine flow sensor, and the signal processing method combining time and frequency domains can improve the anti-interference performance, ensure the measurement accuracy, reduce the operation amount and ensure the real-time performance.
Description
Technical Field
The invention belongs to the technical field of flow detection, and particularly relates to a time-frequency domain digital turbine flow sensor signal processing method.
Background
An impeller-type turbine flow sensor (referred to as a turbine flow sensor for short) is a speed-type flow meter working based on the moment balance principle, and measures flow by using the linear relation between the fluid flow and the frequency of a turbine flow sensor signal. The turbine flow sensor has the characteristics of simple and light structure, high measurement precision, good reproducibility, convenience in installation and use and the like, and is widely applied to industries such as petroleum, chemical engineering, water supply, medical treatment and the like.
In the existing literature, "research and design of a real-time detection method of a turbine flowmeter" (royal vic, university of Guizhou, 2015), a signal amplification shaping circuit and a zero-crossing detection circuit are used for shaping a sensor signal into a square wave signal, and "low-power consumption two-wire turbine flowmeter design" (Shaohilong, Shenyiming, photoelectric information and computer program institute of Shanghai university, 2016), an amplification filter circuit and a Schmidt shaping circuit are used for shaping the sensor signal into an electric pulse signal, and "research and design of a flow remote monitoring system based on APP" (child Liaotong, Changan university, 2020), a filter amplification circuit and a saturation circuit are used for shaping the sensor signal into the pulse signal. In the above documents, an analog circuit is adopted to shape a sensor signal into a pulse or square wave signal for output and measurement, and then the pulse number or frequency of the pulse signal is used to calculate a flow parameter, but if noise exists in a turbine flow sensor signal, the pulse signal is distorted, and the accuracy of flow measurement is reduced.
The prior document "A Zero Crossing Algorithm for the Estimation of the Frequency of the sinusoidal Signal in White Noise" (Friedman V, IEEE Transactions on Signal Processing, 1994, volume 42, phase 6) proposes An Algorithm for determining the Frequency of the sinusoidal Signal affected by additive Gaussian White Noise by calculating the Zero Crossing interval, "An effective adaptive detection based on detection of the Frequency of the sinusoidal Signal in the upper processor" (Ankita guide, Rajeev Thaku, Sachi Murrarka, International Journal of Engineering and Applications (IJERA), volume 3, phase 5) based on hardware circuitry for achieving a slight filtering of the sinusoidal Signal around a fixed Frequency and Zero Crossing detection based on hardware circuitry, "A Zero Crossing Algorithm for the Estimation of the Frequency of the sinusoidal Signal in White Noise" (Noise interference of average Noise calculation, volume 49, volume 2014), and drawing a signal half period histogram to realize the estimation of the signal period, thereby solving the signal frequency. The above literature on zero-crossing detection methods mainly studies single-frequency signals, and the proposed methods are also difficult to implement in a single chip microcomputer.
There are generally two ways to calculate the frequency of the sensor signal in a digital turbine flow sensor: firstly, the signal frequency is directly calculated by using FFT, but when the caliber of the 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 solved 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 difference with the actual signal period.
The invention provides a time-frequency domain digital turbine flow sensor signal processing method, which comprises the following steps: in a frequency domain, after a direct-current component of a signal is removed, firstly, the signal is processed by using digital low-pass filtering, then, the signal frequency is estimated based on FFT, and then, parameters of a band-pass filter are determined according to a table look-up method, so that the noise component in the signal is reduced, the anti-interference performance of the turbine flow sensor is improved, and the measurement precision is ensured; in the time domain, the zero 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 operation amount is reduced and the real-time performance is ensured.
Disclosure of Invention
The invention provides a time-frequency domain digital turbine flow sensor signal processing method, which adopts the technical scheme that: firstly, removing a direct-current component in a signal of a turbine flow sensor, and filtering the signal by adopting a low-pass filter; secondly, estimating the frequency of the sensor signal by using FFT, determining the parameters of a band-pass filter according to the estimated frequency by a table look-up method, and performing band-pass filtering on the signal; and finally, calculating the zero position of the signal based on a linear interpolation method, calculating the signal period based on a zero-crossing detection method, and 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 a signal by adopting a Butterworth second-order digital low-pass filter, wherein the transfer function of the low-pass filter is as follows:
secondly, the frequency corresponding to the maximum peak value in the amplitude spectrum is found by using N-point FFT (fast Fourier transform), and the frequency is regarded as the estimated frequency f of the signale。
Filtering the signal by using a butterworth second-order band-pass filter, wherein the transfer function of the band-pass filter is as follows:
parameters of the band-pass filter being dependent on the estimated frequency feAnd (4) determining.
In the time domain, the specific technical process for processing the digital turbine flow sensor signal is as follows:
the method comprises the steps of calculating a signal zero point coordinate by a linear interpolation method. Finding two adjacent points (x) with opposite amplitude signs in the signal data1,y1)、(x2,y2) Calculating the zero position x using a two-point interpolation methodnNamely:
y1y2≤0
and calculating a signal period based on a zero-crossing detection method. Conveying applianceNumber of points T in nth half period of sensor signalnThe calculation formula of (2) is as follows:
Tn＝xn+1-xn
screening the number of signal half-period points to obtain Tn'. Signal frequency and f corresponding to number of signal half-period pointseEliminating T when the frequency resolution of FFT cannot be exceedednThe number of half-period points which do not meet the condition is obtainedn', then Tn' satisfies the following formula:
in the formula, fsFor the signal sampling rate, Δ f is the frequency resolution of the FFT:
and fourthly, median filtering. Counting the number T of the half period of the screened signalnSorting is carried out, and the average value of the number of the middle 60 percent of signal half-period points is taken as the actual number T of the half-period points of the sensor signal0Then solving for the frequency f of the sensor signal0：
The invention has the beneficial effects that: in a frequency domain, a digital filtering method and an FFT (fast Fourier transform) are combined to reduce noise components in signals, so that 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 operation amount can be reduced, and the real-time performance can be 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 shows a flow rate of 0.6m3When the signal is subjected to the low-pass filtering, the direct-current component of the signal of the turbine flow sensor is removed, and the signal subjected to the low-pass filtering is compared with the signal subjected to the band-pass filtering;
FIG. 4 shows a flow rate of 0.6m3At/h, a signal spectrogram after low-pass filtering;
FIG. 5 shows the flow rate from 0.6m3H to 1.2m3And when the measured value is/h, responding to the test result of the response time of the turbine flow sensor.
Detailed Description
The objects, technical solutions and advantages of the present invention will become more apparent from the following detailed description of the present invention when taken in conjunction with 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, so that 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 0.6m3/h～6.0m3The frequency range is about 136Hz to 1360Hz, and the signal sampling rate needs to satisfy f according to Shannon's sampling theorems2720Hz, in this example fs＝10000Hz。
Before the signal of the turbine flow sensor enters the transmitter, the signal passes through a forward input signal conditioning circuit, and the schematic block diagram of the circuit is shown in fig. 1. A flow chart of a signal processing method of the digital turbine flow sensor is shown in fig. 2, and the specific technical process is as follows:
first, the flow is 0.6m3And when the pressure is/h, acquiring a turbine flow sensor signal.
The method comprises the following steps of filtering a signal by using a butterworth second-order digital low-pass filter, wherein the second-order digital low-pass filter has the parameters:
[a0,a1,a2]＝[1.0,0.369527,0.195816]
[b0,b1,b2]＝[0.391336,0.182672,0.391336]
the equation for the 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 the sensor signal is low-pass filtered, an estimated frequency f of the sensor signal is calculated using 1024-point FFTeAnd is 136.7188 Hz.
At this time, the frequency resolution of the FFT is:
thirdly, designing a band-pass filter bank according to the frequency range of the turbine flow sensor, wherein 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 passband ranges of the two adjacent groups of filters are in 50% of the same interval, and the total number of the digital filter parameters is 44, as shown in Table 1.
TABLE 1 specific passband ranges for each bandpass filter
| Serial number | Passband (Hz) | Serial number | Passband (Hz) | Serial 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 methodeThe center frequency, i.e., the frequency centered in the filter passband, is the least different bandpass filter. Therefore, the passband of the selected bandpass filter is 120Hz to 180 Hz. The parameters of the second order digital band-pass filter are:
α0＝0.018503，β1＝-1.954629，β2＝0.962994
the equation for the 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 of 0.6m3The 1200-point signal sampling data at the/h time is taken as an example to illustrate the effectiveness of the signal processing method of the turbine flow sensor provided by the invention.
The turbine flow sensor signal is low pass filtered, band pass filtered and the signal pair is shown in fig. 3. The filtered signal spectrum is shown in FIG. 4, estimated frequency f of signal calculated by 1024-point FFTeIs 136.7188 Hz. As shown in fig. 3, in the time domain, the flow signal after band-pass filtering becomes smoother, and the glitch noise in the signal is effectively suppressed.
And finding the zero position in the signal data based on a linear interpolation method. In the signal data, if there are two adjacent sample points (x)1,y1)、(x2,y2) And satisfies the following conditions:
y1y2≤0
calculating x using linear interpolation1And x2Zero point position x innComprises the following steps:
fifthly, calculating the signal period based on a zero-crossing detection method. The latter zero position xn+1Minus the previous zero position xnCalculating the number T of sampling points contained in a half period of the signaln：
Tn＝xn+1-xn
Sixthly, screening the number T of the signal half-periodnTo give T'n。T’nCorresponding signal frequency and feThe phase difference does not exceed delta f, then T'nIt should satisfy:
68.2667≤2T’n≤78.7692
just pairs of screened signal half-period points T'nSorting from small to large, and taking the average value of the point number data of the middle 60 percent of the signal half periodNumber of actual half-period points T as sensor signal037.4195, the frequency f of the sensor signal is calculated0：
In order to actually verify the effectiveness of the time-frequency domain digital turbine flow sensor signal processing method provided by the invention, a 0.5-level DN15 turbine flow sensor primary instrument is matched with a transmitter based on the digital turbine flow sensor signal processing method provided by the invention, 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 carried out by adopting a volume method. Table 2 shows the water flow calibration experiment results.
TABLE 2 Water flow calibration test results
The experimental results show that the flow rate range is 0.6m3/h～6.0m3In/h, the maximum value of the indicating value error value of the digital turbine flow sensor using the signal processing method provided by the invention is 0.3565%, the maximum value of the repeatability is 0.0722%, and the accuracy grade reaches 0.5 grade.
In order to examine the response speed of the time-frequency domain digital turbine flow sensor signal processing method, the valve opening of the water flow calibration device is adjusted 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 as a closed state, and then adjusts the valve to increase the flow to vaAbout 0.6m3H, stabilizing for a period of time, adjusting the valve to rapidly increase the flow to a greater flow vbV isbAre respectively 2.4m3/h、4.8m3/h、6.0m3H is used as the reference value. Real-time collecting variable flow signal and sensor output result, calculating flow at vaTo vbTime t at which the output of the transducer begins to change during the process1(as shown in FIG. 5 (b)) until onset is stableAt a fixed time t2(as shown in fig. 5 (c)) so 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 experiment results
In table 3, the variable flow response time of the digital turbine flow sensor using the signal processing method proposed by the present invention is less than 1 s.
In conclusion, the accuracy grade of the digital turbine flow sensor developed based on the signal processing method provided by the invention reaches 0.5 grade, 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 when the flow changes is realized.
Claims (1)
1. A time-frequency domain digital turbine flow sensor signal processing method is characterized in that: in a frequency domain, after a direct-current component in a sensor signal is removed, firstly, a low-pass filter is adopted to filter the signal, then, FFT is used to estimate the frequency of the sensor signal, parameters of the band-pass filter are determined according to the estimated frequency by a table look-up method, band-pass filtering is carried out on the signal, the anti-interference performance of the turbine flow sensor is improved, and the measurement precision is ensured; in the time domain, zero coordinates in signal data are calculated based on a linear interpolation method, a signal period is calculated based on a zero-crossing detection method, and then the signal frequency is solved so as to reduce the operation amount and ensure the real-time property;
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; secondly, estimating the signal frequency f by using N-point FFTe(ii) a According to the estimated frequency feDetermining parameters of a band-pass filter, and performing band-pass filtering on the signal;
the process of calculating the signal frequency in the time domain is:
calculating a signal zero point coordinate by a linear interpolation method: finding two adjacent points of opposite sign of amplitude in the signal: (x1,y1)、(x2,y2) Calculating the zero point coordinate x using linear interpolationnNamely:
y1y2≤0
secondly, calculating the number of signal half-cycle points based on a zero-crossing detection method: the latter zero point coordinate xn+1Minus the previous zero coordinate xnIs counted as the number T of the nth half period of the sensor signaln：
Tn＝xn+1-xn
Screening the number of signal half-period points to obtain T'n: actual frequency of sensor signal and feThe difference does not exceed the frequency resolution of FFT, T is eliminatednThe number of half-period points which do not meet the condition is obtained to obtain T'nThen T'nSatisfies the following formula:
in the formula (f)sFor the signal sampling rate, Δ f is the frequency resolution of the FFT:
fourth, median filtering: counting number T 'of screened signal half period'nSorting, taking the average value of the number of the signal half-period points with the middle of 60 percent as the number T of the actual half-period points of the sensor signal0Then calculating the actual frequency f of the sensor signal0：
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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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| 郝祖龙, 常太华, 田亮, 郝雷: "涡街流量计数字信号处理方法分析与比较", 现代电力, no. 02, 30 April 2005 (2005-04-30) * |
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