A Vortex Street Detection Method Based on Stochastic Resonance for Noise Elimination

Abstract

The present invention discloses a vortex street detection method based on random resonance noise elimination, comprising: S1, collecting the voltage signal S(t) output by the piezoelectric sensor at the vortex street wake; S2, performing fast discrete Fourier transform on the voltage signal of the piezoelectric sensor to obtain a frequency domain signal F(f); S3, calculating the power spectrum F of the frequency domain signal F(f) r (f), and F r (f) Normalize and get the percentage P(f) of the power spectrum corresponding to frequency f in the entire spectrum; S4, calculate the power spectrum entropy Se, through the power spectrum entropy S e Estimate the parameters a, k of the stochastic resonance denoising system; S5, bring the voltage signal S(t) output by the piezoelectric sensor at the vortex wake into the stochastic resonance denoising equation to obtain X(t) as the denoised signal; S6, perform fast discrete Fourier transform on the denoised signal X(t), using the center frequency f of the FFT c Calculate the flow rate Q in the pipeline. According to the present invention, the system's ability to detect vortex signals in complex noise can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vortex street detection, and in particular to a vortex street detection method based on stochastic resonance to eliminate noise. Background Art

[0002] Stochastic resonance was first proposed by Benzi while studying paleometrological glaciology. Since then, it has garnered widespread attention in signal processing research. While suppressing noise, stochastic resonance can also transfer some of the noise energy to a periodic signal at a specific "resonance" point. This not only enhances the signal strength without suppressing the useful signal, but also demonstrates a unique advantage in detecting weak signals in strong noise backgrounds.

[0003] Since there are no moving parts and it is not affected by the physical properties of the fluid (such as density and viscosity), vortex flowmeters are widely used to measure the flow of liquids, gases and steam in industrial pipelines. However, in actual use, a small amount of gas will be drawn in when a water pump pumps water, and a small amount of gas will be carried in the industrial pipelines that transport liquids. This type of working condition is a gas-liquid two-phase flow. When using a vortex flowmeter to detect the volume flow rate, the vortex frequency and signal strength will be affected by the volume gas content. Hulin's experimental results show that when the volume gas content exceeds 25%, the vortex shedding frequency cannot be extracted from the FFT transformed spectrum. [1] Sun Zhiqiang pointed out that the vortex signal amplitude decreases with the increase of gas content, and the vortex frequency cannot be detected when the volume gas content exceeds 18%. [2] Two-phase turbulent flow pulsations enhance pipeline flow noise, resulting in vortex energy being lower than noise energy at high gas fractions, obscuring the signal and making it difficult to identify. This problem falls under the category of weak signal detection. Traditional weak signal detection methods primarily filter out noise, but these methods suppress both useful signals and noise, hindering signal extraction. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the present invention aims to provide a vortex street detection method based on stochastic resonance noise elimination, which can effectively improve the system's ability to detect vortex street signals in complex noise. In order to achieve the above-mentioned purpose and other advantages according to the present invention, a vortex street detection method based on stochastic resonance noise elimination is provided, comprising:

[0005] S1, collects the voltage signal S(t) output by the piezoelectric sensor at the vortex wake;

[0006] S2. Perform fast discrete Fourier transform on the voltage signal of the piezoelectric sensor to obtain a frequency domain signal F(f);

[0007] S3. Calculate the power spectrum F of the frequency domain signal F(f) r(f), and F r (f) Normalize to get the percentage P(f) of the power spectrum corresponding to frequency f in the entire spectrum;

[0008] S4, calculate the power spectrum entropy Se, through the power spectrum entropy S e Estimate the parameters a,k of the stochastic resonance denoising system;

[0009] S5. Substitute the voltage signal S(t) output by the piezoelectric sensor at the vortex wake into the stochastic resonance denoising equation to obtain X(t), which is the denoised signal.

[0010] S6. Perform fast discrete Fourier transform on the denoised signal X(t), using the center frequency f of FFT c Calculate the flow rate Q in the pipe.

[0011] Preferably, in step S1, a voltage signal S(t) output by a piezoelectric sensor at the vortex wake is collected by a high-frequency acquisition card.

[0012] Preferably, the power spectrum F r The formula for (f) is The calculation formula of P(f) is P(f)=F r (f) / ∑F r (f).

[0013] Preferably, in step S4, S e =-∑P(f)*InP(f), using power spectrum entropy S e Estimate the parameters a,k of the stochastic resonance denoising system:

[0014]

[0015] Among them, P max (f) is the maximum value of the normalized power spectrum, and N is the number of sampling points used for Fourier transform.

[0016] Preferably, in step S5, the voltage signal S(t) output by the piezoelectric sensor at the vortex wake is substituted into the stochastic resonance noise removal equation:

[0017]

[0018] Preferably, in step S6, the flow rate Q in the pipeline is calculated: Where W is the vortex shedder width and D is the pipe diameter.

[0019] Compared with the existing technology, the present invention has the following advantages: it uses power spectrum entropy to estimate system parameters a and damping coefficient k to achieve optimal stochastic resonance output. This method can transfer noise energy to the useful signal, not only preventing useful signal damage during noise suppression but also enhancing signal amplitude. It is particularly suitable for detecting weak vortex signals in strong noise backgrounds. This signal processing method provides a new solution to the problem of weak vortex signal detection in complex noise environments, improves the signal-to-noise ratio under strong noise conditions, and can extract vortex frequencies in certain detection blind spots from the stochastic resonance output signal spectrum. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of the structure of the vortex street detection method based on stochastic resonance noise elimination according to the present invention;

[0021] Figure 2 This is a diagram showing the denoising ability of the vortex street detection method based on random resonance noise elimination according to the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] Reference Figure 1-2 This paper utilizes the noise suppression properties of stochastic resonance to propose a new method for detecting weak vortex street signals in complex noise environments. The vortex street signal detected by a piezoelectric sensor serves as the driving excitation and is input into a bistable stochastic resonance nonlinear system. The system output is the noise-suppressed vortex street signal. The power spectral entropy of the original signal is used to adjust system parameters to achieve optimal output from the stochastic resonance system.

[0024] The vortex street detection device and method based on stochastic resonance noise removal has the following specific steps:

[0025] 1) Place a bluff body in the circular pipe, such as Figure 1 A piezoelectric sensor is placed downstream of the bluff body to detect the pressure change of the vortex wake.

[0026] 2) A high-frequency acquisition card is used with a sampling frequency higher than twice the vortex frequency. The number of samples in the calculation cycle is N, and the voltage signal S(t) output by the piezoelectric sensor at the vortex wake is collected.

[0027] 3) Use FFT to convert the piezoelectric sensor's voltage value time domain signal S(t) into a frequency domain signal to estimate the signal's frequency spectrum. That is, the discrete Fourier transform of the sequence S(t) of length N is F(f).

[0028] 4) Calculate the power spectrum of the frequency domain signal F(f):

[0029] 5) F r (f) is normalized to obtain the percentage P(f) of the power spectrum corresponding to frequency f in the entire spectrum:

[0030] P(f)=F r (f) / ∑F r (f)

[0031] 6) If the vortex signal amplitude is large, there is a sharp peak in the power spectrum, corresponding to the power spectrum entropy S e If the vortex amplitude is small and the noise intensity is high, the power spectrum distribution is more uniform, then S e The power spectrum entropy contains information about the vortex amplitude and the intensity of the interference noise. Calculate the power spectrum entropy Se:

[0032] S e =-∑P(f)*InP(f)

[0033] 7) The smaller the vortex amplitude, the lower the matching transition threshold, the smaller the parameter a value is required, and the smaller the signal power spectrum entropy is. The higher the noise intensity, the stronger the system noise suppression capability is required, the larger the parameter k value is required, and the corresponding power spectrum entropy is also larger. Use the power spectrum entropy to estimate the size of the system parameters a and k so that the stochastic resonance system achieves the optimal output. The relationship is as follows:

[0034]

[0035] Among them, P max (f) is the maximum value of the normalized power spectrum, and N is the number of sampling points used for Fourier transform.

[0036] 8) Substitute the voltage signal S(t) output by the piezoelectric sensor at the vortex wake into the stochastic resonance noise elimination equation

[0037]

[0038] 9) X(t) obtained by solving the above equation is the denoised signal.

[0039] 10) Perform fast discrete Fourier transform on the denoised signal X(t), using the center frequency f of FFT c Calculate the flow rate Q in the pipe: Where W is the vortex shedder width and D is the pipe diameter.

[0040] This example is a vortex flow detection method based on stochastic resonance to eliminate noise. The following is a specific implementation in gas-liquid two-phase flow measurement.

[0041] The experiment was conducted using a DN50 vortex flowmeter on a gas-liquid two-phase flow horizontal atmospheric pressure device. The fluid medium was water-air, the sampling frequency was 5kHz, the number of samples was 32768, and each experimental point was sampled 5 times. At two liquid phase volume flow points (4m 3 / h、6m 3 / h) and at different volumetric gas fractions, the sensor output served as the source data for signal analysis. The adaptive stochastic resonance method described in this patent was used to filter and denoise the source data. The comparison of the dominant frequency and signal-to-noise ratio (SNR) obtained by FFT transforming the system output and the source data is listed in Tables 1 and 2.

[0042] Table 1. Liquid phase volume flow rate at 4m 3 / h vortex signal signal-to-noise ratio

[0043]

[0044] Table 2. Liquid phase volume flow rate at 6m 3 / h vortex signal signal-to-noise ratio

[0045]

[0046] As shown in Tables 1 and 2, the vortex signals at the two liquid flow rate points were strongly interfered with by noise, and the vortex spectrum peaks were drowned out by the noise, resulting in a detection blind zone. Within the blind zone, the dominant frequency value of the original signal's frequency spectrum after FFT transformation is not the vortex frequency, but the pipeline noise frequency. After the signal is processed by adaptive stochastic resonance, the vortex amplitude is amplified, the noise energy is suppressed, and the vortex frequency is highlighted. Although the system's output SNR is lower than that of the original signal at some high SNR experimental points, the system's ability to improve the signal-to-noise ratio is more pronounced as the original signal-to-noise ratio decreases at most low SNR experimental points. Furthermore, the stochastic resonance's ability to identify vortex frequencies within the blind zone demonstrates the effectiveness and superiority of this method for detecting weak signals in the presence of strong noise.

[0047] The method proposed in this patent has the ability to denoise the original signal. Figure 2As shown in the figure. When the volumetric air fraction is 0.06, the vortex street amplitude is small due to the entrainment of the wake vortex core, and the excessive noise in the pipeline causes the vortex street signal to be submerged. The signal in the time domain diagram is composed of high-frequency noise and the vortex street frequency cannot be identified. After stochastic resonance, a clear vortex street signal appears in the signal time domain diagram. The vortex street amplitude decreases with increasing air fraction. When the volumetric air fraction is 0.43, the vortex street amplitude is small, and high-frequency fluctuations caused by noise appear at the vortex street peak. The vortex street peak is very unstable, sometimes high and sometimes low. After stochastic resonance filtering, the vortex street amplitude output is amplified, the high-frequency fluctuations are suppressed, and the vortex street signal peak is stable.

[0048] The number of devices and processing scales described herein are intended to simplify the description of the present invention, and applications, modifications, and variations of the present invention will be apparent to those skilled in the art.

[0049] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A vortex street detection method based on stochastic resonance noise elimination, characterized in that: The following steps are involved: S1, collects the voltage signal S(t) output by the piezoelectric sensor at the vortex wake; S2. Perform fast discrete Fourier transform on the voltage signal of the piezoelectric sensor to obtain a frequency domain signal F(f); S3. Calculate the power spectrum F of the frequency domain signal F(f) r (f), and F r (f) Normalize to get the percentage P(f) of the power spectrum corresponding to frequency f in the entire spectrum; S4. Calculate the power spectrum entropy S e , through the power spectrum entropy S e Estimate the parameters a,k of the stochastic resonance denoising system; S5. Substitute the voltage signal S(t) output by the piezoelectric sensor at the vortex wake into the stochastic resonance denoising equation to obtain X(t), which is the denoised signal. S6. Perform fast discrete Fourier transform on the denoised signal X(t), using the center frequency f of FFT c Calculate the flow rate Q in the pipe; In step S1, the voltage signal S(t) output by the piezoelectric sensor at the vortex wake is collected by a high-frequency acquisition card; Power spectrum The formula is , P(f) The calculation formula is: ； In step S4, , using power spectrum entropy S e Estimating the parameters of a stochastic resonance noise removal system a , k : ， Where Pmax(f) is the maximum value of the normalized power spectrum, and N is the number of sampling points used for Fourier transform; Substitute the voltage signal S(t) output by the piezoelectric sensor at the vortex wake into the stochastic resonance noise removal equation: ； In step S6, the flow rate Q in the pipeline is calculated: , where W is the vortex shedder width and D is the pipe diameter.

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