A denoising method for TDLAS gas monitoring system

Abstract

The present invention discloses a denoising method for a TDLAS gas monitoring system. The method first obtains a noisy second harmonic signal collected by the TDLAS gas monitoring system, then removes the distorted signal based on the symmetry of the second harmonic signal, performs baseline calibration on the signal, performs ICEEMDAN decomposition using characteristic selection parameters of the second harmonic signal, calculates the approximate entropy of all IMF components and the second harmonic signal, classifies the IMF components according to defined threshold values, denoises the noisy IMF components using multi-layer wavelet decomposition and a wavelet soft threshold function, and then reconstructs the denoised second harmonic signal to complete the denoising process. The present invention can effectively suppress background noise and improve the signal-to-noise ratio, thereby reducing the minimum detection limit of the gas monitoring system and improving system performance. The method is mainly used in the field of environmental monitoring.

Description

Technical Field

[0001] The present invention relates to the field of TDLAS gas monitoring system signal denoising methods, in particular to a TDLAS gas monitoring system signal denoising method. Background Art

[0002] Among numerous gas detection technologies, TDLAS (tunable diode laser absorption spectroscopy) is widely used for its high precision and fast response speed. However, TDLAS gas monitoring systems are subject to numerous external environmental interference factors, which affect the system's detection performance. The effective signal is often contaminated by background noise, resulting in a low signal-to-noise ratio. Effectively extracting the effective signal from the optical signal is crucial to the detection limit of TDLAS gas monitoring systems.

[0003] Currently, various denoising methods have been applied to TDLAS gas detection, such as wavelet denoising and empirical mode decomposition. However, existing wavelet denoising methods are poorly adaptable to certain noises in non-stationary signals. The IMF components decomposed by the empirical mode decomposition algorithm suffer from severe mode mixing problems. IMF components are easily affected by noise and still contain residual noise and spurious modal components. Summary of the Invention

[0004] The present invention provides a TDLAS gas monitoring system denoising method to solve the problem that residual noise and pseudo-modal components still exist in the signals collected by the TDLAS gas monitoring system in the prior art after wavelet denoising.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A TDLAS gas monitoring system denoising method comprises the following steps:

[0007] Step 1: Obtain the noisy second harmonic signal collected by the TDLAS gas monitoring system;

[0008] Step 2: removing the signal distorted by the fluctuation deformation from the noisy second harmonic signal obtained in step 1, and then performing baseline calibration on the noisy second harmonic signal;

[0009] Step 3: Determine the characteristics of the noise added during ICEEMDAN decomposition, and then add the noise with the determined characteristics to the noisy second harmonic signal obtained in step 2 to perform ICEEMDAN decomposition to obtain multiple IMF components;

[0010] Step 4: Calculate the approximate entropy of each IMF component decomposed in step 3, define an approximate entropy threshold ε, and divide each IMF component into a noisy IMF component and a valid IMF component based on the approximate entropy threshold ε and the calculated approximate entropy of each IMF component.

[0011] Step 5: Determine the wavelet basis function and the number of decomposition layers, and use the wavelet basis function to perform wavelet soft threshold denoising on the noisy IMF component obtained in step 4 according to the determined number of decomposition layers, thereby obtaining the IMF component after wavelet denoising;

[0012] Step 6: Reconstruct the IMF components after wavelet denoising obtained in step 5 and the effective IMF components obtained in step 4 into a denoised second harmonic signal.

[0013] Furthermore, in step 1, the laser of the TDLAS gas monitoring system is wavelength modulated, and the noisy second harmonic signal collected thereby is a signal formed after the wavelength-modulated laser passes through the monitoring gas.

[0014] Furthermore, during wavelength modulation, the modulation signal adopts a low-frequency triangle wave modulation signal and a high-frequency sine wave modulation signal.

[0015] In a further step 2, based on the symmetry characteristics of the noisy second harmonic signal, constraints on the absorption peak parameters of the noisy second harmonic signal are established, and based on the constraints, noisy second harmonic signals with symmetry characteristics are identified, thereby eliminating signals in the noisy second harmonic signal that are distorted due to fluctuation deformation.

[0016] In a further step 2, the baseline of the noisy second harmonic signal is fitted using the least square method, and then the baseline is subtracted from the noisy second harmonic signal with the distortion signal removed, thereby completing the baseline calibration.

[0017] In the further step 3, the noise added during ICEEMDAN decomposition is Gaussian white noise.

[0018] Furthermore, the standard deviation of the Gaussian white noise is 0.1.

[0019] In the further step 4, the approximate entropy calculated for each IMF component is compared with the approximate entropy threshold ε. When the approximate entropy calculated for a certain IMF component is greater than the approximate entropy threshold ε, the corresponding IMF is separated into a noisy IMF component; when the approximate entropy calculated for a certain IMF component is less than or equal to the approximate entropy threshold ε, the corresponding IMF is separated into a valid IMF component.

[0020] In the further step 5, the wavelet basis function used is the db10 wavelet function, and the number of decomposition layers is 7.

[0021] The present invention provides a TDLAS gas monitoring system denoising method based on ICEEMDAN and wavelets. When a noisy second harmonic signal is decomposed by ICEEMDAN to generate IMFs, some of the noisy IMFs contain a small amount of useful signals. The noisy IMFs are subjected to corresponding wavelet denoising and then combined with the remaining IMFs to achieve a better noise reduction effect than empirical mode decomposition and wavelet denoising.

[0022] Compared with the prior art, the present invention has the following advantages:

[0023] The method of the present invention utilizes a method combining ICEEMDAN with wavelet transform filtering to perform noise reduction on the acquired second harmonic signal. Compared with the use of a single wavelet denoising method, this method has better denoising effect and higher signal-to-noise ratio when processing nonlinear and non-stationary signals. This method can effectively improve the correlation coefficient with the real signal, reduce the root mean square error of the signal, do not change the spatial resolution of the signal, and retain the authenticity of the signal to a greater extent. Compared with empirical mode decomposition (EMD) denoising, this method decomposes the signal more thoroughly, effectively reduces residual noise, and decomposes fewer pseudo-modes. This method can minimize the detection limit of the gas detection system measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flowchart of the method of the present invention.

[0025] Figure 2 This is a time domain diagram of the noisy harmonic signal of the present invention.

[0026] Figure 3 This is the result diagram of the decomposition of the ICEEMDAN algorithm of the present invention.

[0027] Figure 4 This is the time domain diagram of the denoised signal of the present invention. DETAILED DESCRIPTION

[0028] The present invention will be further described below with reference to the accompanying drawings and examples.

[0029] like Figure 1 As shown, this embodiment discloses a TDLAS gas monitoring system denoising method, comprising the following steps:

[0030] Step 1: Obtain the noisy second harmonic signal collected by the TDLAS gas monitoring system.

[0031] In the TDLAS gas monitoring system, a DFB laser is used to output a laser signal to the monitored gas. In this embodiment, the wavelength of the laser signal output by the DFB laser is first modulated. The modulation signal uses a low-frequency triangle wave modulation signal and a high-frequency sine wave modulation signal. The modulated laser signal is:

[0032] I(t)=I0(1+U(t)+sin(wt))

[0033] Where I0 is the initial output signal of the laser, U(t) is the superimposed low-frequency triangle wave modulation signal, sin(wt) is the superimposed high-frequency sine wave modulation signal, w is the sine wave frequency, and t is time.

[0034] The modulated laser signal passes through the monitoring gas, where it is absorbed. The absorbed light signal is focused by a lens and converged onto a photodetector, where it is converted into an electrical signal. A lock-in amplifier is then used to measure the electrical signal to obtain the noisy second harmonic of the absorption signal. This is the noisy second harmonic signal required in step 1.

[0035] Step 2: Remove the signal distorted by the fluctuation deformation from the noisy second harmonic signal obtained in step 1, and then perform baseline calibration on the noisy second harmonic signal.

[0036] In this embodiment, based on the symmetry characteristics of the noisy second harmonic signal, constraints on the absorption peak parameters of the noisy second harmonic signal are established. Based on the constraints, noisy second harmonic signals with symmetry characteristics are identified, thereby eliminating signals in the noisy second harmonic signal that are distorted due to fluctuation deformation. The constraints are:

[0037] 0.42<WL / W<0.58

[0038] 0.42<WR / W<0.58

[0039] HL / HR>0.75

[0040] Wherein, WL and WR are the left and right half widths of the noisy second harmonic signal absorption peak, respectively; W is the full width of the noisy second harmonic signal absorption peak; HL and HR are the left and right full heights of the noisy second harmonic signal absorption peak.

[0041] Signals that do not meet the above constraints are considered to be distorted signals with waveform deformation, and these distorted signals are removed from the noisy second harmonic signal.

[0042] In this embodiment, for the baseline drift problem of the noisy second harmonic signal after the distortion signal is removed, the least squares method is used to fit the baseline, and the baseline is subtracted from the noisy second harmonic signal after the distortion signal is removed to complete the baseline calibration. The signal after baseline calibration is as follows: Figure 2 shown.

[0043] Step 3: Determine the characteristics of the noise added during ICEEMDAN decomposition. Then, add the noise with the determined characteristics to the noisy second harmonic signal obtained in step 2 after the distortion signal is removed and the baseline is calibrated, and perform ICEEMDAN decomposition to obtain multiple IMF components.

[0044] In this embodiment, the noise type added during ICEEMDAN decomposition is Gaussian white noise, the standard deviation of the Gaussian white noise is 0.1, and the number of times the noise is added is 50. Let the noisy second harmonic signal obtained in step 2 be S(t), and the expression of ICEEMDAN decomposition is:

[0045]

[0046] Where K represents the number of decomposed IMF components.

[0047] Therefore, the noisy second harmonic signal S(t) is decomposed into a series of IMF components IMF by ICEEMDAN i and a residual signal R. This embodiment uses the ICEEMDAN algorithm to decompose the noisy harmonic signal to obtain multiple IMF components IMF i , refer to Figure 3 shown.

[0048] Step 4: Calculate the approximate entropy of each IMF component decomposed in step 3, define an approximate entropy threshold ε, and divide each IMF component into a noisy IMF component and a valid IMF component based on the approximate entropy threshold ε and the calculated approximate entropy of each IMF component.

[0049] Approximate entropy can be used to describe the complexity of a time series. The greater the complexity of the series, the greater the corresponding approximate entropy. Approximate entropy is stable for the quantitative results of non-stationary and nonlinear series. The calculation method of approximate entropy is:

[0050] Suppose the time series X is of length N = [x1, x2, ..., x N ], arrange the elements of the time series X in order into a vector with m dimensions, that is,

[0051] X i =[x(i),x(i+1),...,x(i+m-1)]

[0052] Where, i=1,2,...,N-m+1.

[0053] Define d[X i ,X j ] is the vector X i and vector X j The distance, then

[0054] d[Xi ,X j ]=max[x(i+k)-x(j+k)],k∈(0,m-1)

[0055] Note B i is d[X i ,X j ]≤r (r is the similarity tolerance), and calculate B i The ratio of the total number of vectors N-m+1, that is

[0056]

[0057] To B i m (r) Take the logarithm and then find its average value for all i, which is recorded as B m (r), then

[0058]

[0059] Let m = m + 1, and we get B m+1 (r)

[0060] Therefore, the approximate entropy can be expressed as

[0061] ApEn(m,r,N)=B m (r)-B m+1 (r)

[0062] Where, ApEn(m,r,N)= is the calculated approximate entropy; m is the embedding dimension; r is the similarity tolerance; N is the data length.

[0063] According to the size of the defined approximate entropy threshold ε, each IMF component is divided into a noisy IMF component and a valid IMF component. In this embodiment, the size of the approximate entropy threshold ε is 0.1. The approximate entropy calculated for each IMF component is compared with the approximate entropy threshold ε. When the approximate entropy calculated for a certain IMF component is greater than the approximate entropy threshold ε = 0.1, the corresponding IMF is separated into a noisy IMF component; when the approximate entropy calculated for a certain IMF component is less than or equal to the approximate entropy threshold ε = 0.1, the corresponding IMF is separated into a valid IMF component.

[0064] Step 5: Determine the wavelet basis function and the number of decomposition layers, and use the wavelet basis function to perform wavelet soft threshold function denoising on the noisy IMF component obtained in step 4 according to the determined number of decomposition layers, thereby obtaining the IMF component after wavelet denoising.

[0065] In this embodiment, the wavelet basis function selected is the db10 wavelet function, and the number of decomposition layers determined is 7. The wavelet denoising function used is the wavelet soft threshold function, and the expression of the wavelet soft threshold function is:

[0066]

[0067] Among them, sgn() is the sign function, y i is the original noisy wavelet coefficient, and δ is the threshold.

[0068] Step 6: Reconstruct the IMF components after wavelet denoising obtained in step 5 and the effective IMF components obtained in step 4 into the denoised second harmonic signal. Figure 4 As shown, it can be seen that the signal after noise reduction is rich in details and close to the real signal.

[0069] The preferred embodiments of the present invention are described in detail above with reference to the accompanying drawings. The embodiments described in the present invention are merely descriptions of the preferred embodiments of the present invention and do not limit the concept and scope of the present invention. The various specific technical features described in the above specific embodiments can be combined in any suitable manner unless there is any contradiction. Such combinations should also be regarded as the contents disclosed in this disclosure as long as they do not violate the concept of the present invention. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0070] The present invention is not limited to the specific details of the above-mentioned embodiments. Within the scope of the technical concept of the present invention and without departing from the design concept of the present invention, various modifications and improvements made to the technical solution of the present invention by those skilled in the art should fall within the scope of protection of the present invention. The technical contents for which protection is sought in the present invention have been fully recorded in the claims.

Claims

1. A TDLAS gas monitoring system denoising method, characterized in that: The following steps are involved: Step 1: Obtain the noisy second harmonic signal collected by the TDLAS gas monitoring system; Step 2: removing the signal distorted by the fluctuation deformation from the noisy second harmonic signal obtained in step 1, and then performing baseline calibration on the noisy second harmonic signal; Step 3: Determine the characteristics of the noise added during ICEEMDAN decomposition, and then add the noise with the determined characteristics to the noisy second harmonic signal obtained in step 2 to perform ICEEMDAN decomposition to obtain multiple IMF components; Step 4: Calculate the approximate entropy of each IMF component decomposed in step 3, define an approximate entropy threshold ε, and divide each IMF component into a noisy IMF component and a valid IMF component based on the approximate entropy threshold ε and the calculated approximate entropy of each IMF component. Step 5: Determine the wavelet basis function and the number of decomposition layers, and use the wavelet basis function to perform wavelet soft threshold function denoising on the noisy IMF component obtained in step 4 according to the determined number of decomposition layers, thereby obtaining the IMF component after wavelet denoising; Step 6: Reconstruct the IMF component after wavelet denoising obtained in step 5 and the effective IMF component obtained in step 4 into a denoised second harmonic signal.

2. A TDLAS gas monitoring system denoising method according to claim 1, characterized in that: In step 1, the laser of the TDLAS gas monitoring system is wavelength modulated, and the noisy second harmonic signal collected thereby is a signal formed after the wavelength modulated laser passes through the monitoring gas.

3. A TDLAS gas monitoring system denoising method according to claim 2, characterized in that: During wavelength modulation, the modulation signal adopts a low-frequency triangle wave modulation signal and a high-frequency sine wave modulation signal.

4. A TDLAS gas monitoring system denoising method according to claim 1, characterized in that: In step 2, based on the symmetry characteristics of the noisy second harmonic signal, constraints on the absorption peak parameters of the noisy second harmonic signal are established, and based on the constraints, noisy second harmonic signals with symmetry characteristics are identified, thereby eliminating signals in the noisy second harmonic signal that are distorted due to fluctuation deformation.

5. A TDLAS gas monitoring system denoising method according to claim 1, characterized in that: In step 2, the baseline of the noisy second harmonic signal is fitted using the least squares method, and then the baseline is subtracted from the noisy second harmonic signal with the distortion signal removed, thereby completing the baseline calibration.

6. A TDLAS gas monitoring system denoising method according to claim 1, characterized in that: In step 3, the noise added during ICEEMDAN decomposition is Gaussian white noise.

7. A TDLAS gas monitoring system denoising method according to claim 6, characterized in that: The standard deviation of the Gaussian white noise is 0.

1.

8. The TDLAS gas monitoring system denoising method according to claim 1, characterized in that: In step 4, the approximate entropy calculated for each IMF component is compared with the approximate entropy threshold ε. When the approximate entropy calculated for a certain IMF component is greater than the approximate entropy threshold ε, the corresponding IMF is separated into a noisy IMF component; when the approximate entropy calculated for a certain IMF component is less than or equal to the approximate entropy threshold ε, the corresponding IMF is separated into a valid IMF component.

9. The TDLAS gas monitoring system denoising method according to claim 1, characterized in that: In step 5, the wavelet basis function used is the db10 wavelet function, and the number of decomposition layers is 7.

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