CN111582132A - Improved EEMD and PCNN-based gas leakage signal noise reduction method - Google Patents
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Abstract
The invention discloses a gas leakage sound wave signal noise reduction method based on improved EEMD and PCNN, and belongs to the field of signal processing and computer simulation application. The sampled signal is decomposed into several orders of IMF components by means of the unique decomposition advantages of EEMD method for non-stationary nonlinear signals. And then, further reducing the noise of each noise component by using a neural network signal transmission mode of the improved PCNN model. And finally, recombining the noise reduction components into a signal. Compared with the traditional noise reduction algorithm, the method overcomes the defect of mode aliasing, overcomes the defect of difficulty in threshold selection, has higher signal-to-noise ratio of processed signals and smaller root mean square error, and has the advantages of strong noise reduction capability, strong effective signal retention capability and small defects of the traditional method.
Description
Technical Field
The invention belongs to the field of one-dimensional signal processing, is applied to preprocessing of gas leakage sound wave signals, and particularly relates to a gas leakage signal noise reduction method based on improved EEMD and PCNN.
Background
Toxic, harmful, inflammable and explosive gases are the biggest potential safety hazards in production and transportation of chemical enterprises, and effective monitoring of hazardous gases is one of the main means for reducing occurrence of serious disastrous events. The premise of timely repairing gas leakage is that the occurrence of accidents is judged according to the characteristics of collected signals, so that the research on a signal noise reduction algorithm with robustness and reliability has great significance for monitoring and remedying dangerous gas leakage accidents.
The gas leak signal generates a primary frequency signal of about 20Hz, and the main sources of gas leak signal noise include mechanical noise and environmental noise. The ultrasonic signal is different from the ambient noise, but in order to effectively monitor the accurate gas leakage signal in the environment, the acquired sound wave signal needs to be subjected to noise reduction processing.
Signal preprocessing is crucial to subsequent signal feature extraction and analysis, in order to research the problem of gas leakage signal noise reduction, noise reduction algorithms are continuously developed in recent years, for example, Wanglong proposes a wavelet semi-soft threshold noise reduction method, which formulates detail coefficients and approximation coefficients of various scales according to signal correlation coefficients obtained by decomposing signals, and reduces the deviation of a wavelet soft threshold function and a hard threshold function in the signal reconstruction process; zhang Xingli proposes an improved threshold function denoising algorithm combining variational modal decomposition and wavelet energy entropy, after VMD decomposition is carried out on an original signal, thresholds of all scale layers are calculated by the wavelet energy entropy to carry out denoising, a threshold selection method is improved, and the signal-to-noise ratio of the signal is improved; the weekly construction provides a self-adaptive wavelet packet threshold function denoising algorithm based on Shannon entropy, a sparse punishment mechanism is formulated, the difficulty caused by threshold selection is avoided, and a better denoising effect is achieved; the method proposes a sequencing data denoising algorithm based on bilinear regression, and is effective in single cell data processing. Therefore, a novel noise reduction method is proposed, and a new idea is provided for reducing the noise of the hazardous gas leakage signal.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problem of limitation of the traditional signal noise reduction algorithm in the aspect of threshold selection, the invention provides a gas leakage signal noise reduction method based on improved integrated empirical Mode Decomposition (EEMD) and Pulse Coupled Neural Network (PCNN). The method is suitable for removing the noise of the non-stationary nonlinear signal.
The technical scheme is as follows: in order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows: a gas leakage signal noise reduction method based on improved EEMD and PCNN comprises the following steps:
and 4, performing signal reconstruction on the IMF component subjected to PCNN noise reduction and the IMF component which is not determined as the noise component in the step 2, and completing the noise reduction of the original signal.
Further, in step 1, the sample signal is constructed and EEMD is decomposed by the following method:
step 1.1, adding equal-length random white gaussian noise g (t) with an amplitude coefficient k into an acquired original gas leakage signal x (t) to form a sample signal n (t), namely n (t) x (t) + g (t);
step 1.2, preliminarily eliminating noise in the sample signal by utilizing the zero-mean characteristic of Gaussian white noise and calculating the mean value of the sample signal, and carrying out EEMD decomposition on the denoised signal to obtain a plurality of IMF components;
and step 1.3, screening the IMF components according to the constraint conditions to obtain IMF components of the characterization signals, and calling a corrcoef function by utilizing matlab to obtain the correlation coefficient between each IMF component and the original signal.
Further, the constraint conditions include:
a) the absolute value of the difference between the zero point and the extreme point is less than or equal to 1;
b) the mean values of the upper and lower envelopes of the sample signal n (t) are zero.
Further, in the step 3, adjusting an internal activity item and a neuron attenuation coefficient of the PCNN model, and performing PCNN noise reduction on each IMF component respectively; the method comprises the following steps:
step 3.1, changing the dimensionality of two parallel channels of neuron feedback input and coupling connection input in a receiving domain of a traditional pulse coupling neural network PCNN model from m & n to 1 & n, and simultaneously changing a connection matrix and a weight matrix into a matrix of 1 & n, so that the weight obtained by performing Euclidean square inverse on the weight matrix is suitable for a one-dimensional vector, and the transfer mechanism of the pulse coupling neural network PCNN is applied to one-dimensional signal noise reduction;
the principle of filtering noise in a signal by using the PCNN model is as follows: when a signal enters a neural network as an input signal, each number in a signal sequence is a neuron, and the relation between an activity item in a PCNN model representing the activity state of the neuron and a threshold value is determined to be output;
step 3.2, initializing the PCNN model, inputting each IMF component as a feedback input signal into a receiving domain of the PCNN model, and forming a feedback input signal Fi[n]＝SiIn which S isiRepresenting a neuron excitation; the coupled connection input signal calculation formula of the PCNN model is as follows:
wherein L isi[n]Representing a coupled input signal, VLDenotes the connection amplitude constant, WiRepresenting a coupling connection matrix, YjRepresenting the firing state of a neuron in the j-th firing process of the PCNN, i representing the time domain serial number of a certain numerical value in the signal sequence, j representing the counting number of the firing process,n is the number of iterations;
3.3, capturing neurons by using the improved PCNN model and determining whether to ignite to generate pulses, changing the size of a threshold value if the pulses are generated by ignition, namely, performing signal denoising processing by using a dynamic threshold value, generating the pulses by ignition, changing the size of the dynamic threshold value and filtering signals with ineffective amplitude values;
capture satisfaction
The neurons jointly determine the state of an activity item in the PCNN model and determine whether the neurons fire or not;
the principle of pulse generation is as follows:
wherein T isi[n]Is a dynamic threshold, αEIs a threshold attenuation coefficient, V, determining a threshold attenuation speedEIs the lifting factor of the dynamic threshold, Ei[n-1]Is a link input Li[n]Last moment state of (Y)i[n-1]Is the last time state of the neuron iteration n times of pulse output;
when αEEnlarged and internal activity item Ui[n]>Ti[n]When the neuron meets the ignition condition, the ith neuron is ignited; y isiThe amplitude of the effective signal at the ith point is used as an output value, when αEWhen reduced, neurons do not meet firing conditions, YiOutput 0, expressed as follows:
step 3.4, calculating a weight matrix of the model, and adjusting the neuron internal salient connection coefficient and the neuron attenuation coefficient in the internal activity item of the PCNN model according to the correlation coefficient of each IMF component and the original signal, so that the PCNN model is suitable for each IMF component; internal activity item Ui[n]Comprises the following steps:
Ui[n]＝Fi[n](1+βLi[n])
wherein Fi[n]Representing the feedback input obtained after n iterations of the neuron, β representing the intra-neuron prominent junction coefficient, Li[n]Representing the coupled connection input after iteration for n times;
step 3.5, judging whether the termination condition is met, namely continuous k times of iteration YiOutputting 0, if the termination condition is met, finishing iteration and outputting a current signal sequence; otherwise, returning to the step 3.2, re-initializing the model, and re-inputting the current signal sequence into the model for the next iteration until the termination condition is met.
Has the advantages that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
the invention changes the dimensionality of two parallel channels of feedback input and coupling connection input of a neuron in a receiving domain of the traditional pulse coupling neural network from m & n to 1 & n, and simultaneously changes a connection matrix and a weight matrix into a matrix of 1 & n. Therefore, the weight obtained by making the weight matrix into the inverse of the Euclidean square is suitable for the one-dimensional vector, and the neural network transfer mechanism of the PCNN excellence is transplanted into the one-dimensional signal processing. Compared with a common modal decomposition noise reduction method, the PCNN noise reduction model is added, and the noise reduction is performed on the signals from different angles, so that the noise reduction capability of the method is stronger. In addition, on the basis of the traditional threshold noise reduction concept, the defect that the threshold is difficult to determine is overcome. Therefore, the noise reduction method has the advantages of strong noise reduction capability, strong effective signal retention capability and small defects of the traditional method.
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FIG. 1 is a flow chart of a method implementation of the present invention;
FIG. 2 is a flow chart of the PCNN algorithm;
FIG. 3 is a graph of a raw signal for gas leakage;
FIG. 4 is an exploded view of the EEMD;
FIG. 5 is a comparison graph of IMF1 before and after noise reduction;
FIG. 6 is a comparison before and after noise reduction of IMF 2;
FIG. 7 is a graph of the original signal spectrum;
FIG. 8 is a graph of the spectrum of the original signal after EEMD-PCNN denoising;
FIG. 9 is a signal diagram of the original signal after EEMD-PCNN denoising;
FIG. 10 is a front-to-back comparison of a signal plus white Gaussian noise with a signal-to-noise ratio of 50 dB;
FIG. 11 is a graph of a spectrum of a noisy signal;
fig. 12 is a signal spectrum diagram after noise reduction.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
The invention relates to a gas leakage signal noise reduction method based on improved EEMD and PCNN, which is implemented by the process shown in figure 1, wherein a sample signal is decomposed by EEMD, IMF components which are determined as noise components are subjected to PCNN noise reduction continuously, and the rest components are used for signal reconstruction subsequently, namely the IMF components which are not determined as the noise components and the noise components subjected to the subsequent PCNN noise reduction are subjected to signal reconstruction together. The specific implementation comprises the following steps:
Constructing a sample signal and EEMD decomposition, wherein the method comprises the following steps:
step 1.1, adding isometric random white gaussian noise g (t) with an amplitude coefficient k of 0.3 into an acquired original gas leakage signal x (t) to form a sample signal n (t), namely n (t) ═ x (t) + g (t);
step 1.2, preliminarily eliminating noise in the sample signal by utilizing the zero-mean characteristic of Gaussian white noise and calculating the mean value of the sample signal, and carrying out EEMD decomposition on the denoised signal to obtain a plurality of IMF components;
and step 1.3, screening the IMF components according to the constraint conditions to obtain IMF components of the characterization signals, and calling a corrcoef function by utilizing matlab to obtain the correlation coefficient between each IMF component and the original signal.
The constraint conditions include:
a) the absolute value of the difference between the zero point and the extreme point is less than or equal to 1;
b) the mean values of the upper and lower envelopes of the sample signal n (t) are zero.
And 2, judging whether each IMF component is a noise component, executing step 3 to further reduce noise for the IMF component judged as the noise component, and reserving the IMF components which are not judged as the noise component to wait for signal reconstruction.
And 3, adjusting internal activity items and neuron attenuation coefficients of the PCNN model according to the correlation coefficients of the IMF components and the original signal, and performing PCNN noise reduction on the IMF components respectively. The method comprises the following steps:
step 3.1, changing the dimensionality of two parallel channels of neuron feedback input and coupling connection input in a receiving domain of a traditional pulse coupling neural network PCNN model from m & n to 1 & n, and simultaneously changing a connection matrix and a weight matrix into a matrix of 1 & n, so that the weight obtained by performing Euclidean square inverse on the weight matrix is suitable for a one-dimensional vector, and the transfer mechanism of the pulse coupling neural network PCNN is applied to one-dimensional signal noise reduction;
the principle of filtering noise in a signal by using the PCNN model is as follows: when a signal enters a neural network as an input signal, each number in a signal sequence is a neuron, and the relation between an activity item in the PCNN model representing the activity state of the neuron and a threshold value determines the output.
Step 3.2, initializing the PCNN model, inputting each IMF component as a feedback input signal into a receiving domain of the PCNN model, and forming a feedback input signal Fi[n]＝SiIn which S isiRepresenting a neuron excitation; the coupled connection input signal calculation formula of the PCNN model is as follows:
wherein L isi[n]Representing a coupled input signal, VLDenotes the connection amplitude constant, WiRepresenting coupling momentArray, YjThe method is characterized in that the method represents the neuron ignition state in the jth ignition process of the PCNN, i represents the time domain serial number of a certain numerical value in a signal sequence, j represents the counting times of the ignition process, and n is the iteration times.
3.3, capturing neurons by using the improved PCNN model and determining whether to ignite to generate pulses, changing the size of a threshold value if the pulses are generated by ignition, wherein the threshold value is a dynamic threshold value, mapping the pulses generated by each ignition, and actually representing the strength of effective signals in signal components, namely, performing signal denoising processing by using the dynamic threshold value, and generating the pulses by ignition, so that the size of a dynamic threshold value is changed, and signals with ineffective amplitude values are filtered;
as shown in FIG. 2, the improved PCNN modifies the dimensions of the neuron channel, capturing the satisfaction
The neurons jointly determine the state of an activity item in the PCNN model and determine whether the neurons fire or not;
the principle of pulse generation is as follows:
wherein T isi[n]Is a dynamic threshold, αEIs the threshold attenuation coefficient for determining the threshold attenuation speed when αEIncrease, Ti[n]With a consequent decrease of VEIs the coefficient of rise of the dynamic threshold, the change of which is in a positive relationship with the dynamic threshold change, Ei[n-1]Is a link input Li[n]Last moment state of (Y)i[n-1]Is the last time state of the neuron iteration n times of pulse output;
when αEEnlarged and internal activity item Ui[n]>Ti[n]When the neuron meets the ignition condition, the ith neuron is ignited; y isiThe amplitude of the effective signal at the ith point is taken as an output value, and therefore, the generation of the pulse is promoted; the useful signal is, in addition to the noise signal, usable for extracting features(ii) a characterised signal;
when αEWhen decreasing, Ti[n]Rapid enlargement, neuron failure to fire condition, YiOutput 0, the generation of pulses is suppressed, and the pulse output state is expressed as follows:
step 3.4, calculating a weight matrix of the model, and adjusting the neuron internal salient connection coefficient and the neuron attenuation coefficient in the internal activity item of the PCNN model according to the correlation coefficient of each IMF component and the original signal, so that the PCNN model is suitable for each IMF component; internal activity item Ui[n]Comprises the following steps:
Ui[n]＝Fi[n](1+βLi[n])
wherein Fi[n]Representing the feedback input obtained after n iterations of the neuron, β representing the intra-neuron prominent junction coefficient, Li[n]Representing the coupled connection input after iteration for n times;
according to the difference of signals and the difference of noise degrees of the signals, the adjustment coefficient values are different, in the embodiment, the PCNN parameter adjustment is performed before the IMF9 component processing, a value with the best effect is obtained, for example, an attenuation coefficient is obtained, when the attenuation coefficient is 0.3, the capability of detecting effective signals is low, the noise is difficult to separate, when the attenuation coefficient is 0.6, the effective signals are filtered together with the noise, and at the moment, a proper value is obtained by using a bisection method.
Step 3.5, judging whether the termination condition is met, namely continuous k times of iteration YiOutputting 0, if the termination condition is met, finishing iteration and outputting a current signal sequence; otherwise, returning to the step 3.2, re-initializing the model, and re-inputting the current signal sequence into the model for the next iteration until the termination condition is met. In this embodiment, k is 50.
And 4, performing signal reconstruction on the IMF component subjected to noise reduction by the pulse coupled neural network model (PCNN) and the IMF component which is not determined as the noise component in the step 2, and completing noise reduction on the original signal.
As shown in fig. 3, the gas leakage raw signal is doped with a large amount of ambient noise. The experiment of this example used a cDAQ data acquisition card developed by NI instruments with an ultrasonic transducer to acquire the simulated gas leak signal. The sampling frequency is 100kHZ, the sampling distance is 2m, and 2000 continuous sampling points are selected from the experimental data.
As shown in fig. 4, the original signal is screened and decomposed into 9 IMF components meeting the conditions:
wherein, the signal W (t) to be detected is obtained by subtracting the local average value from the original signal, N (t) is a sample signal, and the upper envelope H (t) and the lower envelope L (t) of the original signal are obtained by cubic spline interpolation.
As shown in fig. 5 and 6, the comparison between the original IMF1 and IMF2 components and the PCNN noise-reduced components is shown, where the signal-to-noise ratio of the noise components is significantly improved. As shown in fig. 7 and 8, after the noise reduction is completed, the unnecessary noise components are filtered out from the signal, and the main frequency characteristics of the effective signal are relatively more obvious. As shown in FIG. 9, the signal is noise reduced by EEMD-PCNN to improve the signal-to-noise ratio and reduce the root mean square error. As shown in FIG. 10, white Gaussian noise having a signal-to-noise ratio (SNR) of 50dB is added to the signal. The above experiments verify that the EEMD-PCNN noise reduction has good effect of filtering unknown noise, but in order to verify that the method can effectively filter noise instead of effective signals, Gaussian white noise with known signal-to-noise ratio is added to the signals, and EEMD-PCNN noise reduction is carried out. As shown in fig. 11, the signal spectrum has a high frequency component due to the addition of white gaussian noise. As shown in fig. 12, after noise reduction, high frequency components are filtered out, and the effective signal is retained, which confirms that the method can filter noise while retaining the effective signal.
The foregoing is a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (4)
1. A gas leakage signal noise reduction method based on improved EEMD and PCNN is characterized in that: the method comprises the following steps:
step 1, collecting an original gas leakage signal, constructing a sample signal, carrying out integrated empirical mode decomposition (EEMD) on the sample signal to obtain a plurality of IMF components, and solving a correlation coefficient between each IMF component and the original signal;
step 2, judging whether each IMF component is a noise component, executing step 3 to further reduce noise for the IMF component judged as the noise component, and reserving the IMF components which are not judged as the noise component to wait for signal reconstruction;
step 3, adjusting internal activity items and neuron attenuation coefficients of the PCNN model according to correlation coefficients of the IMF components and the original signal, and performing PCNN noise reduction on the IMF components respectively;
and 4, performing signal reconstruction on the IMF component subjected to PCNN noise reduction and the IMF component which is not determined as the noise component in the step 2, and completing the noise reduction of the original signal.
2. The method of claim 1, wherein the method comprises the steps of: in the step 1, a sample signal and EEMD decomposition are constructed, and the method comprises the following steps:
step 1.1, adding equal-length random white gaussian noise g (t) with an amplitude coefficient k into an acquired original gas leakage signal x (t) to form a sample signal n (t), namely n (t) x (t) + g (t);
step 1.2, preliminarily eliminating noise in the sample signal by utilizing the zero-mean characteristic of Gaussian white noise and calculating the mean value of the sample signal, and carrying out EEMD decomposition on the denoised sample signal to obtain a plurality of IMF components;
and step 1.3, screening the IMF components according to the constraint conditions to obtain IMF components of the characterization signals, and calling a corrcoef function by utilizing matlab to obtain the correlation coefficient between each IMF component and the original signal.
3. The method of claim 2, wherein the method comprises the steps of: the constraint conditions include:
a) the absolute value of the difference between the zero point and the extreme point is less than or equal to 1;
b) the mean values of the upper and lower envelopes of the sample signal n (t) are zero.
4. The method of claim 1, wherein the method comprises the steps of: step 3, adjusting an internal activity item and a neuron attenuation coefficient of the PCNN model, and respectively performing PCNN noise reduction on each IMF component; the method comprises the following steps:
step 3.1, changing the dimensionality of neuron feedback input and coupling connection input in a PCNN model receiving domain from m & n to 1 & n, and simultaneously changing a connection matrix and a weight matrix to a 1 & n matrix;
step 3.2, initializing the PCNN model, inputting each IMF component as a feedback input signal into a receiving domain of the PCNN model, and forming a feedback input signal Fi[n]＝SiIn which S isiRepresenting a neuron excitation; the coupled connection input signal calculation formula of the PCNN model is as follows:
wherein L isi[n]Representing a coupled input signal, VLDenotes the connection amplitude constant, WiRepresenting a coupling connection matrix, YjRepresenting the neuron ignition state in the jth ignition process of the PCNN, i representing the time domain serial number of a certain numerical value in the signal sequence, j representing the counting times in the ignition process, and n representing the iteration times;
3.3, capturing neurons by using the improved PCNN model and determining whether to ignite to generate pulses, changing the size of a threshold value if the pulses are generated by ignition, namely, performing signal denoising processing by using a dynamic threshold value, generating the pulses by ignition, changing the size of the dynamic threshold value and filtering signals with ineffective amplitude values;
capture satisfaction
The neurons jointly determine the state of an activity item in the PCNN model and determine whether the neurons fire or not;
pulse generation is represented as follows:
wherein T isi[n]Is a dynamic threshold, αEIs a threshold attenuation coefficient, V, determining a threshold attenuation speedEIs the coefficient of rise of the dynamic threshold, Ei[n-1]Is a link input Li[n]Last moment state of (Y)i[n-1]Is the last time state of the neuron iteration n times of pulse output;
when αEEnlarged and internal activity item Ui[n]>Ti[n]When the neuron meets the ignition condition, the ith neuron is ignited; y isiThe amplitude of the effective signal at the ith point is used as an output value, when αEWhen reduced, neurons do not meet firing conditions, YiOutput 0, expressed as follows:
step 3.4, calculating a weight matrix of the model, and adjusting neuron internal salient connection coefficients and neuron attenuation coefficients in internal activity items of the PCNN model according to correlation coefficients of each IMF component and an original signal; internal activity item Ui[n]Comprises the following steps:
Ui[n]＝Fi[n](1+βLi[n])
wherein Fi[n]Representing the feedback input obtained after n iterations of the neuron, β representing the intra-neuron prominent junction coefficient, Li[n]Representing the coupled connection input after iteration for n times;
step 3.5, judging whether the termination condition is met, namely continuous k times of iteration YiOutputting 0, if the termination condition is met, ending the iterationOutputting a current signal sequence; otherwise, returning to the step 3.2, re-initializing the model, and re-inputting the current signal sequence into the model for the next iteration until the termination condition is met.
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| CN114398926A (en) * | 2022-01-12 | 2022-04-26 | 江苏金晟元控制技术有限公司 | Resistance spot welding plastic ring imaging method based on wavelet analysis and application thereof |
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