A pipeline leakage vibration signal denoising method based on parameter self-information

Abstract

The application belongs to the technical field of structural health monitoring and vibration signal processing, and discloses a pipeline leakage vibration signal denoising method based on parameter self-information, wherein an SAA algorithm is improved to form an ASSA, and a mode decomposition number and a penalty factor in VMD-SI are optimized; then, a normalized self-information distance between an intrinsic mode function and an original signal is calculated to distinguish effective IMF components and ineffective IMF components; and the effective IMF components are used to reconstruct a filtered signal; the method can improve global optimization ability through the ASSA algorithm, avoid local optimal stagnation caused by a traditional algorithm, eliminate irregular interference of vibration signals caused by complex environments of pipelines, avoid mode aliasing and excessive decomposition in traditional methods, better adapt to characteristics of non-stationary signals, improve a signal noise ratio, and thus more accurately extract leakage characteristics.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of structural health monitoring and vibration signal processing, and particularly relates to a pipeline leakage vibration signal denoising method based on parameter self-information. BACKGROUND

[0002] The pipeline bears the key tasks of water supply and energy transportation, and is crucial to the orderly operation of the city. The pipeline is buried underground for a long time and is in a liquid-filled state at all times, and the internal pressure is large. When the pipeline bursts or leaks, the liquid in the pipeline will be ejected in the form of a high-pressure water column, which will not only cause serious resource waste, but also cause the soil structure around the pipeline to loosen, resulting in accidents such as foundation settlement and building subsidence. In the pipeline leakage detection process, various noises will be affected, resulting in false positives, missed detections and other problems, so it is of great significance to pre-process the collected signals to provide signals with a large number of leakage characteristics.

[0003] When the pipeline cracks, the internal pressure appears locally uneven, and the fluid is ejected from the crack and rubs against the pipe wall, exciting the pipeline vibration. The vibration signal characteristics can reflect the damage information of the structure, and the vibration analysis method has been widely used in the field of pipeline leakage damage assessment. However, due to the long-term complex environment of the pipeline, it is affected by many factors, making its vibration signal irregular, increasing the difficulty of leakage detection. The traditional optimization algorithm often falls into a local optimal solution due to the complexity of the problem space when dealing with complex optimization problems, resulting in the inability to find a global optimal solution, and the inability to separate noise and signals in the same frequency band. For example, empirical mode decomposition (EMD) can effectively suppress the noise of the vibration signal, but EMD has problems such as envelope deficiency, over-decomposition and mode mixing in the use process, and EMD method cannot distinguish between effective IMF components and ineffective IMF components, and there is a risk of noise aliasing. SUMMARY

[0004] To solve the above technical problems, the application provides a pipeline leakage vibration signal denoising method based on parameter self-information, which improves the SAA algorithm to form an ASSA (adaptive sparrow search algorithm), and optimizes the modal decomposition number and the penalty factor in VMD (variational modal decomposition). Then, the normalized self-information distance between the intrinsic modal function and the original signal is calculated to distinguish between effective IMF components and ineffective IMF components; and the effective IMF components are used to reconstruct the filtered signal.

[0005] The pipeline leakage vibration signal denoising method based on parameter self-information provided by the application comprises the following steps:

[0006] Step 1, collecting original vibration signals S(t) under different damage conditions of the pipeline j ;

[0007] Step 2, add adaptive sine and Cauchy-Gaussian mutation in sparrow search algorithm SSA to form ASSA algorithm, and the original vibration signal S(t) corresponding to different damage conditions is decomposed into IMF components by ASSA algorithm j As a search space, the number of modal decompositions in VMD-SI and the penalty factor are optimized;

[0008] Step 3, input the obtained number of modal decompositions and penalty factor into VMD-SI algorithm to obtain optimal decomposition parameters;

[0009] Step 4, calculate the correlation coefficient of the intrinsic modal function and the original vibration signal based on the optimal decomposition parameters, and calculate the self-information of the intrinsic modal function and the original vibration signal;

[0010] Step 5, normalize the obtained self-information, obtain effective IMF components according to the normalized self-information distance of the intrinsic modal function and the original signal, reconstruct the effective IMF components, and realize vibration signal denoising.

[0011] Further, step 2 is specifically:

[0012] Step 201, introduce an adaptive search factor γ1 into SSA,

[0013]

[0014] New discoverer position The iteration formula is as follows:

[0015]

[0016] Wherein, Cons is used to adjust the overall amplitude of the adaptive search factor; ξ is an adjustment coefficient; γ2 and γ3 are random numbers in [0, 2π]; t is the current iteration number, and M is the maximum iteration number; H best Indicates the global optimal position in the current population, that is, the position of the sparrow with the highest fitness value; MZ is a safety value;

[0017] Step 202, adopt adaptive Cauchy-Gaussian mutation strategy, wherein the probability density function f1(x) of standard Cauchy distribution and the probability density function f2(x) of standard Gaussian distribution are as follows:

[0018]

[0019] New follower position update As follows:

[0020]

[0021] Wherein, x is the independent variable; Cauchy (0, 1) is a standard Cauchy distribution function; Gauss (0, 1) is a standard Gaussian distribution function; γ4 and γ5 are adaptive dynamic adjustment parameters.

[0022] Step 203, the vibration signal corresponding to different damage levels is taken as a search space, and is input into the ASSA algorithm, the upper and lower limit ranges of the modal decomposition number k are set as [3, 10], the upper and lower limit ranges of the penalty factor α are set as [100, 2500], the iteration number is set, and the optimal values of k and α are obtained.

[0023] Further, in step 3, the optimized modal decomposition number and the penalty factor are input into the VMD-SI algorithm to obtain optimal decomposition parameters, and the original signal S(t) j is decomposed into k IMF components.

[0024] Further, step 4 is specifically as follows:

[0025] Step 401, the correlation R between the IMF component and the original signal is calculated,

[0026]

[0027] Step 402, the self-information I of the IMF component and the original signal is calculated k ,S(t) j ),

[0028] I(IMF k ,S(t) j )=-log2R(IMF k ,S(t) j )

[0029] Wherein, j=1, 2..m, m represents the maximum damage level; k represents the number of reconstructed components.

[0030] Further, step 5 is specifically as follows:

[0031] Step 501, the damage parameter self-information is normalized:

[0032]

[0033] Step 502, the maximum slope Slop of the distance between adjacent IMF original signals S(t) is calculated, the maximum slope is the demarcation between the effective component and the invalid component,

[0034] Slop=MAX|D(i+1)-D(i)|,i=1,2,...k-1;

[0035] Step 503, the effective component is retained, the signal is reconstructed, and the denoised signal is obtained,

[0036]

[0037] wherein D(i) represents the self-information normalized value of the i-th IMF component, I(IMF i ,S(t) j ) represents the self-information of the i-th IMF component, min([I(IMF,S(t) j )] all ) represents the minimum value among the self-information of all components, max([I(IMF,S(t) j )] all ) represents the maximum value among the self-information of all components, represents the reconstructed signal, and n is the number of effective components used for reconstruction.

[0038] The method has the advantages that: the method adds adaptive sine and cosine and Cauchy-Gaussian variation to the traditional SSA algorithm to form an ASSA algorithm, which can improve the global optimization ability in the early stage of algorithm iteration, enhance the speed of obtaining the optimal solution in the late stage of algorithm iteration, avoid the local optimal stagnation caused by the traditional algorithm, effectively optimize the penalty factor and modal decomposition number parameters in VMD-SI, and eliminate the irregularity interference of the vibration signal caused by the pipeline in a complex environment; the self-information is introduced, the VMD and SI algorithms are used to form a VMD-SI decomposition algorithm, which avoids the problems of modal aliasing and excessive decomposition in the traditional method. Meanwhile, the method can better adapt to the characteristics of non-stationary signals, improve the signal-to-noise ratio of the signal, and more accurately extract the leakage characteristics. Meanwhile, the correlation between the decomposed IMF and the original signal can be better represented, and the reconstructed IMF can achieve the effect of denoising. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 is a flowchart of the method;

[0040] Figure 2 is a logic structure diagram of the algorithm;

[0041] Figure 3 is an experimental equipment diagram;

[0042] Figure 4 is a vibration signal diagram corresponding to different working conditions;

[0043] Figure 5 is an iterative curve diagram of the corresponding signal under different working conditions;

[0044] Figure 6 is an IMF decomposition diagram;

[0045] Figure 7 is a correlation coefficient diagram;

[0046] Figure 8 a normalized processing result graph;

[0047] Figure 9 a signal contrast graph;

[0048] Figure 10 an evaluation index result graph. DETAILED DESCRIPTION

[0049] In order to make the content of the present application more easily understood, the present application will be further described in detail below according to specific embodiments and in conjunction with the accompanying drawings.

[0050] As shown in Figure 1 and Figure 2 , a pipeline leakage vibration signal denoising method based on parametric self-information according to the present application comprises the following steps:

[0051] Step 1, collect original vibration signals S(t) under different damage conditions of the pipeline j .

[0052] Step 2, add adaptive sine and cosine and Cauchy-Gaussian mutation to the sparrow search algorithm SSA to form an ASSA algorithm, and take the original vibration signals S(t) j corresponding to different damage conditions as the search space to optimize the modal decomposition number and the penalty factor in VMD-SI;

[0053] Step 3, input the obtained modal decomposition number and penalty factor into the VMD-SI algorithm to obtain optimal decomposition parameters;

[0054] Step 4, calculate the correlation coefficient of the intrinsic modal function and the original vibration signal based on the optimal decomposition parameters, and calculate the self-information of the intrinsic modal function and the original vibration signal;

[0055] Step 5, normalize the obtained self-information, obtain effective IMF components according to the normalized self-information distance of the intrinsic modal function and the original signal, reconstruct the effective IMF components, and realize vibration signal denoising.

[0056] In this embodiment, taking the vibration signals under four kinds of valve opening degrees simulating four damage levels of the pipeline as examples, the present application is further described in detail. Among them, the leakage level of signal 1 is the largest, and the leakage level of signal 4 is the smallest; signal 1 corresponds to level 1, signal 2 corresponds to level 2, signal 3 corresponds to level 3, and signal 4 corresponds to level 4.

[0057] The experimental pipeline is a water supply pipeline, and different opening degrees of fire hydrant valves are set to simulate different cracking states. The experimental system is composed of a vibration sensor, a data transmission cable, a signal acquisition card, and a central control system. The vibration signal propagates from the leakage point to both sides of the pipeline. The vibration sensor is deployed near the leakage point to collect the pipeline vibration signal under the impact of water flow. The experimental equipment is shown in Figure 3 . The vibration signal is amplified, band-pass filtered, and analog-to-digital converted in the signal processing system, and then transmitted to the central control system by RS232 bus. The collected vibration signal is shown in Figure 4 .

[0058] Step 2 specifically includes:

[0059] Step 201, by introducing an adaptive search factor γ1, the weight is larger in the early stage of algorithm iteration, and the decreasing speed is slow, which is beneficial to improve the global optimization ability; in the later stage of algorithm iteration, the weight is smaller, which enhances the advantage of local development of the algorithm, to increase the speed of obtaining the optimal solution:

[0060]

[0061] The new finder position iteration formula is as follows:

[0062]

[0063] Wherein, Cons is used to adjust the overall amplitude of the adaptive search factor, and usually Const = 1; ξ is the adjustment coefficient, ξ = 1.6; γ2 and γ3 are random numbers in [0, 2π], the former determines the moving distance of the sparrow, and the latter is used to control the influence of the optimal individual on the next position of the sparrow; t is the current iteration number, and M is the maximum iteration number; H best represents the global optimal position in the current population, that is, the position of the sparrow with the highest fitness value; MZ is a safety value, usually taking a value of 0.5 to 1, when R2 is less than MZ, it indicates that the current environment is relatively safe, and the sparrow can conduct extensive global search to find better solutions; when R2 is greater than MZ, it indicates that there may be predators in the environment, and the sparrow needs to quickly move to a safer area, at which time local search or random walk will be triggered;

[0064] Step 202, in the later stage of SSA algorithm iteration, the followers will forage around the optimal finder, and food competition may occur, leading to local optimal stagnation. In order to solve this problem, an adaptive Cauchy-Gaussian mutation strategy is adopted. Both Cauchy distribution and standard Gaussian are continuous probability distributions, with small values at the origin and slow zero rate. The probability density function f1(x) of standard Cauchy distribution and the probability density function f2(x) of Gaussian distribution are as follows:

[0065]

[0066] The individual in the sparrow position update is disturbed by using adaptive Cauchy-Gauss variation, the search scale of sparrow algorithm is expanded, and the ability of jumping out of local optimum of the algorithm is improved.

[0067] The new follower position update is as follows:

[0068]

[0069] Wherein, x is the independent variable; Cauchy(0,1) is a standard Cauchy distribution function; Gauss(0,1) is a standard Gaussian distribution function; γ4 and γ5 are adaptive dynamic adjustment parameters, γ4 gradually decreases and γ5 gradually increases in the algorithm iteration process, so that the algorithm can jump out of the current stagnation and strengthen the ability of local development and global exploration of the algorithm;

[0070] In step 203, the vibration signals corresponding to the four damage levels are input into the ASSA algorithm as a search space, the upper and lower limit ranges of the modal decomposition number k are set as [3, 10], the upper and lower limit ranges of the penalty factor α are set as [100, 2500], and the iteration number is set as 100 times to obtain the optimal values of k and α; the algorithm iteration curve is as shown in Figure 5 The SSA iteration curves of signal 2 and signal 4 are straight lines, which are trapped in local optimal solutions; the SSA fitness functions of signal 1 and signal 3 are converged at 71 iterations and 46 iterations respectively, and the convergence speed is slow. This is because SSA is a random search algorithm based on probability, when the search algorithm finds a relatively good solution, a large number of followers will rush to the position, so that the whole sparrow population stagnates, leading to falling into a local optimal solution instead of a global optimal solution. In addition, in the later stage of SSA algorithm iteration, the followers will forage around the optimal discoverer, leading to local optimal stagnation and slow convergence speed of the algorithm. The ASSA iteration curves of working conditions 1, 2, 3 and 4 do not fall into local optimum, and are converged at 12, 19, 37 and 21 iterations respectively. This is because the local development and global development ability of ASSA algorithm is improved, and the probability of jumping out of local optimum of the algorithm is increased, so that the convergence speed of the algorithm is accelerated.

[0071] The step 3 specifically comprises:

[0072] In step 301, the obtained modal decomposition number and penalty factor are input into the VMD-SI algorithm to obtain the optimal decomposition parameter; the original signal S(t) j is decomposed into k IMF components, taking signal 1 and signal 4 as examples, the IMF components and the frequency spectrum of signal 1 are as shown in Figure 6 (a) and Figure 6 (b), and the IMF components and the frequency spectrum of signal 4 are as shown in Figure 6 (c) andFigure 6 As shown in (d). Figure 6 The results show that as the decomposed mode order increases, the frequency of the vibration signal gradually increases, while the energy carried generally decreases. The maximum amplitude of the decomposed signal is 0.02V, which is the IMF1 of signal 4. The signal's spectrum does not show mode duplication or aliasing, and the noise signal is successfully separated, indicating that the ASSA algorithm effectively optimizes the VMD-SI parameters. The correlation R between the IMF components and the original signal is calculated.

[0073]

[0074] like Figure 7 As shown, the IMF5 of signal 1, the IMF2 of signal 2, the IMF2 of signal 3, and the IMF1 of signal 4 have the highest similarity, all greater than 0.7. In the figure, y represents the changing trend of the IMF components fitted by the first-order function, and R0... 2 This indicates whether the fit is good or bad; Figure 7 The results show that as the modal order increases, the correlation between the IMF and the original vibration signal generally decreases, indicating that the signal containing damage information is mainly concentrated in the low-frequency components of the vibration signal.

[0075] Step 302: Calculate the self-information I(IMF) of the IMF component and the original signal. k ,S(t) j ),

[0076] I(IMF k ,S(t) j ) = -log2R(IMF k ,S(t) j )

[0077] Where j = 1, 2, ..., m represents the damage level. k represents the number of reconstructed components.

[0078] Step 4 specifically includes:

[0079] Step 401: Normalize the self-information of the damage parameters.

[0080]

[0081] The results are as follows Figure 8 As shown. Figure 8The normalized values of signal 1 and signal 3 have the maximum slope at IMF4 to IMF6, and the normalized values of signal 2 and signal 4 have the maximum slope at IMF5 to IMF6. This is because the smaller the correlation coefficient, the greater the self-information of the IMF, and the smaller the self-information indicates that the IMF has a stronger correlation with the original signal, that is, the IMF can better capture important features and information in the original signal.

[0082] In step 402, the maximum slope Slop of the distance of the adjacent IMF original signal S(t) is calculated, and the maximum slope is the demarcation point between the effective component and the ineffective component,

[0083] Slop = MAX|D(i+1)-D(i)|, i = 1, 2,... k-1

[0084] In step 403, the effective component is retained, the signal is reconstructed, and the denoised signal is obtained,

[0085]

[0086] The filter signals and the spectrum graphs of signal 1 and signal 4 are obtained by selecting the effective IMF components as the demarcation point for reconstruction, as shown in Figure 9 Figure 9 As shown in the figure, after the ASSA-VMD-SI processing, the amplitude of the vibration signal is locally reduced compared with the original signal, and the amplitudes of the reconstructed signal 1 and signal 4 are 0.05V and 0.032V respectively. This is because the original signal contains a large amount of noise signal irrelevant to the damage information, and the reconstructed signal eliminates the peak interference, making the signal smoother. From the spectrum graphs of signal 1 and signal 4, it can be found that the spectrum graph of the reconstructed signal presents the same overall trend as the spectrum graph of the original signal; as shown in the enlarged part, in the low-frequency stage, the spectrum graph of the reconstructed signal almost reproduces the spectrum graph of the original signal, and in the high-frequency stage, the amplitude of the spectrum graph of the reconstructed signal is smaller than that of the original signal. The signal containing damage information in the original signal is mostly distributed in the low-frequency band, and the noise signal is mostly distributed in the high-frequency band.

[0087] wherein D(i) represents the normalized value of the self-information of the i-th IMF component, I(IMF i , S(t) j ) represents the self-information of the i-th IMF component. min([I(IMF, S(t) j ]) all ) represents the minimum value of the self-information of all components, and max([I(IMF, S(t) j ]) all ) represents the maximum value of the self-information of all components. represents the reconstructed signal, and n is the number of components used for reconstruction. ​

[0088] The original signals under four working conditions are denoised and reconstructed by using the method. The signal-to-noise ratio (SNR), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE) of the four reconstructed signals are calculated, and the output results are compared with other optimization methods.

[0089] Table 1 Comparison of running time of algorithm

[0090]

[0091]

[0092] The comparison of running time of algorithm is shown in Table 1. The optimization algorithm proposed in the application has the shortest average running time. Because the adaptive sine and cosine and Cauchy mutation optimize the ability to find the optimal solution. In addition, the probability of the algorithm jumping out of the local optimal value is also enhanced, thereby accelerating the convergence speed of the algorithm.

[0093] The four evaluation indexes of signals 1-4 are shown in Table 2. Figure 10 Figure 10 It is shown in Table 2 that compared with other denoising methods (VMD, EMD, Fourier) under the same signal, the SNR output by the method adopted in the application is the largest, and the MSE, MAE and RMSE are reduced, verifying that the ASSA-VMD-SI method has good performance in the denoising of pipeline leakage vibration signals.

[0094] The above only describes the preferred scheme of the application, and is not intended to further limit the application, and any equivalent changes made according to the content of the specification and drawings of the application are within the protection scope of the application.​

Claims

1. A pipeline leak vibration signal denoising method based on parametric self-information, characterized in that, Comprising the following steps: Step 1, collect the original vibration signal S(t) of the pipeline under different damage conditions j ; Step 2, add adaptive sine-cosine and Cauchy-Gaussian mutation to the sparrow search algorithm SSA to form the ASSA algorithm, and the original vibration signals S(t) corresponding to different damage conditions j As the search space, the number of modal decomposition in the optimization VMD-SI and the penalty factor are optimized; Step 3, input the obtained modal decomposition number and penalty factor into the VMD-SI algorithm to obtain the optimal decomposition parameter; Step 4, calculate the correlation coefficient of the intrinsic modal function and the original vibration signal based on the optimal decomposition parameter, and calculate the self-information of the intrinsic modal function and the original vibration signal based on the correlation coefficient; Step 5, normalize the obtained self-information, obtain the effective IMF component according to the normalized self-information distance of the intrinsic modal function and the original signal, reconstruct the effective IMF component, and realize vibration signal denoising; wherein, step 2 is specifically: Step 201, introducing an adaptive search factor γ1 in SSA, New finder position The iteration formula is as follows: wherein Cons is used to adjust the overall amplitude of the adaptive search factor; ξ is an adjustment coefficient; γ2 and γ3 are random numbers in [0, 2π]; t is the current iteration number, and M is the maximum iteration number; H best indicates the global optimal position in the current population, i.e. the position of the sparrow with the highest fitness value; MZ is a safety value; Step 202, using an adaptive Cauchy-Gaussian mutation strategy, wherein the probability density function f1(x) of the standard Cauchy distribution and the probability density function f2(x) of the standard Gaussian distribution are as follows: New follower position update As follows: Wherein, x is the independent variable; Cauchy(0,1) is the standard Cauchy distribution function; Gauss(0,1) is the standard Gaussian distribution function; γ4 and γ5 are adaptive dynamic adjustment parameters; Step 203, input the vibration signal corresponding to different damage levels as the search space into the ASSA algorithm, set the upper and lower limit range of the modal decomposition number k as [3, 10], the upper and lower limit range of the penalty factor α as [100, 2500], and the iteration number, to obtain the optimal value of k and α; Step 4 is specifically: Step 401, calculate the correlation R of the IMF component and the original signal, Step 402, calculate the self-information I of the IMF component and the original signal k S(t) j ), I(IMF k ,S(t) j ) = -log2R(IMF k ,S(t) j ), Wherein, j=1,2..m, m represents the maximum damage level; k represents the number of reconstructed components.

2. The pipeline leakage vibration signal denoising method based on the parametric self-information according to claim 1, characterized in that, In step 3, the optimized modal decomposition number and the penalty factor are input into the VMD-SI algorithm to obtain optimal decomposition parameters, and the original signal S(t) j is decomposed into k IMF components.

3. The pipeline leakage vibration signal denoising method based on the parametric self-information according to claim 1, characterized in that, Step 5 is specifically: Step 501, normalize the damage parameter self-information: Step 502, calculate the maximum slope Slop of the distance between adjacent IMF original signals S(t): Slop=MAX|D(i+1)-D(i)|,i=1,2,...k-1; Step 503, retain the effective component, reconstruct the signal, and obtain the denoised signal: where D(i) represents the value of the self-information of the ith IMF component normalized, I(IMF i , S(t) j ) represents the self-information of the ith IMF component, min([I(IMF, S(t) j ]) all ) represents the minimum value among the self-information of all components, max([I(IMF, S(t) j ]) all ) represents the maximum value among the self-information of all components, represents the reconstructed signal, n is the number of effective components used for reconstruction, IMF q represents the qth IMF component, 1≤q≤n.

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