Non-straight pipeline leakage positioning detection method based on self-adaptive morphological analysis

Abstract

The invention relates to the technical field of pipeline leakage positioning, and discloses a non-straight pipeline leakage positioning detection method based on self-adaptive morphological analysis, which comprises the following steps: step S1, performing pre-whitening processing on a collected noise-containing leakage signal; s2, performing frequency domain transformation on a series of signals obtained through processing in the first step; and further processing the observation signal by using the correlation of the second-order statistics in different frequency bands to obtain a mixed signal, X = [X1, X2,..., Xn], and Y = [Y1, Y2,..., Yn]. According to the non-straight pipeline leakage positioning method, the correlation degree between signals can be reduced to the maximum extent through the self-adaptive morphological component analysis blind separation method, the situation that the separation effect is not ideal due to the fact that a traditional blind separation algorithm is sensitive to noise is avoided, and the blind separation algorithm used in the method shows high anti-interference performance to the noise, so that the separation effect is not ideal is avoided. And an excellent separation effect can be obtained, so that a clearer correlation signal source is obtained.

Description

Technical Field

[0001] The invention relates to the technical field of pipeline leakage positioning, and in particular to a non-straight pipeline leakage positioning detection method based on adaptive morphological analysis. Background Art

[0002] Leakage in water supply pipelines is a waste of water resources, increases water supply costs, and reduces the quality of urban life. Therefore, it is particularly important to develop pipeline leakage detection technology.

[0003] In the process of leak location detection, the non-straight working condition of the pipeline is an important reason for the generation of internal noise. When the fluid flows through the non-straight pipeline, the fluid will be affected by diversion, pressure division, and speed change. The fluid will form multiple vortices in the non-straight pipeline. These vortices influence each other, and they stimulate the pipeline vibration to generate noise. When the pipeline leaks, the leakage signal and these internal noise sources will adjust to each other to a certain extent. Therefore, the leakage signal and the internal noise source have a certain correlation, which causes great difficulties for the detection and positioning of the pipeline.

[0004] The existence of non-straight pipelines increases the difficulty of leak detection and positioning. Traditional detection methods have limited leak detection capabilities for non-straight pipelines. It is necessary to propose a method to solve the problem of leak positioning in non-straight pipelines.

[0005] At present, the mainstream detection methods mainly include flow detection, pressure detection, optical detection and acoustic detection.

[0006] The flow detection method refers to using a flow sensor to obtain flow change data in the pipe, and obtaining leakage point location information by analyzing upstream and downstream flow data. The Chinese patent (CN106352246A) installs electromagnetic flow meters upstream and downstream of the pipeline, and the pipeline contains a U-shaped elbow condition. The leakage point location is obtained by correlation analysis of the upstream and downstream flow data. This method does not process the noise generated by the U-shaped elbow, and the leakage point location error is large; the pressure detection method uses a pressure sensor to obtain negative pressure wave data in the leaking pipeline. Since this negative pressure wave is caused by leakage, the leakage point location information can be obtained by analyzing the negative pressure wave data;

[0007] The Chinese patent (CN105844051A) collects negative pressure wave data inside the pipeline, uses the inversion method to learn and train the spatiotemporal variation operator fitting mathematical model, compares the parameters of the mathematical model with the negative pressure wave signal sample data, uses the parameterized collaborative method to perform sparse coding, realizes the training and learning of the over-complete dictionary, judges the bit error of the sparse coding, establishes a sparse representation method for the negative pressure wave signal sample data, and identifies the operating state of the pipeline to be tested according to the sparse representation feature extraction method, and locates the leakage point of the pipeline to be tested according to the sparse representation waveform decomposition method. This method is limited to leakage detection of straight pipelines, and does not process the internal noise source signal in non-straight pipelines, so the positioning effect is not ideal; the optical detection method refers to laying optical sensors such as optical fibers during pipeline construction, and locating the leakage point of the pipeline according to the reflected optical fiber signal;

[0008] The Chinese patent (CN106641739A) installs a distributed optical fiber sensor next to the water supply pipeline, and uses a Raman scattering optical fiber sensor to collect distributed temperature data of the water supply pipeline in real time. The location of the pipeline leakage point can be determined based on the temperature distribution diagram of the pipeline. Although this method can perform distributed temperature measurement on the pipeline, the temperature difference of the fluid in the pipe is small and almost constant in actual working conditions, so it cannot be used for leakage detection of non-straight pipes; the acoustic detection method refers to using an acoustic vibration sensor to pick up the acoustic signal generated by the pipeline leakage, and analyzing the acoustic signal to obtain the leakage point location information;

[0009] The Chinese patent (CN105675216A) uses a vibration sensor to collect noisy leakage sound signals near the leakage point, then extracts features from the noisy leakage sound signals, performs approximate entropy calculation on the selected feature data, obtains a measure of signal complexity, uses the obtained entropy value as the input of the neural network algorithm to identify the leakage event, and then uses a blind system to locate it. Although this method also uses a blind separation algorithm, it fails to effectively process the internal noise generated by non-straight pipe factors, and the positioning error is large;

[0010] The above methods have certain theoretical and technical innovations for straight water supply pipes, but none of them effectively deals with the internal noise sources of non-straight water supply pipes, resulting in large positioning errors. Summary of the invention

[0011] The present invention provides a non-straight pipeline leakage location detection method based on an adaptive morphological analysis, which has the advantages of good separation effect and simplified calculation process, and solves the problems raised by the above-mentioned background technology.

[0012] The present invention provides the following technical solution: a non-straight pipeline leakage location detection method based on an adaptive morphological analysis, comprising the following steps:

[0013] Step S1: pre-whitening the collected noisy leakage signal;

[0014] Step S2: Perform frequency domain transformation on the series of signals obtained by the first step, and further process the observed signals using the correlation of the second-order statistics in different frequency bands to obtain a mixed signal.

[0015] X=[X1,X2,…,X n ],Y=[Y1,Y2,…,Y n ];

[0016] Step S3: Normalize the mixed signal X obtained in the second step and output the mixed matrix:

[0017] A=[A1,A2,…,A n ],B=[B1,B2,…,B n ];

[0018] Step S4: Extract the source signal s according to the mixed signal X and the mixing matrix A, s = [s1, s2, ..., s n ], extract the source signal S from the mixed signal Y and the mixing matrix B, S = [S1, S2, ..., S n ];

[0019] Step S5: Perform random complexity calculation on the extracted source signals S, S, and classify all calculated values ​​as input values ​​of the classifier. The source signals with higher values ​​are classified as leakage source signals.

[0020] Step S6: using the obtained leakage source signal to perform cross-correlation delay estimation, using a bandpass filter based on a coherence function to process the cross-correlation function to make the cross-correlation peak more obvious, and obtaining a delay value τ;

[0021] Step S7: Combine the empirical sound speed v and use the formula Positioning can be completed, L is the propagation path length from the leak point to a certain sensor, and L1 is a known path;

[0022] Preferably, a vibration sensor A and a vibration sensor B are installed at both ends of the non-straight pipe connection respectively.

[0023] Preferably, step S2 specifically includes: the observation signal vector collected by the sensor is X=[X1, X2, ..., X m ],Y=[Y1,Y2,…,Y m ], m represents the number of data observation signals, and the following model mixing matrix is ​​established according to the propagation characteristics of the leakage signal

[0024] Preferably, step S3 specifically includes: i ,Yi , i=1,2,…,m, represents the observed signal, S, S is the frequency domain vector representation of the noisy leakage source signal, its dimension n≥m, A, B are m×n mixing matrix transmission units, N1, N2 are two-dimensional noise disturbance term vectors, pre-whiten the mixed signal matrix X, Y, and then perform the matrix element X i ,Y i Do normalization, that is, A i =X i / ||X i ||2, B i =Y i / ||Y i ||2, output matrix A=[A1,A2,…,A m ],B=[B1,B2,…,B m ].

[0025] Preferably, list all m-1 order submatrix planes of the mixing matrix:

[0026]

[0027] For mixed signal X i ,Y i ,i=1,2,…,m, find the subplane with the smallest distance

[0028] If H i It is composed of A1, A2, .., A m-1 Composition, h i is composed of B1, B2, …, B n-1 Composition, then calculate φ ij ,γ ij , so that it satisfies Then reconstruct the source signal s=[s,s1,…,s n ],S=[S,S1,…,S n ].

[0029] The present invention has the following beneficial effects:

[0030] 1. This non-straight pipeline leakage location detection method based on an adaptive morphological analysis can minimize the correlation between signals through the adaptive morphological component analysis blind separation method, avoiding the traditional blind separation algorithm being sensitive to noise, especially to noise with a wide spectrum such as white noise, which leads to unsatisfactory separation effect. The blind separation algorithm used in this method shows strong anti-interference ability to noise, can achieve excellent separation effect, and thus obtain a clearer correlation signal source.

[0031] 2. This non-straight pipeline leakage location detection method based on an adaptive morphological analysis has a good separation effect on the correlated source signals through the blind separation algorithm, eliminating the step of pre-whitening the signal in the traditional blind separation algorithm. While improving the separation effect, it simplifies the calculation process, thereby better processing the signal and obtaining a more accurate noise signal, so as to better calculate the leakage point of the pipeline. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the sensor arrangement structure of the present invention;

[0033] Figure 2 This is a schematic diagram of the power spectrum structure of a noisy leakage signal according to the present invention;

[0034] Figure 3 It is a schematic structural diagram of the flow chart of the leakage detection process of the present invention;

[0035] Figure 4 It is a schematic diagram of the calculation results of the randomness complexity of the leakage signal and the noise source signal of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0037] See also Figure 1 - Figure 4 , a non-straight pipeline leakage location detection method based on an adaptive morphological analysis comprises the following steps:

[0038] Step S1: pre-whitening the collected noisy leakage signal;

[0039] Step S2: Perform frequency domain transformation on the series of signals obtained by the first step, and further process the observed signals using the correlation of the second-order statistics in different frequency bands to obtain a mixed signal.

[0040] X=[X1,X2,…,X n ],Y=[Y1,Y2,…,Y n ];

[0041] Step S3: Normalize the mixed signal X obtained in the second step and output the mixed matrix:

[0042] A=[A1,A2,…,A n ],B=[B1,B2,…,Bn ];

[0043] Step S4: Extract the source signal s according to the mixed signal X and the mixing matrix A, s = [s1, s2, ..., s n ], extract the source signal S from the mixed signal Y and the mixing matrix B, S = [S1, S2, ..., S n ];

[0044] Step S5: Perform random complexity calculation on the extracted source signals S, S, and classify all calculated values ​​as input values ​​of the classifier. The source signals with higher values ​​are classified as leakage source signals.

[0045] Step S6: using the obtained leakage source signal to perform cross-correlation delay estimation, using a bandpass filter based on a coherence function to process the cross-correlation function to make the cross-correlation peak more obvious, and obtaining a delay value τ;

[0046] Step S7: Combine the empirical sound speed v and use the formula Positioning can be completed, L is the length of the propagation path from the leak point to a certain sensor, and L1 is a known path.

[0047] In a preferred embodiment, a vibration sensor A and a vibration sensor B are respectively installed at both ends of the non-straight pipe connection.

[0048] In a preferred embodiment, step S2 specifically includes: the observation signal vector collected by the sensor is X=[X1, X2, ..., X m ],Y=[Y1,Y2,…,Y m ], m represents the number of data observation signals, and the following model mixing matrix is ​​established according to the propagation characteristics of the leakage signal

[0049] In a preferred embodiment, step S3 specifically includes: i ,Y i , i=1,2,…,m represents the observed signal, S, S is the frequency domain vector representation of the noisy leakage source signal, and its dimension n≥m. A, B are m×n mixing matrix transmission units, N1, N2 are two m-dimensional noise disturbance term vectors, and the mixed signal matrix X, Y is pre-whitened, and then the elements X in the matrix are i ,Y i Do normalization, that is, A i =X i / ||X i ||2, B i =Y i / ||Y i ||2, output matrix A=[A1,A2,…,A m ],B=[B1,B2,…,Bm ].

[0050] In a preferred embodiment, all the m-1 order submatrix planes of the mixing matrix are listed

[0051] For mixed signal X i ,Y i ,i=1,2,…,m, find the subplane with the smallest distance

[0052] If H i It is composed of A1, A2, .., A m-1 Composition, h i is composed of B1, B2, …, B n-1 Composition, then calculate φ ij ,γ ij , to satisfy Then reconstruct the source signal s=[s,s1,…,s n ],S=[S,S1,…,S n ].

[0053] Example

[0054] Two cast iron pipes, both with small hole leaks and with T-type pipe joints and elbows, are taken as research objects. At both ends of the pipe, vibration sensors A and B with magnetic bases are adsorbed on the pipes. The magnetic base should not be tightened too much to avoid the failure to respond to the leakage signal in time due to the sensor and the magnetic base being too tight. The output pipeline vibration signal needs to be amplified and filtered by the data acquisition unit, and the signal X = [X1, X2, X3], Y = [Y1, Y2, Y3] is used as the input data of the detection algorithm for calculation. For the leakage detection problem here, the following model can be established:

[0055]

[0056] Among them, C1 and C2 are two basis transfer functions, which construct the correspondence between the mixing matrices A and B and their sparse coefficients S1 and S2. At this time, the purpose of the algorithm is to find a mixing matrix A and B, and transfer functions C1 and C2 according to the received signals X and Y, so that the sparse coefficients S1 and S2 are as sparse as possible. In order to highlight the effectiveness of sparsity for blind source separation, blind source separation will be divided into two different stages:

[0057] ① Based on high-order statistics, the mixed signals X and Y are extracted by using the correlation in different frequency bands. The mixed source signals are uncorrelated, that is, independent of each other. The uncorrelated source signals are denoted as S I ,S J , therefore, the source separation model is:

[0058]

[0059] Where W1, W2 are separation matrices, and N1, N2 are noise disturbance terms.

[0060] ② Select appropriate transfer basis functions C1, C2, and set the independent source S I ,S J Directly use them as sparse coefficients S1 and S2, and make the sparse coefficients as sparse as possible by selecting appropriate transfer basis functions, so as to improve the separation effect.

[0061] After the host obtains the blind separation result, it uses an algorithm that can measure the random complexity of the signal to calculate the complexity value of each source signal. Here, the sample entropy is used to calculate the random complexity value of all source signals. The calculation results are shown in the attached figure. Figure 4 , the result value is automatically sent to the classifier, such as using support vector machine for screening and classification, to screen out effective leakage source signals, and end the screening process to obtain leakage source signals A1, A2, A3, B1, B2, B3, A1 corresponds to B1, A2 corresponds to B2, A3 corresponds to B3, and these corresponding leakage source signals are cross-correlated and time delays are estimated. The peak enhancement technology based on coherence function bandpass filtering is used for the time delay estimation, and several different time delay values ​​can be obtained. The arithmetic mean is taken as τ. According to the different pipeline materials, the empirical sound velocity v is selected to determine the leakage point location. Finally, the faulty pipeline is excavated according to the leakage point location obtained by this method.

[0062] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0063] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A non-straight pipeline leakage location detection method based on an adaptive morphological analysis, characterized in that: The following steps are involved: Step S1: pre-whitening the collected noisy leakage signal; Step S2: Perform frequency domain transformation on a series of signals obtained through the first step, and further process the observed signals using the correlation of second-order statistics in different frequency bands to obtain a mixed signal; X=[X1,X2,…,X n ],Y=[Y1,Y2,…,Y n ]; Step S3: Normalize the mixed signal X obtained in the second step and output the mixed matrix: <h2 style=";text-align:left;direction:ltr">A=[A1,A2,…,A<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ],B=[B1,B2,…,B<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ]; Step S4: Extract the source signal s according to the mixed signal X and the mixing matrix A, s = [s1, s2, ..., s n ], extract the source signal S from the mixed signal Y and the mixing matrix B, S = [S1, S2, ..., S n ]; Step S5: Perform random complexity calculation on the extracted source signals S, S, and classify all calculated values ​​as input values ​​of the classifier. The source signals with higher values ​​are classified as leakage source signals. Step S6: using the obtained leakage source signal to perform cross-correlation delay estimation, using a bandpass filter based on a coherence function to process the cross-correlation function to make the cross-correlation peak more obvious, and obtaining a delay value τ; Step S7: Combine the empirical sound speed v and use the formula Positioning can be completed, L is the length of the propagation path from the leak point to a certain sensor, and L1 is a known path.

2. The non-straight pipeline leakage location detection method based on an adaptive morphological analysis according to claim 1 is characterized by: Step S1 specifically includes installing a vibration sensor A and a vibration sensor B at both ends of the non-straight pipe connection to collect a noisy leakage signal.

3. The non-straight pipeline leakage location detection method based on an adaptive morphological analysis according to claim 1 is characterized by: Step S2 specifically includes the observation signal vector collected by the sensor being: X=[X1,X2,…,X m ],Y=[Y1,Y2,…,Y m ], m represents the number of data observation signals, and the following model mixing matrix is ​​established according to the propagation characteristics of the leakage signal 4. The non-straight pipeline leakage location detection method based on an adaptive morphological analysis according to claim 1 is characterized by: Step S3 specifically includes: i ,Y i , i=1,2,…,m represents the observed signal, S, S is the frequency domain vector representation of the noisy leakage source signal, and its dimension n≥m, A, B are m×n mixing matrix transmission units, N1, N2 are two m-dimensional noise disturbance term vectors, and the mixed signal matrix X, Y is pre-whitened, and then the elements X in the matrix are i ,Y i Do normalization, that is, A i =X i / ||X i ||2, B i =Y i / ||Y i ||2, output matrix A=[A1,A2,…,A m ,B=[B1,B2,…,B m ].

5. The non-straight pipeline leakage location detection method based on an adaptive morphological analysis according to claim 1 is characterized by: Steps S4-S6 specifically include: List all m-1 order submatrix planes of the mixing matrix For mixed signal X i ,Y i ,i=1,2,…,m, find the subplane with the smallest distance If H i It is composed of A1, A2, .., A m-1 Composition, h i is composed of B1, B2, …, B n-1 Composition, then calculate φ ij ,γ ij , to satisfy Then reconstruct the source signal s=[s,s1,…,s n ],S=[S,S1,…,S n ].

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