CN109340586B - Method and system for detecting leakage of water supply pipeline - Google Patents
Method and system for detecting leakage of water supply pipeline Download PDFInfo
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- CN109340586B CN109340586B CN201811311649.XA CN201811311649A CN109340586B CN 109340586 B CN109340586 B CN 109340586B CN 201811311649 A CN201811311649 A CN 201811311649A CN 109340586 B CN109340586 B CN 109340586B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
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- F17D5/00—Protection or supervision of installations
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Abstract
The embodiment of the invention provides a method and a system for detecting leakage of a water supply pipeline. The method comprises the following steps: performing Hilbert-Huang transformation on a vibration signal acquired by any collector arranged at the periphery of a water supply pipeline to acquire a Hilbert marginal spectrum of the vibration signal; and acquiring approximate entropy of the Hilbert marginal spectrum, and judging whether the water supply pipeline leaks or not based on the approximate entropy. The embodiment of the invention provides a method and a system for detecting leakage of a water supply pipeline. The effectiveness and the accuracy of detection can be guaranteed, so that the detection precision is improved, and the detection error is reduced.
Description
Technical Field
The embodiment of the invention relates to the technical field of signal processing, in particular to a method and a system for detecting leakage of a water supply pipeline.
Background
Urban water supply pipe is because the influence of factors such as the service life is longer, medium corrosion and material ageing inevitably can take place to leak, because water supply pipe buries underground usually, consequently is difficult to know whether water supply pipe takes place to leak, if discovery leak in time not, will cause serious water waste.
At present, relevant researchers have conducted intensive research on water supply pipeline leakage detection technology. The related literature firstly develops experimental research on a water supply pipeline leakage point positioning technology by using a correlation analysis method, and then provides a water supply pipeline leakage positioning method based on the combination of Empirical Mode Decomposition (EMD) and energy feature extraction. Both of the above methods can accurately locate the leak position, but are not suitable for leak detection. In addition, regarding the non-stationary characteristic of the leakage signal of the water supply pipeline, the related document proposes a processing method combining Hilbert-huang transform (HHT) and wavelet packet. However, the decomposition effect of the wavelet packet has a great relationship with the time-frequency characteristics of the signal and the selection of the wavelet basis, when the signal-to-noise ratio is low, the decomposition effect is not ideal, and the method has low detection precision and large error and is not suitable for leakage detection. Therefore, the problem of water supply pipeline leakage detection is a problem to be solved urgently.
Disclosure of Invention
The embodiment of the invention provides a method and a system for detecting leakage of a water supply pipeline, which are used for solving the defects of low detection precision and large error of the leakage of the water supply pipeline in the prior art, improving the detection precision and reducing the detection error.
In a first aspect, an embodiment of the present invention provides a method for detecting leakage of a water supply pipeline, including:
performing Hilbert-Huang transformation on a vibration signal acquired by any collector arranged at the periphery of a water supply pipeline to acquire a Hilbert marginal spectrum of the vibration signal;
and acquiring approximate entropy of the Hilbert marginal spectrum, and judging whether the water supply pipeline leaks or not based on the approximate entropy.
In a second aspect, an embodiment of the present invention provides a system for detecting leakage of a water supply pipeline, including:
the Hilbert marginal spectrum acquisition module is used for performing Hilbert-yellow transformation on a vibration signal acquired by any one collector arranged at the periphery of a water supply pipeline so as to acquire a Hilbert marginal spectrum of the vibration signal;
and the leakage judging module is used for acquiring the approximate entropy of the Hilbert marginal spectrum and judging whether the water supply pipeline leaks or not based on the approximate entropy.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the processor and the memory complete communication with each other through a bus; the memory stores program instructions executable by the processor, which when called by the processor are capable of performing the methods described above.
In a fourth aspect, embodiments of the present invention provide a non-transitory computer-readable storage medium storing computer instructions that cause the computer to perform the above-described method.
According to the method and the system for detecting the leakage of the water supply pipeline, provided by the embodiment of the invention, the Hilbert-Huang transformation is carried out on the vibration signal collected by any collector arranged at the periphery of the water supply pipeline to obtain the Hilbert marginal spectrum of the vibration signal, so that the approximate entropy of the Hilbert marginal spectrum is obtained, whether the water supply pipeline leaks or not is judged based on the approximate entropy, and the effectiveness and the accuracy of detection can be ensured, so that the detection precision is improved, and the detection error is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for detecting leakage in a water supply pipeline according to an embodiment of the present invention;
FIG. 2 is a schematic view of an environmental simulation including a water supply pipeline and a water collector according to an embodiment of the present invention;
FIG. 3 is a Hilbert marginal spectrum of a vibration signal according to an embodiment of the present invention;
FIG. 4 is a time domain waveform diagram of a vibration signal according to an embodiment of the present invention;
FIG. 5 is a power spectrum of a vibration signal according to an embodiment of the present invention;
fig. 6 is an IMF waveform diagram obtained by performing EMD decomposition on a vibration signal according to an embodiment of the present invention;
fig. 7 is a power spectrum of an IMF obtained by performing EMD decomposition on a vibration signal according to an embodiment of the present invention;
fig. 8 is a time domain waveform and a frequency spectrum of a vibration signal in different scenarios according to an embodiment of the present invention;
FIG. 9 is an approximate entropy diagram of Hilbert marginal spectra of vibration signals under different scenarios according to an embodiment of the present invention;
FIG. 10 is a schematic structural view of a water supply pipeline leakage detection system according to an embodiment of the present invention;
fig. 11 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a method for detecting leakage of a water supply pipeline according to an embodiment of the present invention, as shown in fig. 1, the method includes:
102, acquiring approximate entropy of the Hilbert marginal spectrum, and judging whether the water supply pipeline leaks or not based on the approximate entropy.
An embodiment of the present invention will be specifically described with reference to fig. 2. Fig. 2 is an environmental simulation schematic diagram of an intelligent water meter including a water supply pipeline and a collector according to an embodiment of the present invention, where as shown in fig. 2, the water supply pipeline is located below ground, and the collector is integrated in the intelligent water meter and generally located above ground. Because water supply pipe is located the subsurface, be difficult to know whether water supply pipe takes place to reveal, consequently, need use the collector to gather the signal in the environment that contains this water supply pipe, here, will contain the signal in this water supply pipe's the environment and be called vibration signal to whether take place to reveal according to vibration signal judgement water supply pipe. It should be noted that, for a water supply pipeline, a plurality of collectors may be arranged on the ground, but in the embodiment of the present invention, only one collector is taken as an example for illustration.
When the water supply pipeline leaks, due to the action of internal and external pressure of the water supply pipeline, the high-pressure water sprayed out of the water supply pipeline rubs with the medium such as the crack of the water supply pipeline and the surrounding soil, and therefore oscillation signals (namely leakage signals) with different frequencies are generated. In addition, because the acquisition environment is relatively complex, the vibration signal actually acquired by the acquisition unit not only contains a leakage signal, but also is mixed with a random interference noise signal (i.e., a noise signal), and generally can be regarded as an additive combination of the two. The vibration signal x (t) actually collected by the collector can be represented as:
wherein, aiAnd wiRespectively, the ith amplitude and oscillation frequency, r the distance of the leak from the collector, v the propagation velocity of the leakage signal in the water supply pipe, and n (t) the random interference noise signal. The vibration signal x (t) is a combination of a leakage signal and a noise signal, and belongs to a non-stationary random signal.
For step 101, hilbert yellow transform is performed on the vibration signal to obtain a hilbert marginal spectrum of the vibration signal. It should be noted that, if the water supply pipeline leaks, the vibration signal collected by the collector is a superimposed signal of the leakage signal and the noise signal, and if the water supply pipeline does not leak, the vibration signal collected by the collector is only the noise signal.
The Hilbert-Huang transform (HHT) is divided into an Empirical Mode Decomposition (EMD) part and a Hilbert-Hilbert transform part. The essence of EMD is that based on the time scale characteristics of the signal itself, the complex signal can be decomposed into a plurality of Intrinsic Mode Functions (IMFs) and a residual component in sequence from high to low according to frequency without selecting a basis function. The EMD is characterized by being capable of carrying out linearization and smoothing processing on nonlinear and non-smooth signals and preserving the characteristics of the signals in the decomposition process. The Hilbert transform fundamentally breaks through the limitation of the traditional Fourier transform theory, the distribution situation of the amplitude of the signal in a frequency domain can be observed through a Hilbert time frequency spectrum (also called a Hilbert time frequency spectrum) and a Hilbert marginal spectrum (also called a Hilbert marginal frequency spectrum) obtained by the transform, the essence of the signal can be more deeply known, and the Hilbert transform is particularly suitable for processing non-stationary random signals such as vibration signals in the embodiment of the invention.
For step 102, approximate entropy of the Hilbert marginal spectrum is obtained to determine whether a water supply pipeline is leaking based on the approximate entropy. It should be noted that, specifically, determining whether a water supply pipeline leaks based on approximate entropy means: based on the approximate entropy, it is determined whether the vibration signal is a superimposed signal of the leakage signal and the noise signal or only the noise signal. And if the vibration signal is a superposed signal of the leakage signal and the noise signal, judging that the water supply pipeline leaks, and if the vibration signal is only the noise signal, judging that the water supply pipeline does not leak.
Wherein, Approximate Entropy (Approximate Entropy) describes the possibility of generating a new mode when the dimension changes, can fully describe the complexity of the signal, a nonnegative number is used for representing the predictability of the former data to the latter data so as to quantitatively describe the repeatability of the sequence, and the larger the Entropy value is, the more random or irregular the sequence is, the higher the complexity is; the smaller the entropy value, the smaller the complexity.
According to the method provided by the embodiment of the invention, the Hilbert marginal spectrum of the vibration signal is obtained by performing Hilbert-Huang transformation on the vibration signal collected by any collector arranged at the periphery of the water supply pipeline, and the approximate entropy of the Hilbert marginal spectrum is further obtained, so that whether the water supply pipeline leaks or not is judged based on the approximate entropy. The method is a water supply pipeline leakage detection method based on the combination of HHT and approximate entropy, and can ensure the effectiveness and accuracy of detection, thereby improving the detection precision and reducing the detection error.
On the basis of the above embodiments, the embodiments of the present invention will specifically describe a process of performing hilbert-yellow transform on a vibration signal x (t):
step 1011: determining all extreme points in the vibration signal x (t), connecting the maximum point and the minimum point of the vibration signal x (t) in sequence by a cubic spline function to obtain an upper envelope and a lower envelope, and taking the average value of the two envelopes to obtain m1(t) separating m from the vibration signal x (t)1(t) to obtain h1(t)：
h1(t)＝x(t)-m1(t) (2)
If h is1(t) not satisfying the basic condition of IMF, and1(t) repeating the above process as raw data to obtain:
h11(t)＝h1(t)-m11(t) (3)
wherein m is11(t) is h1(t) average of upper and lower envelopes. If h11(t) still unsatisfied, repeat the above process k times until h1k(t) until IMF is satisfied, c1(t)＝h1k(t) mixing c1(t) as a first component.
Step 1012: c is to1(t) separating from the vibration signal x (t) to obtain:
r1(t)＝x(t)-c1(t) (4)
will r is1(t) as new original data, repeating the above process to obtain a second component c2(t) repeating the steps until a predetermined termination condition or r is satisfiedn(t) until no resolubility is possible.
Step 1013: the originally analyzed vibration signal x (t) can be expressed as:
in the formula (5), i represents the decomposition order of IMF; component c1(t)，c2(t)，……，cn(t) the IMFs of the frequency bands which respectively comprise the vibration signals x (t) from high to low; r isn(t) is a residual component, which can reflect the overall variation trend of the vibration signal x (t).
Step 1014: by performing a Hilbert transform on each IMF in equation (4) and constructing an analytical function, another representation of the vibration signal x (t) can be obtained:
wherein, Re [ ·]Representing the real part of the vibration signal, ai(t) and fi(t) represents an amplitude function and a phase function obtained by constructing an analytic function, respectively. Equation (6) writes the vibration signal x (t) as a function of time and frequency, and can accurately reflect the change law of the amplitude of the vibration signal x (t) with time and frequency, called Hilbert time spectrum, which is generally expressed by H (w, t), namely:
step 1015: and (3) performing integration processing on the formula (7) to obtain a Hilbert marginal spectrum as follows:
in equation (8), T represents the duration of the signal. h (w) statistically represents the accumulated amplitude of all data lengths, accurately reflects the change rule of the amplitude of the signal on the whole frequency, and is particularly suitable for processing non-stationary random signals.
On the basis of the foregoing embodiments, obtaining an approximate entropy of the hilbert marginal spectrum, further includes:
step 1021, determining an effective frequency band of the Hilbert marginal spectrum.
And 1022, solving an approximate entropy of the hilbert marginal spectrum in the effective frequency band to serve as the approximate entropy of the hilbert marginal spectrum.
Specifically, for step 1021, it should be noted that the abscissa of the hilbert marginal spectrum is frequency, the ordinate is amplitude, and the effective frequency band refers to a frequency band in the whole frequency range. For two vibration signals, namely, a vibration signal containing a leakage signal and a noise signal and a vibration signal containing only the noise signal, the amplitudes of the two vibration signals in the effective frequency band are different.
For step 1022, due to the different amplitudes of the two vibration signals in the effective frequency band, the approximate entropy of the hilbert spectrum of the two vibration signals in the effective frequency band is also different. Therefore, the approximate entropy of the Hilbert marginal spectrum of the vibration signal in the effective frequency band can be obtained, and whether the water supply pipeline leaks or not can be judged according to the approximate entropy.
The process of finding the approximate entropy is further described below. Assuming a one-dimensional sequence { u (i) } of length N, i ═ 1 … N }, the approximate entropy is specifically defined and the algorithm proceeds as follows:
s1: reconstructing an m-dimensional vector X from an original sequence u (i)i。
Xi＝{u(i),u(i+1),…,u(i+m-1)},i＝1,2,…,N-m+1 (9)
S2: calculating an arbitrary vector XiAnd the remaining vector Xj(j ═ 1, 2., N-m +1, j ≠ i) of the distance d between themij。
dij＝max|u(i+j)-u(j+k)|,k＝0,1,…,m-1 (10)
That is, the maximum value of the absolute value of the difference between the corresponding elements of the two vectors is the distance between the two vectors.
S3: given a threshold r, for each vector XiCounting the corresponding dijThe number of r or less and the ratio of this number to the total distance N-m +1 are recorded
S4: using natural logarithm e as base, pair
Taking logarithm, and averaging all i to obtain phim(r)。
S5: increasing m by 1, repeating the above steps to obtain phim+1(r)。
S6: finally, the approximate entropy ApEn (m, r) is found to be:
ApEn(m,r)＝φm+1(r)-φm(r) (12)
the magnitude of the approximate entropy is related to the vector dimension m and the threshold r, wherein m is 2, and r is 0.1-0.25 times of STD (STD is the standard deviation of the sequence), and the approximate entropy has reasonable statistical characteristics. From the above analysis, it can be seen that the approximate entropy can characterize the complexity of the signal sequence, the greater the approximate entropy, the higher the complexity.
On the basis of the above embodiments, determining whether the water supply pipeline leaks or not based on the approximate entropy further includes:
and comparing the approximate entropy with a preset judgment threshold value, and judging whether the water supply pipeline leaks or not according to a comparison result.
Specifically, in the embodiment of the invention, whether the water supply pipeline leaks or not is judged by comparing the approximate entropy with the preset judgment threshold value. The following describes the acquisition of the preset determination threshold:
and acquiring a plurality of vibration signal samples, wherein the vibration signal samples can be a superimposed signal of a leakage signal and a noise signal or only the noise signal. It should be noted that, for each vibration signal sample, it is known whether the corresponding water supply pipeline is leaking.
For each vibration signal sample, obtaining an approximate entropy corresponding to the vibration signal sample, and counting a plurality of approximate entropies corresponding to a plurality of vibration signal samples to determine a value of a preset judgment threshold. For example, a plurality of approximate entropies corresponding to a plurality of vibration signal samples are counted to obtain: for a plurality of vibration signal samples of which the vibration signal samples are superposed signals of leakage signals and noise signals, corresponding approximate entropies are all located between 1.92 and 2.07, and for a plurality of vibration signal samples of which the vibration signal samples are only noise signals, corresponding approximate entropies are all located between 2.11 and 2.16, and any value between 2.07 and 2.11 can be used as a preset judgment threshold value. For example, with 2.09 as the preset determination threshold value, when leakage detection is performed on the water supply pipeline, as long as the approximate entropy is less than 2.09, it is determined that leakage occurs in the water supply pipeline. It should be noted that the values in the embodiments of the present invention are only exemplary values, and the specific values are different according to different environments and different materials and sizes of water supply pipelines.
On the basis of the above embodiments, the method further includes:
and comparing the approximate entropy with a preset approximate entropy interval, and taking the leakage degree grade corresponding to the preset approximate entropy interval in which the approximate entropy falls as the leakage degree grade of the water supply pipeline.
Specifically, in the embodiment of the present invention, the degree of leakage of the water supply pipeline is obtained by comparing the approximate entropy with a preset approximate entropy interval. The following describes the acquisition of the preset approximate entropy interval:
acquiring a plurality of vibration signal sample sets, for example, including: the method comprises the steps of a leakage-free vibration signal sample set, a small-opening leakage vibration signal sample set, a middle-opening leakage vibration signal sample set and a large-opening leakage vibration signal sample set. It should be noted that the leakage-free vibration signal sample set represents that the water supply pipeline has no leakage, the small-opening leakage vibration signal sample set represents that the water supply pipeline has slight leakage, the medium-opening leakage vibration signal sample set represents that the water supply pipeline has medium leakage, and the large-opening leakage vibration signal sample set represents that the water supply pipeline has severe leakage. It should be noted that for each vibration signal sample, the level of leakage from its corresponding water supply line is known.
And for each vibration signal sample set, determining the approximate entropy corresponding to each vibration signal sample in the vibration signal sample set, and counting a plurality of approximate entropies corresponding to a plurality of vibration signal samples in the vibration signal sample set. If the final statistics result: the approximate entropy corresponding to the leakage-free vibration signal sample set is 2.11-2.16, the approximate entropy corresponding to the small-opening leakage vibration signal sample set is 2.02-2.07, the approximate entropy corresponding to the middle-opening leakage vibration signal sample set is 1.97-2.02, and the approximate entropy corresponding to the large-opening leakage vibration signal sample set is 1.92-1.97. And if the approximate entropy of the vibration signal is 1.95 and falls into 1.92-1.97, judging that the water supply pipeline is seriously leaked. It should be noted that the values in the embodiments of the present invention are only exemplary values, and the specific values are different according to different environments and different materials and sizes of water supply pipelines.
On the basis of the above embodiments, determining the effective frequency band of the hilbert marginal spectrum further includes:
step 10211, a first vibration signal sample set and a second vibration signal sample set are obtained, where each vibration signal sample in the first vibration signal sample set is a superimposed signal of a leakage signal and a noise signal, and each vibration signal sample in the second vibration signal sample set is a noise signal.
Step 10212, determining an effective frequency band of the Hilbert marginal spectrum based on the first set of vibration signal samples and the second set of vibration signal samples.
Specifically, with reference to fig. 3, the embodiment of the present invention is described with only one vibration signal sample in the first vibration signal sample set (hereinafter referred to as vibration signal 1) and only one vibration signal sample in the second vibration signal sample set (hereinafter referred to as vibration signal 2).
Fig. 3 is a hilbert marginal spectrogram of a vibration signal according to an embodiment of the present invention, as shown in fig. 3, a spectral line 1 is the hilbert marginal spectrogram of the vibration signal 1, the vibration signal 1 is a superimposed signal including a leakage signal and a noise signal, a spectral line 2 is the hilbert marginal spectrogram of the vibration signal 2, and the vibration signal 2 is the noise signal. It can be clearly seen by comparing the spectral line 1 and the spectral line 2 that the spectral line 1 has a more obvious energy accumulation phenomenon in the frequency band of 450-1000 Hz, and the spectral line 2 is more stable in the whole frequency band. It should be noted that the energy accumulation phenomenon refers to a phenomenon that the amplitude of a signal in a certain frequency band is significantly higher than the amplitude of a signal outside the frequency band. Therefore, 450-1000 Hz can be used as the effective frequency band of the vibration signal, and the vibration signal 1 and the vibration signal 2 can be distinguished in the effective frequency band. It should be noted that 450 to 1000Hz is only an exemplary frequency band, and the range of the effective frequency band may also vary with the environment, the material and the size of the water supply pipeline, and the range of the effective frequency band is not specifically limited herein.
Because the spectral lines of the vibration signal 1 and the vibration signal 2 in the 450-1000 Hz frequency band are different, the approximate entropy of the vibration signal 1 in the 450-1000 Hz frequency band is also different from the approximate entropy of the vibration signal 2 in the 450-1000 Hz frequency band. Therefore, the approximate entropy of the Hilbert marginal spectrum of the vibration signal in the effective frequency band can be obtained, and whether the water supply pipeline leaks or not can be judged according to the approximate entropy.
On the basis of the above embodiments, the embodiment of the present invention will be described with respect to analyzing the vibration signal by using hilbert-yellow transform to determine whether the water supply pipeline leaks, that is, the embodiment of the present invention provides theoretical support for the above embodiments:
fig. 4 is a time domain waveform diagram of a vibration signal according to an embodiment of the present invention, where (a) is a time domain waveform portion of the vibration signal that only includes a noise signal, and (b) is a time domain waveform portion of the vibration signal that includes a leakage signal and a noise signal, where the time domain waveform is generated according to measured data of a set of small-mouth leakage signals collected by a plastic pipeline experimental system, where a sampling frequency is 5000Hz, and a collection duration is 1 s.
Fig. 5 is a power spectrum of a vibration signal according to an embodiment of the present invention, where (a) is the power spectrum of the vibration signal of fig. 4(a), and (b) is the power spectrum of the vibration signal of fig. 4(b), when viewed from the shape of the power spectrum, there is a certain similarity and no regularity between them, so that it is difficult to distinguish the two vibration signals directly according to the power spectrum.
Fig. 6 is an IMF waveform diagram obtained by performing EMD decomposition on a vibration signal according to an embodiment of the present invention, and it should be noted that 8 waveforms shown in fig. 6 are waveforms of 8 IMFs obtained by performing EMD decomposition on the vibration signal in fig. 4(b), where the IMFs 1 to 8 are sequentially distributed from a high frequency to a low frequency.
Fig. 7 is a power spectrum of an IMF obtained by performing EMD decomposition on a vibration signal according to an embodiment of the present invention, and it should be noted that fig. 7 shows power spectrums of the first 4 IMFs in fig. 6, where the IMF1 is mainly a noise signal, and the leakage signal is mainly distributed on the IMF 2.
Fig. 3 is a hilbert marginal spectrum of a vibration signal according to an embodiment of the present invention, and comparing fig. 5 with fig. 3, it can be seen that the hilbert marginal spectrum has an amplitude resolution in displaying frequency characteristics that is significantly higher than that of a power spectrum. As can be seen from fig. 3, the hilbert marginal spectrum of the vibration signal 1 (including the leakage signal and the noise signal) has a significant energy accumulation phenomenon in a frequency band of 450 to 1000Hz, while the hilbert marginal spectrum of the vibration signal 2 (including only the noise signal) is relatively stable over the entire frequency, and this characteristic can be used as a basis for distinguishing two vibration signals.
Fig. 8 is a time domain waveform and a frequency spectrum of a vibration signal under different scenarios provided in an embodiment of the present invention, and it should be noted that the vibration signal is a superimposed signal including a leakage signal and a noise signal. Wherein, (a), (c) and (e) are time domain waveform parts of the vibration signal under the condition of small-port water leakage, middle-port water leakage and large-port water leakage, respectively, and (b), (d) and (f) are power spectrum parts of the vibration signal under the condition of small-port water leakage, middle-port water leakage and large-port water leakage, respectively. According to the power spectrum analysis, for the large-port leakage situation, the signal-to-noise ratio is large, the energy of the vibration signal is basically concentrated in about 450-1000 Hz, and for the small-port and medium-port leakage situation, the signal-to-noise ratio is relatively small, the energy of the vibration signal is not concentrated, the leakage signal is basically submerged in the noise signal, and therefore the two vibration signals are difficult to distinguish according to the power spectrum.
Fig. 9 is an approximate entropy chart of the hilbert marginal spectrum of the vibration signal under different scenarios, which is provided in the embodiment of the present invention, and it should be noted that (a), (b), and (c) respectively show values of approximate entropies of the hilbert marginal spectrum of multiple groups of vibration signals under a small-mouth water leakage condition, a middle-mouth water leakage condition, and a large-mouth water leakage condition. In contrast, for each of (a), (b), and (c), the approximate entropy of the hilbert marginal spectrum of the vibration signal containing only the noise signal is also shown. Each scene takes 40 groups of vibration signal samples (20 groups of two vibration signals), the approximate entropy curve formed by the vibration signal samples containing leakage signals and noise signals is called a first type curve, and the approximate entropy curve formed by the vibration signal samples containing only noise signals is called a second type curve. As can be seen from fig. 9, the approximate entropy represented by the second kind of curve is different from the approximate entropy represented by the first kind of curve in the case of small-mouth water leakage, middle-mouth water leakage, and large-mouth water leakage, and therefore, the approximate entropy can be used as a standard for distinguishing two kinds of vibration signals.
The detection method provided by the embodiment of the invention is tested by 40 groups of vibration signals which are acquired through experiments and only contain noise signals and the vibration signal samples under the three conditions, and the test results are shown in table 1. Table 1 shows the detection result table of the water supply pipeline leakage, and it can be known from table 1 that the detection method based on the combination of HHT and approximate entropy provided by the embodiment of the present invention has good practicability for the detection of the leakage of the water supply pipeline, and the detection accuracy rate thereof reaches more than 95%.
Table 1 water supply pipeline leakage detection result table
Fig. 10 is a schematic structural diagram of a system for detecting leakage of a water supply pipeline according to an embodiment of the present invention, as shown in fig. 10, the system includes:
a hilbert marginal spectrum obtaining module 1001, configured to perform hilbert yellow transform on a vibration signal collected by any collector arranged around a water supply pipeline, so as to obtain a hilbert marginal spectrum of the vibration signal; and a leakage determining module 1002, configured to obtain an approximate entropy of the hilbert marginal spectrum, and determine whether the water supply pipeline leaks based on the approximate entropy.
Specifically, the hilbert marginal spectrum obtaining module 1001 performs hilbert yellow transform on the vibration signal to obtain a hilbert marginal spectrum of the vibration signal. It should be noted that, if the water supply pipeline leaks, the vibration signal collected by the collector is a superimposed signal of the leakage signal and the noise signal, and if the water supply pipeline does not leak, the vibration signal collected by the collector is only the noise signal. Leakage determination module 1002 obtains the approximate entropy of the hilbert marginal spectrum to determine whether a water supply pipeline is leaking based on the approximate entropy. It should be noted that, specifically, determining whether a water supply pipeline leaks based on approximate entropy means: based on the approximate entropy, it is determined whether the vibration signal is a superimposed signal of the leakage signal and the noise signal or only the noise signal. And if the vibration signal is a superposed signal of the leakage signal and the noise signal, judging that the water supply pipeline leaks, and if the vibration signal is only the noise signal, judging that the water supply pipeline does not leak.
The system provided in the embodiment of the present invention specifically executes the flows of the above-mentioned methods, and for details, the contents of the above-mentioned methods are referred to, and are not described herein again. According to the system provided by the embodiment of the invention, the Hilbert-Huang transformation is carried out on the vibration signals collected by any collector arranged at the periphery of the water supply pipeline to obtain the Hilbert marginal spectrum of the vibration signals, so that the approximate entropy of the Hilbert marginal spectrum is obtained, and whether the water supply pipeline leaks or not is judged based on the approximate entropy. The system is a water supply pipeline leakage detection system based on HHT and approximate entropy combination, and can guarantee effectiveness and accuracy of detection, so that detection precision is improved, and detection errors are reduced.
Fig. 11 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 11, the electronic device may include: a processor (processor)1101, a communication Interface (Communications Interface)1102, a memory (memory)1103 and a communication bus 1104, wherein the processor 1101, the communication Interface 1102 and the memory 1103 are communicated with each other via the communication bus 1104. The processor 1101 may invoke a computer program stored on the memory 1103 and executable on the processor 1101 to perform the methods provided by the various embodiments described above, including, for example: performing Hilbert-Huang transformation on a vibration signal acquired by any collector arranged at the periphery of a water supply pipeline to acquire a Hilbert marginal spectrum of the vibration signal; and acquiring approximate entropy of the Hilbert marginal spectrum, and judging whether the water supply pipeline leaks or not based on the approximate entropy.
In addition, the logic instructions in the memory 1103 can be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Embodiments of the present invention further provide a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the transmission method provided in the foregoing embodiments when executed by a processor, and the method includes: performing Hilbert-Huang transformation on a vibration signal acquired by any collector arranged at the periphery of a water supply pipeline to acquire a Hilbert marginal spectrum of the vibration signal; and acquiring approximate entropy of the Hilbert marginal spectrum, and judging whether the water supply pipeline leaks or not based on the approximate entropy.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (7)
1. A method of detecting a leak in a water supply pipeline, comprising:
performing Hilbert-Huang transformation on a vibration signal acquired by any collector arranged at the periphery of a water supply pipeline to acquire a Hilbert marginal spectrum of the vibration signal;
acquiring approximate entropy of the Hilbert marginal spectrum, and judging whether the water supply pipeline leaks or not based on the approximate entropy;
wherein obtaining an approximate entropy of the Hilbert marginal spectrum further comprises:
determining an effective frequency band of the Hilbert marginal spectrum;
and solving approximate entropy of the Hilbert marginal spectrum in the effective frequency band to serve as the approximate entropy of the Hilbert marginal spectrum.
2. The method of claim 1, wherein determining whether the water supply pipeline is leaking based on the approximate entropy, further comprises:
and comparing the approximate entropy with a preset judgment threshold value, and judging whether the water supply pipeline leaks or not according to a comparison result.
3. The method of claim 1, further comprising:
and comparing the approximate entropy with a preset approximate entropy interval, and taking the leakage degree grade corresponding to the preset approximate entropy interval in which the approximate entropy falls as the leakage degree grade of the water supply pipeline.
4. The method of claim 1, wherein determining an effective frequency band of the Hilbert marginal spectrum further comprises:
acquiring a first vibration signal sample set and a second vibration signal sample set, wherein each vibration signal sample in the first vibration signal sample set is a superimposed signal of a leakage signal and a noise signal, and each vibration signal sample in the second vibration signal sample set is a noise signal;
determining an effective frequency band of the Hilbert marginal spectrum based on the first set of vibration signal samples and the second set of vibration signal samples.
5. A system for detecting water supply line leaks, comprising:
the Hilbert marginal spectrum acquisition module is used for performing Hilbert-yellow transformation on a vibration signal acquired by any one collector arranged at the periphery of a water supply pipeline so as to acquire a Hilbert marginal spectrum of the vibration signal;
the leakage judging module is used for acquiring the approximate entropy of the Hilbert marginal spectrum and judging whether the water supply pipeline leaks or not based on the approximate entropy;
the detection system for the leakage of the water supply pipeline is also used for determining the effective frequency band of the Hilbert marginal spectrum; and solving approximate entropy of the Hilbert marginal spectrum in the effective frequency band to serve as the approximate entropy of the Hilbert marginal spectrum.
6. An electronic device, comprising a memory and a processor, wherein the processor and the memory communicate with each other via a bus; the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1 to 4.
7. A non-transitory computer-readable storage medium storing a computer program that causes a computer to perform the method according to any one of claims 1 to 4.
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Families Citing this family (5)
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| CN109931506A (en) * | 2019-03-14 | 2019-06-25 | 三川智慧科技股份有限公司 | Pipeline leakage detection method and device |
| CN111188999A (en) * | 2020-03-08 | 2020-05-22 | 中信建筑设计研究总院有限公司 | Water supply pipeline leakage detection method and system |
| CN111609324B (en) * | 2020-05-29 | 2021-02-19 | 北京化工大学 | Pipeline leakage detection method and device |
| CN112728427A (en) * | 2020-12-29 | 2021-04-30 | 中冶南方城市建设工程技术有限公司 | Method and system for detecting leakage of water supply pipeline |
| CN113217826B (en) * | 2021-06-03 | 2022-06-14 | 邯郸市水利水电勘测设计研究院 | Pipeline water supply pipe network leakage alarm control method, device and medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102621449A (en) * | 2012-03-16 | 2012-08-01 | 河南理工大学 | Single phase ground fault section locating method in small current grounding system |
| CN104814735A (en) * | 2015-05-22 | 2015-08-05 | 京东方科技集团股份有限公司 | Method and device for judging whether brain is tired |
| CN105675216A (en) * | 2016-01-11 | 2016-06-15 | 中国工程物理研究院激光聚变研究中心 | Detection and location method for leaked sound signals |
| CN105758634A (en) * | 2016-02-26 | 2016-07-13 | 中国航空工业集团公司上海航空测控技术研究所 | Fault detection method applied to helicopter drive system vibration signals |
| CN106361327A (en) * | 2016-09-21 | 2017-02-01 | 广州视源电子科技股份有限公司 | Method and system for detecting waking state in sleep state analysis |
| CN108488637A (en) * | 2018-03-22 | 2018-09-04 | 哈尔滨工业大学 | A kind of water supply line leakage discrimination method based on auto-correlation and approximate Entropy principle |
-
2018
- 2018-11-06 CN CN201811311649.XA patent/CN109340586B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102621449A (en) * | 2012-03-16 | 2012-08-01 | 河南理工大学 | Single phase ground fault section locating method in small current grounding system |
| CN104814735A (en) * | 2015-05-22 | 2015-08-05 | 京东方科技集团股份有限公司 | Method and device for judging whether brain is tired |
| CN105675216A (en) * | 2016-01-11 | 2016-06-15 | 中国工程物理研究院激光聚变研究中心 | Detection and location method for leaked sound signals |
| CN105758634A (en) * | 2016-02-26 | 2016-07-13 | 中国航空工业集团公司上海航空测控技术研究所 | Fault detection method applied to helicopter drive system vibration signals |
| CN106361327A (en) * | 2016-09-21 | 2017-02-01 | 广州视源电子科技股份有限公司 | Method and system for detecting waking state in sleep state analysis |
| CN108488637A (en) * | 2018-03-22 | 2018-09-04 | 哈尔滨工业大学 | A kind of water supply line leakage discrimination method based on auto-correlation and approximate Entropy principle |
Non-Patent Citations (5)
| Title |
|---|
| Ｈｉｌｂｅｒｔ－Ｈｕａｎｇ变换在情感脑电特征提取中的应用;杨鹏圆等;《计算机工程与设计》;20140716;全文 * |
| 基于HHT 的管道阀门内漏声发射检测研究;刘贵杰等;《振动与冲击》;20121215;全文 * |
| 基于希尔伯特黄变换的输气管道泄漏音波时频特性分析;刘翠伟等;《振动与冲击》;20140828;全文 * |
| 基于改进HHT和样本熵的脑电信号特征提取;林海波等;《计算机工程与设计》;20150616;第1608-1612页 * |
| 近似熵及其在机械设备故障诊断中的应用;胥永刚等;《信息与控制》;20021223;全文 * |
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