Water supply pipeline leakage identification method based on linear prediction cepstrum coefficient and lyapunov index

A linear prediction and cepstral coefficient technology, applied in pipeline systems, gas/liquid distribution and storage, mechanical equipment, etc., can solve the problem of low leakage identification accuracy, avoid excessive dependence, save costs, and improve identification The effect of accuracy

Active Publication Date: 2021-01-29
HARBIN INST OF TECH
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Problems solved by technology

[0010] The present invention provides a water supply pipeline leakage identification method based on linear predictive cepstrum coefficient and lyapunov index in order to solve the problem that the existing water supply pipeline leak detection technology relies on human experience identification and the accuracy of leakage identification is not high

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  • Water supply pipeline leakage identification method based on linear prediction cepstrum coefficient and lyapunov index
  • Water supply pipeline leakage identification method based on linear prediction cepstrum coefficient and lyapunov index
  • Water supply pipeline leakage identification method based on linear prediction cepstrum coefficient and lyapunov index

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specific Embodiment approach 1

[0028] Specific implementation mode one: combine figure 1 , figure 2 To illustrate this embodiment, the water supply pipeline leakage identification method based on linear predictive cepstrum coefficient and lyapunov index given in this embodiment specifically includes the following steps:

[0029] Step 1: Collect the environmental background noise signal when the pipeline is not connected to water, and then collect the sound signal when the pipeline is normal and the sound signal when the pipeline is leaking under the same environmental background. The collection process is as figure 1 As shown, the acceleration sensor 3 is connected to the valve plug 2 on the water pipe 1, the acceleration sensor 3 converts the collected sound signal into an electrical signal, and then transmits the electrical signal to the charge amplifier 4 for amplification, and then passes the dynamic acquisition analyzer 5 Connect to host computer 6 and carry out follow-up analysis;

[0030] Step 2,...

specific Embodiment approach 2

[0034] Specific embodiment two: the difference between this embodiment and specific embodiment one is that the calculation process of the lyapunov index specifically includes:

[0035] A1. Calculate the time delay τ of the signal S through the autocorrelation function method;

[0036] A2, seek its average period T' by Fourier transform to signal S;

[0037] A3. Calculate the correlation dimension c of the signal S, and then determine the embedding dimension m;

[0038] A4. Use the time delay τ, the average period T′, and the embedding dimension m to perform phase space reconstruction on the signal S to be measured, and obtain the reconstructed signal phase space Y(t i ); i=0,...,n; where, t 0 Indicates the starting point of the time series, t n is the end point of the time series;

[0039] A5. Calculate the starting point Y(t of the phase space of the reconstructed signal 0 ) and its nearest neighbor Y 0 (t 0 ) distance L 0 ;

[0040] A6. Track the time evolution of t...

specific Embodiment approach 3

[0048] Embodiment 3: This embodiment differs from Embodiment 2 in that the dimension m>2c+1 in step A3.

[0049] Other steps and parameters are the same as those in Embodiment 1 or 2.

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Abstract

The invention provides a water supply pipeline leakage identification method based on linear prediction cepstrum coefficient and lyapunov index, and belongs to the technical field of water supply pipeline network leakage detection and location. The present invention first collects the environmental background noise signal when the pipeline is not connected to water, and then respectively collects the sound signal when the pipeline is normal and the sound signal when the pipeline is leaking under the same environmental background; calculates its lyapunov index, short Time-to-time zero-crossing rate, linear predictive cepstral coefficient LPCC, and establish a B‑P neural network; collect sound signals on the pipeline to be tested, calculate its lyapunov index, short-term zero-crossing rate, and linear predictive cepstral coefficient eigenvalues ​​and input them The established B‑P neural network is used for leakage identification. The invention solves the problem that the existing water supply pipeline leak detection technology relies on human experience identification and the leakage identification accuracy is not high. The invention can be used for accurate identification of water supply pipeline leakage.

Description

technical field [0001] The invention relates to a water supply pipeline leakage identification method, and belongs to the technical field of water supply pipeline network leakage detection and positioning. Background technique [0002] Water is the source of life and the foundation of development, and water resources are an important resource related to the national economy and people's livelihood; the leakage rate of water supply pipe networks in cities in my country is relatively high. According to statistics, the average leakage rate of water supply pipe networks in more than 600 cities in my country (pipeline The network leakage rate (the ratio of the water leakage of the pipe network to the total water supply) exceeds 15%, and the highest is more than 70%. [0003] At present, the mainstream active leakage monitoring methods include the method based on flow monitoring and the method based on acoustic vibration signal monitoring. [0004] Leakage monitoring method based ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): F17D5/06
CPCF17D5/06
Inventor 张鹏赫俊国杨宝明吴晨光袁一星
Owner HARBIN INST OF TECH
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