Water supply pipeline leakage identification method based on linear prediction cepstrum coefficients and lyapunov indexes

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

Active Publication Date: 2019-04-23
HARBIN INST OF TECH
View PDF8 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Water supply pipeline leakage identification method based on linear prediction cepstrum coefficients and lyapunov indexes
  • Water supply pipeline leakage identification method based on linear prediction cepstrum coefficients and lyapunov indexes
  • Water supply pipeline leakage identification method based on linear prediction cepstrum coefficients and lyapunov indexes

Examples

Experimental program
Comparison scheme
Effect test

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.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a water supply pipeline leakage identification method based on linear prediction cepstrum coefficients and lyapunov indexes, and belongs to the technical field of water supply pipe network leakage detection and positioning. Firstly, an environment background noise signal generated when no water flows into a pipeline is acquired firstly, and then a sound signal generated whenthe pipeline is normal and a sound signal generated when the pipeline is in leakage are acquired in the same environment background; the lyapunov indexes, the short-time zero-crossing rates and the linear prediction cepstrum coefficients LPCC of the acquired signals are calculated, and a B-P neutral network is established; and a sound signal is acquired on a to-be-detected pipeline, the characteristic values of the lyapunov index, the short-time zero crossing rate and the linear prediction cepstrum coefficient of the signal are calculated and input in the established B-P neutral network, andleakage identification is carried out. The problems that the existing water supply pipeline leakage detection technology depends on experience identification of people, so that the leakage identification precision is not high are solved. The method can be used for precise water supply pipeline leakage identification.

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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
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
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products