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Water supply pipeline leakage identification method based on signal time-frequency characteristics and support vector machine

A support vector machine, time-frequency feature technology, applied in character and pattern recognition, computer parts, instruments, etc., can solve the problems of high modeling difficulty and high misjudgment rate, to improve the accuracy rate, comprehensive detection effect, Realize the effect of high-precision water leakage location

Active Publication Date: 2019-01-29
INNER MONGOLIA UNIVERSITY
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AI Technical Summary

Problems solved by technology

Use these features to construct a feature matrix as the input of the support vector machine, use the support vector machine as a classifier to identify the signal and output the recognition result, thus solving the problems of high modeling difficulty and high misjudgment rate in the existing pipeline leak detection technology

Method used

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  • Water supply pipeline leakage identification method based on signal time-frequency characteristics and support vector machine
  • Water supply pipeline leakage identification method based on signal time-frequency characteristics and support vector machine
  • Water supply pipeline leakage identification method based on signal time-frequency characteristics and support vector machine

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Embodiment 1

[0145] In the experiment, PVC water supply pipes were selected for signal acquisition. 100 sets of data were collected for leakage and non-leakage conditions at different time periods, and the length of each set of data was 5000, which was used for training and optimizing the support vector machine. At the same time, 100 data were collected for leakage and non-leakage conditions in relatively quiet early morning hours, and the effectiveness of the leakage detection and time delay estimation algorithm studied in the present invention was verified by artificially adding noise.

[0146] First, 50 sets of data are extracted from the leakage signals and non-leakage signals collected at different time periods to form a training sample of 100 sets. Then, use the remaining samples to construct a test sample of 100 sets of data. The parameter (C, γ) of the support vector machine is an integer power of 2, and the value range of C is C∈[2 -5 ,2 15 ], the value range of γ is γ∈[2 -15 ...

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Abstract

The invention discloses a water supply pipeline leakage identification method based on signal time-frequency characteristics and support vector machine, belonging to the technical field of water leakage detection and positioning. The method includes: inputting a detected signal; performing feature extraction of the input signal; the extracted feature set is input to the optimized support vector machine, and the feature is recognized by the support vector machine. The support vector machine outputs a recognition result according to the input signal characteristics, and determines whether the signal is a leakage signal or a non-leakage signal. Based on the intrinsic mode function, approximate entropy and principal component analysis, three time-frequency characteristics of leakage signal areproposed by using its randomness and centralized frequency spectrum. Using these features to construct feature matrix as input of support vector machine, support vector machine is used as classifierto recognize the signal and output recognition results, thus solving the existing pipeline leak detection technology problems such as modeling difficulty coefficient is large, high misjudgment rate ishigh.

Description

technical field [0001] The invention relates to the technical field of water leakage detection and positioning, in particular to a water supply pipeline leakage identification method based on signal time-frequency characteristics and support vector machines. Background technique [0002] Water is the material basis for the survival of human beings and all living things, and an indispensable natural resource for the development of human society. The 2018 World Water Resources Development Report released by the United Nations shows that due to factors such as population growth, economic development, and changes in consumption patterns, the demand for global water resources is growing at a rate of 1% per year, and this rate will continue to grow in the next 20 years. will be greatly accelerated. As the population continues to grow and pollution continues to worsen, water scarcity is increasing. In the case of serious shortage of water resources in the world, the problem of wa...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2135G06F18/2411G06F18/214
Inventor 刘洋吴琼任学利赵婷龚政青春
Owner INNER MONGOLIA UNIVERSITY
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