Pipeline Leakage Signal Recognition Method Based on Improved Elmd Multiscale Entropy

Abstract

The invention provides a pipeline leakage signal identification method based on improved ELMD multi-scale entropy. The pipeline leakage signal identification method includes the steps that firstly, acquired experimental data are subjected to noise pre-processing to eliminate low-correlation components in signals; then pre-processed signals are subjected to ELMD processing to obtain PF components;an endpoint effect problem reserved by ELMD decomposition is weakened through a peak value waveform matching method; the multi-scale entropy of the PF components is calculated correspondingly, and multi-scale entropy values of leakage signals are arrayed and compared to eliminate background noise; a feature vector is constructed by selecting a principal PF component according to the multi-scale entropy values; the feature vector is used as an input vector of a BP neural network to train the network; and a to-be-tested sample is input into a trained BP neural network to obtain a pipeline leakage identification result. The pipeline leakage signal identification method based on the improved ELMD multi-scale entropy can adapt to various conditions of pipelines and has good testing accuracy.

Description

technical field [0001] The invention relates to the technical field of pipeline leakage detection, in particular to a pipeline leakage signal identification method based on improved ELMD multi-scale entropy. Background technique [0002] Urban pipelines have become an indispensable tool for the development of modern cities. With the continuous expansion of its scale, due to the natural aging of equipment, climate and environment, and man-made damage, pipeline failure incidents are on the rise. Especially once the gas pipeline leaks, it is easy to Cause fire, explosion, poisoning, environmental pollution and other vicious accidents. Therefore, it has good economic value and social significance to find an effective pipeline leakage detection method and accurately identify hidden dangers of leakage. [0003] In recent years, with the development of computer technology, pipeline leakage detection technology is developing towards the combination of software and hardware. At pres...

AI Technical Summary

Problems solved by technology

[0004] Because it is difficult to obtain the pressure data of all sections of the entire pipeline, especially in the pipeline sections that cannot be approached, it is almost impossible to obtain pressure data. In the absence of pressure data of the entire pipeline section, the existing methods for pipeline leak detection based on pressure data are It is estimated based on very few pressure sampling points, which leads to unsatisfactory detection results

In recent years, with the development of detection technology, there have been some methods to obtain the pressure data of the entire pipeline, but the method of leak detection based on the pressure data of the entire pipeline has not been proposed, and the BP neural network can be based on the pressure of the entire pipeline. The method of data leakage detection, BP neural network has unique excellent performance such as parallel distributed processing, self-organization, self-adaptation, self-learning and good fault tolerance

Images