Signal identification and classification method

A classification method and signal recognition technology, applied in the direction of gas/liquid distribution and storage, instruments, calculation models, etc., can solve the problem of not finding literature reports with the same or similar topics, so as to overcome the difficulty of determining the network structure, facilitate implementation, simple effect

Inactive Publication Date: 2010-09-15
HARBIN ENG UNIV
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[0005] After searching the existing technical literature, no literature rep

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  • Signal identification and classification method
  • Signal identification and classification method

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Embodiment Construction

[0021] The present invention is described in more detail below in conjunction with accompanying drawing example:

[0022] combine figure 1 , U1 is wavelet transform denoising. Firstly, wavelet transform method is used to denoise the original data containing relatively high noise. In data analysis, the signal is decomposed into high-frequency and low-frequency information. In order to obtain a better denoising effect, adopt The soft threshold method denoises the signal, and then reconstructs the signal to achieve the effect of denoising; while the wavelet packet decomposition U2 inherits the good time-frequency localization advantages of wavelet transform, it has no subdivision for multi-scale analysis. The high-frequency part is further decomposed to have better time-frequency characteristics. In theory, the wavelet packet decomposition can go on indefinitely until there is only one point of data in the bottom-level details. However, in practical applications, according to Si...

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Abstract

The invention provides a signal identification and classification method. The method comprises the followings steps of: carrying out noise reduction on initial data containing higher noise by utilizing a wavelet transform method, decomposing signals into high-frequency information and low-frequency information in data analysis, carrying out noise cancelling on the signals by adopting a soft thresholding method and then carrying out signal reconstruction; carrying out further decomposition on the high-frequency part which is not detailedly classified by multiscale analysis while inheriting allthe favorable time-frequency localization advantages of the wavelet transform; analyzing the signals within different frequency bands after multi-layered decomposition by utilizing the wavelet packettransform to extract out characteristic information reflecting a system state; transforming the characteristic vectors of input signals into a high-dimensional characteristic space through non-lineartransform and then solving for an optimal linear classification plane in the high-dimensional characteristic space. The invention overcomes the defects of difficult determination of a network structure, low convergence rate, requirement on large quantities of data samples during training, and the like in neural network learning and enables the neural network learning to be with the characteristics of high precision and strong real time in the aspect of practical application of engineering.

Description

technical field [0001] The invention relates to the field of modern detection technology, in particular to a pipeline pressure detection and identification technology. Background technique [0002] Because pipeline transportation has the advantages of low cost, energy saving, high safety and stable supply, pipeline transportation has developed rapidly all over the world and has become an indispensable part of modern society. However, due to long-term wear and tear, natural aging of equipment, changes in geography and climate, and man-made damage, leakage failures occur from time to time, which poses a huge potential threat to people's lives, property and living environment. Cause serious waste of resources. Therefore, timely and accurate identification of oil pipeline leakage has important practical significance. [0003] With the development of computer, signal processing and pattern recognition technology, the real-time leak detection technology based on supervisory cont...

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Application Information

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IPC IPC(8): F17D5/06G06N99/00
Inventor 傅荟璇于占东李冰王宇超杜春洋
Owner HARBIN ENG UNIV
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