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Weak signal detection device and method based on wavelets and RBF neural network

A weak signal detection and neural network technology, which is applied in the field of weak signal detection devices, can solve the problem of undetectable leaks and other problems

Inactive Publication Date: 2011-01-12
NORTHEASTERN UNIV
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Problems solved by technology

These methods can only detect when there are large fluctuations in the pipeline, that is, there is a large amount of oil leakage in the pipeline, but if there are small fluctuations, these methods are powerless
For example: We have collected a pressure signal of 1.00-5.00v through a pressure transmitter. The previous method can handle the voltage fluctuation signal of 3% relative to the noise signal. If the fluctuation is smaller than 3%, the conventional leak detection device will not be able to This type of leak was detected

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  • Weak signal detection device and method based on wavelets and RBF neural network
  • Weak signal detection device and method based on wavelets and RBF neural network
  • Weak signal detection device and method based on wavelets and RBF neural network

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Embodiment Construction

[0049] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0050] figure 1 and figure 2 It is a block diagram and circuit schematic diagram of a weak signal detection device based on wavelet and RBF neural network, which includes a 16-bit A / D converter, ARM microprocessor, synchronous dynamic random access memory SDRAM, Nor Flash memory, Nand Flash memory, 16 The output end of bit A / D converter connects the input end of ARM microprocessor, and the first input and output end of ARM microprocessor connects the first synchronous dynamic random access memory SDRAM, and the second input and output end of ARM microprocessor connects the second Synchronous dynamic random access memory SDRAM, the third input and output of the ARM microprocessor is connected to the Nor Flash memory, and the fourth input and output of the ARM microprocessor is connected to the Nand Flash memory; in addition, it also ...

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Abstract

A weak signal detection device and method based on wavelets and a radial basis function (RBF) neural network belong to the technical field of signal detection. The device comprises a fourth-order Butterworth low pass filter, an A / D converter, an ARM microprocessor, a synchronous dynamic random access memory (SDRAM), a Nor Flash memory and a Nand Flash memory. The device is beneficial to inhibiting noises and restoring, enhancing and extracting useful signals. The method can realize detection of a few oil leakage accidents which can not be detected by the conventional leakage detection devices and detect fluctuation less than 3%.

Description

technical field [0001] The invention belongs to the technical field of signal detection, in particular to a weak signal detection device and method based on wavelet and RBF neural network. Background technique [0002] At present, the pipeline transportation industry is developing extremely rapidly, with more and more pipelines and longer and longer transportation distances. However, with the increase of pipeline service time, the probability of pipeline leakage accidents is also increasing. As we all know, the fluid transported in the pipeline is dangerous and polluting, such as oil and natural gas, so once a leakage accident occurs, it will cause huge loss of life and property and environmental pollution. Especially in my country, a considerable part of the oil and gas pipeline network has entered the aging period, and has suffered unprecedented man-made damage in the past ten years, so the loss caused by the leakage accident is very huge, which seriously affects the pipe...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): F17D5/06
Inventor 冯健刘金海张化光鲁忠沂马大中魏向向董良刘振伟
Owner NORTHEASTERN UNIV
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