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Phi-OTDR vibration signal identification algorithm based on STFT-CNN-RVFL

A technology of -OTDR and vibration signal, which is applied in the field of identification and classification of time-frequency diagram of Φ-OTDR vibration signal, can solve problems such as falling into local minimum, and achieve high accuracy and obvious signal identification effect

Active Publication Date: 2019-11-15
BEIJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

It uses the gradient descent method to minimize the error between the actual output value and the expected output value during each training process, thereby approaching the objective function, but often falls into a local minimum

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  • Phi-OTDR vibration signal identification algorithm based on STFT-CNN-RVFL
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  • Phi-OTDR vibration signal identification algorithm based on STFT-CNN-RVFL

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Embodiment Construction

[0037] specific implementation plan

[0038] The present invention will be described in further detail below through examples of implementation.

[0039] In this implementation case, three typical intrusive vibration signals of knocking, climbing, and pedestrian passing and three non-invasive vibration signals of wind, rain, and animal touch were used for experiments. The initially collected Φ-OTDR vibration signal files are binary files, which need to be format converted. The number of acquisitions of each type of vibration signal is 30 times, and the sampling frequency is 10KHz, corresponding to 6 types of vibration signals. There are 180 sets of experimental data in total. Divide each group of data into 10 segments, divide 1 to 5 segments into one sample, 2 to 6 segments into one sample, and so on. Each group of signals can get 6 samples, and 30 groups of data can get 180 samples. 140 samples are randomly selected as training samples, and the rest are used as test samples...

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Abstract

The invention relates to a Phi-OTDR vibration signal identification algorithm based on STFT-CNN-RVFL, and a method for identifying and classifying Phi-OTDR vibration signal time-frequency diagrams, and belongs to the field of picture processing and mode identification. The Phi-OTDR vibration signal identification algorithm is characterized by comprising the following steps: (1) performing STFT onPhi-OTDR vibration signals to obtain time-frequency diagrams; (2) performing gray processing on the time-frequency graph; (3) constructing a CNN network, and extracting image features; (4) randomly initializing a connection weight and a threshold value; (5) constructing an RVFL neural network; and (6) calculating an output weight. According to the invention, the time-frequency image of the vibration signal is used as the input, and the automatic extraction of the vibration signal features is realized through the convolutional neural network, and the powerful image recognition and classification functions of the convolutional neural network are combined into the vibration signal recognition. Experimental results show that the vibration signal recognition algorithm designed by the inventionhas a good signal recognition effect, and an accurate recognition algorithm is provided for the field of vibration signal recognition.

Description

technical field [0001] The invention relates to the field of picture processing and pattern recognition, and mainly relates to a method for recognizing and classifying time-frequency diagrams of Φ-OTDR vibration signals. Background technique [0002] At present, for the classification of Φ-OTDR vibration signals, it is mainly realized by using traditional machine learning algorithms. The general processing flow is to first denoise the signal, decompose it, extract useful features, and finally train the model according to the extracted features to achieve classification. However, Due to the complex and changeable signals, in the process of extracting signal features, low time-frequency accuracy and false component interference may occur, which will affect the accuracy of subsequent signal classification. Although the traditional neural network relying on the gradient descent method has good generalization ability, the convergence speed is too slow during the model training pr...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/00
CPCG06F2218/02G06F2218/08G06F2218/12G06F18/24G06F18/214
Inventor 王松胡燕祝刘娜熊之野
Owner BEIJING UNIV OF POSTS & TELECOMM