Optical fiber vibration signal feature extraction and classification method based on DWT-DFPA-GBDT
A vibration signal and optical fiber vibration technology, applied in instruments, character and pattern recognition, computer parts, etc., can solve problems such as poor model generalization ability, missing values, outliers, unbalanced sample data, etc., to reduce eigenvectors The effect of high dimension, high classification accuracy and high classification accuracy
Active Publication Date: 2019-11-15
BEIJING UNIV OF POSTS & TELECOMM
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However, the generalization ability of the model is poor, especially some models are very sensitive to problems such as missing values, outliers, and imbalanced sample data.
In addition, overfitting may occur during model training
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[0049] specific implementation plan
[0050] The present invention will be described in further detail below through examples of implementation.
[0051] The data set selected in this implementation case includes the optical fiber vibration signals in four situations: knocking, climbing, vehicle passing and natural state. The number of acquisitions of each type of vibration signal is 50 times, and the acquisition frequency is 2KHz, corresponding to four types of vibrations Signal, a total of 200 sets of experimental data. 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 50 sets of data can get 300 samples. Therefore, the total number of samples in the data set is 1200, 960 of which are randomly selected as training samples, and the remaining 240 are used as test samples.
[0052] The overall flow of the optical fiber vibration signal feature extraction ...
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The invention relates to an optical fiber vibration signal feature extraction and classification method based on DWT-DFPA-GBDT, is a method for feature extraction and classification of optical fiber vibration signals, and belongs to the field of signal processing and machine learning. The optical fiber vibration signal feature extraction and classification method is characterized by comprising thefollowing steps: (1) determining time domain features of original vibration signals; (2) determining frequency domain characteristics of the original vibration signal; (3) determining wavelet domaincharacteristics of the original vibration signal; (4) calculating a Mahalanobis distance; (5) obtaining the sorting of the feature vectors according to the separability; (6) determining a loss function; (7) determining a fitting function; and (8) determining a strong learning classifier of the current iteration. According to the optical fiber vibration signal feature extraction and classificationmethod, feature extraction of three different domains of a time domain, a frequency domain and a wavelet domain is realized; a complete vibration signal feature vector is constructed; and GBDT and DWTare combined, and a method for reducing the complexity of a model on the basis of ensuring the classification precision is provided for the field of vibration signal feature extraction and classification.
Description
technical field [0001] The invention relates to the fields of signal processing and machine learning, and mainly relates to a method for feature extraction and classification of optical fiber vibration signals. Background technique [0002] At present, the problem of feature extraction and classification of optical fiber vibration signals 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. For the vibration signal feature extraction problem, the commonly used feature extraction mainly includes the extraction of time domain and frequency domain features. Due to the randomness of optical fiber vibration signals, the features extracted in the time domain and frequency domain may not be able to fully represent the vibration signal. The case of complex distribution features. ...
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IPC IPC(8): G06K9/00
CPCG06F2218/06G06F2218/08G06F2218/12
Inventor 王松胡燕祝刘娜熊之野
Owner BEIJING UNIV OF POSTS & TELECOMM