基于压缩感知的设备多源信息传输与分类算法

doi:10.11959/j.issn.1000-436x.2020040

Abstract

Abstract:

Aiming at the characteristics of various types of equipment in coal preparation plant and the dispersion of monitoring points,a multi-source information wireless transmission and classification algorithm for equipment based on compressed sensing was proposed.By constructing a multi-hop information transmission model,the information transmission problem was transformed into the compressed sensing problem of multi-path measurement signals,thereby the measurement matrix acquisition was transformed into the routing problem of the multi-hop information transmission model.Aiming at the large coherence of the obtained measurement matrix and affecting the signal reconstruction effect,the idea of random routing was introduced into the routing construction,and a random dynamic self-organizing routing algorithm was proposed.In order to solve the problem that the time domain features of the reconstructed signal were difficult to accurately classify the fault type,a new time domain feature,the total variation (TV) of the vibration signal,was introduced for the reconstructed signal,and the compensation distance estimation algorithm was adopted to verify the superiority of the introduction of indicators.The analysis of the measured data of the coal preparation plant shows that the proposed multi-source information transmission and classification algorithm can effectively improve the fault recognition accuracy under the condition of improving the real-time transmission efficiency of the monitoring data.

Key words: compressed sensing, multi-hop network, random routing, total variation, fault classification

CLC Number:

TP18

Xiaohu ZHAO,Gang WANG,Boming SONG,Jiacheng YU. Multi-source information transmission and classification algorithm for equipment based on compressed sensing[J]. Journal on Communications, 2020, 41(2): 13-24.

Figures/Tables 17

序号	特征名称	计算式
1	均值	$\sqrt{\frac{1}{N} \sum_{i = 1}^{N} x_{i}}$
2	标准差	$\sqrt{\frac{1}{N} \sum_{i = 1}^{N} {(x_{i} - μ_{x})}^{2}}$
3	均方根值	$\sqrt{\frac{1}{N} \sum_{i = 1}^{N} {(x_{i})}^{2}}$
4	歪度指标	$\frac{\sum_{i = 1}^{N} {(x_{i} - μ_{x})}^{3}}{N σ_{x}^{3}}$
5	峭度指标	$\frac{\sum_{i = 1}^{N} {(x_{i} - μ_{x})}^{4}}{N σ_{x}^{4}}$
6	波形指标	$\frac{\sqrt{\frac{1}{N} \sum_{i = 1}^{N} {(x_{i})}^{2}}}{\frac{1}{N} \sum_{i = 1}^{N} \| x_{i} \|}$
7	裕度指标	$\frac{\max \| x_{i} \|}{{(\frac{1}{N} \sum_{i = 1}^{N} \sqrt{\| x_{i} \|})}^{2}}$
8	脉冲指标	$\frac{\max \| x_{i} \|}{\frac{1}{N} \sum_{i = 1}^{N} \| x_{i} \|}$
9	峰值指标	$\frac{\max \| x_{i} \|}{\sqrt{\frac{1}{N} \sum_{i = 1}^{N} {(x_{i})}^{2}}}$
10	全变分	$\sum_{i = 1}^{N} \| x_{i + 1} - x_{i} \|$

References 24

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降维算法	SVM分类精度
LDA	94%
PCA	96%
KPCA	97.73%
CDET	99.33%

降维算法	SVM分类精度
LDA	99.46%
PCA	99.46%
KPCA	99.47%
CDET	99.87%

Multi-source information transmission and classification algorithm for equipment based on compressed sensing

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