Accurate Detection of Fault Signal in Large-Scale Communication

Na Shu

doi:10.4028/www.scientific.net/AMR.989-994.3802

Paper Titles

An Improved DFT-Based Channel Estimation Algorithm for OFDM System on Time-Varying Multipath Fading Channels
p.3786

Flattop Broadband Wavelength Conversion Based on Cascaded Second-Harmonic Generation and Difference Frequency Generation in Step-Segmented Quasi-Phase Matched Gratings
p.3790

Investigation of Efficient Wavelength Conversion Based by Difference Frequency Generation in Step-Segmented Quasi-Phase Matched Gratings
p.3794

Wavelet Transform Adjustment Method Study in Color Image Enhancement
p.3798

Accurate Detection of Fault Signal in Large-Scale Communication
p.3802

Long Reach 10-Gbps WDM-PON Based on Carrier Distribution and Coherent Detection for Upstream Transmission
p.3806

Analysis on Signal Extraction under Sudden Changing Noise Interference Based on Time Frequency Analysis
p.3810

Application and Research of Data Mining Technology in Communication Network Environment
p.3814

Study on Optimal Design Approach of One-Card under Multi-Signal Conflict
p.3818

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994Accurate Detection of Fault Signal in Large-Scale...

Accurate Detection of Fault Signal in Large-Scale Communication

Abstract:

Deep data-mining methods of fault signal in large-scale communication system are researched. Although with the characteristic of frequency uniformity as signals distribute in each reaction zone, common method of fault signal detection based on shortwave dispersing is invalid employing in large-scale communication system, which presents the absence or instability of fault signal. For this reason, a method based on particle swarm optimization is proposed for fault signal detection in large-scale communication system. As reaction speed and activity scope within the whole particle swarm are replaced, accurate results are achieved. Taking particle swarm optimization, it is detected that whether there is a fault in communication systems. The experimental results show that proposed method in signal fault detection process can greatly increase accuracy of signal fault detection, as plays a greater role in future.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

3802-3805

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.3802

Citation:

Cite this paper

Online since:

July 2014

Authors:

Na Shu*

Keywords:

Data Mining (DM), Fault Detection, Fault Signal

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Xie Kefu, Luo An. Fuzzy diagnosis system optimized with genetic algorithm for power transformer[J]. Electric Power Automation Equipment, 2005, 25(4): 55-58.

Google Scholar

[2] Yan Xianglian, Wen YuangFang. Research on application of fuzzy neural network to transformer fault diagnosis[J]. Transformer, 2002, 39(7): 41-44.

Google Scholar

[3] Wang Beibei, et al. The power transformer fault site diagnosis based on information fusion technology [J]. High voltage apparatus, 2003, 39(1): 49-52.

Google Scholar

[4] Li Jingxiu, Ma Qinglin, Wangxin. Fault diagnosis of asynchronous induction motor based on fuzzy neural network [J]. Colliery mechanical & electrical technology，2009，4：4-7.

Google Scholar

[5] LiWei, Kuang Peng, Li Yang. A pipeline leak detection method based on fuzzy neural network[J]. Computer simulation，2009，26（2）：190-192.

Google Scholar

[6] Liang Wuke, et al. Fault diagnosis method of hydroelectric units base on rough set & Rbf network[J]. Journal of Northwest A & F University (Nat. Sci. Ed. ), 2007, 35(7): 223-227.

Google Scholar