scholarly journals Sewer Decontamination Mechanism and Pipe Network Monitoring and Fault Diagnosis of Water Network System Based on System Analysis

2012 ◽  
Vol 50 (6) ◽  
pp. 980-987
Author(s):  
OnYu Kang ◽  
SeungChul Lee ◽  
MinJeong Kim ◽  
SuMin Yu ◽  
ChangKyoo Yoo
2008 ◽  
Vol 47 (6) ◽  
pp. 1988-1994 ◽  
Author(s):  
Seong-Rin Lim ◽  
Jong Moon Park

2013 ◽  
Vol 846-847 ◽  
pp. 442-445
Author(s):  
Chun Lin He

The fault diagnosis technology have emerged and developed rapidly with the development of wireless sensor networks and requirements of applications improve. This paper describes two commonly used sensor network fault modeling. What is more, in order to solve this problem that sensor nodes are vulnerable and therefore produce wrong data, the paper proposes a distributed fault detecting algorithm based on spatio-temporal correlation among data of adjacent nodes. The simulation experiment shows that the algorithm can efficiently detect errors in the network and very few errors are introduced.


Author(s):  
Yousef J Alanazi ◽  
Fahad Ibraheem Al-Masoud ◽  
Zaid Q Ababneh

2007 ◽  
Vol 46 (21) ◽  
pp. 6936-6943 ◽  
Author(s):  
Seong-Rin Lim ◽  
Donghee Park ◽  
Jong Moon Park

2018 ◽  
Vol 149 ◽  
pp. 519-528 ◽  
Author(s):  
Minako Nabeshima ◽  
Naoyoshi Koh ◽  
Masaki Nakao ◽  
Masahito Mike

2007 ◽  
Vol 353-358 ◽  
pp. 2632-2635
Author(s):  
Pei Yu Li ◽  
Da Peng Tan ◽  
Tao Qing Zhou ◽  
Bo Yu Lin

Aiming at some problems in the fields of industry monitoring technology (IMT) such as bad dynamic ability and poor versatility, this paper brought forward a kind of intelligent Status monitoring and Fault diagnosis Network System (SFNS) based on UPnP-Universal Plug and Play. The model for fault diagnosis network system was established according to characteristics and requirements of IMT network, and system network architecture was designed and realized by UPnP. Using embedded system technology, real-time data collection node, monitoring center node and data storage server were designed, and that supplies powerful real-time data support for SFNS. Industry fields experiments proved that this system can realize self recognition, seamless linkage and other self adapting ability, and can break through the limitation of real IP address to achieve real-time remote monitoring on line.


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