Distributed System Monitoring and Fault Diagnosis Based on Causal Graphical Model

Author(s):  
Xiaolu Chen ◽  
Jing Wang ◽  
Qiang Liu
Author(s):  
Rodrigo Marino ◽  
Cristian Wisultschew ◽  
Andres Otero ◽  
Jose M. Lanza-Gutierrez ◽  
Jorge Portilla ◽  
...  

2013 ◽  
Vol 73 (3) ◽  
pp. 330-340 ◽  
Author(s):  
Gang Wu ◽  
Huxing Zhang ◽  
Meikang Qiu ◽  
Zhong Ming ◽  
Jiayin Li ◽  
...  

2007 ◽  
Vol 347 ◽  
pp. 245-250 ◽  
Author(s):  
Zhen Xu ◽  
Qian Chen

Cointegration testing method from economics area is introduced for condition monitoring and fault diagnosis for nonstationary engineering systems. The cointegration testing method seeks a a linear combination of a set of nonstationary stochastic variables, which describes long-run dynamic equilibrium relation of the nonstationary variables. This feature provides a possibility for researchers in engineering areas to utilize the cointegration testing method for nonstationary system monitoring and fault diagnosis. To verify the feasibility and performance of the cointegration testing method, an example based on a simulated nonstationary fluid catalytic cracking unit (FCCU) system is discussed. The results of the example show that the cointegration testing method has a potential in engineering system monitoring and fault diagnosis.


Author(s):  
Rakesh Kumar Katare ◽  
Neha Singh ◽  
Aarti Pandey ◽  
Charvi Katare

Communication Complexity, Data Structures and its structural relationship are the key parameters for study of architecture in any interconnection Network. This paper reveals that the study of data structures in Perfect Difference Interconnection Network have some structural relationship between nodes or processors. In the Perfect Difference Network (PDN) with processors as a nodes and links between the processors or processing elements as an edge represents graphical Model for interconnection networks. The relationship between each processor is connected with some mapping functions to map data according to the topology of the PDN


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