Title :
Graphical models for diagnosis knowledge representation and inference
Author :
Luo, Jianhui ; Tu, Haiying ; Pattipati, Krishna ; Qiao, Liu ; Chigusa, Shunsuke
Author_Institution :
Dept. of Electr. & Comput. Eng., Connecticut Univ., Storrs, CT
Abstract :
One popular approach for fault diagnosis is based on reasoning about the behavior of a system in failure space. Diagnosis is performed by considering a set of observations (or symptoms) and by explaining it in terms of a set of root causes. There are many modeling methods to capture the system´s faulty behavior, such as behavioral Petri nets, multi-signal flow graphs, and Bayesian networks. In this paper, we will investigate the equivalence of these three modeling formalism by way of application to a car engine diagnosis problem, and discuss the advantages and disadvantages of each method
Keywords :
Petri nets; equivalence classes; fault diagnosis; graph theory; knowledge representation; car engine diagnosis problem; fault diagnosis; graphical model; inference; knowledge representation; Bayesian methods; Engines; Fault detection; Fault diagnosis; Flow graphs; Graphical models; Knowledge representation; Petri nets; Stochastic processes; Telecommunication computing;
Conference_Titel :
Autotestcon, 2005. IEEE
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-9101-2
DOI :
10.1109/AUTEST.2005.1609185
