The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON SYSTEMS
Transactions ID Number: 29-670
Full Name: Seyed Shahrestani
Position: Senior Lecturer
Age: ON
Sex: Male
Address: Locked Bag 1797
Country: AUSTRALIA
Tel:
Tel prefix:
Fax:
E-mail address: Seyed@computer.org
Other E-mails:
Title of the Paper: Adaptive Categorization of Complex System Fault Patterns
Authors as they appear in the Paper: Seyed Shahrestani
Email addresses of all the authors: Seyed@computer.org
Number of paper pages: 10
Abstract: Due to large amount of information and the inherent intricacy, diagnosis in complex systems is a difficult task. This can be somehow simplified by taking a per-step towards categorizing the system conditions and faults. In this paper, the development and implementation of an approach that establishes class membership conditions, using a labeled training set, is described. More specifically, the use of negative recognition for classification and diagnosis of complex system faults are discussed. The adaptive recognition to achieve the classification is based on discovery of pattern features that make them distinct from objects belonging to different classes. Most of the existing approaches to fault diagnosis, particularly for large or complex systems, depend on heuristic rules. The approach proposed in this work does not resort to any heuristic rules, which makes it more suitable for diagnosis of faults in dynamic and complex systems. For evaluation purposes, using t!
he data provided by the protection simulator of a large power system, its fault diagnosis is carried out. The results of those simulations are also reported. They clearly reveal that even for complex systems, the proposed approach, based on making use of the distinctive features of encountered fault patterns, is capable of fault classification with minimal supervision.
Keywords: Adaptive Recognition, Classification, Complex Systems, Diagnosis, Fault Identification, Fault Pattern, Power Systems
EXTENSION of the file: .pdf
Special (Invited) Session: Complex System Diagnosis through Adaptive Recognition
Organizer of the Session: 624-107
How Did you learn about congress:
IP ADDRESS: 121.217.142.65
