Object-oriented fault-tree models applied to system diagnosis

David L. Iverson; Ann Patterson-Hine

doi:10.1117/12.21059

1 January 1990 Object-oriented fault-tree models applied to system diagnosis

David L. Iverson, Ann Patterson-Hine

Proceedings Volume 1293, Applications of Artificial Intelligence VIII; (1990) https://doi.org/10.1117/12.21059
Event: 1990 Technical Symposium on Optics, Electro-Optics, and Sensors, 1990, Orlando, FL, United States

Abstract

When a diagnosis system is used in a dynamic environment, such as the distributed computer system planned for use on Space Station Freedom, it must execute quickly and its knowledge base must be easily updated. Representing system knowledge as object-oriented augmented fault trees provides both features. The diagnosis system described here is based on the failure cause identification process of the diagnostic system described by Narayanan and Viswanadham. Their system has been enhanced in this implementation by replacing the knowledge base of if-then rules with an object-oriented fault tree representation. This allows the system to perform its task much faster and facilitates dynamic updating of the knowledge base in a changing diagnosis environment. Accessing the information contained in the objects is more efficient than performing a lookup operation on an indexed rule base. Additionally, the object-oriented fault trees can be easily updated to represent current system status. This paper describes the fault tree representation, the diagnosis algorithm extensions, and an example application of this system. Comparisons are made between the object-oriented fault tree knowledge structure solution and one implementation of a rule-based solution. Plans for future work on this system are also discussed.

Citation Download Citation

David L. Iverson and Ann Patterson-Hine "Object-oriented fault-tree models applied to system diagnosis", Proc. SPIE 1293, Applications of Artificial Intelligence VIII, (1 January 1990); https://doi.org/10.1117/12.21059

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available