Development of a hybrid intelligent system for on-line real-time monitoring of nuclear power plant operations

A nuclear power plant (NPP) has an intricate operational domain involving systems, structures and components (SSCs) that vary in scale and complexity. Many of the large scale SSCs contribute to the lost availability in the operation of the NPPs, when their malfunctions cannot be detected in a timely manner. The lost availability can result in millions of dollars economic loss. Currently, one of the main reasons of the incapacity for avoiding such critical system failures is the lack of an appropriate health monitoring system (HMS). A comprehensive HMS can help prevent the system failures, by analyzing the large amount of information for determining the performance status of the SSCs and for providing decision support in the NPP operations. The immediate goal of this work is to design the methodology for the cognition system of an automated multi-faceted HMS to be implemented at NPPs. The tasks of this system are providing efficient and reliable fault diagnosis, failure prediction, and decision support in NPP operations. The ultimate goal of this work is to enhance the NPP operations by increased availability, and consequently, further improved reliability and safety. This work presents the design of the cognition system of the HMS as a unique hybrid intelligent system. In this hybrid structure, we use the Bayesian network (BN) and neural network (NN) techniques in conjunction, for the first time, in order to provide complementary probabilistic performance status estimates and fault diagnosis concerning the monitored SSCs. This strategy makes the real-time implementation of this diagnostic model feasible in large scale, complex problem domains.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Nuclear Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2003

Author dc:creator

• Yildiz, Bilge

Advisor dc:contributor.advisor

• Michael W. Golay.

• Nuclear Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/30002

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/30002