Feasibility Study of Fault Diagnostics Using Multiple Neural Networks

Yukiyasu Shimada; Kazuhiko Suzuki

doi:10.20965/jrm.1999.p0326

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JRM Vol.11 No.4 pp. 326-330

doi: 10.20965/jrm.1999.p0326

(1999)

Paper:

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Feasibility Study of Fault Diagnostics Using Multiple Neural Networks

Yukiyasu Shimada and Kazuhiko Suzuki

Department of Systems Engineering, Faculty of Engineering, Okayama University, 3-1-1, Tsushima-naka, Okayama 700-8530, Japan

Received:

April 5, 1999

Accepted:

June 4, 1999

Published:

August 20, 1999

Keywords:

fault diagnosis, neural network (NN), fault tree (FT)

Abstract

This paper presents a fault diagnostic system (FDS) using multiple neural networks for chemical plants. The fault propagation in progress is modeled by causal relationships between a fault tree (FT) and its minimal cut sets (MCSa). The measurement patterns required for training neural networks (NNs) are obtained from fault propagation model. The FDS has a circuit network and component networks. The circuit network can identify circuit malfunctions that include disturbances. The component networks can identify component malfunctions as root causes of process malfunction. We have constructed an on-line FDS by making use of proposed method and verified the effectiveness of it experimentally.

Cite this article as:

Y. Shimada and K. Suzuki, “Feasibility Study of Fault Diagnostics Using Multiple Neural Networks,” J. Robot. Mechatron., Vol.11 No.4, pp. 326-330, 1999.

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