JRM Vol.11 No.4 pp. 326-330
doi: 10.20965/jrm.1999.p0326


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

April 5, 1999
June 4, 1999
August 20, 1999
fault diagnosis, neural network (NN), fault tree (FT)
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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