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
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.
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