In chemical plants, a process control system (PCS), which is composed of instruments and a distributed control system(DCS), is widely used for the plant automation. Important infrastructure equipment is considered critical to the safe operation of a plant. Failures of the PCS can lead to extremely dangerous accidents such as leakage of poisonous gas or fluid potentially leading to an explosion. Therefore, the plant maintenance department conducts planned activities to prevent failure. However, it is very difficult in actuality to reduce the failure to zero by only planned maintenance. Consequently, to detect a sign of failure early, the plant operators carefully monitor the critical process variables and patrol the facilities in the field as part of their work. Additionlly, nowadays advanced present chemical plants in automation increase the operator’s role such as ensuring safety, saving energy and environment not just controlling amount of production as planned. As the result, their burden of work is higher than before. In this paper, we propose a fault detection method of the instrument devices based on fuzzified neural networks with less adjusting parameters, which helps the operator’s detection.

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[in Japanese]

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[in Japanese]

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[in Japanese]

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[in Japanese]

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