Machine Learning
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Today, there are many needs to replace or maintain many plants built in the 1960s. However, it is difficult to replace or maintain them, because the documents of installed sequential control logic are seldom remain. Therefore, we propose an automatic regeneration method(SPAIR) in order to solve this problem. SPAIR regenerates sequential control logic that is expressed on a ladder diagram from the input and output data of a target control unit and its supplementary specifications, which indicate the information about timers, etc. SPAIR consists of two parts, namely basic logic inferring engine and interior coil logic inferring engine. In basic logic inferring engine, time series data is compressed and translated into training data using the specifications. The training data are processed by inductive learning and transformed into control logic. In the interior coil logic inferring, the target logic of the interior coil is acquired by the selective attachment of logic parts.