Plant abnormality detection method and system

Abstract

The present disclosure provides a plant abnormality detection system and method, which can learn the plant data collected in real time through a plurality of prediction models having different characteristics to generate a prediction value having the highest accuracy to diagnose the abnormality thereof, thus detecting accurately the abnormality of the plant to early provide alarm. The plant abnormality detection system disclosed includes a data collection unit for collecting the plant data, a learning model selection unit for selecting a plurality of models in order to predict a value of the plant data, and an abnormality alarm unit including a prediction algorithm unit having a plurality of prediction algorithms, an ensemble learning unit for outputting a final prediction data by performing ensemble learning based on the prediction data outputted from the prediction algorithm unit, and an alarm logic for determining whether or not the plant is abnormal by comparing the data collected in the data collecting unit with the final prediction data.