Sensor Drift Detection in SNG Plant using Auto-Associative Kernel Regression

With the rapid development of ICTs, condition monitoring has been used as a key technology in the plant industries. For reliable condition monitoring, sensors should output same values under same conditions regardless of time, but the sensitivity of sensors is gradually changed due to several factors such as temperature, humidity, contamination, aging, and etc. This type of situation is called as sensor drift problem. To solve this, several methods such as auto associative neural network, auto-associative support vector regression, and etc. have been developed to detect sensor drifts earlier by estimating new input based on historical data. This study applied the auto-associative kernel regression model into a synthetic natural gas plant which produces synthetic natural gas from coals to detect sensor drifts during operation phase. To validate the auto-associative kernel regression model in the synthetic natural gas plant, a real data collected from an experimental operation are used. Based on the experimental results, the auto-associative kernel regression model can rapidly detect the sensor drift in the synthetic natural gas plant.