Regression Methods for Detecting Anomalies in Flue Gas Desulphurization Installations in Coal-Fired Power Plants Based on Sensor Data

In the industrial world, the Internet of Things produces an enormous amount of data that we can use as a source for machine learning algorithms to optimize the production process. One area of application of this kind of advanced analytics is Predictive Maintenance, which involves early detection of faults based on existing metering. In this paper, we present the concept of a portable solution for a real-time condition monitoring system allowing for early detection of failures based on sensor data retrieved from SCADA systems. Although the data processed in systems, such as SCADA, are not initially intended for purposes other than controlling the production process, new technologies on the edge of big data and IoT remove these limitations and provide new possibilities of using advanced analytics. This paper shows how regression-based techniques can be adapted to fault detection based on actual process data from the oxygenating compressors in the flue gas desulphurization installation in a coal-fired power plant.