Abnormal condition diagnosis for process industry based on virtual sensing technology

Both process changes and sensor faults can result in the unexpected deviation of the sensor reading from its anticipated values. When the anomaly of sensor measurement occurs, in order to take appropriate actions to diminish or eliminate related dangerous factors, identifying the source of abnormal conditions is a very crucial step. To reach that, an abnormal condition diagnosis method based on virtual sensing technology is proposed. Once the abnormal situation of sensor measurement is detected, the virtual sensor is constructed based on the Gauss Process Regression and Moving Windows algorithm. Through analyzing prediction residual, the task of differentiating between sensor and system faults can be performed. In order to verify the performance of this proposal, a case study is carried out on CSTH platform, where both scenarios of sensor faults and process changes are considered. The simulation results show the effectiveness of this method.