On the Detection of Multiple Sensor Abnormalities in Multivariate Processes

The wealth of process information generated from sensor readings can be used to detect bias changes, drift and/or higher levels of noise in various process sensors. New multivariate statistical techniques permit frequent audits of process sensors. These methods are based on the evaluation of residuals generated by utilizing plant models developed with principal components analysis (PCA) or partial least squares (PLS) methods. The fact that the prediction of each variable in the process involves all the other process variables (PLS) and even itself (PCA), may cause false alarms even though the related sensors function properly. A multipass PLS regression technique is proposed to eliminate the false alarms. The sensor with the highest corruption is discarded from both the calibration and the test data when a sensor failure is detected. This eliminates the effect of the corrupted data on the prediction of the remaining process variables and prevents false alarms. The technique is applied to a High Temperature Short Time (HTST) Pasteurization Pilot plant with six temperature and one flow rate measurements.