| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 10402970 | IFAC Proceedings Volumes | 2005 | 8 Pages |
Abstract
A method for the automatic detection of abnormal sensor values in automation systems is described. The method is based on statistical generative models of sensor behavior. A model of normal sensor behavior is automatically constructed. Model parameters are optimized using an on-line maximum-likelihood algorithm. Incoming sensor values are then compared to the model, and an alarm is generated when the sensor value has a low probability under the model. Model parameters are continuously adapted on-line. The system automatically adapts to changing conditions and sensor drift, as well as detecting isolated abnormal sensor values.
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
Brian Sallans, Dietmar Bruckner, Gerhard Russ,