In this paper, we propose a self-validating inferential sensor approach based on principal component analysis (PCA). The input sensors are validated using a fault identification and reconstruction approach proposed in Dunia, et al. (1996). A principal component model is built for the input sensor validation. The validated principal components are used to predict output variables using linear regression or neural networks. If a sensor fails, the sensor is identified and reconstructed with the best estimate from the PCA model. The principal components are also reconstructed accordingly for prediction. The self-validating soft sensor approach is applied to air emission monitoring.
|Name||Proceedings of the American Control Conference|
|Publisher||Institute of Electrical and Electronics Engineers|
|Conference||1997 American Control Conference|
|Period||4/06/97 → 6/06/97|