@inproceedings{97ce41ea84ef4495a07281c2ee4a73b0,
title = "A Self-validating Inferential Sensor for Emission Monitoring",
abstract = "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.",
author = "QIN, \{S. Joe\} and Hongyu YUE and Ricardo DUNIA",
year = "1997",
month = jun,
doi = "10.1109/acc.1997.611844",
language = "English",
isbn = "0780338324",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "473--477",
booktitle = "Proceedings of the 1997 American Control Conference",
address = "United States",
note = "1997 American Control Conference ; Conference date: 04-06-1997 Through 06-06-1997",
}