TY - GEN
T1 - A Self-validating Inferential Sensor for Emission Monitoring
AU - QIN, S. Joe
AU - YUE, Hongyu
AU - DUNIA, Ricardo
PY - 1997/6
Y1 - 1997/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0030661155&partnerID=8YFLogxK
U2 - 10.1109/acc.1997.611844
DO - 10.1109/acc.1997.611844
M3 - Conference paper (refereed)
SN - 0780338324
T3 - Proceedings of the American Control Conference
SP - 473
EP - 477
BT - Proceedings of the 1997 American Control Conference
PB - Institute of Electrical and Electronics Engineers
T2 - 1997 American Control Conference
Y2 - 4 June 1997 through 6 June 1997
ER -