Redundant Sensor Calibration Monitoring Using Independent Component Analysis and Principal Component Analysis

Authors:
Jun Ding;Andrei V. Gribok;J. Wesley Hines;Brandon Rasmussen
Affiliations:
Nuclear Engineering Department, The University of Tennessee, Knoxville, TN 37996-2300, USA jding@utk.edu;Electrical and Computer Engineering Department, 331 Ferris Hall, The University of Tennessee, Knoxville, TN 37996-2100, USA agribok@utk.edu;Nuclear Engineering Department, The University of Tennessee, 212 Pasqua Engineering Building, Knoxville, TN 37996-2300, USA brandonprasmussen@yahoo.com;Nuclear Engineering Department, The University of Tennessee, Knoxville, TN 37996-2300, USA brandonprasmussen@yahoo.com
Venue:
Real-Time Systems
Year:
2004

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Abstract

This paper presents a comparison of methods for industrial on-line sensor calibration monitoring for redundant sensors. Principal component analysis (PCA) and independent component analysis (ICA) techniques are developed and compared using both simulated data and data sets from an operating nuclear power plant. The performance is dependent on the types of noise sources; however, under most conditions ICA outperforms PCA, based on the bias and variance of their respective parameter estimates. A case study is included to demonstrate the usefulness of both techniques for the early detection of sensor drift.