Publication Type:

Journal Article

Source:

Chemical Engineering Research and Design, Elsevier, Volume 83, Number 12, p.1391–1398 (2005)

URL:

http://www.scopus.com/record/display.url?eid=2-s2.0-30744449226&origin=resultslist&sort=plf-f&src=s&st1=Likelihood+and+Bayesian+Methods+for+Accurate+Identification+of+Measurement+Biases+in+Pseudo+Steady-State+Processes&sid=0DDC2A2F2791077BAD3B6EF819CFA7D8

Keywords:

Bayesian statistics, data reconciliation, gross error detection, likelihood ratio test

Abstract:

Two new approaches are presented for improved identification of measurement biases in linear pseudo steady-state processes. Both are designed to detect a change in the mean of a measured variable leading to an inference regarding the presence of a biased measurement. The first method is based on a likelihood ratio test for the presence of a mean shift. The second is based on a Bayesian decision rule (relying on prior distributions for unknown parameters) for the detection of a mean shift. The performance of these two methods is compared with that of a method given by Devanathan et al. (2000). For the process studied, both techniques were found to have higher identification power than the method of Devanathan et al. and appears to have excellent but sightly lower type I error performance than the Devanathan et al. method.

Cite this Research Publication

Dr. Sriram Devanathan, Vardeman, S. B., and Rollins, D. K., “Likelihood and Bayesian Methods for Accurate Identification of Measurement Biases in Pseudo Steady-State Processes”, Chemical Engineering Research and Design, vol. 83, pp. 1391–1398, 2005.

207
PROGRAMS
OFFERED
5
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
8th
RANK(INDIA):
NIRF 2018
150+
INTERNATIONAL
PARTNERS