Methodologies for noise and gross error detection using univariate signal-based approaches in industrial applications
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Methodologies for noise and gross error detection using univariate signal-based approaches in industrial applications. / Mercorelli, Paolo.
Fault Detection: Theory, Methods and Systems. Hrsg. / Léa M. Simon. New York : Nova Science Publishers, Inc., 2011. S. 177-223 (Engineering tools, techniques and tables).Publikation: Beiträge in Sammelwerken › Kapitel › begutachtet
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TY - CHAP
T1 - Methodologies for noise and gross error detection using univariate signal-based approaches in industrial applications
AU - Mercorelli, Paolo
PY - 2011
Y1 - 2011
N2 - This paper addresses Gross Error Detection using uni-variate signal-based approachesand an algorithm for the peak noise level determination in measuredsignals. Gross Error Detection and Replacement (GEDR) may be carried outas a pre-processing step for various model-based or statistical methods. Morespecifically, this work presents developed algorithms and results using twouni-variate, signal-based approaches regarding performance, parameterization,commissioning, and on-line applicability. One approach is based on the MedianAbsolute Deviation (MAD) whereas the other algorithm is based on wavelets.In addition, an algorithm, which was developed for the parameterization of theMAD algorithm, is also utilized to determine an initial variance (or peak noiselevel) estimate of measured variables for other model-based or statistical methods.The MAD algorithm uses a wavelet approach to set the variance of the noise in order to initialize the algorithm.
AB - This paper addresses Gross Error Detection using uni-variate signal-based approachesand an algorithm for the peak noise level determination in measuredsignals. Gross Error Detection and Replacement (GEDR) may be carried outas a pre-processing step for various model-based or statistical methods. Morespecifically, this work presents developed algorithms and results using twouni-variate, signal-based approaches regarding performance, parameterization,commissioning, and on-line applicability. One approach is based on the MedianAbsolute Deviation (MAD) whereas the other algorithm is based on wavelets.In addition, an algorithm, which was developed for the parameterization of theMAD algorithm, is also utilized to determine an initial variance (or peak noiselevel) estimate of measured variables for other model-based or statistical methods.The MAD algorithm uses a wavelet approach to set the variance of the noise in order to initialize the algorithm.
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=84892000151&partnerID=8YFLogxK
M3 - Chapter
SN - 978-1-61728-291-1
T3 - Engineering tools, techniques and tables
SP - 177
EP - 223
BT - Fault Detection
A2 - Simon, Léa M.
PB - Nova Science Publishers, Inc.
CY - New York
ER -