Methodologies for noise and gross error detection using univariate signal-based approaches in industrial applications

Research output: Contributions to collected editions/worksChapterpeer-review

Standard

Methodologies for noise and gross error detection using univariate signal-based approaches in industrial applications. / Mercorelli, Paolo.
Fault Detection: Theory, Methods and Systems. ed. / Léa M. Simon. New York: Nova Science Publishers, Inc., 2011. p. 177-223 (Engineering tools, techniques and tables).

Research output: Contributions to collected editions/worksChapterpeer-review

Harvard

Mercorelli, P 2011, Methodologies for noise and gross error detection using univariate signal-based approaches in industrial applications. in LM Simon (ed.), Fault Detection: Theory, Methods and Systems. Engineering tools, techniques and tables, Nova Science Publishers, Inc., New York, pp. 177-223.

APA

Mercorelli, P. (2011). Methodologies for noise and gross error detection using univariate signal-based approaches in industrial applications. In L. M. Simon (Ed.), Fault Detection: Theory, Methods and Systems (pp. 177-223). (Engineering tools, techniques and tables). Nova Science Publishers, Inc..

Vancouver

Mercorelli P. Methodologies for noise and gross error detection using univariate signal-based approaches in industrial applications. In Simon LM, editor, Fault Detection: Theory, Methods and Systems. New York: Nova Science Publishers, Inc. 2011. p. 177-223. (Engineering tools, techniques and tables).

Bibtex

@inbook{b02def9b42d44d17a1a4321b8d04be0b,
title = "Methodologies for noise and gross error detection using univariate signal-based approaches in industrial applications",
abstract = "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.",
keywords = "Engineering",
author = "Paolo Mercorelli",
year = "2011",
language = "English",
isbn = "978-1-61728-291-1",
series = "Engineering tools, techniques and tables",
publisher = "Nova Science Publishers, Inc.",
pages = "177--223",
editor = "{ Simon}, {L{\'e}a M.}",
booktitle = "Fault Detection",
address = "United States",

}

RIS

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 -

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