Methodologies for Noise and Gross Error Detection using Univariate Signal-Based Approaches in Industrial Application
| Autor: | Mercorelli, Paolo |
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| Přispěvatelé: | Simon, Léa M., Pasqualli , Filippo A. |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: | |
| Zdroj: | Mercorelli, P 2011, Methodologies for Noise and Gross Error Detection using Univariate Signal-Based Approaches in Industrial Application . in L M Simon (ed.), Fault Detection: Theory, Methods and Systems . Engineering tools, techniques and tables, Nova Science Publishers, Inc., New York, pp. 177-223 . Mercorelli, P 2012, Methodologies for Noise and Gross Error Detection using Univariate Signal-Based Approaches in Industrial Application . in F A Pasqualli (ed.), Encyclopedia of Engineering Research . vol. 2, Nova Science Publishers, Inc., New York, pp. 415-445 . |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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