Control quality assessment using fractal persistence measures

Paweł Domański

Abstract

Control Performance Assessment (CPA) has great practical importance. Control quality significantly affects final production throughput, efficiency and environmental impact. There are many approaches starting from time-domain methods, through Minimum Variance, Gaussian and non-Gaussian statistics up to alternative wavelet, fractal or entropy measures. Analysis of production data from process industry shows that signals are often described by non-Gaussian distributions, mostly fat-tail. On the other hand, simulations show that strong disturbances may significantly screen ability of proper detection. This work tests different approaches, i.e. Gaussian standard deviation and fat-tail distribution factors, integral indexes and focuses on persistence measures of rescaled range R/S plot. Robustness of above measures against disturbances with varying statistical properties is investigated. Results confirm that fractal measures may be applied as robust alternative to standard statistics.
Author Paweł Domański (FEIT / AK)
Paweł Domański,,
- The Institute of Control and Computation Engineering
Journal seriesISA Transactions, ISSN 0019-0578, (N/A 140 pkt)
Issue year2019
No90
Pages226-234
Publication size in sheets1.35
Keywords in EnglishControl performance assessment Fractal measures Fat-tail distributions Hurst exponent Crossover
ASJC Classification1706 Computer Science Applications; 2208 Electrical and Electronic Engineering; 2604 Applied Mathematics; 3105 Instrumentation
DOIDOI:10.1016/j.isatra.2019.01.008
URL https://www.sciencedirect.com/science/article/pii/S0019057819300072
Languageen angielski
File
Domanski 2019 (ISA).pdf 4.01 MB
Score (nominal)140
Score sourcejournalList
ScoreMinisterial score = 140.0, 19-12-2019, ArticleFromJournal
Publication indicators Scopus Citations = 1; WoS Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2018 = 1.845; WoS Impact Factor: 2018 = 4.343 (2) - 2018=4.472 (5)
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