Towards anomally detection using stationary and non-stationary signal analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F24%3A63587858" target="_blank" >RIV/70883521:28140/24:63587858 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-981-99-8703-0_49" target="_blank" >http://dx.doi.org/10.1007/978-981-99-8703-0_49</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-99-8703-0_49" target="_blank" >10.1007/978-981-99-8703-0_49</a>
Alternative languages
Result language
angličtina
Original language name
Towards anomally detection using stationary and non-stationary signal analysis
Original language description
This paper focuses on demonstration of an enhanced model for investigating data signals features, i.e., whether the given signal has stationary or non-stationary features. The accurate detection of the features of signals is crucial for the right directions towards methodology of further preprocessing to perform data analysis of the data signal, specifically in the tasks of finding anomalies in the given signal and big data environment. A problem often encountered is the exact determination of the occurrence of stationary or non-stationary data signal features in data processing. Within this research paper, the mathematical foundations of data signal processing are described. Based on the mathematical model of the input signal processing, an improved workflow using the enhanced statistical KPSS test and autocorrelation function (graphical) analysis is demonstrated here, to confirm the accuracy and usability of selected methodology. The alternative approach described here leads to a much lower computational effort and the achievement of accurate identification of signal features in big data environment for possible deployment of A.I. or machine learning anomaly detection pipeline. The obtained dataset and model are based on the real environment and measured signals in the production process of machine tools in company Tajmac-ZPS Zlin.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Electrical Engineering
ISBN
978-981-9987-02-3
ISSN
1876-1100
e-ISSN
1876-1119
Number of pages
10
Pages from-to
595-604
Publisher name
SPRINGER-VERLAG SINGAPORE PTE LTD
Place of publication
SINGAPORE
Event location
Ho Chi Minh City
Event date
Dec 8, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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