Anomaly detection-based condition monitoring
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F22%3A43966182" target="_blank" >RIV/49777513:23520/22:43966182 - isvavai.cz</a>
Result on the web
<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:000860987900007" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:000860987900007</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1784/insi.2022.64.8.453" target="_blank" >10.1784/insi.2022.64.8.453</a>
Alternative languages
Result language
angličtina
Original language name
Anomaly detection-based condition monitoring
Original language description
The impact of an anomaly is domain-dependent. In a dataset of network activities, an anomaly can imply an intrusion attack. Other objectives of anomaly detection are industrial damage detection, data leak prevention, identifying security vulnerabilities or military surveillance. Anomalies are observations or a sequences of observations which distribution deviates remarkably from the general distribution of the whole dataset. The big majority of the dataset consists of normal (healthy) data points. The anomalies form only a very small part of the dataset. Anomaly detection is the technique to find these observations and its methods are specific to the type of data. While there is a wide spectrum of anomaly detection approaches today, it becomes more and more difficult to keep track of all the techniques. As a matter of fact, it is not clear which of the three categories of detection methods, i.e., statistical approaches, machine learning approaches or deep learning approaches is more appropriate to detect anomalies on time-series data which are mainly used in industry. Typical industrial device has multidimensional characteristic. It is possible to measure voltage, current, active power, vibrations, rotational speed, temperature, pressure difference, etc. on such device. Early detection of anomalous behavior of industrial device can help reduce or prevent serious damage leading to significant financial lost. This paper is a summary of the methods used to detect anomalies in condition monitoring applications.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/EF16_026%2F0008389" target="_blank" >EF16_026/0008389: Research Cooperation for Higher Efficiency and Reliability of Blade Machines</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
INSIGHT: Non-Destructive Testing and Condition Monitoring
ISSN
1354-2575
e-ISSN
1754-4904
Volume of the periodical
64
Issue of the periodical within the volume
8
Country of publishing house
GB - UNITED KINGDOM
Number of pages
6
Pages from-to
453-458
UT code for WoS article
000860987900007
EID of the result in the Scopus database
2-s2.0-85137170415