Anomaly detection-based condition monitoring

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Anomaly detection-based condition monitoring

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Anomaly detection-based condition monitoring

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/EF16_026%2F0008389" target="_blank" >EF16_026/0008389: Výzkumná spolupráce pro dosažení vyšší účinnosti a spolehlivosti lopatkových strojů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

INSIGHT: Non-Destructive Testing and Condition Monitoring

ISSN

1354-2575

e-ISSN

1754-4904

Svazek periodika

64

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

6

Strana od-do

453-458

Kód UT WoS článku

000860987900007

EID výsledku v databázi Scopus

2-s2.0-85137170415