Title:	Anomaly detection-based condition monitoring
Authors:	Káš, Martin Wamba, Francis Fomi
Citation:	KÁŠ, M. WAMBA, FF. Anomaly detection-based condition monitoring. INSIGHT: Non-Destructive Testing and Condition Monitoring, 2022, roč. 64, č. 8, s. 453-458. ISSN: 1354-2575
Issue Date:	2022
Publisher:	British Institute of Non-Destructive Testing
Document type:	článek article
URI:	2-s2.0-85137170415 http://hdl.handle.net/11025/51190
ISSN:	1354-2575
Keywords in different language:	Anomaly detection;Deep Learning;Machine Learning
Abstract in different language:	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.
Rights:	Plný text není přístupný. © British Institute of Non-Destructive Testing
Appears in Collections:	Články / Articles (NTIS) Články / Articles (KKY) OBD

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