Authors: Christoph Stemp
Novelty detection is a very important part of Intelligent Systems. Its task is to classify the data produced by the system and identify any new or unknown pattern that were not present during the training of the model. Different algorithms have been proposed over the years using a wide variety of different technologies like probabilistic models and neural networks. Novelty detection and reaction is used to enable self*-properties in technical systems to cope with increasingly complex processes. Using the notion of Organic Computing, industrial factories are getting more and more advanced and intelligent. Machines gain the capability of self-organization, self-configuration and self-adaptation to react to outside influences. This survey paper looks at the state-of-the-art technologies used in Industry 4.0 and assesses different novelty detection algorithms and their usage in such systems. Therefore, different data-sources and consequently applications for potential novelty detection are analyzed. Three different novelty detection algorithms are then present using different underlying technologies and the applicability of these algorithms in combination with the defined scenarios is analyzed.
Comments: 7 Pages.
[v1] 2019-03-05 15:43:42
Unique-IP document downloads: 3 times
Vixra.org is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.
Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.