Short review of dimensionality reduction methods for failure detection
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RIS BIB ENDNOTEShort review of dimensionality reduction methods for failure detection
Publication date: 16.02.2018
Schedae Informaticae, 2017, Volume 26, pp. 69-78
https://doi.org/10.4467/20838476SI.17.006.8152Authors
Short review of dimensionality reduction methods for failure detection
Size of the data is often a challenge in real-life applications. Especially when working with time series data, when next sample is produced every few milliseconds and can include measurement from hundreds of sensors, one has to take the dimensionality of the data into consideration. In this work, we compare various dimensionality reduction methods for time series data and check their performance on failure detection task. We work on sensory data coming from existing machines.
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Information: Schedae Informaticae, 2017, Volume 26, pp. 69-78
Article type: Original article
Titles:
Short review of dimensionality reduction methods for failure detection
Short review of dimensionality reduction methods for failure detection
Faculty of Mathematics and Computer Science, Jagiellonian University, Cracow, Poland
Faculty of Mathematics and Computer Science, Jagiellonian University, Krakow, Poland
Reliability Solutions, ul. Lublańska 34, 31-476 Kraków, Poland; AGH University of Science and Technology, Faculty of Applied Mathematics, al. Mickiewicza 30, 30-059 Kraków, Poland
Published at: 16.02.2018
Article status: Open
Licence: CC BY-NC-ND
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