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Short 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.17.006.8152

Authors

,
Agnieszka Pocha
Faculty of Mathematics and Computer Science, Jagiellonian University, Cracow, Poland
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,
Krzysztof Misztal
Faculty of Mathematics and Computer Science, Jagiellonian University, Krakow, Poland
All publications →
Paweł Morkisz
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
All publications →

Titles

Short review of dimensionality reduction methods for failure detection

Abstract

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

Information: Schedae Informaticae, 2017, Volume 26, pp. 69-78

Article type: Original article

Titles:

Polish:

Short review of dimensionality reduction methods for failure detection

English:

Short review of dimensionality reduction methods for failure detection

Authors

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  licence icon

Percentage share of authors:

Agnieszka Pocha (Author) - 33%
Krzysztof Misztal (Author) - 33%
Paweł Morkisz (Author) - 34%

Article corrections:

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Publication languages:

English