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Identifying Discriminating Features in Time Series Data for Diagnosis of Industrial Machines

Fulltext:


Authors:


Publication Type:

Conference/Workshop Paper

Venue:

The 24th annual workshop of the Swedish Artificial Intelligence Society, May, 2007


Abstract

Reducing the inherent high dimensionality in time series data is a desirable goal. Algorithms used for classi¯cation can easily be misguided if presented with data of to high dimension. E.g. the k-nearest neighbor algorithm which is often used for case-based classi¯cation per- forms best on smaller dimensions with less than 20 attributes. In this paper we address the problem using a time series case base and a feature discrimination approach incorporating an unsupervised combination of a search function based on statistical feature discrimination and a crite- rion function ¯nding the global maximum of discriminating power in the range the search function. Feature vectors for case indexing is computed with respect to this information. For evaluation, previously classi¯ed cur- rent measurements from an electrical motor driving the gearbox of axis 4 on an industrial robot were used. The results were promising and we managed to correctly classify measurements from healthy and unhealthy gearboxes.

Bibtex

@inproceedings{Olsson1083,
author = {Ella Olsson},
title = {Identifying Discriminating Features in Time Series Data for Diagnosis of Industrial Machines},
month = {May},
year = {2007},
booktitle = {The 24th annual workshop of the Swedish Artificial Intelligence Society, May, 2007},
url = {http://www.es.mdh.se/publications/1083-}
}