v Fault Detection via modified Principal Direction Divisive Partitioning and application to aerospace electro-mechanical actuators · Mirko Mazzoleni

# Fault Detection via modified Principal Direction Divisive Partitioning and application to aerospace electro-mechanical actuators

### Abstract

In this paper, the use of the Principal Direction Divisive Partitioning (PDDP) method for unsupervised learning is discussed and analyzed with a focus on fault detection applications. Specifically, a geometric limit of the standard algorithm is highlighted by means of a simulation example and a modified version of PDDP is introduced. Such a method is shown to correcly perform data clustering also when the standard algorithm fails. The modified strategy is based on the use of a Chi-squared statistical test and offers more guarantees in terms of detection of a wrong functioning of the system. The proposed algorithm is finally experimentally tested on a fault detection application for aerospace electro-mechanical actuators, for which a comparison with k-means and fuzzy k-means approaches is also provided. [Paper, Code]

#### Reference

M. Mazzoleni, S. Formentin, F. Previdi and S. M. Savaresi, "Fault Detection via modified Principal Direction Divisive Partitioning and application to aerospace electro-mechanical actuatorss 53rd IEEE Conference on Decision and Control (CDC), Los Angeles, CA, USA, 2014, doi:10.1109/CDC.2014.7040292 , ISBN: 978-1-4673-6090-6, pp. 5770-5775.

#### Bibtex

@INPROCEEDINGS{7040292,
author={M. Mazzoleni and S. Formentin and F. Previdi and S. M. Savaresi},
booktitle={53rd IEEE Conference on Decision and Control},
title={Fault Detection via modified Principal Direction Divisive Partitioning and application to aerospace electro-mechanical actuators},
year={2014},
volume={},
number={},
pages={5770-5775},
doi={10.1109/CDC.2014.7040292},
ISSN={0191-2216},
ISBN={978-1-4673-6090-6},
month={Dec},
}