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 kmeans approaches is also provided.

(2014). Fault detection via modified Principal Direction Divisive Partitioning and application to aerospace electro-mechanical actuators [conference presentation - intervento a convegno]. Retrieved from http://hdl.handle.net/10446/32426

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

MAZZOLENI, Mirko;FORMENTIN, Simone;PREVIDI, Fabio;
2014-12-01

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 kmeans approaches is also provided.
dic-2014
Mazzoleni, Mirko; Formentin, Simone; Previdi, Fabio; Savaresi, SERGIO MATTEO
File allegato/i alla scheda:
File Dimensione del file Formato  
2014 IEEE CDC - FDI PDDP.pdf

Solo gestori di archivio

Descrizione: publisher's version - versione dell'editore
Dimensione del file 404.27 kB
Formato Adobe PDF
404.27 kB Adobe PDF   Visualizza/Apri
Pubblicazioni consigliate

Aisberg ©2008 Servizi bibliotecari, Università degli studi di Bergamo | Terms of use/Condizioni di utilizzo

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/32426
Citazioni
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 7
social impact