Minimum rotation partitioning for data analysis and its application to fault detection

Murat Yasar, Asok Ray, Harry G. Kwatny

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Symbolic dynamics provide a new set of tools for data analysis, fault detection and investigation of the dynamical systems. The main concept is partitioning the phase space into a finite number of non-overlapping segments that provide a low-dimensional representation of time series. By simplifying the dynamics this way, a novel method for nonlinear analysis of systems, including fault progression, can be constructed from observed data. This paper presents a novel space partitioning technique, referred as minimum rotation partitioning for the purpose of fault detection and quantification. The results obtained from a permanent magnet synchronous machine is presented as an example of fault detection and quantification.

Original languageEnglish (US)
Title of host publicationProceedings of the 2010 American Control Conference, ACC 2010
PublisherIEEE Computer Society
Pages5439-5444
Number of pages6
ISBN (Print)9781424474264
DOIs
StatePublished - 2010

Publication series

NameProceedings of the 2010 American Control Conference, ACC 2010

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

Fingerprint Dive into the research topics of 'Minimum rotation partitioning for data analysis and its application to fault detection'. Together they form a unique fingerprint.

Cite this