Anomaly Prediction in Mechanical Systems Using Symbolic Dynamics

David Friedlander, Ishanu Chattopadhyay, Asok Ray, Shashi Phoha, Noah Jacobson

Research output: Contribution to journalConference article

3 Scopus citations

Abstract

This paper presents anomaly prediction in complex mechanical systems at an early stage where anomaly is defined as an observable deviation from the nominal dynamical response. The anomaly prediction algorithm is built upon two-time-scale analysis of time series data and relies on a combination of Nonlinear Systems theory and Language theory. The algorithm has been validated for anomaly prediction on a rotorcraft gearbox testbed for two different types of anomalies.

Original languageEnglish (US)
Pages (from-to)4275-4280
Number of pages6
JournalProceedings of the American Control Conference
Volume5
StatePublished - Nov 7 2003
Event2003 American Control Conference - Denver, CO, United States
Duration: Jun 4 2003Jun 6 2003

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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