Advanced time-frequency mutual information measures for condition-based maintenance of helicopter drivetrains

David Coats, Kwangik Cho, Yong June Shin, Nicholas Goodman, Vytautas Blechertas, Abdel Moez E. Bayoumi

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

A new concept of nonparametric signal detection and classification technique is proposed using mutual information measures in the time-frequency domain. The time-frequency-based self-information and mutual information are defined in terms of the cross time-frequency distribution. Based on time-frequency mutual information theory, this paper presents applications of the proposed technique to real-world vibration data obtained from a dedicated condition-based-maintenance experimental test bed. Baseline, unbalanced, and misaligned experimental settings of helicopter drivetrain bearings and shafts are quantitatively distinguished by the proposed techniques. With imbalance quantifiable by variance in the in-phase mutual information and misalignment quantifiable by variance in the quadrature mutual information developed and presented herein, machine health classification can be accomplished by use of statistical bounding regions.

Original languageEnglish
Article number5746644
Pages (from-to)2984-2994
Number of pages11
JournalIEEE Transactions on Instrumentation and Measurement
Volume60
Issue number8
DOIs
Publication statusPublished - 2011 Aug

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Electrical and Electronic Engineering

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