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

doi:10.1109/TIM.2011.2122370

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

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

15 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 language	English
Article number	5746644
Pages (from-to)	2984-2994
Number of pages	11
Journal	IEEE Transactions on Instrumentation and Measurement
Volume	60
Issue number	8
DOIs	https://doi.org/10.1109/TIM.2011.2122370
Publication status	Published - 2011 Aug

Bibliographical note

Funding Information:
Manuscript received March 17, 2010; revised September 20, 2010; accepted November 12, 2010. This work was supported in part by the South Carolina Army National Guard, by the United States Army Aviation and Missile Command via the Condition-Based Maintenance Research Center, Department of Mechanical Engineering, University of South Carolina, Columbia, and by the National Science Foundation under Grant 0747681, “CAREER: Diagnostics and Prognostics of Electric Cables in Aging Power Infrastructure.” The work of D. Coats was supported by a National Science Foundation Graduate Research Fellowship Program. The Associate Editor coordinating the review process for this paper was Dr. Jiong Tang.

All Science Journal Classification (ASJC) codes

Instrumentation
Electrical and Electronic Engineering

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10.1109/TIM.2011.2122370

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title = "Advanced time-frequency mutual information measures for condition-based maintenance of helicopter drivetrains",

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.",

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note = "Funding Information: Manuscript received March 17, 2010; revised September 20, 2010; accepted November 12, 2010. This work was supported in part by the South Carolina Army National Guard, by the United States Army Aviation and Missile Command via the Condition-Based Maintenance Research Center, Department of Mechanical Engineering, University of South Carolina, Columbia, and by the National Science Foundation under Grant 0747681, “CAREER: Diagnostics and Prognostics of Electric Cables in Aging Power Infrastructure.” The work of D. Coats was supported by a National Science Foundation Graduate Research Fellowship Program. The Associate Editor coordinating the review process for this paper was Dr. Jiong Tang.",

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

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