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.
|Number of pages||11|
|Journal||IEEE Transactions on Instrumentation and Measurement|
|Publication status||Published - 2011 Aug|
Bibliographical noteFunding 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
- Electrical and Electronic Engineering