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Proceedings Paper

Statistical monitoring of rotating machinery by cumulant spectral analysis
Author(s): Richard W. Barker; Melvin J. Hinich
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Paper Abstract

Background on the rotating drill wear problem, including approaches using a combination of sensors and signal features, are briefly summarized prior to sharing results from a higher- order statistical study of accelerometer data. Vibroacoustic signals of rotating machinery are composed of sums of modulated periodicities, broadband random components, and occasionally a set of transient responses. These signals are not ergodic as the modulated periodicities are partially coherent. Progressive wear of the rotating machine causes the nonlinear structure of the received signal to intensify, and nonlinearity results in transfer of energy between harmonics of the signal's periodic components. Statistics developed from bispectrum and second-order cumulant spectrum estimates of the measured signal are combined with power spectrum amplitudes as feature inputs for standard multivariate classifiers. The higher-order statistics measure, respectively, the extent of nonlinearity and intermodulation of the received signal. Classification results of actual drill wear data collected from a controlled experiment reveal that statistics from estimates of the second order cumulant spectrum have increased discrimination power for detecting incipient drill wear.

Paper Details

Date Published: 28 October 1994
PDF: 9 pages
Proc. SPIE 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V, (28 October 1994); doi: 10.1117/12.190854
Show Author Affiliations
Richard W. Barker, Office of Secretary of Defense/Joint Tactical Missile Signatures (United States)
Melvin J. Hinich, Applied Research Labs. (United States)

Published in SPIE Proceedings Vol. 2296:
Advanced Signal Processing: Algorithms, Architectures, and Implementations V
Franklin T. Luk, Editor(s)

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