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Proceedings Paper

Analysis of the Westland data set
Author(s): Fang Wen; Peter K. Willett; Somnath Deb
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Paper Abstract

The Westland set of empirical accelerometer helicopter data with seeded and labeled faults is analyzed with the aim of condition monitoring. The autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; and it has also been found that augmentation of these by harmonic and other parameters can improve classification significantly. Several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior on training data and is thus able to quantify probability of error in an exact manner, such that features may be discarded or coarsened appropriately.

Paper Details

Date Published: 20 July 2001
PDF: 12 pages
Proc. SPIE 4389, Component and Systems Diagnostics, Prognosis, and Health Management, (20 July 2001); doi: 10.1117/12.434240
Show Author Affiliations
Fang Wen, Univ. of Connecticut (United States)
Peter K. Willett, Univ. of Connecticut (United States)
Somnath Deb, Qualtech Systems, Inc. (United States)


Published in SPIE Proceedings Vol. 4389:
Component and Systems Diagnostics, Prognosis, and Health Management
Peter K. Willett; Thiagalingam Kirubarajan, Editor(s)

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