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Proceedings Paper

A Gaussian process based prognostics framework for composite structures
Author(s): Yingtao Liu; Subhasish Mohanty; Aditi Chattopadhyay
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Paper Abstract

Prognostic algorithms indicate the remaining useful life based on fault detection and diagnosis through condition monitoring framework. Due to the wide-spread applications of advanced composite materials in industry, the importance of prognosis on composite materials is being acknowledged by the research community. Prognosis has the potential to significantly enhance structural monitoring and maintenance planning. In this paper, a Gaussian process based prognostics framework is presented. Both off-line and on-line methods combined state estimation and life prediction of composite beam subject to fatigue loading. The framework consists of three main steps: 1) data acquisition, 2) feature extraction, 3) damage state prediction and remaining useful life estimation. Active piezoelectric and acoustic emission (AE) sensing techniques are applied to monitor the damage states. Wavelet transform is used to extract the piezoelectric sensing features. The number of counts from AE system was used as a feature. Piezoelectric or AE sensing features are used to build the input and output space of the Gaussian process. The future damage states and remaining useful life are predicted by Gaussian process based off-line and on-line algorithms. Accuracy of the Gaussian process based prognosis method is improved by including more training sets. Piezoelectric and AE features are also used for the state prediction. In the test cases presented, the piezoelectric features lead to better prognosis results. On-line prognosis is completed sequentially by combining experimental and predicted features. On-line damage state prediction and remaining useful life estimation shows good correlation with experimental data at later stages of fatigue life.

Paper Details

Date Published: 3 April 2009
PDF: 12 pages
Proc. SPIE 7286, Modeling, Signal Processing, and Control for Smart Structures 2009, 72860J (3 April 2009); doi: 10.1117/12.815889
Show Author Affiliations
Yingtao Liu, Arizona State Univ. (United States)
Subhasish Mohanty, Arizona State Univ. (United States)
Aditi Chattopadhyay, Arizona State Univ. (United States)


Published in SPIE Proceedings Vol. 7286:
Modeling, Signal Processing, and Control for Smart Structures 2009
Douglas K. Lindner, Editor(s)

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