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Proceedings Paper

Proposed health state awareness of helicopter blades using an artificial neural network strategy
Author(s): Andrew Lee; Ed Habtour; S. Andrew Gadsden
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Paper Abstract

Structural health prognostics and diagnosis strategies can be classified as either model or signal-based. Artificial neural network strategies are popular signal-based techniques. This paper proposes the use of helicopter blades in order to study the sensitivity of an artificial neural network to structural fatigue. The experimental setup consists of a scale aluminum helicopter blade exposed to transverse vibratory excitation at the hub using single axis electrodynamic shaker. The intent of this study is to optimize an algorithm for processing high-dimensional data while retaining important information content in an effort to select input features and weights, as well as health parameters, for training a neural network. Data from accelerometers and piezoelectric transducers is collected from a known system designated as healthy. Structural damage will be introduced to different blades, which they will be designated as unhealthy. A variety of different tests will be performed to track the evolution and severity of the damage. A number of damage detection and diagnosis strategies will be implemented. A preliminary experiment was performed on aluminum cantilever beams providing a simpler model for implementation and proof of concept. Future work will look at utilizing the detection information as part of a hierarchical control system in order to mitigate structural damage and fatigue. The proposed approach may eliminate massive data storage on board of an aircraft through retaining relevant information only. The control system can then employ the relevant information to intelligently reconfigure adaptive maneuvers to avoid harmful regimes, thus, extending the life of the aircraft.

Paper Details

Date Published: 19 May 2016
PDF: 8 pages
Proc. SPIE 9872, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2016, 98720C (19 May 2016); doi: 10.1117/12.2223356
Show Author Affiliations
Andrew Lee, Univ. of Maryland, Baltimore County (United States)
Ed Habtour, U.S. Army Research Lab. (United States)
S. Andrew Gadsden, Univ. of Maryland, Baltimore County (United States)

Published in SPIE Proceedings Vol. 9872:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2016
Jerome J. Braun, Editor(s)

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