Proposed Health State Awareness of Helicopter Blades using an Artificial Neural Network Strategy

Andrew Lee, Ed Habtour, S. Andrew Gadsden*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationMultisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2016
EditorsJerome J. Braun
PublisherSPIE International
Number of pages8
ISBN (Print)9781510601130
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventSPIE Defense+ Commercial Sensing 2016 - Baltimore Convention Center, Baltimore, United States
Duration: 17 Apr 201621 Apr 2016

Publication series

NameProceedings of SPIE
PublisherSPIE
Volume9872
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceSPIE Defense+ Commercial Sensing 2016
CountryUnited States
CityBaltimore
Period17/04/1621/04/16

Keywords

  • State awareness
  • Fault detection
  • Sensor network
  • System identification
  • Aerospace

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  • Cite this

    Lee, A., Habtour, E., & Gadsden, S. A. (2016). Proposed Health State Awareness of Helicopter Blades using an Artificial Neural Network Strategy. In J. J. Braun (Ed.), Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2016 (Proceedings of SPIE; Vol. 9872). SPIE International. https://doi.org/10.1117/12.2223356