Reduced-Order Modeling and Wavelet Analysis of Turbofan Engine Structural Response Due to Foreign Object Damage (FOD) Events

Abstract

The development of a wavelet-based feature extraction technique specifically targeting FOD-event induced vibration signal changes in gas turbine engines is described. The technique performs wavelet analysis of accelerometer signals from specified locations on the engine and is shown to be robust in the presence of significant process and sensor noise. It is envisioned that the technique will be combined with Kalman filter thermal/health parameter estimation for FOD-event detection via information fusion from these (and perhaps other) sources. Due to the lack of high-frequency FOD-event test data in the open literature, a reduced-order turbofan structural model (ROM) was synthesized from a finite element model modal analysis to support the investigation. In addition to providing test data for algorithm development, the ROM is used to determine the optimal sensor location for FOD-event detection. In the presence of significant noise, precise location of the FOD event in time was obtained using the developed wavelet-based feature.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2004
Accession Number
ADA426768

Entities

People

  • Charles Lawrence
  • James Turso
  • Jonathan Litt

Organizations

  • National Aeronautics and Space Administration

Tags

Communities of Interest

  • Air Platforms
  • Biomedical
  • Sensors

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Artificial Intelligence
  • Computational Science
  • Detection
  • Engines
  • Event Detection
  • Feature Extraction
  • Filters
  • Frequency
  • Gas Turbines
  • Kalman Filters
  • Modal Analysis
  • Resonant Frequency
  • Signal Processing
  • Structural Response
  • Turbines

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Aerospace Engineering
  • Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference