Modeling, Diagnostics and Prognostics of a Two-Spool Turbofan Engine

Abstract

Model-based diagnostic/prognostic techniques have the potential to predict, within reasonable bounds, the remaining useful life of critical system components. Due to the numerous uncertainties in the operation of a turbine engine and unavailability of accurate engine models, prognostics continue to pose a significant challenge. There is a need to develop an engine prognostic approach that can accommodate different damage modes, sensor failures, material properties, dynamic load histories and damage accumulation. Using an accurate physics-based model of the engine one can develop such a prognostic approach. We present a nonlinear dynamical model of a two-spool turbine engine developed from first principles. The simulation model has been implemented using MATLAB/Simulink. It is used with the Kalman Filter-based diagnostic technique previously discussed in literature to detect and isolate sensor faults. A literature review of the developments in the area of prognostics is also presented, along with the problems and challenges.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2005
Accession Number
ADA437921

Entities

People

  • Al Behbahani
  • Majid Siddiqi
  • Praveen Shankar
  • Rama K. Yedavalli

Organizations

  • Ohio State University

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Artificial Intelligence
  • Auxiliary Power Units
  • Detection
  • Dynamic Loads
  • Engines
  • Gas Turbines
  • High Pressure
  • Kalman Filters
  • Literature Surveys
  • Mathematical Models
  • Measurement
  • Neural Networks
  • Simulations
  • Turbines
  • Turbofan Engines

Fields of Study

  • Engineering

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