eSTORM: Enhanced Self Tuning On-board Real-time Engine Model

Abstract

A key to producing reliable engine diagnostics and prognostics resides in the fusion of different processing techniques. Fusion of techniques has been shown to improve diagnostic performance while simultaneously reducing false alarms. Presented here is an approach that fuses a physical model called STORM (Self Tuning Onboard, Real-time engine Model) developed by Pratt & Whitney, with an empirical neural net model to provide a unique hybrid model called enhanced STORM (eSTORM) for engine diagnostics. STORM is a piecewise linear approximation of the engine cycle deck. Though STORM provides significant improvement over existing real-time engine model methods, there are several effects that impact engine performance that STORM does not capture. Integrating an empirical model with STORM accommodates the modeling errors. This paper describes the development of eSTORM for a Pratt & Whitney high bypass turbofan engine. Results of using STORM and eSTORM on simulated engine data are presented and compared. eSTORM is shown to work extremely well in reducing STORM modeling errors and biases for the conditions considered.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2003
Accession Number
ADA511669

Entities

People

  • Al Volponi
  • Donald L. Simon
  • Rob Luppold
  • Tom Brotherton

Tags

Communities of Interest

  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Control Systems
  • Detection
  • Detectors
  • Electrical Engineering
  • Engine Components
  • Engineering
  • Equations
  • Gas Turbines
  • High Bypass Turbofans
  • Kalman Filters
  • Measurement
  • Neural Networks
  • Signal Processing
  • Supervised Machine Learning
  • Turbofan Engines

Readers

  • Atmospheric Science/Meteorology
  • Combustion and Flow Dynamics.
  • Robotics and Automation.