Neural Network-Based Sensor Validation for Turboshaft Engines

Abstract

Sensor failure detection, isolation, and accommodation using a neural network approach is described. An autoassociative neural network is configured to perform dimensionality reduction on the sensor measurement vector and provide estimated sensor values. The sensor validation scheme is applied in a simulation of the T700 turboshaft engine in closed loop operation. Performance is evaluated based on the ability to detect faults correctly and maintain stable and responsive engine operation. The set of sensor outputs used for engine control forms the network input vector. Analytical redundancy is verified by training networks of successively smaller bottleneck layer sizes. Training data generation and strategy are discussed. The engine maintained stable behavior in the presence of sensor hard failures. With proper selection of fault determination thresholds, stability was maintained in the presence of sensor soft failures.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1998
Accession Number
ADA357644

Entities

People

  • James Moller
  • Jonathan S. Litt
  • Ten-huei Guo

Organizations

  • National Aeronautics and Space Administration

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Astronautics
  • Classification
  • Closed Loop Systems
  • Computing System Architectures
  • Control Systems
  • Databases
  • Dimensionality Reduction
  • Engineering
  • Generators
  • Network Architecture
  • Neural Networks
  • Reliability
  • Space Sciences
  • Steady State
  • Turboshaft Engines
  • United States

Fields of Study

  • Engineering

Readers

  • Computational Modeling and Simulation
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks