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Sensor Validation Using Nonlinear Minor Component Analysis

  • Roger Xu
  • Guangfan Zhang
  • Xiaodong Zhang
  • Leonard Haynes
  • Chiman Kwan
  • Kenneth Semega
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

In this paper, we present a unified framework for sensor validation, which is an extremely important module in the engine health management system. Our approach consists of several key ideas. First, we applied nonlinear minor component analysis (NLMCA) to capture the analytical redundancy between sensors. The obtained NLMCA model is data driven, does not require faulty data, and only utilizes sensor measurements during normal operations. Second, practical fault detection and isolation indices based on Squared Weighted Residuals (SWR) are employed to detect and classify the sensor failures. The SWR yields more accurate and robust detection and isolation results as compared to the conventional Squared Prediction Error (SPE). Third, an accurate fault size estimation method based on reverse scanning of the residuals is proposed. Extensive simulations based on a nonlinear prototype non-augmented turbofan engine model have been performed to validate the excellent performance of our approach.

Keywords

Fault Detection Sensor Fault Engine Model Fault Isolation Squared Prediction Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Roger Xu
    • 1
  • Guangfan Zhang
    • 1
  • Xiaodong Zhang
    • 2
  • Leonard Haynes
    • 1
  • Chiman Kwan
    • 1
  • Kenneth Semega
    • 3
  1. 1.Intelligent Automation, Inc.Rockville
  2. 2.GM R & D and PlanningWarrenUSA
  3. 3.1950 Fifth Street, Building 18, RM D036, WPAFBUSA

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