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Kalman Filter Based Neural Network Methodology for Predictive Maintenance: A Case Study on Steam Turbine Blade Performance Prognostics

[+] Author Affiliations
Jihong Yan, Min Lv

Harbin Institute of Technology

Pengxiang Wang

University of Wisconsin at Milwaukee

Meiying Wang

Harbin Turbine Company

Paper No. IMECE2006-15805, pp. 271-276; 6 pages
doi:10.1115/IMECE2006-15805
From:
  • ASME 2006 International Mechanical Engineering Congress and Exposition
  • Manufacturing Engineering and Textile Engineering
  • Chicago, Illinois, USA, November 5 – 10, 2006
  • Conference Sponsors: Manufacturing Engineering Division and Textile Engineering Division
  • ISBN: 0-7918-4774-8 | eISBN: 0-7918-3790-4
  • Copyright © 2006 by ASME

abstract

Predictive maintenance involves condition monitoring, fault detection and prediction of remaining useful life or forthcoming failures. Predictive maintenance systems for steam turbine engines offer detection, classification, and prediction (or prognosis) of potential critical component failures, and ensures substantially reducing the cost of repair and replacement of defective parts, and may even result in saving lives. This paper describes a Kalman filter based neural network approach to provide performance evaluation and residual life prediction with the objectives of improving availability and implementing maintenance before failure occurs by estimating degradation severity and the proper timing for replacement. The approach has been applied to a steam turbine blade fatigue experiment testbed to illustrate the prognostic functionalities of the methodology.

Copyright © 2006 by ASME

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