Intelligent approach to prognostic enhancements of diagnostic systems

George Vachtsevanos; Peng Wang; Noppadon Khiripet; Ash Thakker; Thomas R. Galie

doi:10.1117/12.434227

20 July 2001 Intelligent approach to prognostic enhancements of diagnostic systems

George Vachtsevanos, Peng Wang, Noppadon Khiripet, Ash Thakker, Thomas R. Galie

Proceedings Volume 4389, Component and Systems Diagnostics, Prognosis, and Health Management; (2001) https://doi.org/10.1117/12.434227
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

This paper introduces a novel methodology to prognostics based on a dynamic wavelet neural network construct and notions from the virtual sensor area. This research has been motivated and supported by the U.S. Navy's active interest in integrating advanced diagnostic and prognostic algorithms in existing Naval digital control and monitoring systems. A rudimentary diagnostic platform is assumed to be available providing timely information about incipient or impending failure conditions. We focus on the development of a prognostic algorithm capable of predicting accurately and reliably the remaining useful lifetime of a failing machine or component. The prognostic module consists of a virtual sensor and a dynamic wavelet neural network as the predictor. The virtual sensor employs process data to map real measurements into difficult to monitor fault quantities. The prognosticator uses a dynamic wavelet neural network as a nonlinear predictor. Means to manage uncertainty and performance metrics are suggested for comparison purposes. An interface to an available shipboard Integrated Condition Assessment System is described and applications to shipboard equipment are discussed. Typical results from pump failures are presented to illustrate the effectiveness of the methodology.

Citation Download Citation

George Vachtsevanos, Peng Wang, Noppadon Khiripet, Ash Thakker, and Thomas R. Galie "Intelligent approach to prognostic enhancements of diagnostic systems", Proc. SPIE 4389, Component and Systems Diagnostics, Prognosis, and Health Management, (20 July 2001); https://doi.org/10.1117/12.434227

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