RESEARCH PAPERS: Gas Turbines: Structures and Dynamics

Machine Health Monitoring and Life Management Using Finite-Element-Based Neural Networks

[+] Author and Article Information
M. J. Roemer, C. Hong, S. H. Hesler

Stress Technology Inc., 1800 Brighton-Henrietta Town Line Rd., Rochester, NY 14623

J. Eng. Gas Turbines Power 118(4), 830-835 (Oct 01, 1996) (6 pages) doi:10.1115/1.2817002 History: Received February 27, 1995; Online November 19, 2007


This paper demonstrates a novel approach to condition-based health monitoring for rotating machinery using recent advances in neural network technology and rotordynamic, finite-element modeling. A desktop rotor demonstration rig was used as a proof of concept tool. The approach integrates machinery sensor measurements with detailed, rotordynamic, finite-element models through a neural network that is specifically trained to respond to the machine being monitored. The advantage of this approach over current methods lies in the use of an advanced neural network. The neural network is trained to contain the knowledge of a detailed finite-element model whose results are integrated with system measurements to produce accurate machine fault diagnostics and component stress predictions. This technique takes advantage of recent advances in neural network technology that enable real-time machinery diagnostics and component stress prediction to be performed on a PC with the accuracy of finite-element analysis. The availability of the real-time, finite-element-based knowledge on rotating elements allows for real-time component life prediction as well as accurate and fast fault diagnosis.

Copyright © 1996 by The American Society of Mechanical Engineers
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