Machine health monitoring and life management using finite-element-based neural networks

Journal Article · Tue Oct 01 04:00:00 EDT 1996 · Journal of Engineering for Gas Turbines and Power

DOI:https://doi.org/10.1115/1.2817002· OSTI ID:397913

Roemer, M J; Hong, C; Hesler, S H ^[1]

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1. Stress Technology Inc., Rochester, NY (United States)

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.

• Sponsoring Organization: USDOE
• OSTI ID: 397913
• Report Number(s): CONF-950629--
• Journal Information: Journal of Engineering for Gas Turbines and Power, Journal Name: Journal of Engineering for Gas Turbines and Power Journal Issue: 4 Vol. 118; ISSN JETPEZ; ISSN 0742-4795
• Country of Publication: United States
• Language: English

Related Subjects

20 FOSSIL-FUELED POWER PLANTS
ELECTRIC MOTORS
FORECASTING
GAS TURBINES
NEURAL NETWORKS
ON-LINE MEASUREMENT SYSTEMS
SERVICE LIFE
TURBOGENERATORS