NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
A real time neural net estimator of fatigue lifeA neural network architecture is proposed to estimate, in real-time, the fatigue life of mechanical components, as part of the intelligent Control System for Reusable Rocket Engines. Arbitrary component loading values were used as input to train a two hidden-layer feedforward neural net to estimate component fatigue damage. The ability of the net to learn, based on a local strain approach, the mapping between load sequence and fatigue damage has been demonstrated for a uniaxial specimen. Because of its demonstrated performance, the neural computation may be extended to complex cases where the loads are biaxial or triaxial, and the geometry of the component is complex (e.g., turbopumps blades). The generality of the approach is such that load/damage mappings can be directly extracted from experimental data without requiring any knowledge of the stress/strain profile of the component. In addition, the parallel network architecture allows real-time life calculations even for high-frequency vibrations. Owing to its distributed nature, the neural implementation will be robust and reliable, enabling its use in hostile environments such as rocket engines.
Document ID
19920039070
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Troudet, T.
(NASA Lewis Research Center; Sverdrup Technology, Inc. Cleveland, OH, United States)
Merrill, W.
(NASA Lewis Research Center Cleveland, OH, United States)
Date Acquired
August 15, 2013
Publication Date
January 1, 1990
Subject Category
Cybernetics
Meeting Information
Meeting: IJCNN - International Joint Conference on Neural Networks
Location: San Diego, CA
Country: United States
Start Date: June 17, 1990
End Date: June 21, 1990
Sponsors: International Nueral Network Society, IEEE
Accession Number
92A21694
Distribution Limits
Public
Copyright
Other

Available Downloads

There are no available downloads for this record.
No Preview Available