Innovative Methods for Engine Health Monitoring
Abstract
The University of Texas San Antonio (UTSA), including Southwest Research Institute, is developing engine health monitoring (EHM) technology that compliments the ongoing and planned research within AFRL. The program consists of three distinct but related task areas that span EHM from a systems engineering level, to a specific damage-based life prediction processor, to a durability assessment of sensing materials. Task 1 is a systems level capstone effort focused on the information management, diagnostics and prognostics of EHM systems. The objectives are to develop Bayesian learning and neural networks for learning the unknown aspects of nonlinear engine systems and sensor sensitivity analysis. Task 2 is focused on developing a probabilistic fracture mechanics model and ASIC (application specific integrated circuit) implementation for efficient on-board and real-time assessment of the damage state of critical engine components. The effort is to develop hardware such that sophisticated probabilistic fracture mechanics algorithms can be placed on-board for evaluation of detected defects. Task 3 is focused on the development of much-needed durability models for thin film sensors that are either in common use or likely candidates for monitoring changes in engine performance or detecting and monitoring defects in fracture critical engine components.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 30, 2006
- Accession Number
- ADA463539
Entities
People
- C. L. Chen
- David B. Tanner
- E. John
- H. Millwater
- K. Chan
- P. Cotae
- S. Hudak
- Xufeng Wang
- Yihe Huang
- Z. Pantic-tanner
Organizations
- University of Texas at San Antonio