Failure prognostics and risk analysis facilitate maintenance scheduling and operation planning of aging mechanical systems (e.g., power and processing plants, aircraft, and civil infrastructures such as bridges and industrial buildings). Decision tools for failure prognostics must have the capability of incorporating the dynamic behavior of material damage under both normal and off-normal operating conditions. This paper presents a nonlinear stochastic model of fatigue damage dynamics and a filter for on-line estimation of the first two moments of the time-dependent damage accumulation. The results have been verified with experimental data of fatigue damage statistics for the 2024-T3 Aluminum alloy.
|Original language||English (US)|
|Number of pages||5|
|Journal||Proceedings of the American Control Conference|
|State||Published - Jan 1 1995|
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering