Abstract
In conventional approaches to life prognosis, damage tolerance and fatigue life predictions are obtained based on assumed structural flaws, regardless of whether they actually occur in service. Consequently, a large degree of conservatism is incorporated into structural designs due to these uncertainties. In a real time environment, keeping track of the damage growth in a complex structural component manually is quite difficult and requires automatic damage state estimation. The current research on structural health monitoring (or on-line damage state estimation) techniques offers condition-based damage state prediction and corresponding residual useful life assessment. The real-time damage state information from an on-line state estimation model can be regularly fed to a predictive model to update the residual useful life estimation in the event of a new prevailing situation. This article discusses the use of an adaptive prognosis procedure, which integrates an on-line state estimation algorithm with an off-line predictive algorithm to estimate the condition-based residual useful life of structural hotspots such as a lug joint.
Original language | English (US) |
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Pages (from-to) | 321-335 |
Number of pages | 15 |
Journal | Journal of Intelligent Material Systems and Structures |
Volume | 21 |
Issue number | 3 |
DOIs | |
State | Published - Feb 2010 |
Keywords
- 2024-T351 aluminum alloy
- Adaptive prognosis
- Bayesian inference
- Condition-based residual useful life estimate
- Fatigue crack growth
- Gaussian process
- Off-line state prediction
- On-line state estimation
ASJC Scopus subject areas
- Materials Science(all)
- Mechanical Engineering