Adaptive Residual Useful Life Estimation of a Structural Hotspot
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Structural Health Monitoring Techniques · Fatigue and fracture mechanics · Non-Destructive Testing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Mechanical and Aerospace Engineering, Arizona State University, Tempe, AZ 85287, USA,
Mechanical and Aerospace Engineering, Arizona State University, Tempe, AZ 85287, USA
References (15)
Show source-provided references
1. Farrar, C.R. · 2003
2. 10.2172/92253210.2172/922532 ↗
3. 10.21236/ADA37043110.21236/ADA370431 ↗
4. Hess, A.
5. 10.1016/j.ijfatigue.2007.03.01710.1016/j.ijfatigue.2007.03.017 ↗
6. MacKay, D.J.C. · Neural Networks and Machine Learning · 1998 · 168 · 133
7. Mohanty, S.
8. Mohanty, S.
9. 10.1177/1045389X0809960210.1177/1045389X08099602 ↗
10. Newman Jr, J.C. · 1992
11. Rasmussen, C. · 2006
12. Saxena, A.
13. Smith, L. · 2002
14. 10.1109/TSMCC.2008.200698810.1109/TSMCC.2008.2006988 ↗
15. Williams, C.K.I. · Advances in Neural Information Processing Systems · 1997 · 9 · 295