Title :
Stochastic modeling of fatigue crack dynamics for on-line failure prognostics
Author :
Ray, Asok ; Tangirala, Sekhar
Author_Institution :
Dept. of Mech. Eng., Pennsylvania State Univ., University Park, PA, USA
fDate :
7/1/1996 12:00:00 AM
Abstract :
This paper presents a nonlinear stochastic model of fatigue crack dynamics for real-time computation of the time-dependent damage rate and accumulation in mechanical structures. The model configuration allows construction of a filter for estimation of the current damage state and prediction of the remaining service life based on the underlying principle of extended Kalman filtering instead of solving the Kolmogorov forward equation. This approach is suitable for online damage sensing, failure prognosis, life prediction, reliability analysis, decision-making for condition-based maintenance and operation planning, and life extending control in complex dynamical systems. The model results have been verified by comparison with experimentally generated statistical data of time-dependent fatigue cracks in specimens made of 2024-T3 aluminum alloy
Keywords :
Kalman filters; aluminium alloys; crack detection; failure analysis; fatigue cracks; fatigue testing; filtering theory; life testing; stochastic processes; 2024-T3 aluminum alloy; complex dynamical systems; condition-based maintenance; damage accumulation; decision-making; experimentally generated statistical data; extended Kalman filtering; fatigue crack dynamics; life prediction; life-extending control; mechanical structures; nonlinear stochastic model; online damage sensing; online failure prognostics; operation planning; reliability analysis; stochastic modeling; time-dependent damage rate; time-dependent fatigue cracks; Decision making; Equations; Failure analysis; Fatigue; Filtering; Kalman filters; Life estimation; Predictive models; State estimation; Stochastic processes;
Journal_Title :
Control Systems Technology, IEEE Transactions on
