Stochastic modeling of fatigue crack dynamics for on-line failure prognostics

Asok Ray; Sekhar Tangirala

doi:10.1109/87.508893

Stochastic modeling of fatigue crack dynamics for on-line failure prognostics

Asok Ray, Sekhar Tangirala

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

127 Citations (SciVal)

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 on-line 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.

Original language	English (US)
Pages (from-to)	443-451
Number of pages	9
Journal	IEEE Transactions on Control Systems Technology
Volume	4
Issue number	4
DOIs	https://doi.org/10.1109/87.508893
State	Published - 1996

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Electrical and Electronic Engineering

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10.1109/87.508893

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title = "Stochastic modeling of fatigue crack dynamics for on-line failure prognostics",

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 on-line 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.",

author = "Asok Ray and Sekhar Tangirala",

note = "Funding Information: Manuscript received September 14, 1995; revised January 2.5, 1996. Recommended by Associate Editor, E. 0. King. This work was supported by the National Science Foundation Grant ECS-9216386, the National Science Foundation Grant DMI-9424587, the NASA Lewis Research Center Grant NAG 3-1673, the Office of Naval Research Grant N00014-90-J-1513, and the Electric Power Research Institute Contract EPRI RP8030-05. The authors are with the Mechanical Engineering Department, Pennsylvania State University, University Park, PA 16802 USA. Publisher Item Identifier S 1063-6.536(96)0491 7-2.",

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AU - Ray, Asok

AU - Tangirala, Sekhar

N1 - Funding Information: Manuscript received September 14, 1995; revised January 2.5, 1996. Recommended by Associate Editor, E. 0. King. This work was supported by the National Science Foundation Grant ECS-9216386, the National Science Foundation Grant DMI-9424587, the NASA Lewis Research Center Grant NAG 3-1673, the Office of Naval Research Grant N00014-90-J-1513, and the Electric Power Research Institute Contract EPRI RP8030-05. The authors are with the Mechanical Engineering Department, Pennsylvania State University, University Park, PA 16802 USA. Publisher Item Identifier S 1063-6.536(96)0491 7-2.

PY - 1996

Y1 - 1996

N2 - 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 on-line 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.

AB - 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 on-line 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.

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Stochastic modeling of fatigue crack dynamics for on-line failure prognostics

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