Imperial College London
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DrJosephCorcoran

Faculty of Engineering, Department of Mechanical Engineering

Honorary Lecturer
 
 
 
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Contact

 

joseph.corcoran

 
 
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Location

 

563City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{ODowd:2021:10.1016/j.ijfatigue.2020.105943,
author = {ODowd, NM and Madarshahian, R and Leung, MSH and Corcoran, J and Todd, MD},
doi = {10.1016/j.ijfatigue.2020.105943},
journal = {International Journal of Fatigue},
title = {A probabilistic estimation approach for the failure forecast method using Bayesian inference},
url = {http://dx.doi.org/10.1016/j.ijfatigue.2020.105943},
volume = {142},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Positive-feedback mechanisms such as fatigue induce a self-accelerating behavior, captured by models displaying infinite limit-state asymptotics, collectively known as the failure forecast method (FFM). This paper presents a Bayesian model parameter estimation approach to the fully nonlinear FFM implementation and compares the results to the classic linear regression formulation, including a regression uncertainty model. This process is demonstrated in a cyclic loading fatigue crack propagation application, both on a synthetic data set and on a full fatigue experiment. A novel ”switch point” parameter is included in the Bayesian formulation to account for nonstationary changes in the growth parameter.
AU  - ODowd,NM
AU  - Madarshahian,R
AU  - Leung,MSH
AU  - Corcoran,J
AU  - Todd,MD
DO  - 10.1016/j.ijfatigue.2020.105943
PY  - 2021///
SN  - 0142-1123
TI  - A probabilistic estimation approach for the failure forecast method using Bayesian inference
T2  - International Journal of Fatigue
UR  - http://dx.doi.org/10.1016/j.ijfatigue.2020.105943
VL  - 142
ER  -
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