Fatigue crack prognosis using Bayesian probabilistic modelling

Prognosis of fatigue crack growth for mechanical and structural components is vital for aging military aircraft operated near or beyond their original design lives. For modern aircraft, prognostics and health management is supposed to be a designed-in capability; however, prognosis of mechanical and...

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Autores principales:	Wenyi WANG, Weiping HU, Nicholas ARMSTRONG
Formato:	article
Lenguaje:	EN
Publicado:	The Japan Society of Mechanical Engineers 2017
Materias:	fatigue crack crack prediction bayesian model extended kalman filter gear fault prognosis vibration analysis Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/d933da40b7f94b93b03f5fb9ca110454
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spelling	oai:doaj.org-article:d933da40b7f94b93b03f5fb9ca1104542021-11-26T07:11:27ZFatigue crack prognosis using Bayesian probabilistic modelling2187-974510.1299/mej.16-00702https://doaj.org/article/d933da40b7f94b93b03f5fb9ca1104542017-03-01T00:00:00Zhttps://www.jstage.jst.go.jp/article/mej/4/5/4_16-00702/_pdf/-char/enhttps://doaj.org/toc/2187-9745Prognosis of fatigue crack growth for mechanical and structural components is vital for aging military aircraft operated near or beyond their original design lives. For modern aircraft, prognostics and health management is supposed to be a designed-in capability; however, prognosis of mechanical and structural damage is yet to fully mature. This paper presents a scheme adopting Bayesian probabilistic modelling, extended Kalman filter (EKF) in particular, to predict fatigue crack growth in a common aircraft structural material: 2024-T3 aluminum alloy. In this scheme, the state model is the widely adopted Paris law in fracture mechanics (used to model the physics of crack growth), and the measurement model is a simple random walk model. The scheme is validated using a set of published crack growth test data, often referred to as the Virkler data, where the state model parameters are derived from one half of the data and the crack length prediction is made on the other half of the data. The EKF framework is further validated using a set of gear tooth crack propagation test data, where the crack length is the unobservable (or hidden) state variable, and the observable variable is a feature extracted from the gear vibration signal. The state model is also derived from the Paris law and the measurement model is developed using the observed relationship between the known crack length, the applied stress, and the energy of the impulsive signature extracted from an optimized sinusoidal model for gear vibration signals. Using the recursive EKF solution, we are able to achieve promising prognostic results in terms of the accuracy of the prediction, and demonstrate the method’s robustness in dealing with uncertainties in the parameters defining the Paris law and the uncertainties in the measurements. Compared to other studies, the proposed method is a much simpler and more robust approach to the prognosis of fatigue crack size in mechanical structures and rotating components.Wenyi WANGWeiping HUNicholas ARMSTRONGThe Japan Society of Mechanical Engineersarticlefatigue crackcrack predictionbayesian modelextended kalman filtergear fault prognosisvibration analysisMechanical engineering and machineryTJ1-1570ENMechanical Engineering Journal, Vol 4, Iss 5, Pp 16-00702-16-00702 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	fatigue crack crack prediction bayesian model extended kalman filter gear fault prognosis vibration analysis Mechanical engineering and machinery TJ1-1570
spellingShingle	fatigue crack crack prediction bayesian model extended kalman filter gear fault prognosis vibration analysis Mechanical engineering and machinery TJ1-1570 Wenyi WANG Weiping HU Nicholas ARMSTRONG Fatigue crack prognosis using Bayesian probabilistic modelling
description	Prognosis of fatigue crack growth for mechanical and structural components is vital for aging military aircraft operated near or beyond their original design lives. For modern aircraft, prognostics and health management is supposed to be a designed-in capability; however, prognosis of mechanical and structural damage is yet to fully mature. This paper presents a scheme adopting Bayesian probabilistic modelling, extended Kalman filter (EKF) in particular, to predict fatigue crack growth in a common aircraft structural material: 2024-T3 aluminum alloy. In this scheme, the state model is the widely adopted Paris law in fracture mechanics (used to model the physics of crack growth), and the measurement model is a simple random walk model. The scheme is validated using a set of published crack growth test data, often referred to as the Virkler data, where the state model parameters are derived from one half of the data and the crack length prediction is made on the other half of the data. The EKF framework is further validated using a set of gear tooth crack propagation test data, where the crack length is the unobservable (or hidden) state variable, and the observable variable is a feature extracted from the gear vibration signal. The state model is also derived from the Paris law and the measurement model is developed using the observed relationship between the known crack length, the applied stress, and the energy of the impulsive signature extracted from an optimized sinusoidal model for gear vibration signals. Using the recursive EKF solution, we are able to achieve promising prognostic results in terms of the accuracy of the prediction, and demonstrate the method’s robustness in dealing with uncertainties in the parameters defining the Paris law and the uncertainties in the measurements. Compared to other studies, the proposed method is a much simpler and more robust approach to the prognosis of fatigue crack size in mechanical structures and rotating components.
format	article
author	Wenyi WANG Weiping HU Nicholas ARMSTRONG
author_facet	Wenyi WANG Weiping HU Nicholas ARMSTRONG
author_sort	Wenyi WANG
title	Fatigue crack prognosis using Bayesian probabilistic modelling
title_short	Fatigue crack prognosis using Bayesian probabilistic modelling
title_full	Fatigue crack prognosis using Bayesian probabilistic modelling
title_fullStr	Fatigue crack prognosis using Bayesian probabilistic modelling
title_full_unstemmed	Fatigue crack prognosis using Bayesian probabilistic modelling
title_sort	fatigue crack prognosis using bayesian probabilistic modelling
publisher	The Japan Society of Mechanical Engineers
publishDate	2017
url	https://doaj.org/article/d933da40b7f94b93b03f5fb9ca110454
work_keys_str_mv	AT wenyiwang fatiguecrackprognosisusingbayesianprobabilisticmodelling AT weipinghu fatiguecrackprognosisusingbayesianprobabilisticmodelling AT nicholasarmstrong fatiguecrackprognosisusingbayesianprobabilisticmodelling
_version_	1718409747015663616

Fatigue crack prognosis using Bayesian probabilistic modelling

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