A nonlinear stochastic model of fatigue crack dynamics

Ray, Asok; Tangirala, S.

doi:10.1016/s0266-8920(96)00012-4

Cited by 28 publications

(18 citation statements)

References 14 publications

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“…Researchers have since pursued more significant developmental strides based on Paris law. Ray et al [13] and Mohanty et al [14] proposed a nonlinear stochastic model of crack growth based on the measured signals, which could estimate the length of crack, the propagation rate of crack and residual life.…”

Section: Physics Modelmentioning

confidence: 99%

Remaining Useful Life Model and Assessment of Mechanical Products: A Brief Review and a Note on the State Space Model Method

Liu

et al. 2019

Chin. J. Mech. Eng.

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The remaining useful life (RUL) prediction of mechanical products has been widely studied for online system performance reliability, device remanufacturing, and product safety (safety awareness and safety improvement). These studies incorporated many different models, algorithms, and techniques for modeling and assessment. In this paper, methods of RUL assessment are summarized and expounded upon using two major methods: physics model based and data driven based methods. The advantages and disadvantages of each of these methods are deliberated and compared as well. Due to the intricacy of failure mechanism in system, and difficulty in physics degradation observation, RUL assessment based on observations of performance variables turns into a science in evaluating the degradation. A modeling method from control systems, the state space model (SSM), as a first order hidden Markov, is presented. In the context of non-linear and non-Gaussian systems, the SSM methodology is capable of performing remaining life assessment by using Bayesian estimation (sequential Monte Carlo). Being effective for non-linear and non-Gaussian dynamics, the methodology can perform the assessment recursively online for applications in CBM (condition based maintenance), PHM (prognostics and health management), remanufacturing, and system performance reliability. Finally, the discussion raises concerns regarding online sensing data for SSM modeling and assessment of RUL.

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Section: Physics Modelmentioning

confidence: 99%

Remaining Useful Life Model and Assessment of Mechanical Products: A Brief Review and a Note on the State Space Model Method

Liu

et al. 2019

Chin. J. Mech. Eng.

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“…Various studies in the literature [16,32] have mentioned that the time to failure for the component would be between 30 hours to 700 hours depending on the severity of the operating condition.…”

Section: Development Of Frameworkmentioning

confidence: 99%

“…The contributing factor for failure mechanism of the crankshaft is due of the bending and torsional stresses with the fluctuation of loads during the operational time period [1,2,3,5,6,32]. The optimal desired reliability life prediction (Fig.…”

Section: Reliability Analysis and Predictionmentioning

confidence: 99%

Reliability analysis and prediction for time to failure distribution of an automobile crankshaft

Singh

Abdullah

Mohamed

2015

EiN

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“…Model-based methods use an explicit mathematical model of the degradation process to predict the future evolution of the degradation state and, thus, the RUL of the system [6]. Examples of degradation models are the non-linear stochastic model of fatigue crack dynamics [7] [8] or the creep growth model based on the Norton law [9]. In practice, even when the model of the degradation process is known, the RUL estimate may be difficult to obtain, since the degradation state of the system may not be directly observable and/or the measurements may be affected by noise and disturbances.…”

Section: Introductionmentioning

confidence: 99%

Model-based and data-driven prognostics under different available information

Baraldi

Cadini

Mangili

et al. 2013

Probabilistic Engineering Mechanics

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In practical industrial applications, different prognostic approaches can be used depending on the information available for the model development. In this paper, we consider three different cases: 1) a physics-based model of the degradation process is available; 2) a set of degradation observations measured on components similar to the one of interest is available; 3) degradation observations are available only for the component of interest.The objective of the present work is to develop prognostic approaches properly tailored for these three cases and to evaluate them in terms of the assumptions they require, the accuracy of the Remaining Useful Life (RUL) predictions they provide and their ability of providing measures of confidence in the predictions. The first case is effectively handled within a particle filtering (PF) scheme, whereas the second and third cases are addressed by bootstrapped ensembles of empirical models.The main methodological contributions of this work are i) the proposal of a strategy for selecting the prognostic approach which best suits the information setting, even in presence of mixed information sources;ii) the development of a bootstrap method able to assess the confidence in the RUL prediction in the third case characterized by the unavailability of any degradation observations until failure.A case study is analyzed, concerning the prediction of the RUL of turbine blades affected by a developing creep.

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A nonlinear stochastic model of fatigue crack dynamics

Cited by 28 publications

References 14 publications

Remaining Useful Life Model and Assessment of Mechanical Products: A Brief Review and a Note on the State Space Model Method

Remaining Useful Life Model and Assessment of Mechanical Products: A Brief Review and a Note on the State Space Model Method

Reliability analysis and prediction for time to failure distribution of an automobile crankshaft

Model-based and data-driven prognostics under different available information

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