Stochastic modelling and prediction of fatigue crack propagation using piecewise-deterministic Markov processes

Fatigue crack propagation is a stochastic phenomenon due to the inherent uncertainties originating from material properties, environmental conditions and cyclic mechanical loads. Stochastic processes offer thus an appropriate framework for modelling and predicting crack propagation. In this paper, the fatigue crack growth is modelled and predicted by a piecewise-deterministic Markov process associated with deterministic crack laws. First, a regime-switching model is used to express the transition between Paris' regime and rapid propagation which occurs before failure. Both regimes of propagation are governed by a deterministic equation whose parameters are randomly selected in a finite state space. This one has been adjusted from real data available in the literature. The crack growth behaviour is well-captured and the transition between both regimes is well-estimated by a critical stress intensity factor range. The second purpose of our investigation deals with the prediction of the fatigue crack path and its variability based on measurements taken at the beginning of the propagation. The results show that our method based on this class of stochastic models associated with an updating method provides a reliable prediction and can be an efficient tool for safety analysis of structures in a large variety of engineering applications. In addition, the proposed strategy requires only few information to be effective and is not time-consuming

Test de la vraisemblance entre deux motifs de points

Test de la vraisemblance entre deux motifs de point

Stochastic modelling and prediction of fatigue crack propagation using piecewise-deterministic Markov processes

Fatigue crack propagation is a stochastic phenomenon due to the inherent uncertainties originating from material properties, environmental conditions and cyclic mechanical loads. Stochastic processes thus offer an appropriate framework for modelling and predicting crack propagation. In this paper, fatigue crack growth is modelled and predicted by a piecewise-deterministic Markov process associated with deterministic crack laws. First, a regime-switching model is used to express the transition between the Paris regime and rapid propagation that occurs before failure. Both regimes of propagation are governed by a deterministic equation whose parameters are randomly selected in a finite state space. This one has been adjusted from real data available in the literature. The crack growth behaviour is well-captured and the transition between both regimes is well-estimated by a critical stress intensity factor range. The second purpose of our investigation deals with the prediction of the fatigue crack path and its variability based on measurements taken at the beginning of the propagation. The results show that our method based on this class of stochastic models associated with an updating method provides a reliable prediction and can be an efficient tool for safety analysis of structures in a large variety of engineering applications. In addition, the proposed strategy requires only little information to be effective and is not time-consuming

Statistical analysis of the grapevines mortality associated with foliar expression of Esca or Eutypa dieback

International audienc

Stochastic modelling and prediction of fatigue crack propagation using piecewise-deterministic Markov processes

Fatigue crack propagation is a stochastic phenomenon due to the inherent uncertainties originating from material properties, environmental conditions and cyclic mechanical loads. Stochastic processes offer thus an appropriate framework for modelling and predicting crack propagation. In this paper, the fatigue crack growth is modelled and predicted by a piecewise-deterministic Markov process associated with deterministic crack laws. First, a regime-switching model is used to express the transition between Paris' regime and rapid propagation which occurs before failure. Both regimes of propagation are governed by a deterministic equation whose parameters are randomly selected in a finite state space. This one has been adjusted from real data available in the literature. The crack growth behaviour is well-captured and the transition between both regimes is well-estimated by a critical stress intensity factor range. The second purpose of our investigation deals with the prediction of the fatigue crack path and its variability based on measurements taken at the beginning of the propagation. The results show that our method based on this class of stochastic models associated with an updating method provides a reliable prediction and can be an efficient tool for safety analysis of structures in a large variety of engineering applications. In addition, the proposed strategy requires only few information to be effective and is not time-consuming.Contrôle tolérant aux pannes pour les systèmes embarqué

Statistical analysis of the grapevines mortality associated with foliar expression of Esca or Eutypa dieback

International audienc

Stochastic modelling and prediction of fatigue crack propagation using piecewise-deterministic Markov processes

International audienceFatigue crack propagation is a stochastic phenomenon due to the inherent uncertainties originating from material properties, environmental conditions and loads. Stochastic processes offer an appropriate framework for modelling and predicting crack propagation. In this work, we propose to model and to predict the fatigue crack growth with Piecewise Deterministic Markov Processes (PDMP) associated with deterministic crack laws. First, we propose a regime-switching model with one jump to express the transition between Paris' regime and rapid crack propagation which occurs before failure. Parameters are adjusted from real data available in the literature. For this, we have to capture the change of regime of the observed failure trough an optimisation algorithm. The second purpose of this investigation is to predict the fatigue crack path and its variability based on the global model and knowledge of some informations retrieved at the beginning of crack propagation. We show that our method based on PDMP associated with an updating method provides a reliable prediction and can be an efficient tool for safety structures in a large variety of engineering applications. In addition, the proposed strategy requires only few information to be effective and is cheaper in terms of computing time

Effective connectivity: Influence, causality and biophysical modeling

This is the final paper in a Comments and Controversies series dedicated to “The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution”. We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener–Akaike–Granger–Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments