No abstract

During the lifetime of a component, microstructural changes emerge at its material level and evolve through time. Classical empirical degradation models (e.g. Paris Law in fatigue crack growth) are usually established based on monitoring and estimating well-known direct damage indicators such as crack size. However, by the time the usual inspection techniques efficiently identify such damage indicators, most of the life of the component would have been expended, and usually it would be too late to save the component. Therefore, it is important to detect damage at the earliest possible time. This article presents a new structural health monitoring and damage prognostics framework based on evolution of damage precursors representing the indirect damage indicators, when conventional direct damage indicator, such as a crack, is unobservable, inaccessible, or difficult to measure. Dynamic Bayesian network is employed to represent all the related variables as well as their causal or correlation relationships. Since the degradation model based on damage precursor evolution is not fully recognized, the methodology needs to be capable of online-learning the degradation process as well as estimating the damage state. Therefore, the joint particle filtering technique is implemented as an inference method inside the dynamic Bayesian network to assess both model parameters and damage states simultaneously. The proposed framework allows the integration of any related sources of information in order to reduce the inherent uncertainties. Incorporating different types of evidences in dynamic Bayesian network entails advance techniques to identify and formulate the possible interaction between potentially nonhomogenous variables. This article uses the support vector regression in order to define generally unknown nonparametric and nonlinear correlation between the input variables. The methodology is successfully applied to damage estimation and prediction of crack initiation in a metallic alloy under fatigue. The proposed framework is intended to be general and comprehensive so that it can be implemented in different applications.Chilean National Fund for Scientific and Technological Development (Fondecyt) 116049

No abstract

A fully adaptive particle filtering algorithm is proposed in this paper which is capable of updating both state process models and measurement models separately and simultaneously. The approach is a significant step toward more realistic online monitoring or tracking damage. The majority of the existing methods for Bayes filtering are based on predefined and fixed state process and measurement models. Simultaneous estimation of both state and model parameters has gained attention in recent literature. Some works have been done on updating the state process model. However, not many studies exist regarding an update of the measurement model. In most of the real-world applications, the correlation between measurements and the hidden state of damage is not defined in advance and, therefore, presuming an offline fixed measurement model is not promising. The proposed approach is based on optimizing relative entropy or Kullback-Leibler divergence through a particle filtering algorithm. The proposed algorithm is successfully applied to a case study of online fatigue damage estimation in composite materials.

No abstract