Hugin bayesian how to#
Hugin bayesian crack#

Hugin bayesian crack#

To complement the test specimen, a DBN is constructed to predict the crack length with input observations. The results of the hexagon experiment demonstrate that the designed specimen successfully simulates the interaction among cracks and structural redundancy. A standard eccentrically-loaded single edge crack tension specimen is designed and tested to validate the performance of the developed computer vision-based method for measuring crack length. Two new methods of measuring crack length are developed based on computer vision and digital image correlation. The applied loading cycles and corresponding crack lengths are recorded as the major time-varying data of degradation state. To mimic the interdependence, redundancy and component-to-system level performance of marine structures in degradation, a hexagon tension specimen with four propagating fatigue cracks, one on each corner, is designed and tested. Therefore, the PhD research designs an experiment to mimic the properties of marine structure and develops a corresponding numerical model based on DBN whose performance is evaluated by the designed experiment. DBN can model the degradation process of structure but its performance has not been validated by experiments. Dynamic Bayesian networks (DBN) expand the Ordinary Bayesian networks (BN) with slices representing the state of the system at different time intervals. Thus, an experiment must be designed to mimic the multiple degradation process and retain structural redundancy so that a single element failure will not remove all load carrying capacity. Due to the infeasiblity of full-scale testing of marine structures, scale experiments are developed but few of them reflect many of the properties of large and complex marine structures. Digital twins also require data fusion from observations or experiments to improve the numerical model agreement with the real-world structure. Such numerical models are the essential part of digital twins representing complex marine structures and providing enhanced forecasts of risk and lifecycle performance. Numerical models of such structural systems are developed and relied on to simulate and ensure system integrity. Vessels and marine structures are subjected to degradation during their service, jeopardizing structural safety and shortening their service life.

Hugin bayesian how to#

In particular, we describe how battery monitoring and control problems related to an underwater and a ground vehicle are solved and how to solve the problem, of assessing the quality of a sonar image related to an underwater vehicle. Based on the HUGIN tool, limited memory influence diagrams (LIMIDs) are used to represent and solve complex problems of diagnosis and control of autonomous ground and underwater vehicles. We describe the application of PGMs to three problems of diagnosis and control of autonomous vehicles. To handle uncertainty, we focus on the use of probabilistic graphical models (PGMs) as implemented in the HUGIN tool. The purpose of the architecture is to assist the operator or piloting system in managing fault detection, risk assessment, and recovery plans under uncertainty. The application is based on a distributed architecture supporting diagnosis and control of autonomous units. We present an application of HUGIN to solve problems related to diagnosis and control of autonomous vehicles. ConclusionĪlthough regression-based methods are still popular and widely used, network-based approaches should be paid more attention, since they capture the complex relationship between variables.

Further application on GWAS of leprosy show Bayesian network still outperforms other methods. For the special wheel network structure, logistic regression had a considerable performance compared to others. ResultsThe simulation results reveal that Bayesian network showed a better performance when the variables were in a network relationship or in a chain structure. Simulations under different scenarios (the input variables are independent or in network relationship) as well as an application were conducted to assess the prediction performance of four typical methods including Bayesian network, neural network, logistic regression and regression splines. It remains a controversy whether the network-based methods have advantageous performance than regression-based methods, and to what extent do they outperform. In stark contrast to network-centric view for complex disease, regression-based methods are preferred in disease prediction, especially for epidemiologists and clinical professionals.