With the increasing complexity of today's engineering systems that contain various component dependencies and degradation behaviors, there has been increasing interest in on-line System Health Management (SHM) capability to continuously monitor (via sensors and other methods of observation) system software, and hardware components for detection and diagnostic of safety-critical systems. Bayesian Network (BN) and their extension for time-series modeling known as Dynamic Bayesian Network (DBN) have been shown by recent studies to be capable of providing a unified framework for system health diagnosis and prognosis. BN has many modeling features, such as multi-state variables, noisy gates, dependent failures, and general posterior analysis. BN...
L'analyse de fiabilité fait partie intégrante de la conception et du fonctionnement du système, en p...
Bayesian networks have established themselves as an indispensable tool in artificial intelligence, ...
In this paper, we review the application of dynamic Bayesian networks to prognostic modelling. An e...
This paper presents a new dependency computational algorithm for reliability inference with dynamic ...
The maintenance optimization of complex systems is a key question. One important objective is to be ...
International audienceThis paper presents a procedure for failure prognostic by using Dynamic Bayesi...
The maintenance optimization of complex systems is a key question. One important objective is to be ...
abstract: Bayesian networks are powerful tools in system reliability assessment due to their flexibi...
Bayesian networks have been applied to many different domains to perform prognostics, reduce risk an...
Dynamic Bayesian networks (DBNs) represent complex time-dependent causal relationships through the u...
Dynamic Bayesian Networks (DBNs) are temporal probabilistic models for reasoning over time. They oft...
In this paper, we present an approach to reliability modeling and analysis based on the automatic c...
Complex engineering systems (CESes), such as nuclear power plants or manufacturing plants, are criti...
AbstractIn this paper, we present an approach to reliability modeling and analysis based on the auto...
This paper develops a generic degradation model based on Dynamic Bayesian Networks (DBN) which predi...
L'analyse de fiabilité fait partie intégrante de la conception et du fonctionnement du système, en p...
Bayesian networks have established themselves as an indispensable tool in artificial intelligence, ...
In this paper, we review the application of dynamic Bayesian networks to prognostic modelling. An e...
This paper presents a new dependency computational algorithm for reliability inference with dynamic ...
The maintenance optimization of complex systems is a key question. One important objective is to be ...
International audienceThis paper presents a procedure for failure prognostic by using Dynamic Bayesi...
The maintenance optimization of complex systems is a key question. One important objective is to be ...
abstract: Bayesian networks are powerful tools in system reliability assessment due to their flexibi...
Bayesian networks have been applied to many different domains to perform prognostics, reduce risk an...
Dynamic Bayesian networks (DBNs) represent complex time-dependent causal relationships through the u...
Dynamic Bayesian Networks (DBNs) are temporal probabilistic models for reasoning over time. They oft...
In this paper, we present an approach to reliability modeling and analysis based on the automatic c...
Complex engineering systems (CESes), such as nuclear power plants or manufacturing plants, are criti...
AbstractIn this paper, we present an approach to reliability modeling and analysis based on the auto...
This paper develops a generic degradation model based on Dynamic Bayesian Networks (DBN) which predi...
L'analyse de fiabilité fait partie intégrante de la conception et du fonctionnement du système, en p...
Bayesian networks have established themselves as an indispensable tool in artificial intelligence, ...
In this paper, we review the application of dynamic Bayesian networks to prognostic modelling. An e...