Predicting system lifetime is important to ensure safe and reliable operation of products, which requires integrated modeling based on multi-level, multi-sensor information. However, lifetime characteristics of equipment in a system are different and failure mechanisms are inter-coupled, which leads to complex logical correlations and the lack of a uniform lifetime measure. Based on a Bayesian network (BN), a lifetime prediction method for systems that combine multi-level sensor information is proposed. The method considers the correlation between accidental failures and degradation failure mechanisms, and achieves system modeling and lifetime prediction under complex logic correlations. This method is applied in the lifetime prediction of ...
This paper proposes a methodology to apply Bayesian networks to structural system reliability reasse...
This article presents a methodology for reliability prediction during the design phase of mechatroni...
Testing the reliability of Smart Power semiconductor devices is highly time and cost consuming. Neve...
This paper develops a generic degradation model based on Dynamic Bayesian Networks (DBN) which predi...
Accurate prediction of remaining useful life (RUL) plays a critical role in optimizing condition-bas...
The increased system complexity in electronic products brings challenges in a system level reliabili...
Reliability prediction is crucial for aircraft maintenance and spare part inventory decisions. These...
Predictive maintenance based on performance degradation is a crucial way to reduce maintenance costs...
IIn recent years, reliability of DIO modules has been drawing much attention from manufacturing comp...
For system failure prediction, automatically modeling from historical failure dataset is one of the ...
International audienceIn this paper, a data-driven method for remaining useful life (RUL) prediction...
In this paper a generic degradation model based on Dynamic Bayesian Networks (DBN) which predicts th...
International audienceFor dealing with uncertainty in Remaining Useful Life (RUL) predictions, numer...
The maintenance optimization of complex systems is a key question. One important objective is to be ...
Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate...
This paper proposes a methodology to apply Bayesian networks to structural system reliability reasse...
This article presents a methodology for reliability prediction during the design phase of mechatroni...
Testing the reliability of Smart Power semiconductor devices is highly time and cost consuming. Neve...
This paper develops a generic degradation model based on Dynamic Bayesian Networks (DBN) which predi...
Accurate prediction of remaining useful life (RUL) plays a critical role in optimizing condition-bas...
The increased system complexity in electronic products brings challenges in a system level reliabili...
Reliability prediction is crucial for aircraft maintenance and spare part inventory decisions. These...
Predictive maintenance based on performance degradation is a crucial way to reduce maintenance costs...
IIn recent years, reliability of DIO modules has been drawing much attention from manufacturing comp...
For system failure prediction, automatically modeling from historical failure dataset is one of the ...
International audienceIn this paper, a data-driven method for remaining useful life (RUL) prediction...
In this paper a generic degradation model based on Dynamic Bayesian Networks (DBN) which predicts th...
International audienceFor dealing with uncertainty in Remaining Useful Life (RUL) predictions, numer...
The maintenance optimization of complex systems is a key question. One important objective is to be ...
Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate...
This paper proposes a methodology to apply Bayesian networks to structural system reliability reasse...
This article presents a methodology for reliability prediction during the design phase of mechatroni...
Testing the reliability of Smart Power semiconductor devices is highly time and cost consuming. Neve...