Abstract Discrete Bayesian Belief Network (BBN) has become a popular method for the
analysis of complex systems in various domains of application. One of its pillar is the …

Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and
troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals …

Probabilistic graphical models (PGMs) can effectively deal with the problems of energy
consumption and occupancy prediction, fault detection and diagnosis, reliability analysis …

Autonomous Vehicles have the potential to change the urban transport scenario. However,
to be able to safely navigate autonomously they need to deal with faults that its components …

This paper proposes a systematized presentation and a terminology for observations in a
Bayesian network. It focuses on the three main concepts of uncertain evidence, namely …

This paper investigates a new scheme for fault detection and isolation based on time series
and data analysis. This scheme is applied in wind turbines which are used to tap the …

Bayesian network (BN) is a popularly used approach for risk analysis. Because it is a
graphic model being able to deal with randomness yet unable to model ambiguity, the fuzzy …

Successful diagnosis of system failures in nuclear power plants plays a central role in
emergency response. Existing research focuses on diagnosis of operational failures that …

This paper proposes the definition of the architecture of an evolving fuzzy neural network
based on self-organizing direction aware data partitioning through stochastic processes …

Faulty feeder detection is essential for maintaining the security and stability of energy supply
in distribution networks. However, it is rather difficult to identify a specific faulty feeder owing …