Helge Langseth

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Over the last decade, Bayesian Networks (BNs) have become a popular tool for modelling many kinds of statistical problems. We have also seen a growing interest for using BNs in the reliability analysis community. In this paper we will discuss the properties of the modelling framework that make BNs particularly well suited for reliability applications, and(More)
We investigate the mathematical modelling of maintenance and repair of components that can fail due to a variety of failure mechanisms. Our motivation is to build a model, which can be used to unveil aspects of the quality of the maintenance performed. The model we propose is motivated by imperfect repair models, but extended to model preventive maintenance(More)
We present an approach to efficiently generating an inspection strategy for fault diagnosis. We extend the traditional troubleshooting framework to model nonperfect repair actions, and we include questions. Questions are troubleshooting steps that do not aim at repairing the device, but merely are performed to capture information about the failed equipment,(More)
Classification problems have a long history in the machine learning literature. One of the simplest, and yet most consistently well-performing set of classifiers is the Naïve Bayes models. However, an inherent problem with these classifiers is the assumption that all attributes used to describe an instance are conditionally independent given the class of(More)
Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees and reliability block diagrams). However, limitations in the(More)
We consider the competing risks problem for a repairable unit which at each sojourn may be subject to either a critical failure, or a preventive maintenance (PM) action, where the latter will prevent the failure. It is reasonable to expect a dependence between the failure mechanism and the PM regime. The paper presents a new model, called the repair alert(More)
In this paper we study the problem of exact inference in hybrid Bayesian networks using mixtures of truncated basis functions (MoTBFs). We propose a structure for handling probability potentials called Sum-Product factorized potentials, and show how these potentials facilitate efficient inference based on i) properties of the MoTBFs and ii) ideas similar to(More)
This paper describes a method for parameter learning in Object-Oriented Bayesian Networks (OOBNs). We propose a methodology for learning parameters in OOBNs, and prove that maintaining the object orientation imposed by the prior model will increase the learning speed in object-oriented domains. We also propose a method to efficiently estimate the(More)