Similarity Networks for the Construction of Multiple-Faults Belief Networks
A similarity network is a tool for constructing belief networks for the diagnosis of a single fault. In this paper, we examine modifications to the similarity-network representation that facilitate the construction of belief networks for the diagnosis of multiple coexisting faults.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment
We develop and extend existing decision-theoretic methods for troubleshooting a nonfunctioning device. Traditionally, diagnosis with Bayesian networks has focused on belief updating---determining the probabilities of var…
A Counterfactual Reasoning Framework for Fault Diagnosis in Robot Perception Systems
Perception systems provide a rich understanding of the environment for autonomous systems, shaping decisions in all downstream modules. Hence, accurate detection and isolation of faults in perception systems is important…
Fault DiagnosisModeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification
Detecting faults in electrical power grids is of paramount importance, either from the electricity operator and consumer viewpoints. Modern electric power grids (smart grids) are equipped with smart sensors that allow to…
General ClassificationOne-Class ClassificationOne-class classifierConditioning on Disjunctive Knowledge: Defaults and Probabilities
Many writers have observed that default logics appear to contain the "lottery paradox" of probability theory. This arises when a default "proof by contradiction" lets us conclude that a typical X is not a Y where Y is an…
Canonical Variate Dissimilarity Analysis for Process Incipient Fault Detection
Early detection of incipient faults in industrial processes is increasingly becoming important, as these faults can slowly develop into serious abnormal events, an emergency situation, or even failure of critical equi…
Fault Detection