Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory

By O. Basir, Xiaohong Yuan. Information Fusion, 2007

469 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/eee55b044876596a98a5dab4861e32865eace82c

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In Artificial Intelligence

In Control and Systems Engineering

In Statistics and Probability

Citations in the corpus

A Modified TOPSIS Method Based onDNumbers and Its Applications in Human Resources Selection (2016, background): “With the superiority in dealing with uncertain information and the practicability in engineering, a number of applications of D-S evidence theory have been published in the literature indicating its widespread for fault diagnosis [50, 51], pattern recognition [52–54], supplier selection [55, 56], an”
Supplier selection based on evidence theory and analytic network process (2016, methodology): “It has been applied in many fields of science and engineering to describe uncertainty: decision analysis, pattern recognition, risk assessment, supplier selection and others.(19,52,67) For example, Beynon et al.”
Fusion Techniques for Reliable Information: A Survey (2010, background): “Failures normally produce the change in system dynamic and pose a significant risk [5].”
Trusted Multi-View Classification With Dynamic Evidential Fusion (2022, background): “DS evidence theory has also been extensively studied in multiview learning, which mainly focuses on improving the Dempster’s combination rule [17], [76], [77], [78], [79], [80].”
Nonintrusive appliance load monitoring: Review and outlook (2011, methodology): “Several tools and solutions, e.g., those based on the Dempster-Shafer evidence theory [ 41 ] have been developed in this field of science.”
A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm (2017, background): “Basir and Yuan (2007) proposed a preliminary review of evidence theory and explained how the multi-sensor engine diagnosis problem can be framed in the context of this theory, in terms of faults frame of discernment, mass functions and the rule for combining pieces of evidence.”
Fault Diagnosis of Rotating Machinery Based on Multisensor Information Fusion Using SVM and Time-Domain Features (2014, methodology): “The decision vectors are then fused using decision-level fusion techniques such as voting strategy, Bayesian method, behaviorknowledge space, andDempster-Shafer theory [11].”