Abstract
Graphical models are statistical models supported on a graph structure: nodes represent random variables, and missing edges represent probabilistic relationship of conditional independence. This makes them suited to model the behavior of complex systems that are difficult to model through mathematical equations. In this work, this possibility is exploited in a context of diagnostics and fault detection. Specifically, the fault detection problem is reduced to the evaluation of a conditional probability. The relevant conditional distribution is derived from the analysis of a suitable graphical model taking advantage of the so‐called Markov properties.As a case study, we consider diagnostics of a hybrid bus, characterized by the combined use of a diesel engine and an electrical engine. The aim of the study is to verify the correct operation of the electrical system, in particular the status of the battery. Copyright © 2014 John Wiley & Sons, Ltd.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
