Next Article in Journal
Small-Signal Modeling and Analysis for a Wirelessly Distributed and Enabled Battery Energy Storage System of Electric Vehicles
Previous Article in Journal
Viscoelastic Properties of Asphalt Mixtures with Different Modifiers at Different Temperatures Based on Static Creep Tests
Open AccessArticle

Fault Inference of Electronic Equipment Based on Multi-State Fuzzy Bayesian Network

1
College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China
2
Department of Communication, National Digital Switching System Engineering and Technology R&D Center (NDSC), Zhengzhou 450002, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(20), 4248; https://doi.org/10.3390/app9204248
Received: 1 August 2019 / Revised: 20 September 2019 / Accepted: 29 September 2019 / Published: 11 October 2019
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
The aim of this study is to extend the directive function of fault inference in test and diagnosis system for electronic equipment. There are many problems, such as presence of various types of uncertain information in test set of electronic equipment, frequent degenerative faults, complex relationships of modules, multiple fault modes, existence of fuzzy interval in fault state, and interaction of each module. In view of these problems, the total membership degree of faults is commonly synthesized based on weights of multiple test indicators and normal membership degree of a single indicator. On this basis, this study builds the model for inferring fault states of leaf and root nodes based on multi-state triangular fuzzy Bayesian network (BN). Finally, this research carried out feasibility analysis on fault inference of a super-heterodyne receiver, thus verifying the efficiency and applicability of the method proposed in the study. View Full-Text
Keywords: multi-state Bayesian network; electronic equipment; fault inference; membership degree of fault multi-state Bayesian network; electronic equipment; fault inference; membership degree of fault
Show Figures

Figure 1

MDPI and ACS Style

Wang, L.; Zhou, D.; Zhang, H.; Tian, H.; Zou, C.; Wang, X. Fault Inference of Electronic Equipment Based on Multi-State Fuzzy Bayesian Network. Appl. Sci. 2019, 9, 4248.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop