scholarly journals Building a Bayesian Network Model Based on the Combination of Structure Learning Algorithms and Weighting Expert Opinions Scheme

10.5772/38586 ◽  
2012 ◽  
Author(s):  
Wichian Premchaiswadi ◽  
Nipat Jongsawat
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Structure Learning ◽  
Learning Algorithms ◽  
Bayesian Network Model ◽  
Model Based ◽  
Expert Opinions

Probabilistic Model-Based Fault Diagnosis of the Rotor System

2007 ◽  
Author(s):  
Jiye Shao ◽  
Rixin Wang ◽  
Jingbo Gao ◽  
Minqiang Xu
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Network Model ◽  
Rotating Machinery ◽  
Rotor System ◽  
Bayesian Network Model ◽  
Diagnosis System ◽  
Model Based ◽  
Core Components ◽  
Fault Diagnosis System

The rotor is one of the most core components of the rotating machinery and its working states directly influence the working states of the whole rotating machinery. There exists much uncertainty in the field of fault diagnosis in the rotor system. This paper analyses the familiar faults of the rotor system and the corresponding faulty symptoms, then establishes the rotor’s Bayesian network model based on above information. A fault diagnosis system based on the Bayesian network model is developed. Using this model, the conditional probability of the fault happening is computed when the observation of the rotor is presented. Thus, the fault reason can be determined by these probabilities. The diagnosis system developed is used to diagnose the actual three faults of the rotor of the rotating machinery and the results prove the efficiency of the method proposed.

scholarly journals Analyzing the Effects of the Dwelling Characteristics on Space Heating Costs with the Rank Correlation Bayesian Network Model

2021 ◽  
Author(s):  
Volkan Sevinç
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Network Model ◽  
Building Materials ◽  
Structure Learning ◽  
Rank Correlation ◽  
Space Heating ◽  
Bayesian Network Model ◽  
Heating Systems ◽  
External Wall

Abstract Energy is one of the main concerns of humanity because energy resources are limited and costly. In order to reduce the costs and to use the energy for space heating effectively, new building materials, techniques and insulations facilities are being developed. Therefore, it is important to know which factors affect the space heating costs. This study aims to introduce the novel Rank Correlation Bayesian Network model and its application in analyzing the effects of dwelling characteristics on the space heating costs. The results show that the constructed Rank Correlation Bayesian Network model performed better than the Bayesian networks models estimated by Bayesian search, PC and Greedy Thick Thinning algorithms, which are kinds of structure learning algorithms having different kinds of estimation mechanisms to build Bayesian networks. The constructed Rank Correlation Bayesian Network model shows that the space heating costs of the dwellings are mostly affected by the heating systems used. Coal stoves, air conditioners and electric stoves appear to be the costliest heating systems. The second most important factor appears to be the existence of external wall insulation. The lack of external wall insulation almost doubles the space heating costs. The third most important factor is the building age. Dwellings on the ground floors and the first floors appear to pay the highest space heating costs. Therefore, dwellings on these floors need to be more effectively insulated. As the size of the dwelling increases the heating cost increases too. Another result is that facing directions and floor levels of the dwellings have the least effects on their space heating.

scholarly journals Performance of a Bayesian-Network-Model-Based BCI Using Single-Trial EEGs

2015 ◽  
Vol E98.D (11) ◽  
pp. 1976-1981
Author(s):  
Maiko SAKAMOTO ◽  
Hiromi YAMAGUCHI ◽  
Toshimasa YAMAZAKI ◽  
Ken-ichi KAMIJO ◽  
Takahiro YAMANOI
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Single Trial ◽  
Bayesian Network Model ◽  
Model Based
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