Developing a Bayesian Network Model Based on a State and Transition Model for Software Defect Detection

2012 ◽  
Author(s):  
Nipat Jongsawat ◽  
Wichian Premchaiswadi
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Defect Detection ◽  
Transition Model ◽  
Bayesian Network Model ◽  
Model Based ◽  
Software Defect ◽  
State And Transition Model

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 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

Cognitive Diagnostic Assessment of Power Supply Magnetic Components Design Based on Bayesian Network

2020 ◽  
Vol 12 (8) ◽  
pp. 1044-1053
Author(s):  
Yi Kuang ◽  
Bin Duan ◽  
Mengping Lv ◽  
Junfeng Wu
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Power Supply ◽  
Evaluation Method ◽  
Assessment System ◽  
Diagnostic Assessment ◽  
Bayesian Network Model ◽  
Model Based ◽  
Magnetic Components ◽  
Cognitive Diagnostic Assessment

The power electronics engineering education is aimed at helping students in becoming qualified electronics engineers. However, the existing evaluation method cannot reflect the students’ performance in knowledge-structure and skillsets objectively and accurately. To address these issues and improve the effectiveness of the current evaluation method in the field, we propose a Bayesian network model-based cognitive diagnostic assessment method and demonstrate it to evaluate students’ knowledge and skills condition with the switched-mode power supply (SMPS) magnetic components design task. The paper starts with a brief introduction to the SMPS inductor design. It continues with the Bayesian network model-based inductor proficiency model, inductor evidence model, and the task model for power magnetics volume and weight in the aerospace SMPS. Then we identify the parameters in the graded response model, relations among variables, and calculate the conditional probability between variables. Finally, we use Markov Chain Monte Carlo estimation method to get the posterior probability distribution of proficiency variables by OpenBUGS. The results show that this cognitive diagnostic assessment system can effectively reflect the students’ study performances, scientifically advise their future study plans, and effectively achieve the education goals.

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