Fault Diagnosis of the Foundation Brake Rigging System Based on Fault Tree and Bayesian Network

2016 ◽  
Vol 693 ◽  
pp. 1734-1740 ◽  
Author(s):  
Dan Wang ◽  
Ying Tian ◽  
Tai Yong Wang ◽  
Shi Feng Ye ◽  
Qiong Liu
Keyword(s):  
Complex System ◽  
Fault Diagnosis ◽  
Bayesian Network ◽  
Fault Tree ◽  
Fault Analysis ◽  
Inference Process ◽  
Tree Algorithm ◽  
Junction Tree ◽  
Bayesian Tree

Based on the analysis of the advantages and limits of the traditional fault tree and Bayesian network in fault diagnosis, the method that building the fault Bayesian network based on fault tree is proposed in this paper. The paper introduces the correspondences between elements of the fault tree and the fault Bayesian network, also describes the inference process of the junction tree algorithm in the fault Bayesian network. Then with the foundation brake rigging system of CRH380AL EMU as an example, we build up the fault tree, complete its transmission to the fault Bayesian network, proving the superiority of the fault Bayesian tree in fault analysis of the complex system at last.

Bayesian Diagnostic Network Model for Sliding Plug Door

2011 ◽  
Vol 219-220 ◽  
pp. 1496-1499 ◽  
Author(s):  
Hui Chao Shi ◽  
Long Tian ◽  
Liang Wang
Keyword(s):  
Complex System ◽  
Fault Diagnosis ◽  
Expert System ◽  
Bayesian Network ◽  
Knowledge Base ◽  
Network Model ◽  
Model Building ◽  
Bayesian Network Model ◽  
Structure Tree ◽  
System Knowledge

For constructing Bayesian diagnostic network model of complex system is a difficult course, we propose a Bayesian network model auto-construction method based on expert system knowledge base. Bayesian diagnostic network model was built by using the CM structure, and the diagnostic knowledge was organized by product structure tree. We have applied this method to fault diagnosis for sliding plug door, and tested our methodology on many examples of diagnostic problems of sliding plug door, which prove the efficiency of the Bayesian diagnostic network model and model-building method．

scholarly journals Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Zhuqing Bi ◽  
Chenming Li ◽  
Xujie Li ◽  
Hongmin Gao
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Historical Data ◽  
Complex Structure ◽  
Fault Tree ◽  
Uncertain Information ◽  
New Approach ◽  
Pumping Station ◽  
The One ◽  
Tree Method

According to the characteristics of fault diagnosis for pumping station, such as the complex structure, multiple mappings, and numerous uncertainties, a new approach combining T-S fuzzy gate fault tree and Bayesian network (BN) is proposed. On the one hand, traditional fault tree method needs the logical relationship between events and probability value of events and can only represent the events with two states. T-S fuzzy gate fault tree method can solve these disadvantages but still has weaknesses in complex reasoning and only one-way reasoning. On the other hand, the BN is suitable for fault diagnosis of pumping station because of its powerful ability to deal with uncertain information. However, it is difficult to determine the structure and conditional probability tables of the BN. Therefore, the proposed method integrates the advantages of the two methods. Finally, the feasibility of the method is verified through a fault diagnosis model of the rotor in the pumping unit, the accuracy of the method is verified by comparing with the methods based on traditional Bayesian network and BP neural network, respectively, when the historical data is sufficient, and the results are more superior to the above two when the historical data is insufficient.

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