Research of Fault Diagnosis Based on Bayesian Network for Air Brake System

San Tong Zhang

doi:10.4028/www.scientific.net/AMR.143-144.629

Paper Titles

The Numerical Simulation of Pure-Oil Lubrication Journal Bearing
p.609

Research on Identification Theories of Bridge Damage
p.614

A Novel Heuristic Genetic Algorithm Based on Simulated Annealing Strategy for Intelligent Computing
p.619

Compression Resistance of Magnetorheological Fluid
p.624

Research of Fault Diagnosis Based on Bayesian Network for Air Brake System
p.629

Multi-Step Prediction Algorithm for State Prediction Model
p.634

Hybrid Effect of Carbon Fiber on Piezoresistivity of Carbon Nanotube Cement-Based Composite
p.639

Self-Sensing Property of Cementitious Nanocomposites Hybrid with Nanophase Carbon Nanotube and Carbon Black
p.644

Anomalous Human Activity Detection Based on Online One-Class SVM with N-Grams Kernel
p.648

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 143-144Research of Fault Diagnosis Based on Bayesian...

Research of Fault Diagnosis Based on Bayesian Network for Air Brake System

Abstract:

A method for solving the fault diagnosis problem of air brake system based on probabilistic approach is presented. The fault diagnosis model based on Bayesian network was built for the uncertainty characteristic of fault in the air brake system. Through evaluating the characteristic of Bayesian networks in the diagnosis inference and model expression, it is demonstrated that this method can solve the uncertain problems in fault diagnosis. The test result has shown that the Bayesian network model is effective in fault diagnosis of the air brake system.

You might also be interested in these eBooks

Smart Materials and Intelligent Systems, SMIS2010

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 143-144)

Pages:

629-633

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.143-144.629

Citation:

Cite this paper

Online since:

October 2010

Authors:

San Tong Zhang

Keywords:

Air Brake System, Bayesian Network (BN), Fault Diagnosis

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Zhang Bing. Theory and Analysis Research of Monitoring and Detecting the Key Components of Train[Ph.D. ]. (in Chinese). Chengdu: Southwest Jiaotong University, (2007).

Google Scholar

[2] Jun Fu, He Wei, et al. A Study of Fault Diagnosis Based on Bayesian Networks[J](in Chinese). Journal of Shanghai Maritime University. 2001, 22 (3) : 68-72.

Google Scholar

[3] J Pearl Fusion. Propagation and Structuring in Belief Networks[J]. Artificial Intelligence, 1986, 29(3): 241-288.

DOI: 10.1016/0004-3702(86)90072-x

Google Scholar

[4] Andrews J D, Morgan J M. Application of the Digraph Method of Fault Tree Construction to Process Plant[J]. Reliability Engineering and System Safety, 2003, 14: 85 -106.

DOI: 10.1016/0143-8174(86)90045-4

Google Scholar

[5] Andrews J D, Brennan G. Application of the Digraph Method of Fault Tree Construction to a Complex Control Configuration[J]. Reliability Engineering and System Safety, 2002, 28: 357-384.

DOI: 10.1016/0951-8320(90)90120-c

Google Scholar

[6] Kevin Patrick Murphy Dynamic Bayesian Networks: Representation, Inference and Learning. Computer Science[Ph.D. ]. University of California, Berkeley, (2002).

Google Scholar

[7] Hua Bin. Application Research of Bayesian Networks into Coudition-Based Maintenance System of Hydroelectric Set[Ph.D. ](in Chinese). Wuhan: Huazhong University of Science and Technology, (2004).

Google Scholar