This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2009 WRI World Congress on Computer Science and Information Engineering
Long-Distance Oil/Gas Pipeline Failure Rate Prediction Based on Fuzzy Neural Network Model
Los Angeles, California USA
March 31-April 02
ISBN: 978-0-7695-3507-4
With an aging underground long-distance oil/gas pipeline, ever-encroaching population and increasing oil price, the burden on pipeline agencies to efficiently prioritize and maintain the rapidly deteriorating underground utilities is increasing. Failure rate prediction is the most important part of risk assessment, and the veracity of the failure rate impacts the rationality and applicability of the result of the risk assessment. This paper developed a fuzzy artificial neural network model, which is based on failure tree and fuzzy number computing model, for predicting the failure rates of the long-distance oil/gas pipeline. The neural network model was trained and tested with acquired Lanzhou - Chengdu - Chongqing product oil pipeline data, and the developed model was intended to aid in pipeline risk assessment to identify distressed pipeline segments. The gained result based on fuzzy artificial neural network model would be comparatively analyzed with fuzzy failure tree analysis to verify the accuracy of fuzzy artificial neural network model.
Index Terms:
Oil/Gas Pipeline, Failure Rate Prediction, Fuzzy Neural Network
Citation:
Xing-yu Peng, Peng Zhang, Li-qiong Chen, "Long-Distance Oil/Gas Pipeline Failure Rate Prediction Based on Fuzzy Neural Network Model," csie, vol. 5, pp.651-655, 2009 WRI World Congress on Computer Science and Information Engineering, 2009
Usage of this product signifies your acceptance of the Terms of Use.