Neural networks for railway hazard analysis

Neural networks for railway hazard analysis

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Article ID: iaor20063518
Country: Spain
Volume: 4
Issue: 2
Start Page Number: 21
End Page Number: 34
Publication Date: Nov 1999
Journal: Fuzzy Economic Review
Authors: ,
Keywords: fuzzy sets, economics, risk, neural networks
Abstract:

The ability of neural networks to learn by example instead of simply applying a set of formalised rules represents an innovative factor of great interest in terms of studying railway reliability. The proposed method overcomes the functional limits of traditional techniques of hazard rate analysis by taking into account several influencing factors. The primary purpose of this case study is to examine the applicability of neural-network methodology to reliability analysis and obtain an estimate of the failures occurring in a system, however complex it may be. Such an estimate is based on outline parameters (functional, environmental, maintenance, etc.) on which the probability of failure potentially depends. The approach thus allows evaluating the robustness of the design/product under consideration with the aim of making continuous improvement during its future application.

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