Design of Dynamic Systems Based on Dynamic Fault Trees and Neural Networks

Reliability design of static systems has been widely developed in recent years. Fault trees (FT), genetic algorithms (GA) and neural networks (NN) are among the most common methodologies used in such tasks. However, because of the complex behavior of dynamic systems, less attention has been paid on their design. With the thorough research into dynamic fault trees (DFT) and neural networks, which could be used in reliability analysis of dynamic systems, the design of dynamic systems becomes possible. In this paper, a hierarchically modular design method based on DFT and NN are proposed to solve this problem. Fault tree of the system is constructed, a linear-time modular method is performed to find out all the static and dynamic subtrees and the reliability demand of each subtree could be determined. Then the static subtrees are optimized using traditional methods, and each dynamic subtree are mapped into feed-forward recursive neural networks, which could be trained to obtain the optimal design parameters

[1]  A. Bobbio,et al.  Dynamic Bayesian Networks for modeling advanced Fault Tree features in dependability analysis , 2005 .

[2]  Joanne Bechta Dugan.  Design of Reliable Systems Using Static & Dynamic Fault , 1998 .

[3]  Yves Dutuit,et al.  A linear-time algorithm to find modules of fault trees , 1996, IEEE Trans. Reliab..

[4]  Manolis Papadrakakis,et al.  Design optimization of steel structures considering uncertainties , 2005 .

[5]  Yansong Ren,et al.  Design of reliable systems using static and dynamic fault trees , 1998 .

[6]  Salvatore J. Bavuso,et al.  Fault trees and Markov models for reliability analysis of fault-tolerant digital systems , 1993 .

[7]  J. Bechta Dugan,et al.  Bridging the gap between systems and dynamic fault tree models , 1999, Annual Reliability and Maintainability. Symposium. 1999 Proceedings (Cat. No.99CH36283).

[8]  Dong Hyawn Kim,et al.  Neural network for design and reliability analysis of rubble mound breakwaters , 2005 .

[9]  J. B. Dugan,et al.  Sensitivity analysis of modular dynamic fault trees , 2000, Proceedings IEEE International Computer Performance and Dependability Symposium. IPDS 2000.

[10]  Mamoun Suliman,et al.  Markov availability models on neural networks , 1992 .

[11]  Salvatore J. Bavuso,et al.  Dynamic fault-tree models for fault-tolerant computer systems , 1992 .

[12]  Ricardo O. Foschi,et al.  Performance-based design and seismic reliability analysis using designed experiments and neural networks , 2004 .

[13]  Mamoun Suliman,et al.  Neural network realization of Markov reliability and fault-tolerance models , 1991 .

[14]  J. Dugan,et al.  A modular approach for analyzing static and dynamic fault trees , 1997, Annual Reliability and Maintainability Symposium.