APPROXIMATE RELIABILITY EXPRESSIONS USING A DECISION TREE APPROACH

In this paper a Decision Tree (DT) algorithm, belonging to the family of rule generation methods, is employed to obtain an Approximate Reliability Expression (ARE) of a network. The main idea is to employ a classification technique, trained on a restricted subset of data, to produce an estimate of the Reliability Expression (RE), which provides reasonably accurate values of the reliability. The algorithm develops a tree by recursively dividing a collection of random examples (training set) for the network at hand, on the basis of the state (operating or failed) of network components. This produces a classifier that can be easily transformed into a set of intelligible disjoint rules. In the examples presented the AREs, built from a very small fraction of the total state space, produce very close reliability estimates, with errors less than 2 %. The excellent results obtained in the experiments show the potential of the method for the evaluation of the reliability of a system through an ARE.

[1]  E. Reingold,et al.  Combinatorial Algorithms: Theory and Practice , 1977 .

[2]  K.K. Aggarwal,et al.  Capacity Consideration in Reliability Analysis of Communication Systems , 1982, IEEE Transactions on Reliability.

[3]  W. J. Rueger.  Reliability Analysis of Networks with Capacity-Constraints and Failures at Branches & Nodes , 1986, IEEE Transactions on Reliability.

[4]  Charles J. Colbourn,et al.  The Combinatorics of Network Reliability , 1987 .

[5]  Y. B. Yoo,et al.  A comparison of algorithms for terminal-pair reliability , 1988 .

[6]  Sieteng Soh,et al.  A computer approach for reliability evaluation of telecommunication networks with heterogeneous link-capacities , 1991 .

[7]  Roy Billinton,et al.  Reliability evaluation of engineering systems : concepts and techniques , 1992 .

[8]  Wenyuan Li,et al.  Reliability Assessment of Electric Power Systems Using Monte Carlo Methods , 1994 .

[9]  Simon Kasif,et al.  A System for Induction of Oblique Decision Trees , 1994, J. Artif. Intell. Res..

[10]  Stavros A. Papathanassiou,et al.  Decision trees for fast security assessment of autonomous power systems with a large penetration from renewables , 1995 .

[11]  David W. Aba,et al.  Comparing Simplification Procedures for Decision Trees on an Economics Classification , 1998 .

[12]  T. Gomez,et al.  Enhancing optimal transmission or subtransmission planning by using decision trees , 1999, PowerTech Budapest 99. Abstract Records. (Cat. No.99EX376).

[13]  Nello Cristianini,et al.  Controlling the Sensitivity of Support Vector Machines , 1999 .

[14]  L. Torgo,et al.  Inductive learning of tree-based regression models , 1999 .

[15]  D. Liberati,et al.  Training digital circuits with Hamming clustering , 2000 .

[16]  João Gama,et al.  Combining classification algorithms , 2000 .

[17]  K. Misra,et al.  AN EFFICIENT MULTI-VARIABLE INVERSION ALGORITHM FOR RELIABILITY EVALUATION OF COMPLEX SYSTEMS USING PATH SETS , 2002 .

[18]  Zhi-Hua Zhou,et al.  Hybrid decision tree , 2002, Knowl. Based Syst..

[19]  Marco Muselli,et al.  Binary Rule Generation via Hamming Clustering , 2002, IEEE Trans. Knowl. Data Eng..

[20]  R. Siciliano,et al.  A statistical approach to growing a reliable honest tree , 2002 .

[21]  José Alí Moreno,et al.  Reliability Evaluation Using Monte Carlo Simulation and Support Vector Machine , 2002, International Conference on Computational Science.

[22]  Marcello Braglia,et al.  The classification and regression tree approach to pump failure rate analysis , 2003, Reliab. Eng. Syst. Saf..

[23]  S ClaudioM.Rocco.  A rule induction approach to improve Monte Carlo system reliability assessment , 2003 .

[24]  Christophe Bérenguer,et al.  A practical comparison of methods to assess sum-of-products , 2003, Reliab. Eng. Syst. Saf..