Title: Toward the fault identification method for diagnosing strongly t-diagnosable systems under the PMC model
Authors: Tzu-Liang Kung; Hsing-Chung Chen
Addresses: Department of Computer Science and Information Engineering, Asia University, Wufeng, Taichung City, Taiwan ' Department of Computer Science and Information Engineering, Asia University, Wufeng, Taichung City, Taiwan
Abstract: System-level diagnosis is a crucial subject for maintaining the reliability of interconnected systems. Based on the classical notion of one-step diagnosability, strong and conditional diagnosabilities are proposed to reflect a systems' self-diagnostic capability under more realistic assumptions. Zhu et al. (2014) studied the strong networks, which are n-regular and (n - 1)-connected, and in which any two nodes share at most n - 3 common neighbours, and then they proved that a t-regular strong network is strongly t-diagnosable if and only if its conditional diagnosability is greater than t. In this paper, a fault identification algorithm is proposed to diagnose strongly t-diagnosable strong networks under the PMC model.
Keywords: one-step diagnosability; system reliability; strongly diagnosability; conditional diagnosability; PMC model; strong networks; fault identification; fault diagnosis; self-diagnosis.
International Journal of Communication Networks and Distributed Systems, 2015 Vol.15 No.4, pp.386 - 399
Received: 05 Feb 2015
Accepted: 29 Apr 2015
Published online: 12 Oct 2015 *