Toward the fault identification method for diagnosing strongly t-diagnosable systems under the PMC model. (2015)

• Record Type: Journal Article
• Authors: Kung, Tzu-Liang; Chen, Hsing-Chung
• Is Part Of: International journal of communication networks and distributed systems. Volume 15:Number 4(2015)
• Page Start: 386
• Page End: 399
• Publication Date: 2015
• Subjects: one-step diagnosability -- system reliability -- strongly diagnosability -- conditional diagnosability -- PMC model -- strong networks -- fault identification -- fault diagnosis -- self-diagnosis; Computer networks -- Periodicals; Telecommunication systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; 004.6
• Languages: English
• ISSNs: 1754-3916

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.