Multistate phased-mission systems (MS-PMS) are multistate systems subject to multiple, consecutive, and nonoverlapping phases of operation. The challenges in analyzing MS-PMS reside in the dynamic system configuration, failure criteria, and component state transition behavior in different phases, as well as thes-dependence across different phases and among different states of a given component. Existing methods for the reliability analysis of MS-PMS are either based on monolithic Markov models that suffer from the well-known state explosion problem, or using a hierarchical strategy that can only handle ordered component states. This paper presents integrated modeling approaches for the reliability analysis of repairable MS-PMS with both ordered and unordered component states. The proposed methods integrate efficient decision diagram models for representing the system structure function and incorporating the unordered/ordered component states at the system level, and Markov models for describing dependence and transition behaviors at the component level. The application and advantages of the proposed approaches are illustrated through a case study in which the reliability for a sequence of tasks in a multistate distributed computing system is analyzed.

A distinct characteristic of multistate systems (MSS) is that the systems and/or their components may exhibit multiple performance levels (or states) varying from perfect operation to complete failure. MSS can model behaviors such as shared loads, performance degradation, imperfect fault coverage, standby redundancy, limited repair resources, and limited link capacities. The nonbinary state property of MSS and their components as well as dependencies existing among different states of the same component make the analysis of MSS difficult. This paper proposes efficient algorithms for analyzing MSS using multivalued decision diagrams (MDD). Various reliability, availability, and performability measures based on state probabilities or failure frequencies are considered. The application and advantages of the proposed algorithms are demonstrated through two examples. Furthermore, experimental results on a set of benchmark examples are presented to illustrate the advantages of the proposed MDD-based method for the performability analysis of MSS, as compared to the existing methods.

Multistate systems (MSS) are systems in which the system and its components are characterized by multiple states or performance levels. Component importance or sensitivity analysis facilitates the identification of vulnerabilities within the system, and aids in the quantification of criticalities of the system components. Multistate component importance analysis poses unique challenges to existing methods that are primarily based on binary-state applications. This paper presents an analytical method based on multistate multivalued decision diagrams (MMDD) for multistate component importance analysis. The contribution of this work is two-fold: 1) a novel, efficient algorithm for directly generating an MMDD model from multistate capacity network specifications without inefficient enumeration of multistate minimal path or cut vectors; and 2) an efficient, exact MMDD-based approach for evaluating MSS reliability and importance measures. The advantages of the proposed method are illustrated through a comparison with existing methods, and through detailed analyses of three case studies.

Decision diagrams are graphical structures based on Shannon's decomposition. They have been extensively used for representing and manipulating logic functions in areas such as circuit verification, compact Markov chain representation, and symbolic model checking. However, their applicability in reliability modeling and analysis has only been recently studied. Moreover, the study had been mostly restricted to binary-state systems in which both the system and its components are either operational, or failed. Nevertheless, many practical systems are multi-state systems (MSS) in which both the system and its components may reside at multiple (more than two) performance levels (or states), varying from perfect operation to complete failure. This paper presents three forms of decision diagrams for the modeling and analysis of MSS: binary decision diagrams, logarithmically encoded binary decision diagrams, and multi-valued decision diagrams. We present both separated, and shared methods based on these decision diagrams. Comprehensive complexity analysis, and performance comparisons among these methods, are conducted with both mathematical, and empirical approaches.

This paper considers the problem of evaluating the reliability of hierarchical systems subject to common-cause failures (CCF); and dynamic failure behavior such as spares, functional dependence, priority dependence, and dependence caused by multi-phased operations. We present a separable solution that has low computational complexity, and which is easy to integrate into existing analytical methods. The resulting approach is applicable to Markov analyses, and combinatorial models for the modular analysis of the system reliability. We illustrate the approach, and the advantages of the proposed approach, through the detailed analyses of two examples of dynamic hierarchical systems subject to CCF.

Multistate systems (MSS) are systems in which both the systems, and/or their components may exhibit multiple performance levels or states. MSS can model complex behaviors such as shared loads, performance degradation, imperfect fault coverage, standby redundancy, and limited repair resources. The non-binary state property of MSS, and their components makes the analysis of MSS challenging. In this paper, we propose efficient logarithmically-encoded binary decision diagram (LBDD)-based methods for analysing MSS. The application and advantages of the proposed LBDD-based approaches, as compared to the existing binary decision diagram-based approaches, are demonstrated through the analyses of practical MSS examples, and a set of benchmark examples.

Many combinatorial reliability models assume binary designation of states for both systems and their components. In many real applications, however, systems and their components may have more than two states (or levels of performance) varying from perfect operation to complete failure. In this paper, we present two combinatorial decision diagram based models that have been proposed for the analysis of multistate systems: multistate binary decision diagrams (MBDD) based approach and multistate multivalued decision diagrams (MMDD) based approach. And we conduct an empirical performance comparison between those two methods in terms of model size and computational complexity via two illustrative examples.

In this paper we consider the problem of modeling and evaluating the infrastructure communication reliability (ICR) of wireless sensor networks (WSN). Our approach is progressive and based on reduced ordered binary decision diagrams (ROBDD). Due to the nature of the ROBDD and the reduction scheme used in the progressive process, the approach is computationally efficient and is easy to implement. We also incorporate the consideration of common cause failures (CCF) into the reliability analysis of WSN. We illustrate the application and advantages of our approach by working through the analysis of an example WSN

We consider the problem of reliability modeling and analysis of hierarchical clustered wireless sensor networks (WSN) in this paper. We propose reliability measures that integrate the conventional connectivity-based network reliability with the sensing coverage measure indicating the quality of service (QoS) of the WSN. And we propose a progressive approach for evaluating such coverage-oriented QoS reliability. Our approach is unique in that we analyze a more practical and generic hierarchical clustered WSN. We also incorporate the consideration of some dynamic dependencies, like the standby spare behavior, by proposing a modular scheme