The optimal rate of periodic preventive maintenance to achieve the best availability is studied for Markov systems with multiple degraded operational stages, where the time-to-failure has a hypo-exponential distribution. An analytical expression is developed for the availability of such systems havingnoperational stages, and a necessary and sufficient condition is derived for a non-trivial optimal rate of periodic maintenance to exist. Numerical procedures for finding the optimal rate of periodic maintenance are given, and examples are presented.

This study is motivated by the unexpected failure of a network-based system during the performance of corrective maintenance. This incident highlights the importance of incorporating network connectivity analysis in maintenance planning. This study shows that it is crucial to consider network connectivity when planning maintenance. Procedures to conduct maintenance planning with network connectivity consideration is proposed and discussed

This paper addresses the conditional probability for undetectable burst errors when using two CRC (cyclic redundancy check) codes in tandem, given that non-guaranteed-detectable burst errors have occurred. Three distinctive cases are discussed based on the error positions and the conditional probability of undetectable errors under each case is derived. Because this probability depends on the exact CRC polynomials selected, an upper-bound assessment is provided to address general cases

Assumptions about independence are made when modeling the availability and reliability of large systems. The challenge, then, is to show that the loss of fidelity due to the independence assumptions is not significant. A sensitivity analysis that can cover all the situations for a dynamic system is the focus of this study. We attack this problem by conducting a series of sensitivity analyses on the dependencies identified in the system hierarchy. By showing the importance of the dependencies that have been modeled and the insignificance of the differences caused by the independence assumptions, the appropriateness of the modeling approach is demonstrated. This sensitivity analysis thus earns the analysts confidence in the model.

Systems with global spares, in which a spare can replace any of multiple identical primary modules, are widely used. We present efficient algorithms for approximating the probability distribution of performance level (number of working modules) in systems with global spares and arbitrary module failure distribution. For nondegradable systems with global spares and arbitrary module failure distribution, our algorithms provide the first efficient solution for reliability. For degradable systems with global spares, our algorithms can be used to produce upper and lower bounds on various dependability measures.

Most research on semantic integrity has taken place in the traditional database fields, specifically the relational data model. Advanced models, such as semantic and object-oriented data models, have developed higher level abstractions to increase their expressive power in order to meet the needs of newly emerging application domains. This allows them to incorporate some semantic constraints directly into their schemas. There are, however, many types of restrictions that cannot be expressed solely by these high-level constructs. Therefore we extend the potential of advanced models by augmenting their abstractions with useful set restrictions. In particular, we identify and formulate four of their most common semantic groupings: set groupings, is-a related set groupings, power set groupings, and Cartesian product groupings. For each, we define a number of restrictions that control its structure and composition. We exploit the notion of object identity for the definition of these semantic restrictions. This permits each grouping to capture more subtle distinctions of the concepts in the application environment, as demonstrated by numerous examples throughout this paper. The resulting set of restrictions forms a general framework for integrity constraint management in advanced data models.>

Three of the most common fundamental groupings that are utilized in semantic database models are considered: set groupings, power set groupings, and Cartesian aggregation groupings. For each, useful restrictions that control its structure and composition are defined. This permits each grouping to capture more subtle distinctions of the concepts or situations in the application environment. The resulting set of restrictions forms a framework for integrity constraints in semantic data models. This framework is targeted towards advanced applications, such as computer-aided design, office automation, and artificial intelligence, which require the support of more sophisticated relationships among data than traditional database domains.>