5,158 research outputs found
An architecture-based dependability modeling framework using AADL
For efficiency reasons, the software system designers' will is to use an
integrated set of methods and tools to describe specifications and designs, and
also to perform analyses such as dependability, schedulability and performance.
AADL (Architecture Analysis and Design Language) has proved to be efficient for
software architecture modeling. In addition, AADL was designed to accommodate
several types of analyses. This paper presents an iterative dependency-driven
approach for dependability modeling using AADL. It is illustrated on a small
example. This approach is part of a complete framework that allows the
generation of dependability analysis and evaluation models from AADL models to
support the analysis of software and system architectures, in critical
application domains
On cost-effective reuse of components in the design of complex reconfigurable systems
Design strategies that benefit from the reuse of system components can reduce costs while maintaining or increasing dependabilityâwe use the term dependability to tie together reliability and availability. D3H2 (aDaptive Dependable Design for systems with Homogeneous and Heterogeneous redundancies) is a methodology that supports the design of complex systems with a focus on reconfiguration and component reuse. D3H2 systematizes the identification of heterogeneous redundancies and optimizes the design of fault detection and reconfiguration mechanisms, by enabling the analysis of design alternatives with respect to dependability and cost. In this paper, we extend D3H2 for application to repairable systems. The method is extended with analysis capabilities allowing dependability assessment of complex reconfigurable systems. Analysed scenarios include time-dependencies between failure events and the corresponding reconfiguration actions. We demonstrate how D3H2 can support decisions about fault detection and reconfiguration that seek to improve dependability while reducing costs via application to a realistic railway case study
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Current capabilities, requirements and a proposed strategy for interdependency analysis in the UK
The UK government recently commissioned a research study to identify the state-of-the-art in Critical Infrastructure modelling and analysis, and the government/industry requirements for such tools and services. This study (Cetifs) concluded with a strategy aiming to bridge the gaps between the capabilities and requirements, which would establish interdependency analysis as a commercially viable service in the near future. This paper presents the findings of this study that was carried out by CSR, City University London, Adelard LLP, a safety/security consultancy and Cranfield University, defense academy of the UK
Supporting group maintenance through prognostics-enhanced dynamic dependability prediction
Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry
Model-based dependability analysis : state-of-the-art, challenges and future outlook
Abstract: Over the past two decades, the study of model-based dependability analysis has gathered significant research interest. Different approaches have been developed to automate and address various limitations of classical dependability techniques to contend with the increasing complexity and challenges of modern safety-critical system. Two leading paradigms have emerged, one which constructs predictive system failure models from component failure models compositionally using the topology of the system. The other utilizes design models - typically state automata - to explore system behaviour through fault injection. This paper reviews a number of prominent techniques under these two paradigms, and provides an insight into their working mechanism, applicability, strengths and challenges, as well as recent developments within these fields. We also discuss the emerging trends on integrated approaches and advanced analysis capabilities. Lastly, we outline the future outlook for model-based dependability analysis
Rich Interfaces for Dependability: Compositional Methods for Dynamic Fault Trees and Arcade models
This paper discusses two behavioural interfaces for reliability analysis: dynamic fault trees, which model the system reliability in terms of the reliability of its components and Arcade, which models the system reliability at an architectural level. For both formalisms, the reliability is analyzed by transforming the DFT or Arcade model to a set of input-output Markov Chains. By using compositional aggregation techniques based on weak bisimilarity, significant reductions in the state space can be obtained
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