About the Project
Model-based dependability analysis (MBDA) techniques have improved the manageability of the analysis of large systems by (semi-)automatically synthesising safety artefacts such as fault trees from system models. MBDA approaches are typically compositional, meaning that system-level failure analyses can be generated from component-level failure logic and the topology of the system. This compositionality lends itself well to automation and reuse of component failure models across applications, thus allowing rapid evaluation of speculative changes to the system model. For probabilistic analysis of systems, Bayesian networks (BNs) have been increasingly used in the dependability analysis domain because of their ability to combine different sources of information to provide a global safety assessment and to enable robust, probabilistic reasoning in conditions of uncertainty. In the literature, the advantages of BNs have been utilised for fault prediction, detection, identification, and recovery of autonomous systems.
Currently, for dependability analysis of a system, BNs are created either by translating other models like fault trees and reliability block diagram into BNs or based on the opinions of experts regarding the behaviour of the system or based on the historical data about the system. In these approaches, system architectures are rarely associated with BN models themselves. As a result, when the system design evolves over the life-cycle of the system, it would be challenging to adapt the BN models to maintain consistency of the analysis. As MBDA has the ability to associate system models with safety artefacts and can facilitate coevolution of system architecture and safety models, the primary goal of this project is to develop a model-driven approach to automatically generate BN models from formal system models. This will allow rapid probabilistic analysis of systems and enable to produce more meaningful results by refining and synchronising the dependability analysis results with the evolving system designs. Moreover, both predictive and diagnostic analyses can be performed using the BN models. Predictive analysis can determine the failure probability of a system given the failure
probability of the components. At the same time, diagnostic analysis will help to update the prior-belief about the probability of the failure modes based on real time evidence obtained during system operation.
 A. Bobbio, L. Portinale, M. Minichino, and E. Ciancamerla, “Improving the analysis of dependable systems by mapping fault trees into Bayesian networks,” Reliability Engineering & System Safety, 71(3), pp.249-260, 2001.
 A. Amrin, V. Zarikas, and C. Spitas, “Reliability analysis and functional design using Bayesian networks generated automatically by an “Idea Algebra” framework,” Reliability Engineering & System Safety, 180, pp.211-225, 2018.
 D. Codetta-Raiteri and L. Portinale, “Dynamic Bayesian networks for fault detection, identification, and recovery in autonomous spacecraft,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(1), 13-24, 2015.
 P. Weber, G. Medina-Oliva, C. Simon, and B. Iung, “Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas,” Engineering Applications of Artificial Intelligence, 25(4), pp.671-682, 2012.
 S. Getir, L. Grunske, A. van Hoorn, T. Kehrer, Y. Noller, and M. Tichy, “Supporting semi-automatic co-evolution of architecture and fault tree models,” Journal of Systems and Software, 142, 115-135, 2018.
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