Diagnosability Verification Using Compositional Branching Bisimulation

This paper presents an efficient diagnosability verification technique, based on a general abstraction approach. More specifically, branching bisimulation including state labels with explicit divergence (BBSD) is defined. This bisimulation preserves the temporal logic property that verifies diagnosability. Based on a proposed BBSD algorithm, compositional abstraction for modular diagnosability verification is shown to offer a significant state space reduction in comparison to state-of-the-art techniques. This is illustrated by verifying non-diagnosability analytically for a set of synchronized components, where the abstracted solution is independent of the number of components and the number of observable events

Conflict-driven Hybrid Observer-based Anomaly Detection

This paper presents an anomaly detection method using a hybrid observer -- which consists of a discrete state observer and a continuous state observer. We focus our attention on anomalies caused by intelligent attacks, which may bypass existing anomaly detection methods because neither the event sequence nor the observed residuals appear to be anomalous. Based on the relation between the continuous and discrete variables, we define three conflict types and give the conditions under which the detection of the anomalies is guaranteed. We call this method conflict-driven anomaly detection. The effectiveness of this method is demonstrated mathematically and illustrated on a Train-Gate (TG) system

Reliability analysis of dynamic systems by translating temporal fault trees into Bayesian networks

Classical combinatorial fault trees can be used to assess combinations of failures but are unable to capture sequences of faults, which are important in complex dynamic systems. A number of proposed techniques extend fault tree analysis for dynamic systems. One of such technique, Pandora, introduces temporal gates to capture the sequencing of events and allows qualitative analysis of temporal fault trees. Pandora can be easily integrated in model-based design and analysis techniques. It is, therefore, useful to explore the possible avenues for quantitative analysis of Pandora temporal fault trees, and we identify Bayesian Networks as a possible framework for such analysis. We describe how Pandora fault trees can be translated to Bayesian Networks for dynamic dependability analysis and demonstrate the process on a simplified fuel system model. The conversion facilitates predictive reliability analysis of Pandora fault trees, but also opens the way for post-hoc diagnostic analysis of failures