Rare event simulation for dynamic fault trees

Fault trees (FT) are a popular industrial method for reliability engineering, for which Monte Carlo simulation is an important technique to estimate common dependability metrics, such as the system reliability and availability. A severe drawback of Monte Carlo simulation is that the number of simulations required to obtain accurate estimations grows extremely large in the presence of rare events, i.e., events whose probability of occurrence is very low, which typically holds for failures in highly reliable systems. This paper presents a novel method for rare event simulation of dynamic fault trees with complex repairs that requires only a modest number of simulations, while retaining statistically justified confidence intervals. Our method exploits the importance sampling technique for rare event simulation, together with a compositional state space generation method for dynamic fault trees. We demonstrate our approach using two parameterized sets of case studies, showing that our method can handle fault trees that could not be evaluated with either existing analytical techniques, nor with standard simulation techniques

Supervisory Control Theory in System Safety Analysis

Development of safety critical systems requires a risk management strategy to identify and analyse hazards, and apply necessary actions to eliminate or control them as malfunctions could be catastrophic. Fault Tree Analysis (FTA) is one of the most widely used methods for safety analysis in industrial use. However, the standard FTA is manual, informal, and limited to static analysis of systems. In this paper, we present preliminary results from a model-based approach to address these limitations using Supervisory Control Theory. Taking an example from the Fault Tree Handbook, we present a systematic approach to incrementally obtain formal models from a fault tree and verify them in the tool Supremica. We present a method to calculate minimal cut sets using our approach. These compositional techniques could potentially be very beneficial in the safety analysis of highly complex safety critical systems, where several components interact to solve different tasks

A Hierarchy of Scheduler Classes for Stochastic Automata

Stochastic automata are a formal compositional model for concurrent stochastic timed systems, with general distributions and non-deterministic choices. Measures of interest are defined over schedulers that resolve the nondeterminism. In this paper we investigate the power of various theoretically and practically motivated classes of schedulers, considering the classic complete-information view and a restriction to non-prophetic schedulers. We prove a hierarchy of scheduler classes w.r.t. unbounded probabilistic reachability. We find that, unlike Markovian formalisms, stochastic automata distinguish most classes even in this basic setting. Verification and strategy synthesis methods thus face a tradeoff between powerful and efficient classes. Using lightweight scheduler sampling, we explore this tradeoff and demonstrate the concept of a useful approximative verification technique for stochastic automata

Fault Trees from Data: Efficient Learning with an Evolutionary Algorithm

Cyber-physical systems come with increasingly complex architectures and failure modes, which complicates the task of obtaining accurate system reliability models. At the same time, with the emergence of the (industrial) Internet-of-Things, systems are more and more often being monitored via advanced sensor systems. These sensors produce large amounts of data about the components' failure behaviour, and can, therefore, be fruitfully exploited to learn reliability models automatically. This paper presents an effective algorithm for learning a prominent class of reliability models, namely fault trees, from observational data. Our algorithm is evolutionary in nature; i.e., is an iterative, population-based, randomized search method among fault-tree structures that are increasingly more consistent with the observational data. We have evaluated our method on a large number of case studies, both on synthetic data, and industrial data. Our experiments show that our algorithm outperforms other methods and provides near-optimal results.Comment: This paper is an extended version of the SETTA 2019 paper, Springer-Verla

One Net Fits All: A unifying semantics of Dynamic Fault Trees using GSPNs

Dynamic Fault Trees (DFTs) are a prominent model in reliability engineering. They are strictly more expressive than static fault trees, but this comes at a price: their interpretation is non-trivial and leaves quite some freedom. This paper presents a GSPN semantics for DFTs. This semantics is rather simple and compositional. The key feature is that this GSPN semantics unifies all existing DFT semantics from the literature. All semantic variants can be obtained by choosing appropriate priorities and treatment of non-determinism.Comment: Accepted at Petri Nets 201

Pleiotropic Benefit of Monomeric and Oligomeric Flavanols on Vascular Health - A Randomized Controlled Clinical Pilot Study

BACKGROUND: Cardiovascular diseases are expanding to a major social-economic burden in the Western World and undermine man's deep desire for healthy ageing. Epidemiological studies suggest that flavanol-rich foods (e.g. grapes, wine, chocolate) sustain cardiovascular health. For an evidenced-based application, however, sound clinical data on their efficacy are strongly demanded. METHODS: In a double-blind, randomized, placebo-controlled intervention study we supplemented 28 male smokers with 200 mg per day of monomeric and oligomeric flavanols (MOF) from grape seeds. At baseline, after 4 and 8 weeks we measured macro- and microvascular function and a cluster of systemic biomarkers for major pathological processes occurring in the vasculature: disturbances in lipid metabolism and cellular redox balance, and activation of inflammatory cells and platelets. RESULTS: In the MOF group serum total cholesterol and LDL decreased significantly (P ≤ 0.05) by 5% (n = 11) and 7% (n = 9), respectively in volunteers with elevated baseline levels. Additionally, after 8 weeks the ratio of glutathione to glutathione disulphide in erythrocytes rose from baseline by 22% (n = 15, P<0.05) in MOF supplemented subjects. We also observed that MOF supplementation exerts anti-inflammatory effects in blood towards ex vivo added bacterial endotoxin and significantly reduces expression of inflammatory genes in leukocytes. Conversely, alterations in macro- and microvascular function, platelet aggregation, plasma levels of nitric oxide surrogates, endothelin-1, C-reactive protein, fibrinogen, prostaglandin F2alpha, plasma antioxidant capacity and gene expression levels of antioxidant defense enzymes did not reach statistical significance after 8 weeks MOF supplementation. However, integrating all measured effects into a global, so-called vascular health index revealed a significant improvement of overall vascular health by MOF compared to placebo (P ≤ 0.05). CONCLUSION: Our integrative multi-biomarker approach unveiled the pleiotropic vascular health benefit of an 8 weeks supplementation with 200 mg/d MOF in humans. TRIAL REGISTRATION: ClinicalTrials.gov NCT00742287