A compositional semantics for Repairable Fault Trees with general distributions

Fault Tree Analysis (FTA) is a prominent technique in industrial and scientific risk assessment. Repairable Fault Trees (RFT) enhance the classical Fault Tree (FT) model by introducing the possibility to describe complex dependent repairs of system components. Usual frameworks for analyzing FTs such as BDD, SBDD, and Markov chains fail to assess the desired properties over RFT complex models, either because these become too large, or due to cyclic behaviour introduced by dependent repairs. Simulation is another way to carry out this kind of analysis. In this paper we review the RFT model with Repair Boxes as introduced by Daniele Codetta-Raiteri. We present compositional semantics for this model in terms of Input/Output Stochastic Automata, which allows for the modelling of events occurring according to general continuous distribution. Moreover, we prove that the semantics generates (weakly) deterministic models, hence suitable for discrete event simulation, and prominently for Rare Event Simulation using the FIG tool

Fault Tree Analysis: a survey of the state-of-the-art in modeling, analysis and tools

Fault tree analysis (FTA) is a very prominent method to analyze the risks related to safety and economically critical assets, like power plants, airplanes, data centers and web shops. FTA methods comprise of a wide variety of modelling and analysis techniques, supported by a wide range of software tools. This paper surveys over 150 papers on fault tree analysis, providing an in-depth overview of the state-of-the-art in FTA. Concretely, we review standard fault trees, as well as extensions such as dynamic FT, repairable FT, and extended FT. For these models, we review both qualitative analysis methods, like cut sets and common cause failures, and quantitative techniques, including a wide variety of stochastic methods to compute failure probabilities. Numerous examples illustrate the various approaches, and tables present a quick overview of results

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

Smart railroad maintenance engineering with stochastic model checking

RAMS (reliability, availability, maintenance and safety) requirements are of utmost important for safety-critical systems like railroad infrastructure and signaling systems. Fault tree analysis (FTA) is a widely applied industry standard for RAMS analysis and is often one of the techniques preferred by railways organizations. FTA yields system availability and reliability, and can be used for critical path analysis. It can however not yet deal with a pressing aspect of railroad engineering: maintenance. While railroad infrastructure providers are focusing more and more on managing cost/performance ratios, RAMS can be considered as the performance specification, and maintenance the main cost driver. Methods facilitating the management of this ratio are still very uncommon. This paper presents a powerful, flexible and transparent technique to incorporate maintenance aspects in fault tree analysis, based on stochastic model checking. The analysis and comparison of different maintenance strategies (such as age-based, clockbased and condition-dependent maintenance) and their impact on reliability and availability metrics are thus enabled. Thus, the trade off between cost and RAMS performance is facilitated. To keep the underlying state space small, two aggressive state space reduction techniques are employed namely: compositional aggregation and smart semantics. The approach presented is illustrated using several existing, large fault tree models in a case study from Movares, a major RAMS consultancy firm in the Netherlands

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

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

Rare Event Simulation for non-Markovian repairable Fault Trees

Dynamic Fault Trees (DFT) are widely adopted in industry to assess the dependability of safety-critical equipment. Since many systems are too large to be studied numerically, DFTs dependability is often analysed using Monte Carlo simulation. A bottleneck here is that many simulation samples are required in the case of rare events, e.g. in highly reliable systems where components fail seldomly. Rare Event Simulation (RES) provides techniques to reduce the number of samples in the case of rare events. We present a RES technique based on importance splitting, to study failures in highly reliable DFTs. Whereas RES usually requires meta-information from an expert, our method is fully automatic: by cleverly exploiting the fault tree structure we extract the so-called importance function. We handle DFTs with Markovian and non-Markovian failure and repair distributions (for which no numerical methods exist) and show the efficiency of our approach on several case studies

A Hierarchical Approach for Dynamic Fault Trees Solution Through Semi-Markov Process

Dynamic fault tree (DFT) is a top-down deductive technique extended to model systems with complex failure behaviors and interactions. In two last decades, different methods have been applied to improve its capabilities, such as computational complexity reduction, modularization, intricate failure distribution, and reconfiguration. This paper uses semi-Markov process (SMP) theorem for DFT solution with the motivation of obviating the model state-explosion, considering nonexponential failure distribution through a hierarchical solution. In addition, in the proposed method, a universal SMP for static and dynamic gates is introduced, which can generalize dynamic behaviors like functional dependencies, sequences, priorities, and spares in a single model. The efficiency of the method regarding precision and competitiveness with commercial tools, repeated events consideration, computational complexity reduction, nonexponential failure distribution consideration, and repairable events in DFT is studied by a number of examples, and the results are then compared to those of the selected existing methods