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

Modelling and Resolution of Dynamic Reliability Problems by the Coupling of Simulink and the Stochastic Hybrid Fault Tree Object Oriented (SHyFTOO) Library

Dependability assessment is one of the most important activities for the analysis of complex systems. Classical analysis techniques of safety, risk, and dependability, like Fault Tree Analysis or Reliability Block Diagrams, are easy to implement, but they estimate inaccurate dependability results due to their simplified hypotheses that assume the components’ malfunctions to be independent from each other and from the system working conditions. Recent contributions within the umbrella of Dynamic Probabilistic Risk Assessment have shown the potential to improve the accuracy of classical dependability analysis methods. Among them, Stochastic Hybrid Fault Tree Automaton (SHyFTA) is a promising methodology because it can combine a Dynamic Fault Tree model with the physics-based deterministic model of a system process, and it can generate dependability metrics along with performance indicators of the physical variables. This paper presents the Stochastic Hybrid Fault Tree Object Oriented (SHyFTOO), a Matlab® software library for the modelling and the resolution of a SHyFTA model. One of the novel features discussed in this contribution is the ease of coupling with a Matlab® Simulink model that facilitates the design of complex system dynamics. To demonstrate the utilization of this software library and the augmented capability of generating further dependability indicators, three di erent case studies are discussed and solved with a thorough description for the implementation of the corresponding SHyFTA models

Addressing Complexity and Intelligence in Systems Dependability Evaluation

Engineering and computing systems are increasingly complex, intelligent, and open adaptive. When it comes to the dependability evaluation of such systems, there are certain challenges posed by the characteristics of “complexity” and “intelligence”. The first aspect of complexity is the dependability modelling of large systems with many interconnected components and dynamic behaviours such as Priority, Sequencing and Repairs. To address this, the thesis proposes a novel hierarchical solution to dynamic fault tree analysis using Semi-Markov Processes. A second aspect of complexity is the environmental conditions that may impact dependability and their modelling. For instance, weather and logistics can influence maintenance actions and hence dependability of an offshore wind farm. The thesis proposes a semi-Markov-based maintenance model called “Butterfly Maintenance Model (BMM)” to model this complexity and accommodate it in dependability evaluation. A third aspect of complexity is the open nature of system of systems like swarms of drones which makes complete design-time dependability analysis infeasible. To address this aspect, the thesis proposes a dynamic dependability evaluation method using Fault Trees and Markov-Models at runtime.The challenge of “intelligence” arises because Machine Learning (ML) components do not exhibit programmed behaviour; their behaviour is learned from data. However, in traditional dependability analysis, systems are assumed to be programmed or designed. When a system has learned from data, then a distributional shift of operational data from training data may cause ML to behave incorrectly, e.g., misclassify objects. To address this, a new approach called SafeML is developed that uses statistical distance measures for monitoring the performance of ML against such distributional shifts. The thesis develops the proposed models, and evaluates them on case studies, highlighting improvements to the state-of-the-art, limitations and future work

Dynamic Fault Tree Analysis: State-of-the-Art in Modeling, Analysis, and Tools

YesSafety and reliability are two important aspects of dependability that are needed to be rigorously evaluated throughout the development life-cycle of a system. Over the years, several methodologies have been developed for the analysis of failure behavior of systems. Fault tree analysis (FTA) is one of the well-established and widely used methods for safety and reliability engineering of systems. Fault tree, in its classical static form, is inadequate for modeling dynamic interactions between components and is unable to include temporal and statistical dependencies in the model. Several attempts have been made to alleviate the aforementioned limitations of static fault trees (SFT). Dynamic fault trees (DFT) were introduced to enhance the modeling power of its static counterpart. In DFT, the expressiveness of fault tree was improved by introducing new dynamic gates. While the introduction of the dynamic gates helps to overcome many limitations of SFT and allows to analyze a wide range of complex systems, it brings some overhead with it. One such overhead is that the existing combinatorial approaches used for qualitative and quantitative analysis of SFTs are no longer applicable to DFTs. This leads to several successful attempts for developing new approaches for DFT analysis. The methodologies used so far for DFT analysis include, but not limited to, algebraic solution, Markov models, Petri Nets, Bayesian Networks, and Monte Carlo simulation. To illustrate the usefulness of modeling capability of DFTs, many benchmark studies have been performed in different industries. Moreover, software tools are developed to aid in the DFT analysis process. Firstly, in this chapter, we provided a brief description of the DFT methodology. Secondly, this chapter reviews a number of prominent DFT analysis techniques such as Markov chains, Petri Nets, Bayesian networks, algebraic approach; and provides insight into their working mechanism, applicability, strengths, and challenges. These reviewed techniques covered both qualitative and quantitative analysis of DFTs. Thirdly, we discussed the emerging trends in machine learning based approaches to DFT analysis. Fourthly, the research performed for sensitivity analysis in DFTs has been reviewed. Finally, we provided some potential future research directions for DFT-based safety and reliability analysis

Contribution à l'analyse de sûreté de fonctionnement basée sur les modèles des systèmes dynamiques, réparables et reconfigurables

Existing works on Model Based Safety Analysis of an automated system generally focus on the process part. Process reconfiguration strategies that are driven by the control are often modeled without failure and with a lack of accuracy. However these strategies have a real impact on the safety of the closed-loop system. In order to improve the relevance of analysis, this impact has to be captured in models. This thesis contributes to modeling and analysis of dynamic repairable reconfigurable systems. Firstly a new modeling formalism is proposed to relevantly take into account different reconfiguration strategies that can fail. This formalism develops and generalizes the principle of Boolean logic Driven Markov Processes (BDMP), and enriches it with Moore machine for formally specifying reconfiguration strategies. In a second stage, two analysis techniques based on a Generalized BDMP (GBDMP) model are described. These techniques allow to obtain a qualitative result: the set of shortest Minimal Cut Sequences (MCS), and a quantitative result: probabilistic indicator of system availability. Finally, a case study coming from the electric power production field is addressed. This case study shows how several industrial problems can be solved in GBDMP framework.Dans les travaux existants, les analyses basées sur les modèles de la Sûreté de Fonctionnement (SdF) d'un système automatisé sont généralement focalisées uniquement sur la partie procédé. Aussi, les stratégies de reconfiguration du procédé - réalisées par le contrôle-commande - ne sont souvent pas modélisées, sinon de manière imprécises et sans échec possible. Pourtant, ces stratégies ont un impact certain sur la SdF du système bouclé, qui doit être pris en compte dans les modèles afin d'améliorer la pertinence des analyses. Le travail dont rend compte cette thèse contribue à la modélisation et à l'analyse de la SdF des systèmes dynamiques, réparables et reconfigurables. Premièrement, un nouveau formalisme de modélisation est proposé pour prendre en compte avec précision les différentes stratégies de reconfiguration du système avec leurs possibles échecs. Ce formalisme développe et généralise le principe des BDMP (Boolean logic Driven Markov Processes en anglais), auxquels il associe des machines de Moore afin de spécifier formellement les stratégies de reconfiguration. Dans un second temps, deux techniques d'analyse basées sur un modèle GBDMP (BDMP Généralisé) sont décrites. Ces techniques permettent d'obtenir un résultat qualitatif : l'ensemble des plus courtes Séquences de Coupe Minimales (SCM), ainsi qu'un résultat quantitatif : indicateur probabiliste de la disponibilité du système. Finalement, la modélisation GBDMP et l'analyse de SdF basée sur un modèle GBDMP sont expérimentées sur un cas d'étude représentatif de plusieurs problématiques industrielles liées au secteur de la production d'énergie électrique

Algorithms and Data Structures for de novo Sequence Assembly

Despite the prodigious throughput of the sequencing instruments currently on the market, the assembly problem remains very challenging, mainly due to the repetitive content of large genomes, uneven sequencing coverage, and the presence of (non-uniform) sequencing errors and chimeric reads. The third generation of sequencing technology such as Pacific Biosciences and Oxford Nanopore offers very long reads at a higher cost per base, but sequencing error rate is much higher. As a consequence, the final assembly is very rarely entirely finished, with one solid sequence per chromosome. Instead, the typical output is an unordered/unoriented set of contiguous regions called contigs. We examine two different but related problems in this study; merging multiple assemblies produced using different assemblers/parameters, and stitching assembled BACs to create a genome-wide assembly.The contribution of this dissertation is twofold. First, compact encoding of finite sets of strings is a classic problem. The manipulation of large sets requires compact data structures that allow for efficient set operations. We defined sequence decision diagrams (SeqDDs), which can encode arbitrary finite sets of strings over an alphabet.Second, reassembly of existing overlapping contigs with the intent to produce a higher quality genome-wide assembly. Second, merge multiple assemblies to produce a higher quality consensus is a compelling problem. We conducted a comparative study of state of the art assembly reconciliation tools, with the intent to use them in assembling a set of approximately four thosands Vigna unguiculata (cowpea) assembled BACs. To accomplish this task, we developed Colored-Positioned de bruijn graph, a variant of the classic de bruijn graph to stitch overlapped assemblies.In this Dissertation we studied and developed data structures and algorithms to merge overlapping assemblies. In particular: (1) Introduced sequence decision diagrams (SeqDDs) to enable compact encoding of finite sets of strings that allow for efficient set operations, among which detecting overlaps. (2) carried a comparative study of state of the art assembly reconciliation tools. and (3) developed tools to cluster overlapped BACs and assemble said clusters. Our assembler implements colored-positioned de bruijn graph, an augmented variant of the classic de bruijn graph, defined in this study