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

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

Vers une Génération Efficace d’Analyses de Sûreté de Fonctionnement dans le Cadre du Déploiement de l’ISO 26262

Cars embed a steadily increasing number of Electric and Electronic Systems. The ISO 26262 standard dis-cusses at length the requirements that these systems must follow in order to guaranty their functional safety.One of the means at hand to ensure the automotive systems safety is to perform safety analyses. During these analyses, practitioners perform FTA and FMEDA in order to evaluate the “trust” that we have in a system. As big quantities of data are handled in those analyses, it would be of great help for them to have the possibility to efficiently generate a part of them and check their consistency.This manuscript is the result of a thesis led on this subject. It focuses on the formalization of the data handled during the safety analyses in order to propose an efficient methodology for their generation. It presents the different works done, from the proposition of formal models for the safety related element behavior representation to the design and implementation of a process for consistent FMEDA generation based on Fault tree patterns.La complexité et la criticité des systèmes électroniques embarqués automobiles est en augmentation constante. Un nouveau standard concernant la sûreté de fonctionnement automobile (ISO 26262) permet d'établir un cadre et de définir des exigences sur les systèmes concernés afin de garantir leur sécurité.Un des moyens permettant de vérifier la sûreté de ces systèmes consiste à effectuer des analyses dites de sureté de fonctionnement. Au cours de ces analyses, les praticiens effectuent des analyses de type FTA et FMEDA afin d’évaluer robustesse et la sûreté de ces systèmes. Lors de ces analyses, les praticiens manipulent une masse de données de plus en plus conséquente ; Ce qui a créé le besoin d’avoir un moyen de générer une partie de ces données efficacement et de vérifier leur cohérence.Dans ce manuscrit, nous détaillons les travaux que nous avons effectués sur ce sujet, en nous concentrant principalement sur la formalisation des données manipulées durant les analyses de sûreté de fonctionnement afin de proposer une méthode efficace pour leur génération. Nous y présentons les différents travaux réalisés, de la proposition de modèles formels pour la représentation du comportement dysfonctionnel « d’élément lié à la sûreté » à la conception et mise en œuvre d'un processus pour la génération de FMEDA cohérentes à partir d’arbres de défaillances

Automated generation of partial Markov chain from high level descriptions

International audienceWe propose an algorithm to generate partial Markov chains from high level implicit descriptions, namely AltaRica models. This algorithm relies on two components. First, a variation on Dijkstra's algorithm to compute shortest paths in a graph. Second, the definition of a notion of distance to select which states must be kept and which can be safely discarded. The proposed method solves two problems at once. First, it avoids a manual construction of Markov chains, which is both tedious and error prone. Second, up the price of acceptable approximations, it makes it possible to push back dramatically the exponential blow-up of the size of the resulting chains. We report experimental results that show the efficiency of the proposed approach

Methodology for automated Petri Net model generation to support Reliability Modelling

As the complexity of engineering systems and processes increases, determining their optimal performance also becomes increasingly complex. There are various reliability methods available to model performance but generating the models can become a significant task that is cumbersome, error-prone and tedious. Hence, over the years, work has been undertaken into automatically generating reliability models in order to detect the most critical components and design errors at an early stage, supporting alternative designs. Earlier work lacks full automation resulting in semi-automated methods since they require user intervention to import system information to the algorithm, focus on specific domains and cannot accurately model systems or processes with control loops and dynamic features. This thesis develops a novel method that can generate reliability models for complex systems and processes, based on Petri Net models. The process has been fully automated with software developed that extracts the information required for the model from a topology diagram that describes the system or process considered and generates the corresponding mathematical and graphical representations of the Petri Net model. Such topology diagrams are used in industrial sectors, ranging from aerospace and automotive engineering to finance, defence, government, entertainment and telecommunications. Complex real-life scenarios are studied to demonstrate the application of the proposed method, followed by the verification, validation and simulation of the developed Petri Net models. Thus, the proposed method is seen to be a powerful tool to automatically obtain the PN modelling formalism from a topology diagram, commonly used in industry, by: - Handling and efficiently modelling systems and processes with a large number of components and activities respectively, dependent events and control loops. - Providing generic domain applicability. - Providing software independence by generating models readily understandable by the user without requiring further manipulation by any industrial software. Finally, the method documented in this thesis enables engineers to conduct reliability and performance analysis in a timely manner that ensures the results feed into the design process

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen