41 research outputs found

    Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review

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    YesSystem safety, reliability and risk analysis are important tasks that are performed throughout the system lifecycle to ensure the dependability of safety-critical systems. Probabilistic risk assessment (PRA) approaches are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to, Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). Growing complexity of modern systems and their capability of behaving dynamically make it challenging for classical PRA techniques to analyse such systems accurately. For a comprehensive and accurate analysis of complex systems, different characteristics such as functional dependencies among components, temporal behaviour of systems, multiple failure modes/states for components/systems, and uncertainty in system behaviour and failure data are needed to be considered. Unfortunately, classical approaches are not capable of accounting for these aspects. Bayesian networks (BNs) have gained popularity in risk assessment applications due to their flexible structure and capability of incorporating most of the above mentioned aspects during analysis. Furthermore, BNs have the ability to perform diagnostic analysis. Petri Nets are another formal graphical and mathematical tool capable of modelling and analysing dynamic behaviour of systems. They are also increasingly used for system safety, reliability and risk evaluation. This paper presents a review of the applications of Bayesian networks and Petri nets in system safety, reliability and risk assessments. The review highlights the potential usefulness of the BN and PN based approaches over other classical approaches, and relative strengths and weaknesses in different practical application scenarios.This work was funded by the DEIS H2020 project (Grant Agreement 732242)

    Model-based development of energy-efficient automation systems

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    Der Energieverbrauch ist ein immer wichtigeres Entscheidungskriterium, das bei der Suche nach guten architektonischen und gestalterischen Alternativen technischer Systeme einbezogen werden muss. Diese Monographie stellt eine Methodik für das modellbasierte Engineering energieeffizienter Automatisierungssysteme vor. In dieser Monografie wird ein eingebettetes System als eine Kombination der Prozessorhardware und des Softwareteils betrachtet. Im entwickelten Verfahren wird der erste Teil durch ein Betriebsmodell (operational model) beschrieben, das alle möglichen Zustände und Übergänge des betrachteten Systems darstellt. Der letzte Teil wird durch ein Anwendungsmodell (application model) repräsentiert, das den Arbeitsablauf eines konkreten für dieses System erstellten Programms widerspiegelt. Gemeinsam werden die beiden Modelle in ein stochastisches Petri-Netz umgewandelt, um eine Analyse des Systems zu ermöglichen. Die entwickelten Transformationsregeln werden vorgestellt und mathematisch beschrieben. Es ist dann möglich, die Leistungsaufnahme des Systems mittels einer Standardauswertung von Petri-Netzen vorherzusagen. Die UML (vereinheitlichte Modellierungssprache) wird in dieser Monographie für die Modellierung der Echtzeitsysteme verwendet. Die mit dem MARTE-Profil (Modellierung und Analyse der Echtzeit- und eingebetteten Systeme) erweiterten Zustandsübergangsdiagramme sind für die Modellierung und Leistungsbewertung ausgewählt. Die vorgestellte Methodik wird durch eine Implementierung der notwendigen Algorithmen und grafischen Editoren in der integrierten Entwicklungsumgebung TimeNET unterstützt. Die entwickelte Erweiterung implementiert die vorgestellte Methode zur Modellierung und Bewertung des Energieverbrauchs basierend auf den erweiterten UML-Modellen, die nun automatisch in ein stochastisches Petri-Netz transformiert werden können. Der Energieverbrauch des Systems kann dann durch die Analyse-Module für stochastische Petri-Netze von TimeNET vorhergesagt werden. Die Vorteile der vorgeschlagenen Methode werden anhand von Anwendungsbeispielen demonstriert.Power consumption is an increasingly important decision criterion that has to be included in the search for good architectural and design alternatives of technical systems. This monograph presents a methodology for the model-based engineering of energy-aware automation systems. In this monograph, an embedded system is considered as an alliance of the processor hardware and the software part. In the developed method, the former part is described by an operational model, which depicts all possible states and transitions of the system under consideration. The latter part is represented by an application model, which reflects the workflow of a concrete program created for this system. Together, these two models are translated into one stochastic Petri net to make analyzing of the system possible. The developed transformation rules are presented and described mathematically. It is then possible to predict the system’s power consumption by a standard evaluation of Petri nets. The Unified Modeling Language (UML) is used in this monograph for modeling of real-time systems. State machine diagrams extended with the MARTE profile (Modeling and Analysis of Real-Time and Embedded Systems) are chosen for modeling and performance evaluation. The presented methodology is supported by an implementation of the necessary algorithms and graphical editors in the software tool TimeNET. The developed extension implements the presented method for power consumption modeling and evaluation based on the extended UML models, which now can be automatically transformed into a stochastic Petri net. The system’s power consumption can be then predicted by the standard Petri net analysis modules of TimeNET. The methodology is validated and its advantages are demonstrated using application examples

    Modeling IT Availability Risks in Smart Factories

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    In the course of the ongoing digitalization of production, production environments have become increasingly intertwined with information and communication technology. As a consequence, physical production processes depend more and more on the availability of information networks. Threats such as attacks and errors can compromise the components of information networks. Due to the numerous interconnections, these threats can cause cascading failures and even cause entire smart factories to fail due to propagation effects. The resulting complex dependencies between physical production processes and information network components in smart factories complicate the detection and analysis of threats. Based on generalized stochastic Petri nets, the paper presents an approach that enables the modeling, simulation, and analysis of threats in information networks in the area of connected production environments. Different worst-case threat scenarios regarding their impact on the operational capability of a close-to-reality information network are investigated to demonstrate the feasibility and usability of the approach. Furthermore, expert interviews with an academic Petri net expert and two global leading companies from the automation and packaging industry complement the evaluation from a practical perspective. The results indicate that the developed artifact offers a promising approach to better analyze and understand availability risks, cascading failures, and propagation effects in information networks in connected production environments

    Compositional dependability analysis of dynamic systems with uncertainty

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    Over the past two decades, research has focused on simplifying dependability analysis by looking at how we can synthesise dependability information from system models automatically. This has led to the field of model-based safety assessment (MBSA), which has attracted a significant amount of interest from industry, academia, and government agencies. Different model-based safety analysis methods, such as Hierarchically Performed Hazard Origin & Propagation Studies (HiP-HOPS), are increasingly applied by industry for dependability analysis of safety-critical systems. Such systems may feature multiple modes of operation where the behaviour of the systems and the interactions between system components can change according to what modes of operation the systems are in.MBSA techniques usually combine different classical safety analysis approaches to allow the analysts to perform safety analyses automatically or semi-automatically. For example, HiP-HOPS is a state-of-the-art MBSA approach which enhances an architectural model of a system with logical failure annotations to allow safety studies such as Fault Tree Analysis (FTA) and Failure Modes and Effects Analysis (FMEA). In this way it shows how the failure of a single component or combinations of failures of different components can lead to system failure. As systems are getting more complex and their behaviour becomes more dynamic, capturing this dynamic behaviour and the many possible interactions between the components is necessary to develop an accurate failure model.One of the ways of modelling this dynamic behaviour is with a state-transition diagram. Introducing a dynamic model compatible with the existing architectural information of systems can provide significant benefits in terms of accurate representation and expressiveness when analysing the dynamic behaviour of modern large-scale and complex safety-critical systems. Thus the first key contribution of this thesis is a methodology to enable MBSA techniques to model dynamic behaviour of systems. This thesis demonstrates the use of this methodology using the HiP-HOPS tool as an example, and thus extends HiP-HOPS with state-transition annotations. This extension allows HiP-HOPS to model more complex dynamic scenarios and perform compositional dynamic dependability analysis of complex systems by generating Pandora temporal fault trees (TFTs). As TFTs capture state, the techniques used for solving classical FTs are not suitable to solve them. They require a state space solution for quantification of probability. This thesis therefore proposes two methodologies based on Petri Nets and Bayesian Networks to provide state space solutions to Pandora TFTs.Uncertainty is another important (yet incomplete) area of MBSA: typical MBSA approaches are not capable of performing quantitative analysis under uncertainty. Therefore, in addition to the above contributions, this thesis proposes a fuzzy set theory based methodology to quantify Pandora temporal fault trees with uncertainty in failure data of components.The proposed methodologies are applied to a case study to demonstrate how they can be used in practice. Finally, the overall contributions of the thesis are evaluated by discussing the results produced and from these conclusions about the potential benefits of the new techniques are drawn

    ESTABLISHMENT OF CYBER-PHYSICAL CORRELATION AND VERIFICATION BASED ON ATTACK SCENARIOS IN POWER SUBSTATIONS

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    Insurance businesses for the cyberworld are an evolving opportunity. However, a quantitative model in today\u27s security technologies may not be established. Besides, a generalized methodology to assess the systematic risks remains underdeveloped. There has been a technical challenge to capture intrusion risks of the cyber-physical system, including estimating the impact of the potential cascaded events initiated by the hacker\u27s malicious actions. This dissertation attempts to integrate both modeling aspects: 1) steady-state probabilities for the Internet protocol-based substation switching attack events based on hypothetical cyberattacks, 2) potential electricity losses. The phenomenon of sequential attacks can be characterized using a time-domain simulation that exhibits dynamic cascaded events. Such substation attack simulation studies can establish an actuarial framework for grid operation. The novelty is three-fold. First, the development to extend features of steady-state probabilities is established based on 1) modified password models, 2) new models on digital relays with two-step authentications, and 3) honeypot models. A generalized stochastic Petri net is leveraged to formulate the detailed statuses and transitions of components embedded in a Cyber-net. Then, extensive modeling of steady-state probabilities is qualitatively performed. Methodologies on how transition probabilities and rates are extracted from network components and actuarial applications are summarized and discussed. Second, dynamic models requisite for switching attacks against multiple substations or digital relays deployed in substations are formulated. Imperative protection and control models to represent substation attacks are clarified with realistic model parameters. Specifically, wide-area protections, i.e., special protection systems (SPSs), are elaborated, asserting that event-driven SPSs may be skipped for this type of case study. Third, the substation attack replay using a proven commercially available time-domain simulation tool is validated in IEEE system models to study attack combinations\u27 critical paths. As the time-domain simulation requires a higher computational cost than power flow-based steady-state simulation, a balance of both methods is established without missing the critical dynamic behavior. The direct impact of substation attacks, i.e., electricity losses, is compared between steady-state and dynamic analyses. Steady-state analysis results are prone to be pessimistic for a smaller number of compromised substations. Finally, simulation findings based on the risk-based metrics and technical implementation are extensively discussed with future work

    Towards semantics-driven modelling and simulation of context-aware manufacturing systems

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    Systems modelling and simulation are two important facets for thoroughly and effectively analysing manufacturing processes. The ever-growing complexity of the latter, the increasing amount of knowledge, and the use of Semantic Web techniques adhering meaning to data have led researchers to explore and combine together methodologies by exploiting their best features with the purpose of supporting manufacturing system's modelling and simulation applications. In the past two decades, the use of ontologies has proven to be highly effective for context modelling and knowledge management. Nevertheless, they are not meant for any kind of model simulations. The latter, instead, can be achieved by using a well-known workflow-oriented mathematical modelling language such as Petri Net (PN), which brings in modelling and analytical features suitable for creating a digital copy of an industrial system (also known as "digital twin"). The theoretical framework presented in this dissertation aims to exploit W3C standards, such as Semantic Web Rule Language (SWRL) and Web Ontology Language (OWL), to transform each piece of knowledge regarding a manufacturing system into Petri Net modelling primitives. In so doing, it supports the semantics-driven instantiation, analysis and simulation of what we call semantically-enriched PN-based manufacturing system digital twins. The approach proposed by this exploratory research is therefore based on the exploitation of the best features introduced by state-of-the-art developments in W3C standards for Linked Data, such as OWL and SWRL, together with a multipurpose graphical and mathematical modelling tool known as Petri Net. The former is used for gathering, classifying and properly storing industrial data and therefore enhances our PN-based digital copy of an industrial system with advanced reasoning features. This makes both the system modelling and analysis phases more effective and, above all, paves the way towards a completely new field, where semantically-enriched PN-based manufacturing system digital twins represent one of the drivers of the digital transformation already in place in all companies facing the industrial revolution. As a result, it has been possible to outline a list of indications that will help future efforts in the application of complex digital twin support oriented solutions, which in turn is based on semantically-enriched manufacturing information systems. Through the application cases, five key topics have been tackled, namely: (i) semantic enrichment of industrial data using the most recent ontological models in order to enhance its value and enable new uses; (ii) context-awareness, or context-adaptiveness, aiming to enable the system to capture and use information about the context of operations; (iii) reusability, which is a core concept through which we want to emphasize the importance of reusing existing assets in some form within the industrial modelling process, such as industrial process knowledge, process data, system modelling primitives, and the like; (iv) the ultimate goal of semantic Interoperability, which can be accomplished by adding data about the metadata, linking each data element to a controlled, shared vocabulary; finally, (v) the impact on modelling and simulation applications, which shows how we could automate the translation process of industrial knowledge into a digital manufacturing system and empower it with quantitative and qualitative analytical technics

    Practical Use of High-level Petri Nets

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    This booklet contains the proceedings of the Workshop on Practical Use of High-level Petri Nets, June 27, 2000. The workshop is part of the 21st International Conference on Application and Theory of Petri Nets organised by the CPN group at the Department of Computer Science, University of Aarhus, Denmark. The workshop papers are available in electronic form via the web pages: http://www.daimi.au.dk/pn2000/proceeding

    Compositional construction and analysis of Petri net systems

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    Software agents & human behavior

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    People make important decisions in emergencies. Often these decisions involve high stakes in terms of lives and property. Bhopal disaster (1984), Piper Alpha disaster (1988), Montara blowout (2009), and explosion on Deepwater Horizon (2010) are a few examples among many industrial incidents. In these incidents, those who were in-charge took critical decisions under various ental stressors such as time, fatigue, and panic. This thesis presents an application of naturalistic decision-making (NDM), which is a recent decision-making theory inspired by experts making decisions in real emergencies. This study develops an intelligent agent model that can be programed to make human-like decisions in emergencies. The agent model has three major components: (1) A spatial learning module, which the agent uses to learn escape routes that are designated routes in a facility for emergency evacuation, (2) a situation recognition module, which is used to recognize or distinguish among evolving emergency situations, and (3) a decision-support module, which exploits modules in (1) and (2), and implements an NDM based decision-logic for producing human-like decisions in emergencies. The spatial learning module comprises a generalized stochastic Petri net-based model of spatial learning. The model classifies routes into five classes based on landmarks, which are objects with salient spatial features. These classes deal with the question of how difficult a landmark turns out to be when an agent observes it the first time during a route traversal. An extension to the spatial learning model is also proposed where the question of how successive route traversals may impact retention of a route in the agent’s memory is investigated. The situation awareness module uses Markov logic network (MLN) to define different offshore emergency situations using First-order Logic (FOL) rules. The purpose of this module is to give the agent the necessary experience of dealing with emergencies. The potential of this module lies in the fact that different training samples can be used to produce agents having different experience or capability to deal with an emergency situation. To demonstrate this fact, two agents were developed and trained using two different sets of empirical observations. The two are found to be different in recognizing the prepare-to-abandon-platform alarm (PAPA ), and similar to each other in recognition of an emergency using other cues. Finally, the decision-support module is proposed as a union of spatial-learning module, situation awareness module, and NDM based decision-logic. The NDM-based decision-logic is inspired by Klein’s (1998) recognition primed decision-making (RPDM) model. The agent’s attitudes related to decision-making as per the RPDM are represented in the form of belief, desire, and intention (BDI). The decision-logic involves recognition of situations based on experience (as proposed in situation-recognition module), and recognition of situations based on classification, where ontological classification is used to guide the agent in cases where the agent’s experience about confronting a situation is inadequate. At the planning stage, the decision-logic exploits the agent’s spatial knowledge (as proposed in spatial-learning module) about the layout of the environment to make adjustments in the course of actions relevant to a decision that has already been made as a by-product of situation recognition. The proposed agent model has potential to be used to improve virtual training environment’s fidelity by adding agents that exhibit human-like intelligence in performing tasks related to emergency evacuation. Notwithstanding, the potential to exploit the basis provided here, in the form of an agent representing human fallibility, should not be ignored for fields like human reliability analysis

    Practical Use of High-level Petri Nets

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