154 research outputs found

    On cost-effective reuse of components in the design of complex reconfigurable systems

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    Design strategies that benefit from the reuse of system components can reduce costs while maintaining or increasing dependability—we use the term dependability to tie together reliability and availability. D3H2 (aDaptive Dependable Design for systems with Homogeneous and Heterogeneous redundancies) is a methodology that supports the design of complex systems with a focus on reconfiguration and component reuse. D3H2 systematizes the identification of heterogeneous redundancies and optimizes the design of fault detection and reconfiguration mechanisms, by enabling the analysis of design alternatives with respect to dependability and cost. In this paper, we extend D3H2 for application to repairable systems. The method is extended with analysis capabilities allowing dependability assessment of complex reconfigurable systems. Analysed scenarios include time-dependencies between failure events and the corresponding reconfiguration actions. We demonstrate how D3H2 can support decisions about fault detection and reconfiguration that seek to improve dependability while reducing costs via application to a realistic railway case study

    Application of the D3H2 Methodology for the Cost-Effective Design of Dependable Systems

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    The use of dedicated components as a means of achieving desirable levels of fault tolerancein a system may result in high costs. A cost effective way of restoring failed functions is to use heterogeneous redundancies: components that, besides performing their primary intended design function, can also restore compatible functions of other components. In this paper, we apply a novel design methodology called D3H2 (aDaptive Dependable Design for systems with Homogeneous and Heterogeneous redundancies) to assist in the systematic identification of heterogeneous redundancies, the design of hardware/software architectures including fault detection and reconfiguration, and the systematic dependability and cost assessments of the system. D3H2 integrates parameter uncertainty and criticality analyses to model inexact failure data in dependability assessment. The application to a railway case study is presented with a focus on analysing different reconfiguration strategies as well as types and levels of redundancies

    A unified methodology of maintenance management for repairable systems based on optimal stopping theory

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    This dissertation focuses on the study of maintenance management for repairable systems based on optimal stopping theory. From reliability engineering’s point of view, all systems are subject to deterioration with age and usage. System deterioration can take various forms, including wear, fatigue, fracture, cracking, breaking, corrosion, erosion and instability, any of which may ultimately cause the system to fail to perform its required function. Consequently, controlling system deterioration through maintenance and thus controlling the risk of system failure becomes beneficial or even necessary. Decision makers constantly face two fundamental problems with respect to system maintenance. One is whether or when preventive maintenance should be performed in order to avoid costly failures. The other problem is how to make the choice among different maintenance actions in response to a system failure. The whole purpose of maintenance management is to keep the system in good working condition at a reasonably low cost, thus the tradeoff between cost and condition plays a central role in the study of maintenance management, which demands rigorous optimization. The agenda of this research is to develop a unified methodology for modeling and optimization of maintenance systems. A general modeling framework with six classifying criteria is to be developed to formulate and analyze a wide range of maintenance systems which include many existing models in the literature. A unified optimization procedure is developed based on optimal stopping, semi-martingale, and lambda-maximization techniques to solve these models contained in the framework. A comprehensive model is proposed and solved in this general framework using the developed procedure which incorporates many other models as special cases. Policy comparison and policy optimality are studied to offer further insights. Along the theoretical development, numerical examples are provided to illustrate the applicability of the methodology. The main contribution of this research is that the unified modeling framework and systematic optimization procedure structurize the pool of models and policies, weed out non-optimal policies, and establish a theoretical foundation for further development

    Dynamic model-based safety analysis: from state machines to temporal fault trees

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    Finite state transition models such as State Machines (SMs) have become a prevalent paradigm for the description of dynamic systems. Such models are well-suited to modelling the behaviour of complex systems, including in conditions of failure, and where the order in which failures and fault events occur can affect the overall outcome (e.g. total failure of the system). For the safety assessment though, the SM failure behavioural models need to be converted to analysis models like Generalised Stochastic Petri Nets (GSPNs), Markov Chains (MCs) or Fault Trees (FTs). This is particularly important if the transformed models are supported by safety analysis tools.This thesis, firstly, identifies a number of problems encountered in current safety analysis techniques based on SMs. One of the existing approaches consists of transforming the SMs to analysis-supported state-transition formalisms like GSPNs or MCs, which are very powerful in capturing the dynamic aspects and in the evaluation of safety measures. But in this approach, qualitative analysis is not encouraged; here the focus is primarily on probabilistic analysis. Qualitative analysis is particularly important when probabilistic data are not available (e.g., at early stages of design). In an alternative approach though, the generation of combinatorial, Boolean FTs has been applied to SM-based models. FTs are well-suited to qualitative analysis, but cannot capture the significance of the temporal order of events expressed by SMs. This makes the approach potentially error prone for the analysis of dynamic systems. In response, we propose a new SM-based safety analysis technique which converts SMs to Temporal Fault Trees (TFTs) using Pandora — a recent technique for introducing temporal logic to FTs. Pandora provides a set of temporal laws, which allow the significance of the SM temporal semantics to be preserved along the logical analysis, and thereby enabling a true qualitative analysis of a dynamic system. The thesis develops algorithms for conversion of SMs to TFTs. It also deals with the issue of scalability of the approach by proposing a form of compositional synthesis in which system large TFTs can be generated from individual component SMs using a process of composition. This has the dual benefits of allowing more accurate analysis of different sequences of faults, and also helping to reduce the cost of performing temporal analysis by producing smaller, more manageable TFTs via the compositionality.The thesis concludes that this approach can potentially address limitations of earlier work and thus help to improve the safety analysis of increasingly complex dynamic safety-critical systems

    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

    Addressing Complexity and Intelligence in Systems Dependability Evaluation

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    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

    The Impact of Protection System Failures on Power System Reliability Evaluation

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    The reliability of protection systems has emerged as an important topic because protection failures have critical influence on the reliability of power systems. The goal of this research is to develop novel approaches for modeling and analysis of the impact of protection system failures on power system reliability. It is shown that repairable and non-repairable assumptions make a remarkable difference in reliability modeling. A typical all-digital protection system architecture is modeled and numerically analyzed. If an all-digital protection system is indeed repairable but is modeled in a non-repairable manner for analysis, the calculated values of reliability indices could be grossly pessimistic. The smart grid is emerging with the penetration of information-age technologies and the development of the Special Protection System (SPS) will be greatly influenced. A conceptual all-digital SPS architecture is proposed for the future smart grid. Calculation of important reliability indices by the network reduction method and the Markov modeling method is illustrated in detail. Two different Markov models are proposed for reliability evaluation of the 2-out-of-3 voting gates structure in a generation rejection scheme. If the model with consideration of both detectable and undetectable logic gate failures is used as a benchmark, the simple model which only considers detectable failures will significantly overestimate the reliability of the 2-out-of-3 voting gates structure. The two types of protection failures, undesired-tripping mode and fail-to-operate mode are discussed. A complete Markov model for current-carrying components is established and its simplified form is then derived. The simplified model can appropriately describe the overall reliability situation of individual components under the circumstances of complex interactions between components due to protection failures. New concepts of the self-down state and the induced-down state are introduced and utilized to build up the composite unit model. Finally, a two-layer Markov model for power systems with protection failures is proposed. It can quantify the impact of protection failures on power system reliability. Using the developed methodology, we can see that the assumption of perfectly reliable protection can introduce errors in reliability evaluation of power systems

    FRAMEWORK FOR RELIABILITY, MAINTAINABILITY AND AVAILABILITY ANALYSIS OF GAS PROCESSING SYSTEM DURING OPERATION PHASE

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    In facing many operation challenges such as increased expectation in bottom line performances and escalating overhead costs, petrochemical plants nowadays need to continually strive for higher reliability and availability by means of effective improvement tools. Reliability, maintainability and availability (RAM) analysis has been recognised as one of the strategic tools to improve plant's reliability at operation phase. Nevertheless, the application of RAM among industrial practitioners is still limited generally due to the impracticality and complexity of existing approaches. Hence, it is important to enhance the approaches so that they can be practically applied by companies to assist them in achieving their operational goals. The objectives of this research are to develop frameworks for applying reliability, maintainability and availability analysis of gas processing system at operation phase to improve system operational and maintenance performances. In addition, the study focuses on ways to apply existing statistical approach and incorporate inputs from field experts for prediction of reliability related measures. Furthermore, it explores and highlights major issues involved in implementing RAM analysis in oil and gas industry and offers viable solutions. In this study, systematic analysis on each RAM components are proposed and their roles as strategic improvement and decision making tools are discussed and demonstrated using case studies of two plant systems. In reliability and maintainability (R&M) analysis, two main steps; exploratory and inferential are proposed. Tools such as Pareto, trend plot and hazard functions; Kaplan Meier (KM) and proportional hazard model (PHM), are used in exploratory phase to identify critical elements to system's R&M performances. In inferential analysis, a systematic methodology is presented to assess R&M related measures

    ADEPS: a methodology for designing prognostic applications

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    Prognostics applications predict the future evolution of an asset under study, by diagnosing the actual health state and modeling the future degradation. Due to rapidly growing interest in prognostics, different prediction techniques have been developed independently without a consistent and systematic design. In this paper we formalize the prognostics design process with a novel methodology entitled ADEPS (Assisted Design for Engineering Prognostic Systems). ADEPS combines prognostics concepts with model-based safety assessment, criticality analysis, knowledge engineering and formal verification approaches. The main activities of ADEPS include synthesis of the safety assessment model from the design model, prioritization of the system failure modes, systematic prognostics model selection and verification of the adequacy of the prognostics results with respect to design requirements. By linking system-level safety assessment models and prognostics results, design and safety models are updated with online information about different failure modes. This step enables system-level health assessment including prognostics predictions of different failure modes. The end-to-end application of the methodology for the design and evaluation of a power transformer demonstrates the benefits of the proposed approach including reduced design time and effort, complete consideration of prognostics algorithms and updated system-level health assessment
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