CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

A HYBRID METHODOLOGY FOR MODELING RISK OF ADVERSE EVENTS IN COMPLEX HEALTHCARE SETTINGS

Despite efforts to provide safe, effective medical care, adverse events still occur with some regularity. While risk cannot be entirely eliminated from healthcare activities, an important goal is to develop effective and durable mitigation strategies to render the system `safer'. In order to do this, though, we must develop models that comprehensively and realistically characterize the risk. In the healthcare domain, this can be extremely challenging due to the wide variability in the way that healthcare processes and interventions are executed and also due to the dynamic nature of risk in this particular domain. In this study we have developed a generic methodology for evaluating dynamic changes in adverse event risk in acute care hospitals as a function of organizational and non-organizational factors, using a combination of modeling formalisms. First, a system dynamics (SD) framework is used to demonstrate how organizational level and policy level contributions to risk evolve over time, and how policies and decisions may affect the general system-level contribution to adverse event risk. It also captures the feedback of organizational factors and decisions over time and the non-linearities in these feedback effects. Second, Bayesian Belief Network (BBN) framework is used to represent patient-level factors and also physician level decisions and factors in the management of an individual patient, which contribute to the risk of hospital-acquired adverse event. The model is intended to support hospital decisions with regards to staffing, length of stay, and investment in safeties, which evolve dynamically over time. The methodology has been applied in modeling the two types of common adverse events; pressure ulcers and vascular catheter-associated infection, and has been validated with eight years of clinical data

AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges

Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes, particularly in cloud infrastructures, to provide actionable insights with the primary goal of maximizing availability. There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be leveraged to enhance operational efficiency. Here we provide a review of the AIOps vision, trends challenges and opportunities, specifically focusing on the underlying AI techniques. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions. We discuss the problem formulation for each task, and then present a taxonomy of techniques to solve these problems. We also identify relatively under explored topics, especially those that could significantly benefit from advances in AI literature. We also provide insights into the trends in this field, and what are the key investment opportunities

Model-Based Diagnostic Frameworks for Fault Detection and System Monitoring in Nuclear Engineering Systems

The high operations and maintenance (O&M) cost for nuclear plants is one of the most significant challenges facing the industry today. The research in this thesis is motivated by the ongoing effort to utilize automation and improved operator support technologies to reduce O&M costs in nuclear power plants. A diagnostic framework is first developed for the problem of monitoring equipment health and sensor calibration status in nuclear engineering systems. This is achieved by utilizing real-time data from sensors that are already in place for system monitoring to perform automated diagnostics of equipment degradation. Given the long-time scale over which component degradation typically proceeds, some of the sensors may also inevitably degrade and become unreliable. The need to simultaneously consider equipment and instrument faults is both a technical necessity and a desired capability. The automation of these monitoring tasks contributes to the reduction of the overall O&M cost by reducing the required human resources and by providing better maintenance scheduling. Early detection of slow degradation over the course of plant operation requires sufficient detection sensitivity from the diagnostic framework. The problem is more complicated in the presence of various sources of uncertainty and possible changes of operating conditions due to plant drifts. To resolve these difficulties and provide the desired capability, the proposed framework is a hybrid integration of quantitative model-based diagnosis, statistical change detection and probabilistic reasoning. Physics-based models are developed to describe the fault-free behavior of system components. Quantitative residuals are generated from the analytical redundancy in each model and serve as fault symptoms for model-based diagnosis. Statistical change detection methods are used to detect changes in the residuals in the presence of uncertainty. Measurement and modelling uncertainty are robustly treated by methods of statistical change detection and probabilistic reasoning. A system level diagnosis framework is proposed to deal with the lack of local sensors to each component. The overall framework has been implemented and demonstrated with a high-pressure feedwater system whose available sensor set is insufficient for the construction of standalone models for most major components. Results from the demonstration showed that the system level approach can be used to construct models and perform diagnostics for systems with limited instrumentation. Both component faults and sensor faults can be detected, and the effects of uncertainty can be mitigated by the proposed probabilistic reasoning framework. Areas for future work were identified and include the investigation of a dynamic Bayesian network to treat the effects of uncertainty in the diagnosis as well as the investigation of using high fidelity simulation codes to construct simulation-based surrogate models of the basic plant components.PHDNuclear Engineering & Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155193/1/nghiant_1.pd

Bayesian Network Analysis for Diagnostics and Prognostics of Engineering Systems

Bayesian networks have been applied to many different domains to perform prognostics, reduce risk and ultimately improve decision making. However, these methods have not been applied to military field and human performance data sets in an industrial environment. Methods frequently rely on a clear understanding of causal connections leading to an undesirable event and detailed understanding of the system behavior. Methods may also require large amount of analyst teams and domain experts, coupled with manual data cleansing and classification. The research performed utilized machine learning algorithms (such as Bayesian networks) and two existing data sets. The primary objective of the research was to develop a diagnostic and prognostic tool utilizing Bayesian networks that does not require the need for detailed causal understanding of the underlying system. The research yielded a predictive method with substantial benefits over reactive methods. The research indicated Bayesian networks can be trained and utilized to predict failure of several important components to include potential malfunction codes and downtime on a real-world Navy data set. The research also considered potential error within the training data set. The results provided credence to utilization of Bayesian networks in real field data – which will always contain error that is not easily quantified. Research should be replicated with additional field data sets from other aircraft. Future research should be conducted to solicit and incorporate domain expertise into subsequent models. Research should also consider incorporation of text based analytics for text fields, which was considered out of scope for this research project

Dynamic safety analysis of decommissioning and abandonment of offshore oil and gas installations

The global oil and gas industry have seen an increase in the number of installations moving towards decommissioning. Offshore decommissioning is a complex, challenging and costly activity, making safety one of the major concerns. The decommissioning operation is, therefore, riskier than capital projects, partly due to the uniqueness of every offshore installation, and mainly because these installations were not designed for removal during their development phases. The extent of associated risks is deep and wide due to limited data and incomplete knowledge of the equipment conditions. For this reason, it is important to capture every uncertainty that can be introduced at the operational level, or existing hazards due to the hostile environment, technical difficulties, and the timing of the decommissioning operations. Conventional accident modelling techniques cannot capture the complex interactions among contributing elements. To assess the safety risks, a dynamic safety analysis of the accident is, thus, necessary. In this thesis, a dynamic integrated safety analysis model is proposed and developed to capture both planned and evolving risks during the various stages of decommissioning. First, the failure data are obtained from source-to-source and are processed utilizing Hierarchical Bayesian Analysis. Then, the system failure and potential accident scenarios are built on bowtie model which is mapped into a Bayesian network with advanced relaxation techniques. The Dynamic Integrated Safety Analysis (DISA) allows for the combination of reliability tools to identify safetycritical causals and their evolution into single undesirable failure through the utilisation of source to-source variability, time-dependent prediction, diagnostic, and economic risk assessment to support effective recommendations and decisions-making. The DISA framework is applied to the Elgin platform well abandonment and Brent Alpha jacket structure decommissioning and the results are validated through sensitivity analysis. Through a dynamic-diagnostic and multi-factor regression analysis, the loss values of accident contributory factors are also presented. The study shows that integrating Hierarchical Bayesian Analysis (HBA) and dynamic Bayesian networks (DBN) application to modelling time-variant risks are essential to achieve a well-informed decommissioning decision through the identification of safety critical barriers that could be mitigated against to drive down the cost of remediation.The global oil and gas industry have seen an increase in the number of installations moving towards decommissioning. Offshore decommissioning is a complex, challenging and costly activity, making safety one of the major concerns. The decommissioning operation is, therefore, riskier than capital projects, partly due to the uniqueness of every offshore installation, and mainly because these installations were not designed for removal during their development phases. The extent of associated risks is deep and wide due to limited data and incomplete knowledge of the equipment conditions. For this reason, it is important to capture every uncertainty that can be introduced at the operational level, or existing hazards due to the hostile environment, technical difficulties, and the timing of the decommissioning operations. Conventional accident modelling techniques cannot capture the complex interactions among contributing elements. To assess the safety risks, a dynamic safety analysis of the accident is, thus, necessary. In this thesis, a dynamic integrated safety analysis model is proposed and developed to capture both planned and evolving risks during the various stages of decommissioning. First, the failure data are obtained from source-to-source and are processed utilizing Hierarchical Bayesian Analysis. Then, the system failure and potential accident scenarios are built on bowtie model which is mapped into a Bayesian network with advanced relaxation techniques. The Dynamic Integrated Safety Analysis (DISA) allows for the combination of reliability tools to identify safetycritical causals and their evolution into single undesirable failure through the utilisation of source to-source variability, time-dependent prediction, diagnostic, and economic risk assessment to support effective recommendations and decisions-making. The DISA framework is applied to the Elgin platform well abandonment and Brent Alpha jacket structure decommissioning and the results are validated through sensitivity analysis. Through a dynamic-diagnostic and multi-factor regression analysis, the loss values of accident contributory factors are also presented. The study shows that integrating Hierarchical Bayesian Analysis (HBA) and dynamic Bayesian networks (DBN) application to modelling time-variant risks are essential to achieve a well-informed decommissioning decision through the identification of safety critical barriers that could be mitigated against to drive down the cost of remediation

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Confronting climate crisis: A framework for understanding the criteria for addressing dangerous climate change

Despite wide acknowledgement of the threats from human-induced climate change to human societies and the wider ecosystem, no comprehensive long-term global agreement to tackle the problem has yet been reached to replace the Kyoto Protocol. In arguing for a replacement, evaluative claims are often made that certain policy proposals are more environmentally effective, equitable or efficient than others. However, these three dominant criteria are subject to a range of interpretations, and can come into conflict with one another. This limits their use for guiding policy. Philosophy can and should play a role in scrutinising alternative conceptions, their justifications and assumptions, and help develop justifiable formulations of the criteria. Existing philosophical contributions have focused on aspects of the equity criterion, but have largely overlooked the other two criteria and have not considered how they should be prioritised overall. This thesis, for the first time, considers and proposes an ordering of these three criteria (focusing on mitigation), drawing on a Green Economic conceptual framework. This places ecological effectiveness first, defining the ecological limits of economic greenhouse gas-emitting activity; equity is then applied second, to define equitable resource sharing of the emissions space; and efficiency last, to imply genuinely efficient use of emissions space in contributing to equitable human well-being. The thesis then examines in detail how each criterion should be interpreted within this context, so that they are mutually consistent. As well as offering a set of ordered evaluative criteria for a climate change mitigation agreement, it aims to highlight the role of the conventional political-economic framework in climate policy debates and draw out the hidden conceptual and ethical assumptions it imports. This thesis also, therefore, aims to further the development of Green Economic thinking and show its relevance to the current substantial threat of dangerous anthropogenic climate change

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