Research in several domains has shown that the implementation of computerized decision support aids is often associated with issues of human-automation interaction, which can have disastrous consequences. One often-cited reason for these issues is the poor quality of the feedback that is provided to the operators through these tools. The objective of the proposed investigation is to examine how providing feedback through a decision support tool affects operator knowledge and performance in the context of a fault management task for naval gunfire support. A one-way between-groups comparison was made to investigate differences between providing decision support feedback (logic trace, mission impact, both, no feedback) in a fault management task. Logic trace feedback was posited to provide users with a representation of the logic that the decision support tool used in reaching a conclusion about the best course of action to perform and is posited to support better diagnostic performance. Mission impact feedback was posited to provide the operator with a description of the potential effects that a taking a course of action will have on the pre-planned mission and is expected to support better prognoses of the outcome of a particular fault. Finally, providing both feedback types was posited to support better compensatory actions for fault situations. Results indicated that decision support feedback has potential improve diagnosis and decrease errors of commission in these tasks.

The use of service-oriented (SO) distributed systems is increasing. Within service orientation web services (WS) are the de facto standard for implementing service-oriented systems. The consumers of WS want to get uninterrupted and reliable service from the service providers. But WS providers cannot always provide services in the expected level due to faults and failures in the system. As a result the fault management of these systems is becoming crucial. This work presents a distributed event-driven architecture for fault management of Web Services. According to the architecture the managed WS report different events to the event databases. From event databases these events are sent to the event processors. The event processors are distributed over the network. They process the events, detect fault scenarios in the event stream and manage faults in the WS.

The use of service-oriented (SO) distributed systems is increasing. Within service orientation web services (WS) are the de facto standard for implementing service-oriented systems. The consumers of WS want to get uninterrupted and reliable service from the service providers. But WS providers cannot always provide services in the expected level due to faults and failures in the system. As a result the fault management of these systems is becoming crucial. This work presents a distributed event-driven architecture for fault management of Web Services. According to the architecture the managed WS report different events to the event databases. From event databases these events are sent to the event processors. The event processors are distributed over the network. They process the events, detect fault scenarios in the event stream and manage faults in the WS.

The purpose of this research is to investigate fault management methodologies within Integrated Modular Avionics (IMA) systems, and develop techniques by which the use of dynamic reconfiguration can be implemented to restore higher levels of systems redundancy in the event of a systems fault. A proposed concept of dynamic configuration has been implemented on a test facility that allows controlled injection of common faults to a representative IMA system. This facility allows not only the observation of the response of the system management activities to manage the fault, but also analysis of real time data across the network to ensure distributed control activities are maintained. IMS technologies have evolved as a feasible direction for the next generation of avionic systems. Although federated systems are logical to design, certify and implement, they have some inherent limitations that are not cost beneficial to the customer over long life-cycles of complex systems, and hence the fundamental modular design, i.e. common processors running modular software functions, provides a flexibility in terms of configuration, implementation and upgradability that cannot be matched by well-established federated avionic system architectures. For example, rapid advances of computing technology means that dedicated hardware can become outmoded by component obsolescence which almost inevitably makes replacements unavailable during normal life-cycles of most avionic systems. To replace the obsolete part with a newer design involves a costly re-design and re-certification of any relevant or interacting functions with this unit. As such, aircraft are often known to go through expensive mid-life updates to upgrade all avionics systems. In contrast, a higher frequency of small capability upgrades would maximise the product performance, including cost of development and procurement, in constantly changing platform deployment environments. IMA is by no means a new concept and work has been carried out globally in order to mature the capability. There are even examples where this technology has been implemented as subsystems on service aircraft. However, IMA flexible configuration properties are yet to be exploited to their full extent; it is feasible that identification of faults or failures within the system would lead to the exploitation of these properties in order to dynamically reconfigure and maintain high levels of redundancy in the event of component failure. It is also conceivable to install redundant components such that an IMS can go through a process of graceful degradation, whereby the system accommodates a number of active failures, but can still maintain appropriate levels of reliability and service. This property extends the average maintenance-free operating period, ensuring that the platform has considerably less unscheduled down time and therefore increased availability. The content of this research work involved a number of key activities in order to investigate the feasibility of the issues outlined above. The first was the creation of a representative IMA system and the development of a systems management capability that performs the required configuration controls. The second aspect was the development of hardware test rig in order to facilitate a tangible demonstration of the IMA capability. A representative IMA was created using LabVIEW Embedded Tool Suit (ETS) real time operating system for minimal PC systems. Although this required further code written to perform IMS middleware functions and does not match up to the stringent air safety requirements, it provided a suitable test bed to demonstrate systems management capabilities. The overall IMA was demonstrated with a 100kg scale Maglev vehicle as a test subject. This platform provides a challenging real-time control problem, analogous to an aircraft flight control system, requiring the calculation of parallel control loops at a high sampling rate in order to maintain magnetic suspension. Although the dynamic properties of the test rig are not as complex as a modern aircraft, it has much less stringent operating requirements and therefore substantially less risk associated with failure to provide service. The main research contributions for the PhD are: 1. A solution for the dynamic reconfiguration problem for assigning required systems functions (namely a distributed, real-time control function with redundant processing channels) to available computing resources whilst protecting the functional concurrency and time critical needs of the control actions. 2. A systems management strategy that utilises the dynamic reconfiguration properties of an IMA System to restore high levels of redundancy in the presence of failures. The conclusion summarises the level of success of the implemented system in terms of an appropriate dynamic reconfiguration to the response of a fault signal. In addition, it highlights the issues with using an IMA to as a solution to operational goals of the target hardware, in terms of design and build complexity, overhead and resources.

In the last decade, the world of telecommunications has seen the value ofservices definitively affirmed and the loss of the connectivity value. This changeof pace in the use of the network (and available hardware resources) has ledto continuous, unlimited growth in data traffic, increased incomes for serviceproviders, and a constant erosion of operators’ incomes for voice and ShortMessage Service (SMS) traffic.The change in mobile service consumption is evident to operators. Themarket today is in the hands of over the top (OTT) media content deliverycompanies (Google, Meta, Netflix, Amazon, etc.), and The fifth generation ofmobile networks (5G), the latest generation of mobile architecture, is nothingother than how operators can invest in system infrastructure to participate in theprosperous service business.With the advent of 5G, the worlds of cloud and telecommunications havefound their meeting point, paving the way for new infrastructures and ser-vices, such as smart cities, industry 4.0, industry 5.0, and Augmented Reality(AR)/Virtual Reality (VR). People, infrastructures, and devices are connected toprovide services that we even struggle to imagine today, but a highly intercon-nected system requires high levels of reliability and resilience.Hardware reliability has increased since the 1990s. However, it is equallycorrect to mention that the introduction of new technologies in the nanometerdomain and the growing complexity of on-chip systems have made fault man-agement critical to guarantee the quality of the service offered to the customerand the sustainability of the network infrastructure. In this thesis, our first contribution is a review of the fault managementimplementation framework for the radio access network domain. Our approachintroduces a holistic vision in fault management where there is increasingly moresignificant attention to the recovery action, the crucial target of the proposedframework. A new contribution underlines the attention toward the recoverytarget: we revisited the taxonomy of faults in mobile systems to enhance theresult of the recovery action, which, in our opinion, must be propagated betweenthe different layers of an embedded system ( hardware, firmware, middleware,and software). The practical adoption of the new framework and the newtaxonomy allowed us to make a unique contribution to the thesis: the proposalof a new algorithm for managing system memory errors, both temporary (soft)and permanent (hard)The holistic vision of error management we introduced in this thesis involveshardware that proactively manages faults. An efficient implementation of faultmanagement is only possible if the hardware design considers error-handlingtechniques and methodologies. Another contribution of this thesis is the def-inition of the fault management requirements for the RAN embedded systemhardware design.Another primary function of the proposed fault management framework isfault prediction. Recognizing error patterns means allowing the system to reactin time, even before the error condition occurs, or identifying the topology of theerror to implement more targeted and, therefore, more efficient recovery actions.The operating temperature is always a critical characteristic of embedded radioaccess network systems. Base stations must be able to work in very differenttemperature conditions. However, the working temperature also directly affectsthe probability of error for the system. In this thesis, we have also contributed interms of a machine-learning algorithm for predicting the working temperature ofbase stations in radio access networks — a first step towards a more sophisticatedimplementation of error prevention and prediction.

The internet growth rapidly increased by the years, and the traffic is increasing daily. The management of the network is becoming more and more complexed for humans to handle on their own, with that being said a new direction of using Artificial Intelligence (AI) technologies is being implemented in the direction of network fault management. In order to keep up with the development network, new solutions need to be implemented. Traditional network fault management are dependent of system administrators and there is too much human error that can happen during operations. That’s why AI is a great tool to be used in future network fault management. There are currently many challenges within network fault management, and this makes an opportunity for AI to be implemented. The studies shows that AI subpart “supervised learning” is the most popular used in network fault management. AI have shown that there is potential to tackle problems such as detection, prediction and also improve the system as whole.

No description available.

Shipboard Power Systems (SPS) play a significant role in next-generation Navy fleets. With the increasing power demand from propulsion loads, ship service loads, weaponry systems and mission systems, a stable and reliable SPS is critical to support different aspects of ship operation. It also becomes the technology-enabler to improve ship economy, efficiency, reliability, and survivability. Moreover, it is important to improve the reliability and robustness of the SPS while working under different operating conditions to ensure safe and satisfactory operation of the system. This dissertation aims to introduce novel and effective approaches to respond to different types of possible faults in the SPS. According to the type and duration, the possible faults in the Medium Voltage DC (MVDC) SPS have been divided into two main categories: transient and permanent faults. First, in order to manage permanent faults in MVDC SPS, a novel real-time reconfiguration strategy has been proposed. Onboard postault reconfiguration aims to ensure the maximum power/service delivery to the system loads following a fault. This study aims to implement an intelligent real-time reconfiguration algorithm in the RTDS platform through an optimization technique implemented inside the Real-Time Digital Simulator (RTDS). The simulation results demonstrate the effectiveness of the proposed real-time approach to reconfigure the system under different fault situations. Second, a novel approach to mitigate the effect of the unsymmetrical transient AC faults in the MVDC SPS has been proposed. In this dissertation, the application of combined Static Synchronous Compensator (STATCOM)-Super Conducting Fault Current Limiter (SFCL) to improve the stability of the MVDC SPS during transient faults has been investigated. A Fluid Genetic Algorithm (FGA) optimization algorithm is introduced to design the STATCOM's controller. Moreover, a multi-objective optimization problem has been formulated to find the optimal size of SFCL's impedance. In the proposed scheme, STATCOM can assist the SFCL to keep the vital load terminal voltage close to the normal state in an economic sense. The proposed technique provides an acceptable post-disturbance and postault performance to recover the system to its normal situation over the other alternatives.

Large-scale distributed computing systems such as data centers are hosted on heterogeneous and networked servers that execute in a dynamic and uncertain operating environment, caused by factors such as time-varying user workload and various failures. Therefore, achieving stringent quality-of-service goals is a challenging task, requiring a comprehensive approach to performance control, fault diagnosis, and failure recovery. This work presents a model-based approach for fault management, which integrates limited lookahead control (LLC), diagnosis, and fault-tolerance concepts that: (1) enables systems to adapt to environment variations, (2) maintains the availability and reliability of the system, (3) facilitates system recovery from failures. We focused on memory leak errors in this thesis. A characterization function is designed to detect memory leaks. Then, a LLC is applied to enable the computing system to adapt efficiently to variations in the workload, and to enable the system recover from memory leaks and maintain functionality.

Com o crescimento do número e da heterogeneidade dos equipamentos presentes nas atuais redes de computadores, o gerenciamento eficaz destes recursos toma-se crítico. Esta atividade exige dos gerentes de redes a disponibilidade de uma grande quantidade de informações sobre os seus equipamentos, as tecnologias envolvidas e os problemas associados a elas. Sistemas de registro de problemas (trouble ticket systems) tem lido utilizados para armazenar os incidentes ocorridos, servindo como uma memória histórica da rede e acumulando o conhecimento derivado do processo de diagnose e resolução de problemas. Todavia, o crescente número de registros armazenados torna a busca manual nestes sistemas por situações similares ocorridas anteriormente muito morosa e imprecisa. Assim, uma solução apropriada para consolidar a memória histórica das redes é o desenvolvimento de um sistema especialista que utilize o conhecimento armazenado nos sistemas de registro de problemas para propor soluções para um problema corrente. Uma abordagem da Inteligência Artificial que tem atraído enorme atenção nos últimos anos e que pode ser utilizada para tal fim é o raciocínio baseado em casos (casebased reasoning). Este paradigma de raciocínio visa propor soluções para novos problemas através da recuperação de um caso similar ocorrido no passado, cuja solução pode ser reutilizada na nova situação. Além disso, os benefícios deste paradigma incluem a capacidade de aprendizado com a experiência, permitindo que novos problemas sejam incorporados e se tomem disponíveis para use em situações futuras, aumentando com isso o conhecimento presente no sistema. Este trabalho apresenta um sistema que utiliza o paradigma de raciocínio baseado em casos aplicado a um sistema de registro de problemas para propor soluções para um novo problema. Esse sistema foi desenvolvido com o propósito de auxiliar no diagnostico e resolução dos problemas em redes. Os problemas típicos deste domínio, a abordagem adotada e os resultados obtidos com o protótipo construído são descritos. / With the increasing number of computer equipments and their increasing heterogeneity, the efficient management of those resources has become a hard job. This activity demands from the network manager a big amount of expertise on network equipments, technologies involved, and eventual problems that may arise. So far, trouble ticket systems (TTS) have been used to store network problems, working like a network historical memory and accumulating the knowledge derived from the diagnosis and troubleshooting of such problems. However, the increasing number of stored tickets makes the manual search of similar situations very slow and inaccurate in these kind of systems. So, an adequate approach to consolidate the network historic memory is the development of an expert system that uses the knowledge stored in the trouble ticket systems to propose a solution for a current problem. Case-based reasoning (CBR), an approach borrowed from Artificial Intelligence that recently had attracted many researchers attention, may be applied to help diagnosing and troubleshooting networking management problems. This reasoning paradigm proposes solution to new problems by retrieving a similar case occurred in the past, whose solution can be reused in the new situation. Furthermore, the benefits of this paradigm include the experience learning capability, allowing new problems being added and becoming available to use in future situations, expanding the knowledge of the system. This work presents a system that uses case-based reasoning applied to a trouble ticket system to propose solutions for a new problem in the network. This system was developed with the aim of helping the diagnostic and troubleshooting of network problems. It describes the typical problems of this domain, the adopted approach and the results obtained with the prototype built.