Detection of inter-turn faults in multi-phase ferrite-PM assisted synchronous reluctance machine

Inter-turn winding faults in five-phase ferrite-permanent magnet-assisted synchronous reluctance motors (fPMa-SynRMs) can lead to catastrophic consequences if not detected in a timely manner, since they can quickly progress into more severe short-circuit faults, such as coil-to-coil, phase-to-ground or phase-to-phase faults. This paper analyzes the feasibility of detecting such harmful faults in their early stage, with only one short-circuited turn, since there is a lack of works related to this topic in multi-phase fPMa-SynRMs. Two methods are tested for this purpose, the analysis of the spectral content of the zero-sequence voltage component (ZSVC) and the analysis of the stator current spectra, also known as motor current signature analysis (MCSA), which is a well-known fault diagnosis method. This paper compares the performance and sensitivity of both methods under different operating conditions. It is proven that inter-turn faults can be detected in the early stage, with the ZSVC providing more sensitivity than the MCSA method. It is also proven that the working conditions have little effect on the sensitivity of both methods. To conclude, this paper proposes two inter-turn fault indicators and the threshold values to detect such faults in the early stage, which are calculated from the spectral information of the ZSVC and the line currentsPeer ReviewedPostprint (published version

A review of physics-based models in prognostics: application to gears and bearings of rotating machinery

Health condition monitoring for rotating machinery has been developed for many years due to its potential to reduce the cost of the maintenance operations and increase availability. Covering aspects include sensors, signal processing, health assessment and decision-making. This article focuses on prognostics based on physics-based models. While the majority of the research in health condition monitoring focuses on data-driven techniques, physics-based techniques are particularly important if accuracy is a critical factor and testing is restricted. Moreover, the benefits of both approaches can be combined when data-driven and physics-based techniques are integrated. This article reviews the concept of physics-based models for prognostics. An overview of common failure modes of rotating machinery is provided along with the most relevant degradation mechanisms. The models available to represent these degradation mechanisms and their application for prognostics are discussed. Models that have not been applied to health condition monitoring, for example, wear due to metal–metal contact in hydrodynamic bearings, are also included due to its potential for health condition monitoring. The main contribution of this article is the identification of potential physics-based models for prognostics in rotating machinery

Theory of reliable systems

An attempt was made to refine the current notion of system reliability by identifying and investigating attributes of a system which are important to reliability considerations. Techniques which facilitate analysis of system reliability are included. Special attention was given to fault tolerance, diagnosability, and reconfigurability characteristics of systems

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

A survey on online monitoring approaches of computer-based systems

This report surveys forms of online data collection that are in current use (as well as being the subject of research to adapt them to changing technology and demands), and can be used as inputs to assessment of dependability and resilience, although they are not primarily meant for this use

The Role of Semantic Technologies in Diagnostic and Decision Support for Service Systems

In this research, we utilize semantic technology for robust early diagnosis and decision support. We present a light-weight platform that provides the end-user with direct access to the data through an ontology, and enables detection of any forthcoming faults by considering the data only from the reliable sensors. Concurrently, it indicates the actual sources of the detected faults, enabling mitigation action to be taken. Our work is focused on systems that require only real-time data and a restricted part of the historic data, such as fuel cell stack systems. First, we present an upper-level ontology that captures the semantics of such monitored systems and then we present the structure of the platform. Next, we specialize on the fuel cell paradigm and we provide a detailed description of our platform’s functionality that can aid future servicing problem reporting applications

The role of intelligent systems in delivering the smart grid

The development of "smart" or "intelligent" energy networks has been proposed by both EPRI's IntelliGrid initiative and the European SmartGrids Technology Platform as a key step in meeting our future energy needs. A central challenge in delivering the energy networks of the future is the judicious selection and development of an appropriate set of technologies and techniques which will form "a toolbox of proven technical solutions". This paper considers functionality required to deliver key parts of the Smart Grid vision of future energy networks. The role of intelligent systems in providing these networks with the requisite decision-making functionality is discussed. In addition to that functionality, the paper considers the role of intelligent systems, in particular multi-agent systems, in providing flexible and extensible architectures for deploying intelligence within the Smart Grid. Beyond exploiting intelligent systems as architectural elements of the Smart Grid, with the purpose of meeting a set of engineering requirements, the role of intelligent systems as a tool for understanding what those requirements are in the first instance, is also briefly discussed

On the use of context information for an improved application of data-based algorithms in condition monitoring

xi, 124 p.En el campo de la monitorización de la condición, los algoritmos basados en datos cuentan con un amplio recorrido. Desde el uso de los gráficos de control de calidad que se llevan empleando durante casi un siglo a técnicas de mayor complejidad como las redes neuronales o máquinas de soporte vectorial que se emplean para detección, diagnóstico y estimación de vida remanente de los equipos. Sin embargo, la puesta en producción de los algoritmos de monitorización requiere de un estudio exhaustivo de un factor que es a menudo obviado por otros trabajos de la literatura: el contexto. El contexto, que en este trabajo es considerado como el conjunto de factores que influencian la monitorización de un bien, tiene un gran impacto en la algoritmia de monitorización y su aplicación final. Por este motivo, es el objeto de estudio de esta tesis en la que se han analizado tres casos de uso. Se ha profundizado en sus respectivos contextos, tratando de generalizar a la problemática habitual en la monitorización de maquinaria industrial, y se ha abordado dicha problemática de monitorización de forma que solucionen el contexto en lugar de cada caso de uso. Así, el conocimiento adquirido durante el desarrollo de las soluciones puede ser transferido a otros casos de uso que cuenten con contextos similares