1,008 research outputs found

    A survey of machine learning techniques applied to self organizing cellular networks

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    In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

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

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    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

    Industrial data-driven monitoring based on incremental learning applied to the detection of novel faults

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    The detection of uncharacterized events during electromechanical systems operation represents one of the most critical data challenges dealing with condition-based monitoring under the Industry 4.0 framework. Thus, the detection of novelty conditions and the learning of new patterns are considered as mandatory competencies in modern industrial applications. In this regard, this article proposes a novel multifault detection and identification scheme, based on machine learning, information data-fusion, novelty-detection, and incremental learning. First, statistical time-domain features estimated from multiple physical magnitudes acquired from the electrical motor under inspection are fused under a feature-fusion level scheme. Second, a self-organizing map structure is proposed to construct a data-based model of the available conditions of operation. Third, the incremental learning of the condition-based monitoring scheme is performed adding self-organizing structures and optimizing their projections through a linear discriminant analysis. The performance of the proposed scheme is validated under a complete set of experimental scenarios from two different cases of study, and the results compared with a classical approach.Peer ReviewedPostprint (author's final draft

    Data-driven Soft Sensors in the Process Industry

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    In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

    A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals

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    This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results

    Learning for predictions: Real-time reliability assessment of aerospace systems

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    Prognostics and Health Management (PHM) aim to predict the Remaining Useful Life (RUL) of a system and to allow a timely planning of replacement of components, limiting the need for corrective maintenance and the down time of equipment. A major challenge in system prognostics is the availability of accurate physics based representations of the grow rate of faults. Additionally, the analysis of data acquired during flight operations is traditionally time consuming and expensive. This work proposes a computational method to overcome these limitations through the dynamic adaptation of the state-space model of fault propagation to on-board observations of system’s health. Our approach aims at enabling real-time assessment of systems health and reliability through fast predictions of the Remaining Useful Life that account for uncertainty. The strategy combines physics-based knowledge of the system damage propagation rate, machine learning and real-time measurements of the health status to obtain an accurate estimate of the RUL of aerospace systems. The RUL prediction algorithm relies on a dynamical estimator filter, which allows to deal with nonlinear systems affected by uncertainties with unknown distribution. The proposed method integrates a dynamical model of the fault propagation, accounting for the current and past measured health conditions, the past time history of the operating conditions (such as input command, load, temperature, etc.), and the expected future operating conditions. The model leverages the knowledge collected through the record of past fault measurements, and dynamically adapts the prediction of the damage propagation by learning from the observed time history. The original method is demonstrated for the RUL prediction of an electromechanical actuator for aircraft flight controls. We observe that the strategy allows to refine rapid predictions of the RUL in fractions of seconds by progressively learning from on-board acquisitions

    Design and validation of structural health monitoring system based on bio-inspired algorithms

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    The need of ensure the proper performance of the structures in service has made of structural health monitoring (SHM) a priority research area. Researchers all around the world have focused efforts on the development of new ways to continuous monitoring the structures and analyze the data collected from the inspection process in order to provide information about the current state and avoid possible catastrophes. To perform an effective analysis of the data, the development of methodologies is crucial in order to assess the structures with a low computational cost and with a high reliability. These desirable features can be found in biological systems, and these can be emulated by means of computational systems. The use of bio-inspired algorithms is a recent approach that has demonstrated its effectiveness in data analysis in different areas. Since these algorithms are based in the emulation of biological systems that have demonstrated its effectiveness for several generations, it is possible to mimic the evolution process and its adaptability characteristics by using computational algorithms. Specially in pattern recognition, several algorithms have shown good performance. Some widely used examples are the neural networks, the fuzzy systems and the genetic algorithms. This thesis is concerned about the development of bio-inspired methodologies for structural damage detection and classification. This document is organized in five chapters. First, an overview of the problem statement, the objectives, general results, a brief theoretical background and the description of the different experimental setups are included in Chapter 1 (Introduction). Chapters 2 to 4 include the journal papers published by the author of this thesis. The discussion of the results, some conclusions and the future work can be found on Chapter 5. Finally, Appendix A includes other contributions such as a book chapter and some conference papers.La necesidad de asegurar el correcto funcionamiento de las estructuras en servicio ha hecho de la monitorización de la integridad estructural un área de gran interés. Investigadores en todas las partes del mundo centran sus esfuerzos en el desarrollo de nuevas formas de monitorización contínua de estructuras que permitan analizar e interpretar los datos recogidos durante el proceso de inspección con el objetivo de proveer información sobre el estado actual de la estructura y evitar posibles catástrofes. Para desarrollar un análisis efectivo de los datos, es necesario el desarrollo de metodologías para inspeccionar la estructura con un bajo coste computacional y alta fiabilidad. Estas características deseadas pueden ser encontradas en los sistemas biológicos y pueden ser emuladas mediante herramientas computacionales. El uso de algoritmos bio-inspirados es una reciente técnica que ha demostrado su efectividad en el análisis de datos en diferentes áreas. Dado que estos algoritmos se basan en la emulación de sistemas biológicos que han demostrado su efectividad a lo largo de muchas generaciones, es posible imitar el proceso de evolución y sus características de adaptabilidad al medio usando algoritmos computacionales. Esto es así, especialmente, en reconocimiento de patrones, donde muchos de estos algoritmos brindan excelentes resultados. Algunos ejemplos ampliamente usados son las redes neuronales, los sistemas fuzzy y los algoritmos genéticos. Esta tesis involucra el desarrollo de unas metodologías bio-inspiradas para la detección y clasificación de daños estructurales. El documento está organizado en cinco capítulos. En primer lugar, se incluye una descripción general del problema, los objetivos del trabajo, los resultados obtenidos, un breve marco conceptual y la descripción de los diferentes escenarios experimentales en el Capítulo 1 (Introducción). Los Capítulos 2 a 4 incluyen los artículos publicados en diferentes revistas indexadas. La revisión de los resultados, conclusiones y el trabajo futuro se encuentra en el Capítulo 5. Finalmente, el Anexo A incluye otras contribuciones tales como un capítulo de libro y algunos trabajos publicados en conferencias
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