1,059 research outputs found

    Toward a unified PNT, Part 1: Complexity and context: Key challenges of multisensor positioning

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    The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Known environmental features, such as signs, buildings, terrain height variation, and magnetic anomalies, may or may not be available for positioning. The system could be stationary, carried by a pedestrian, or on any type of land, sea, or air vehicle. Furthermore, for many applications, the environment and host behavior are subject to change. A multi-sensor solution is thus required. The expert knowledge problem is compounded by the fact that different modules in an integrated navigation system are often supplied by different organizations, who may be reluctant to share necessary design information if this is considered to be intellectual property that must be protected

    Fault detection in operating helicopter drive train components based on support vector data description

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    The objective of the paper is to develop a vibration-based automated procedure dealing with early detection of mechanical degradation of helicopter drive train components using Health and Usage Monitoring Systems (HUMS) data. An anomaly-detection method devoted to the quantification of the degree of deviation of the mechanical state of a component from its nominal condition is developed. This method is based on an Anomaly Score (AS) formed by a combination of a set of statistical features correlated with specific damages, also known as Condition Indicators (CI), thus the operational variability is implicitly included in the model through the CI correlation. The problem of fault detection is then recast as a one-class classification problem in the space spanned by a set of CI, with the aim of a global differentiation between normal and anomalous observations, respectively related to healthy and supposedly faulty components. In this paper, a procedure based on an efficient one-class classification method that does not require any assumption on the data distribution, is used. The core of such an approach is the Support Vector Data Description (SVDD), that allows an efficient data description without the need of a significant amount of statistical data. Several analyses have been carried out in order to validate the proposed procedure, using flight vibration data collected from a H135, formerly known as EC135, servicing helicopter, for which micro-pitting damage on a gear was detected by HUMS and assessed through visual inspection. The capability of the proposed approach of providing better trade-off between false alarm rates and missed detection rates with respect to individual CI and to the AS obtained assuming jointly-Gaussian-distributed CI has been also analysed

    Anomaly detection on data streams from vehicular networks

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    As redes veiculares são compostas por nós com elevada mobilidade que apenas estão ativos quando o veículo se encontra em movimento, tornando a rede imprevisível e em constante mudança. Num cenário tão dinâmico, detetar anomalias na rede torna-se uma tarefa exigente, mas crucial. A Veniam opera uma rede veicular que garante conexão fiável através de redes heterogéneas como LTE, Wi-Fi e DSRC, conectando os veículos à Internet e a outros dispositivos espalhados pela cidade. Ao longo do tempo, os nós enviam dados para a Cloud tanto por tecnologias em tempo real como por tecnologias tolerantes a atraso, aumentando a dinâmica da rede. O objetivo desta dissertação é propor e implementar um método para detetar anomalias numa rede veicular real, através de uma análise online dos fluxos de dados enviados dos veículos para a Cloud. Os fluxos da rede foram explorados de forma a caracterizar os dados disponíveis e selecionar casos de uso. Os datasets escolhidos foram submetidos a diferentes técnicas de deteção de anomalias, como previsão de séries temporais e deteção de outliers baseados na densidade da vizinhança, seguido da análise dos trade-offs para selecionar os algoritmos que melhor se ajustam às características dos dados. A solução proposta engloba duas etapas: uma primeira fase de triagem seguida de uma classificação baseada no método dos vizinhos mais próximos. O sistema desenvolvido foi implementado no cluster distribuído da Veniam, que executa Apache Spark, permitindo uma solução rápida e escalável que classifica os dados assim que chegam à Cloud. A performance do método foi avaliada pela sua precisão, i.e., a percentagem de verdadeiras anomalias dentro das anomalias detetadas, quando foi submetido a datasets com anomalias artificiais provenientes de fontes de dados diferentes, recebidas tanto por tecnologias em tempo real como por tecnologias tolerantes a atraso.Vehicular networks are characterized by high mobility nodes that are only active when the vehicle is moving, thus making the network unpredictable and in constant change. In such a dynamic scenario, detecting anomalies in the network is a challenging but crucial task. Veniam operates a vehicular network that ensures reliable connectivity through heterogeneous networks such as LTE, Wi-Fi and DSRC, connecting the vehicles to the Internet and to other devices spread throughout the city. Over time, nodes send data to the cloud either by real time technologies or by delay tolerant ones, increasing the network's dynamics. The aim of this dissertation is to propose and implement a method for detecting anomalies in a real-world vehicular network through means of an online analysis of the data streams that come from the vehicles to the cloud. The network's streams were explored in order to characterize the available data and select target use cases. The chosen datasets were submitted to different anomaly detection techniques, such as time series forecasting and density-based outlier detection, followed by the trade-offs' analysis to select the algorithms that best modeled the data characteristics. The proposed solution comprises two stages: a lightweight screening step, followed by a Nearest Neighbor classification. The developed system was implemented on Veniam's distributed cluster running Apache Spark, allowing a fast and scalable solution that classifies the data as soon as it reaches the Cloud. The performance of the method was evaluated by its precision, i.e., the percentage of true anomalies within the detected outliers, when it was submitted to datasets presenting artificial anomalies from different data sources, received either by real-time or delay tolerant technologies

    Comparison of new anomaly detection technique for wind turbine condition monitoring using gearbox SCADA data

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    Anomaly detection for wind turbine condition monitoring is an active area of research within the wind energy operations and maintenance (O&M) community. In this paper three models were compared for multi-megawatt operational wind turbine SCADA data. The models used for comparison were One-Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Elliptical Envelope (EE). Each of these were compared for the same fault, and tested under various different data configurations. IF and EE have not previously been used for fault detection for wind turbines, and OCSVM has not been used for SCADA data. This paper presents a novel method of condition monitoring that only requires two months of data per turbine. These months were separated by a year, the first being healthy and the second unhealthy. The number of anomalies is compared, with a greater number in the unhealthy month being considered correct. It was found that for accuracy IF and OCSVM had similar performances in both training regimes presented. OCSVM performed better for generic training, and IF performed better for specific training. Overall, IF and OCSVM had an average accuracy of 82% for all configurations considered, compared to 77% for EE

    Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning

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    Terabytes of data are collected every day by wind turbine manufacturers from their fleets. The data contain valuable real-time information for turbine health diagnostics and performance monitoring, for predicting rare failures and the remaining service life of critical parts. And yet, this wealth of data from wind turbine fleets remains inaccessible to operators, utility companies, and researchers as manufacturing companies prefer the privacy of their fleets' turbine data for business strategic reasons. The lack of data access impedes the exploitation of opportunities, such as improving data-driven turbine operation and maintenance strategies and reducing downtimes. We present a distributed federated machine learning approach that leaves the data on the wind turbines to preserve the data privacy, as desired by manufacturers, while still enabling fleet-wide learning on those local data. We demonstrate in two case studies that wind turbines which are scarce in representative training data benefit from more accurate fault detection models with federated learning, while no turbine experiences a loss in model performance by participating in the federated learning process. When comparing conventional and federated training processes, the average model training time rises significantly by a factor of up to 14 in the federated training due to increased communication and overhead operations. Thus, model training times might constitute an impediment that needs to be further explored and alleviated in federated learning applications, especially for large wind turbine fleets

    Learning Informative Health Indicators Through Unsupervised Contrastive Learning

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    Condition monitoring is essential to operate industrial assets safely and efficiently. To achieve this goal, the development of robust health indicators has recently attracted significant attention. These indicators, which provide quantitative real-time insights into the health status of industrial assets over time, serve as valuable tools for fault detection and prognostics. In this study, we propose a novel and universal approach to learn health indicators based on unsupervised contrastive learning. Operational time acts as a proxy for the asset's degradation state, enabling the learning of a contrastive feature space that facilitates the construction of a health indicator by measuring the distance to the healthy condition. To highlight the universality of the proposed approach, we assess the proposed contrastive learning framework in two distinct tasks - wear assessment and fault detection - across two different case studies: a milling machines case study and a real condition monitoring case study of railway wheels from operating trains. First, we evaluate if the health indicator is able to learn the real health condition on a milling machine case study where the ground truth wear condition is continuously measured. Second, we apply the proposed method on a real case study of railway wheels where the ground truth health condition is not known. Here, we evaluate the suitability of the learned health indicator for fault detection of railway wheel defects. Our results demonstrate that the proposed approach is able to learn the ground truth health evolution of milling machines and the learned health indicator is suited for fault detection of railway wheels operated under various operating conditions by outperforming state-of-the-art methods. Further, we demonstrate that our proposed approach is universally applicable to different systems and different health conditions
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