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Real-time anomaly detection with in-flight data : streaming anomaly detection with heterogeneous communicating agents

Abstract : With the rise of the number of sensors and actuators in an aircraft and the development of reliable data links from the aircraft to the ground, it becomes possible to improve aircraft security and maintainability by applying real-time analysis techniques. However, given the limited availability of on-board computing and the high cost of the data links, current architectural solutions cannot fully leverage all the available resources limiting their accuracy.Our goal is to provide a distributed algorithm for failure prediction that could be executed both on-board of the aircraft and on a ground station and that would produce on-board failure predictions in near real-time under a communication budget. In this approach, the ground station would hold fast computation resources and historical data and the aircraft would hold limited computational resources and current flight's data.In this thesis, we will study the specificities of aeronautical data and what methods already exist to produce failure prediction from them and propose a solution to the problem stated. Our contribution will be detailed in three main parts.First, we will study the problem of rare event prediction created by the high reliability of aeronautical systems. Many learning methods for classifiers rely on balanced datasets. Several approaches exist to correct a dataset imbalance and we will study their efficiency on extremely imbalanced datasets.Second, we study the problem of log parsing as many aeronautical systems do not produce easy to classify labels or numerical values but log messages in full text. We will study existing methods based on a statistical approach and on Deep Learning to convert full text log messages into a form usable as an input by learning algorithms for classifiers. We will then propose our own method based on Natural Language Processing and show how it outperforms the other approaches on a public benchmark.Last, we offer a solution to the stated problem by proposing a new distributed learning algorithm that relies on two existing learning paradigms Active Learning and Federated Learning. We detail our algorithm, its implementation and provide a comparison of its performance with existing methods
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Submitted on : Tuesday, July 23, 2019 - 3:29:27 PM
Last modification on : Friday, October 23, 2020 - 5:02:33 PM


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  • HAL Id : tel-02191646, version 1


Nicolas Aussel. Real-time anomaly detection with in-flight data : streaming anomaly detection with heterogeneous communicating agents. Networking and Internet Architecture [cs.NI]. Université Paris-Saclay, 2019. English. ⟨NNT : 2019SACLL007⟩. ⟨tel-02191646⟩



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