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Switched System Diagnosis Based on Intelligent State Filtering with Unknown Models
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/></div></noscript> <!-- /Yandex.Metrika counter --> <!-- Matomo --> <!-- End Matomo Code --> <title>Switched System Diagnosis Based on Intelligent State Filtering with Unknown Models</title> <meta name="description" content="Switched System Diagnosis Based on Intelligent State Filtering with Unknown Models"> <meta name="keywords" content="clustering, diagnosis, Kalman Filtering, k-means, regularized regression"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav 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class="card"> <div class="card-body"><strong>Paper Count:</strong> 87481</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Switched System Diagnosis Based on Intelligent State Filtering with Unknown Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nada%20Slimane">Nada Slimane</a>, <a href="https://publications.waset.org/abstracts/search?q=Foued%20Theljani"> Foued Theljani</a>, <a href="https://publications.waset.org/abstracts/search?q=Faouzi%20Bouani"> Faouzi Bouani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper addresses the problem of fault diagnosis for systems operating in several modes (normal or faulty) based on states assessment. We use, for this purpose, a methodology consisting of three main processes: 1) sequential data clustering, 2) linear model regression and 3) state filtering. Typically, Kalman Filter (KF) is an algorithm that provides estimation of unknown states using a sequence of I/O measurements. Inevitably, although it is an efficient technique for state estimation, it presents two main weaknesses. First, it merely predicts states without being able to isolate/classify them according to their different operating modes, whether normal or faulty modes. To deal with this dilemma, the KF is endowed with an extra clustering step based fully on sequential version of the k-means algorithm. Second, to provide state estimation, KF requires state space models, which can be unknown. A linear regularized regression is used to identify the required models. To prove its effectiveness, the proposed approach is assessed on a simulated benchmark. <iframe src="https://publications.waset.org/abstracts/104370.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnosis" title=" diagnosis"> diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20Filtering" title=" Kalman Filtering"> Kalman Filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=regularized%20regression" title=" regularized regression"> regularized regression</a> </p> <a href="https://publications.waset.org/abstracts/104370/switched-system-diagnosis-based-on-intelligent-state-filtering-with-unknown-models" 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