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The purpose of this article is to present a method for industrial process diagnosis. We are interested in fault diagnosis considered as a supervised classification task. The interest of the proposed method is to take into account new features (and so new informations) in the classifier. These new features are probabilities extracted from a Bayesian network comparing the faulty observations to the...
Faulty automotive systems significantly degrade the performance and efficiency of vehicles and are often major contributors of vehicle breakdown; they result in large expenditures for repair and maintenance. Therefore, intelligent vehicle health-monitoring schemes are needed for effective fault diagnosis in automotive systems. Previously, we developed a data-driven approach using a data-reduction...
Rotating machinery anomaly detection is of paramount significance for industries to prevent catastrophic breakdown and improve productivity and personnel safety. The kernel classifier support vector machine (SVM) has shown excellent performance towards this purpose, but it is difficult to optimize relevant hyper-parameters. In this paper, we propose a new anomaly detection approach by merging Gaussian...
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