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• W2561034198 abstract "This paper presents an application of a Hidden Markov Model for fault detection and diagnosis on a testbed that emulates an AUV thruster system. The testbed consists in circuit board with two DC motors that represent the thrusters and embedded features to produce malfunctions. We present how the model is learned using the Expectation Maximization algorithm for Gaussian Mixtures and how the testbed is monitored probabilistic inference. Diagnosis is also performed using GMM classifiers. We describe how the framework deals with non-Gaussian data and how it reflects in the accuracy overall." @default.
• W2561034198 created "2017-01-06" @default.
• W2561034198 creator A5042862102 @default.
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• W2561034198 date "2016-09-01" @default.
• W2561034198 modified "2023-09-25" @default.
• W2561034198 title "A Hidden Markov Model application with Gaussian Mixture emissions for fault detection and diagnosis on a simulated AUV platform" @default.
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• W2561034198 doi "https://doi.org/10.1109/oceans.2016.7761280" @default.
• W2561034198 hasPublicationYear "2016" @default.
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