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• W2462954776 abstract "Cognitive fault detection and diagnosis systems are systems able to provide timely information about possibly occurring faults without requiring any a priori knowledge about the process generating the data or the possible faults. This ability is crucial in sensor network scenarios where a priori information about the data generating process, the noise level or the dictionary of the possibly occurring faults is generally hard to obtain. We here present a novel cognitive fault detection and isolation system for sensor networks. The proposed solution relies on the modeling of spatial and temporal relationships present in the acquired datastreams and an ensemble of Hidden Markov Model change-detection tests working in the space of estimated parameters for fault detection and isolation purposes. The effectiveness of the proposed solution has been evaluated on both synthetically generated and real datasets." @default.
• W2462954776 created "2016-07-22" @default.
• W2462954776 creator A5035547226 @default.
• W2462954776 creator A5039887680 @default.
• W2462954776 date "2017-02-27" @default.
• W2462954776 modified "2023-10-13" @default.
• W2462954776 title "An Ensemble Approach for Cognitive Fault Detection and Isolation in Sensor Networks" @default.
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• W2462954776 doi "https://doi.org/10.1142/s0129065716500477" @default.
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• W2462954776 hasPublicationYear "2017" @default.
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