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- W3210281612 abstract "AbstractIn this paper, we introduce a recurrent neural network-based method to track machine degradation without using any expert knowledge. Contrary to previous studies, the designed method allows to detect an anomalous state by training on the raw non-anomalous vibrational data only and does not require complex feature engineering of the input. Using this method, it is possible to detect degradation on two unrelated datasets with minor changes in the RNN parameters. It is therefore a promising architecture for machine health monitoring in settings where expert knowledge is either unavailable or unable to give enough insight to obtain good results.KeywordsCondition monitoringRecurrent neural networkUnsupervisedMachine health" @default.
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- W3210281612 date "2021-10-22" @default.
- W3210281612 modified "2023-10-17" @default.
- W3210281612 title "Predictive Maintenance Using Recurrent Neural Network Without Feature Engineering" @default.
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- W3210281612 doi "https://doi.org/10.1007/978-981-16-3934-0_13" @default.
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