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      Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks

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          Speech Recognition with Deep Recurrent Neural Networks

          Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates \emph{deep recurrent neural networks}, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score.
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            A review on machinery diagnostics and prognostics implementing condition-based maintenance

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              Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis

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                Author and article information

                Journal
                IEEE/ASME Transactions on Mechatronics
                IEEE/ASME Trans. Mechatron.
                Institute of Electrical and Electronics Engineers (IEEE)
                1083-4435
                1941-014X
                February 2018
                February 2018
                : 23
                : 1
                : 101-110
                Article
                10.1109/TMECH.2017.2728371
                9c8442c0-f484-445b-b21c-f8f2b366c7dc
                © 2018
                History

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