Bearing fault diagnosis base on multi-scale CNN and LSTM model
Bearing fault diagnosis base on multi-scale CNN and LSTM model
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Publisher
New York: Springer US
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Language
English
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Publisher
New York: Springer US
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Scope and Contents
Contents
Intelligent fault diagnosis methods based on signal analysis have been widely used for bearing fault diagnosis. These methods use a pre-determined transformation (such as empirical mode decomposition, fast Fourier transform, discrete wavelet transform) to convert time-series signals into frequency domain signals, the performance of dignostic system...
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Full title
Bearing fault diagnosis base on multi-scale CNN and LSTM model
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TN_cdi_proquest_journals_2500913550
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2500913550
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ISSN
0956-5515
E-ISSN
1572-8145
DOI
10.1007/s10845-020-01600-2
