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Chen Lu et al.
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Chen Lu et al.
ADVANCED ENGINEERING INFORMATICS (2017)
An on-line condition monitoring system for induction motors via instantaneous power analysis
M. Irfan et al.
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY (2015)
Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study
Wade A. Smith et al.
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Geoffrey Hinton et al.
IEEE SIGNAL PROCESSING MAGAZINE (2012)
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Guangming Dong et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2012)
A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis
Yang Yang et al.
EXPERT SYSTEMS WITH APPLICATIONS (2011)
Rolling element bearing diagnostics-A tutorial
Robert B. Randall et al.
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Yongxiang Zhang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2009)
A fast learning algorithm for deep belief nets
Geoffrey E. Hinton et al.
NEURAL COMPUTATION (2006)
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B Li et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2000)
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