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Jie Duan et al.
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Hamed Tajammolian et al.
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Miao He et al.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS (2017)
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Luyang Jing et al.
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A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals
Wei Zhang et al.
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Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification
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.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2015)
Deep Neural Networks for Acoustic Modeling in Speech Recognition
Geoffrey Hinton et al.
IEEE SIGNAL PROCESSING MAGAZINE (2012)
Noise resistant time frequency analysis and application in fault diagnosis of rolling element bearings
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.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2011)
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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)
Neural-network-based motor rolling bearing fault diagnosis
B Li et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2000)