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Biao Wang et al.
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Min Xia et al.
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Liang Guo et al.
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Yuting Wu et al.
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Xiang Li et al.
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David Lira Nunez et al.
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Jun Wu et al.
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Liang Guo et al.
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He Fei et al.
JOURNAL OF PROCESS CONTROL (2016)
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Funa Zhou et al.
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Rodney K. Singleton et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2015)
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Ruhi Sarikaya et al.
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Bearing degradation process prediction based on the PCA and optimized LS-SVM model
Shaojiang Dong et al.
MEASUREMENT (2013)
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Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods
Runqing Huang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2007)