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    Semi-Supervised Learning for Anomaly Classification Using Partially Labeled Subsets 

    Source: Journal of Manufacturing Science and Engineering:;2021:;volume( 144 ):;issue: 006:;page 61008-1
    Author(s): Cohen, Joseph; Ni, Jun
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Machine learning and other data-driven methods have developed at a prolific rate for industrial applications due to the advent of industrial big data. However, industrial datasets may not be especially well-suited to ...
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    Machine Learning for Diagnosis of Event Synchronization Faults in Discrete Manufacturing Systems 

    Source: Journal of Manufacturing Science and Engineering:;2021:;volume( 144 ):;issue: 007:;page 71006-1
    Author(s): Cohen, Joseph; Jiang, Baoyang; Ni, Jun
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Common in discrete manufacturing, timed event systems often have strict synchronization requirements for healthy operation. Discrete event system methods have been used as mathematical tools to detect known faults, but do ...
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