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Evolving From Predictive to Liquid Maintenance in Postmodern Industry
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Author(s): Manuel Lozano Rodriguez (American University of Sovereign Nations, Spain)and Carlos E. Torres (Power-MI, USA)
Copyright: 2023
Pages: 17
Source title:
Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch130
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Abstract
PdM is unready to face the near-future incoming challenges since it is anchored in an obsolete paradigm alien to the incoming cyber-physical reality and unfit for unbelievable data density. In addition to this, PdM is wormed by philosophical hiddenness around time and taxonomies abuse; it is not the sound subdiscipline it appears to be. So, we are doomed to dialogue and get along with AIs if we want to break our human predictiveness ceiling glass and keep PdM improving. In this article, the authors explain not only the turn of the tide but how to flow towards a non-essentialist PdM paradigm.
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