Visualization and Explainable Machine Learning for Efficient Manufacturing and System Operations

Dy D. Le, Huyen N. Nguyen, Tommy Dang

Research output: Contribution to journalArticlepeer-review

Abstract

To enable Industry 4.0 successfully, there is a need to build a resilient automation system that can quickly recover after having been attacked or robustly sustain continued operations while being threatened, enable an automated monitoring evolution via various sensor channels in real-time, and use advanced machine learning and data analytics to formulate strategies to mitigate and eliminate faults, threats, and malicious attacks. It is envisioned that if we can develop an intelligent model that (a) represents a meaningful, realistic environment and complex entity containing manufacturing Internet of Things interdependent and independent properties that are stepping-stones of the cyber kill chain or precursors of the onset of cyberattacks; (b) can learn and predict potential errors and formulate offense/defense strategies and healing solutions; (c) can enable cognitive ability and human-in-the-loop analytics in real-time; and (d) can facilitate system behavior changes to disrupt the a
Original languageEnglish
Pages (from-to)127-147
JournalASTM
StatePublished - Dec 1 2019

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