Visualization and Explainable Machine Learning for Efficient Manufacturing and System Operations

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 attack cascade, then the hosting system can learn how to neutralize threats and attacks and self-repair infected or damaged links autonomously. In this article, our preliminary work presents a visual analytics framework and technique for situational awareness, including autonomously monitoring, diagnosing, and prognosticating the state of cyber-physical systems. Our approach, presented in this article, relies on visual characterizations of multivariate time series and real-time predictive analytics to highlight potential faults, threats, and malicious attacks. To validate the usefulness of our approach, we demonstrate the developed technique using various aviation datasets obtained from the Prognostics Center of Excellence at the National Aeronautics and Space Administration Ames.