Industrial AI: Introduction and motivation
Data sources, business problems, challenges, and restrictions
Taxonomy of Industrial AI problems
Problem formulations and modeling challenges
Introduction to Prognostics
Prognostics using sequence deep learning models [5, 8, 22]
Functional neural networks for modeling sensors and spatiotemporal measurements [10, 14, 15]
Prognostics in industrial networks using graph CNN [4, 7]
Generative adversarial networks for prognostics and sensor generation [18–20]
Visual inspection for maintenance, manufacturing and quality enhancement [3, 13]
Natural language understanding and knowledge base construction for industrial domains [2, 11]
Multi-task learning for achieving consistency between related tasks [1]
Ensemble learning for improving consistency of deep learning models [6, 9]
Deep RL for dynamic dispatching [21]
Health indicator learning using deep RL [16]
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