GraphEL: A Graph-Based Ensemble Learning Method for Distributed Diagnostics and Prognostics in the Industrial Internet of Things

Ensemble learning (EL)methods have been shown to be effective for diagnostics and prognostics in industrial systems. By combining the learning ability of different base learners, EL has the potential to reduce the total complexity of the learning system while solving a difficult problem satisfactorily. Recent advantages in Industrial Internet of Things (IIoT)and edge computing technologies have started a new paradigm of distributed diagnostics and prognostics. However, existing EL methods that mainly focus on a centralized setting cannot adapt to the edge computing scenario, which significantly constrains the application of these EL methods in real industrial environments. In this paper, we present a new approach to EL called Graph-based Ensemble Learning (GraphEL)to enable distributed diagnostics and prognostics in real industrial environments. Comparing with existing methods, the proposed GraphEL framework builds different base learners for different subsystems. Furthermore, a graphical model is constructed to define the correlation structures among the outputs of different base learners in the ensemble such that they can be collaboratively trained to optimize the learning performance of the ensemble. Via simple message passing, the proposed GraphEL framework can be executed in a fully distributed manner which is suitable for edge computing. The performance of the proposed GraphEL framework is evaluated using two real-world industrial data sets where we demonstrate the advantages of GraphEL compared to existing EL methods. Index Terms-Industrial Internet of Things (IIoT), smart manufacturing, diagnostics and prognostics, distributed ensemble learning.