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首页> 外文会议>IEEE International Conference on Parallel and Distributed Systems >GraphEL: A Graph-based Ensemble Learning Method for Distributed Diagnostics and Prognostics in the Industrial Internet of Things
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GraphEL: A Graph-based Ensemble Learning Method for Distributed Diagnostics and Prognostics in the Industrial Internet of Things

机译：Graphel：基于图形的集合学习方法，用于商业互联网上的分布式诊断和预测

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摘要

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.
机译：集合学习（EL）方法已被证明在工业系统中的诊断和预测有效。通过结合不同基础学习者的学习能力，EL有可能降低学习系统的总复杂性，同时解决困难问题。最近的事业互联网（IIT）和Edge Computing Technologies的优势已经开始了一种新的分布式诊断和预测的范例。然而，主要关注集中式设置的现有EL方法不能适应边缘计算场景，这显着约束了这些EL方法在真正的工业环境中的应用。在本文中，我们向EL称为基于图形的集合学习（Graphel）的新方法，以使实际工业环境中的分布式诊断和预测。与现有方法相比，所提出的石墨框架为不同的子系统构建不同的基础学习者。此外，构造图形模型以在集合中定义不同基础学习者的输出中的相关结构，使得它们可以协作训练以优化集合的学习性能。通过简单的消息传递，所提出的图形框架可以以完全分布式方式执行，该方法适合于边缘计算。使用两个现实世界的工业数据集进行评估所提出的石墨框架的性能，我们展示了与现有的EL方法相比图形的优点。

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