An Adaptive Multihop Branch Ensemble-Based Graph Adaptation Framework With Edge-Cloud Orchestration for Condition Monitoring

被引：6

作者：

Liu, Bufan ^{[1
,2
]}

Chen, Chun-Hsien ^{[1
,2
]}

机构：

[1] Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore 639798, Singapore

[2] Nanyang Technol Univ, HP NTU Digital Mfg Corp Lab, Singapore 637460, Singapore

来源：

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | 2023年 / 19卷 / 10期

基金：

新加坡国家研究基金会;

关键词：

Condition monitoring; cyber-physical system; domain adaptation; edge-cloud orchestration; graph convolutional network; INTELLIGENT FAULT-DIAGNOSIS; SYSTEM; MODEL;

D O I：

10.1109/TII.2022.3230684

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Condition monitoring plays a crucial role in securing smooth production, which has been facilitated into a cyber-physical system (CPS) integration paradigm with the new information and communication technologies and data-driven intelligence. However, traditional methods limit its successful deployment from the different distribution of training data and testing data, the missing relationships between the input signals, and the insufficient data size. To overcome these limitations, a novel graph-based adaptation framework with edge-cloud orchestration is proposed. A three-stage edge-cloud orchestration mechanism is encapsulated with CPS architecture. The proposed graph-based approach mainly consists of an adaptive multihop branch ensemble module to intelligently aggregate the node information, a distance metric learner to autonomously align the data distribution, and a classifier module to automatically generate the pseudolabeled data to guide the edge-cloud orchestration and output results. Finally, a real-life case study and extensive experiments are conducted to prove the effectiveness of the proposed approach.

引用

页码：10102 / 10113

页数：12