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A Learning-Based Context-Aware Quality Test System in B5G-Aided Advanced Manufacturing

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posted on 2025-02-11, 02:16 authored by Sige Liu, Peng ChengPeng Cheng, Zhuo Chen, Kan YuKan Yu, Wei XiangWei Xiang, Jun Li, Branka Vucetic, Yonghui Li
The booming of the industrial Internet of Things (IIoT) brings an exponential increase in industrial devices, calling for more flexible and low-cost communications. The fifth generation and beyond (B5G) communication technologies provide a dedicated solution by supporting two industry-targeted technologies: Massive machine-type communications (mMTC) and ultra reliable low-latency communications (URLLC). In this article, we design a B5G-aided quality test system in advanced manufacturing, where various sensors are connected to the base station (BS) and send contextual information via mMTC. The BS and quality test machine transmit short length commands and small size feedback to each other, respectively, via URLLC. We formulate a long-term optimization problem to improve the product qualification rate by maximizing the expected average reward with limited testing capacity and changing configurations. To address this problem, we develop a novel context-aware combinatorial quality test (CC-QT) algorithm based on bandit learning (BL), which integrates contextual information to predict the product quality, and a combinatorial method to decrease the complexity of the BL process. Furthermore, we derive a performance upper bound of the proposed CC-QT and analyze its computational complexity. Experimental results illustrate the performance of CC-QT and substantiate its superiority over the existing algorithms.

Funding

The work of Peng Cheng was supported by ARC under Grant DE190100162 and Grant DP210103410. The work of Yonghui Li was supported by ARC under Grant DP190101988 and Grant DP210103410. The work of Jun Li was supported in part by the National Natural Science Foundation of China under Grant 61872184, and in part by the Fundamental Research Funds for the Central Universities under Grant 30921013104. Paper no. TII-21-5797.

History

Publication Date

2023-02-01

Journal

IEEE Transactions on Industrial Informatics

Volume

19

Issue

2

Pagination

1548 - 1558

Publisher

Institute of Electrical and Electronics Engineers

ISSN

1551-3203

Rights Statement

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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