Wang, Y, Li, K orcid.org/0000-0001-6657-0522, Gan, S et al. (2 more authors) (2019) Data Augmentation for Intelligent Manufacturing with Generative Adversarial Framework. In: Proceedings of the 2019 1st International Conference on Industrial Artificial Intelligence (IAI). The 1st International Conference on Industrial Artificial Intelligence (IAI 2019), 23-27 Jul 2019, Shenyang, China. IEEE ISBN 978-1-7281-3593-9
Abstract
The global economy is greatly shaped by the unprecedented booming of ICT and artificial intelligence technologies. Their applications in manufacturing has led to the advent of intelligent manufacturing and industry 4.0. Data has become a precious asset for modern industry. This paper first introduces an energy monitoring and data acquisition system namely the Point Energy Technology, which has been developed by the team and installed in several industrial partners, including a local bakery. The lack of data always exists due to various reasons, such as measurement or transmission errors at data collection and transmission stage, leading to the loss of varied length of data samples that are key for process monitoring and control. To solve this problem, we introduce a generative adversarial framework which is based on a game theory for data augmentation. This framework consists of two multilayer perceptron networks, namely generator and discriminator. An improved framework with Q-net that extracts the latent variables from real data is also proposed, in which the Q-net shares the structure with discriminator except for the last layer. In addition, the two optimization methods, namely mini-batch gradient descent and adaptive moment estimation are adopted to tune the parameters. To evaluate the performance of these algorithms, energy consumption data collected from a bakery process is used in the experiment. The experimental results confirm that the latent generative adversarial framework with adaptive moment estimation could generate good quality data samples to compensate the random loss of samples in time series data.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019, 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. |
Keywords: | Generators; Estimation; Monitoring; Industries; Training; Manufacturing; Gallium nitride |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Funding Information: | Funder Grant number EPSRC EP/P004636/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Nov 2019 11:44 |
Last Modified: | 12 Nov 2019 09:54 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/iciai.2019.8850773 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:153020 |