Mr DIPTO ARIFEEN d.arifeen@rgu.ac.uk
Research Student
Bayesian optimized autoencoder for predictive maintenance of smart packaging machines.
Arifeen, Murshedul; Petrovski, Andrei
Authors
Andrei Petrovski
Abstract
Smart packaging machines incorporate various components (blades, motors, films) to accomplish the packaging process and are involved in almost all types of the manufacturing industry. Proper maintenance and monitoring of the components over time can help industries to maintain a sustainable production environment. On the contrary, a faulty system may degrade production efficiency and increase the cost. Smart packaging machines comprising several sensors can generate time series data and leverage data driven condition monitoring models to overcome faulty conditions. In this work, we have studied the application of Autoencoder as a data driven condition monitoring tool for the predictive maintenance of packaging machines. The trained Autoencoder on the new system's data can detect worn or degraded components over time. We have also used the Bayesian optimization algorithm to tune the hyper-parameters of the Autoencoder for better predictive performance. Moreover, the reconstruction error is analyzed to identify the worn components in the packaging machine.
Citation
ARIFEEN, M. and PETROVSKI, A. 2023. Bayesian optimized autoencoder for predictive maintenance of smart packaging machines. In Proceedings of the 6th IEEE (Institute of Electrical and Electronics Engineers) International conference on Industrial cyber-physical systems 2023 (ICPS 2023), 8-11 May 2023, Wuhan, China. Piscataway: IEEE [online], 10128064. Available from: https://doi.org/10.1109/icps58381.2023.10128064
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | 6th IEEE (Institute of Electrical and Electronics Engineers) International conference on Industrial cyber-physical systems 2023 (ICPS 2023) |
Start Date | May 8, 2023 |
End Date | May 11, 2023 |
Acceptance Date | Feb 28, 2023 |
Online Publication Date | May 8, 2023 |
Publication Date | May 23, 2023 |
Deposit Date | Jun 22, 2023 |
Publicly Available Date | Jun 22, 2023 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Peer Reviewed | Peer Reviewed |
Series ISSN | 2769-3899 |
Book Title | Proceedings of the 6th IEEE (Institute of Electrical and Electronics Engineers) International conference on Industrial cyber-physical systems 2023 (ICPS 2023) |
DOI | https://doi.org/10.1109/ICPS58381.2023.10128064 |
Keywords | Autoencoder; Bayesian optimization; Predictive maintenance; Packaging machine; Fault detection |
Public URL | https://rgu-repository.worktribe.com/output/1993376 |
Files
ARIFEEN 2023 Bayesian optimized autoencoder (AAM)
(826 Kb)
PDF
Copyright Statement
© 20XX 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.
You might also like
Deep learning models for the diagnosis and screening of COVID-19: a systematic review.
(2022)
Journal Article
Autoencoder based consensus mechanism for blockchain-enabled industrial Internet of Things.
(2022)
Journal Article
Automated microsegmentation for lateral movement prevention in industrial Internet of Things (IIoT).
(2021)
Presentation / Conference Contribution
Performance analysis of different loss function in face detection architectures.
(2020)
Presentation / Conference Contribution
Downloadable Citations
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search