Skip to main content

Research Repository

Advanced Search

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



Downloadable Citations