PreprintArticleVersion 1This version is not peer-reviewed
Enhancement of Sustainable Maintenance in Hydrogen Compressor Monitoring by the Utilization of Fractional Factorial Experiments to Optimize the Hyperparameters of a Self-Attention-Based Neural Network
Version 1
: Received: 31 July 2024 / Approved: 31 July 2024 / Online: 31 July 2024 (15:15:50 CEST)
How to cite:
Pérez, S.; González-Gaya, C.; Sebastián, M. A. Enhancement of Sustainable Maintenance in Hydrogen Compressor Monitoring by the Utilization of Fractional Factorial Experiments to Optimize the Hyperparameters of a Self-Attention-Based Neural Network. Preprints2024, 2024072609. https://doi.org/10.20944/preprints202407.2609.v1
Pérez, S.; González-Gaya, C.; Sebastián, M. A. Enhancement of Sustainable Maintenance in Hydrogen Compressor Monitoring by the Utilization of Fractional Factorial Experiments to Optimize the Hyperparameters of a Self-Attention-Based Neural Network. Preprints 2024, 2024072609. https://doi.org/10.20944/preprints202407.2609.v1
Pérez, S.; González-Gaya, C.; Sebastián, M. A. Enhancement of Sustainable Maintenance in Hydrogen Compressor Monitoring by the Utilization of Fractional Factorial Experiments to Optimize the Hyperparameters of a Self-Attention-Based Neural Network. Preprints2024, 2024072609. https://doi.org/10.20944/preprints202407.2609.v1
APA Style
Pérez, S., González-Gaya, C., & Sebastián, M. A. (2024). Enhancement of Sustainable Maintenance in Hydrogen Compressor Monitoring by the Utilization of Fractional Factorial Experiments to Optimize the Hyperparameters of a Self-Attention-Based Neural Network. Preprints. https://doi.org/10.20944/preprints202407.2609.v1
Chicago/Turabian Style
Pérez, S., Cristina González-Gaya and Miguel A. Sebastián. 2024 "Enhancement of Sustainable Maintenance in Hydrogen Compressor Monitoring by the Utilization of Fractional Factorial Experiments to Optimize the Hyperparameters of a Self-Attention-Based Neural Network" Preprints. https://doi.org/10.20944/preprints202407.2609.v1
Abstract
Making sustainable business models, especially in manufacturing, is a key approach for organizations to adopt in order to create more sustainable processes. New technologies are needed to implement new policies that can minimize the economic, environmental, and social consequences of industrial activity. Sustainable maintenance is decisive to guarantee the availability, reliability, and safety of assets, surpassing traditional maintenance policies and embracing the principles of the circular economy. The main objective of Maintenance 4.0 is to increase the lifespan and availability of the equipment by preventing unplanned downtime, reducing planned shutdowns, and improving safety. This research focuses on the integration of advanced Industry 4.0 tools with conventional monitoring and control techniques in process industries. The article suggests that while complex neural networks might provide good predictions, simpler solutions can generalize well and have lower prediction errors because they can capture the underlying relationships between variables in a specific asset better. The combination of maintenance function, Industry 4.0 tools, and advanced statistical solutions to obtain sustainable processes can facilitate organizational change and aid decision-making in equipment management. These solutions seek to reduce interruptions and expenses through the unnecessary utilization of labor and spare parts. All these considerations improve communication and data processing, resulting in improved maintenance of a hydrogen compressor in the petrochemical industry.
Keywords
sustainable maintenance; Industry 4.0; forecast; neural network; hyperparameter.
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
