Version 1
: Received: 27 September 2024 / Approved: 27 September 2024 / Online: 29 September 2024 (11:04:07 CEST)
How to cite:
Zhao, Y.; Lin, L.; Schlarb, A. K. Long Short-Term Memory (LSTM) Networks for Automated Identification the Stationary Phase in Tribological Experiments. Preprints2024, 2024092269. https://doi.org/10.20944/preprints202409.2269.v1
Zhao, Y.; Lin, L.; Schlarb, A. K. Long Short-Term Memory (LSTM) Networks for Automated Identification the Stationary Phase in Tribological Experiments. Preprints 2024, 2024092269. https://doi.org/10.20944/preprints202409.2269.v1
Zhao, Y.; Lin, L.; Schlarb, A. K. Long Short-Term Memory (LSTM) Networks for Automated Identification the Stationary Phase in Tribological Experiments. Preprints2024, 2024092269. https://doi.org/10.20944/preprints202409.2269.v1
APA Style
Zhao, Y., Lin, L., & Schlarb, A. K. (2024). Long Short-Term Memory (LSTM) Networks for Automated Identification the Stationary Phase in Tribological Experiments. Preprints. https://doi.org/10.20944/preprints202409.2269.v1
Chicago/Turabian Style
Zhao, Y., Leyu Lin and Alois K. Schlarb. 2024 "Long Short-Term Memory (LSTM) Networks for Automated Identification the Stationary Phase in Tribological Experiments" Preprints. https://doi.org/10.20944/preprints202409.2269.v1
Abstract
This study outlines the development and optimization of a Long Short-Term Memory (LSTM) network designed to analyze and classify time-series data from tribological experiments, with a particular emphasis on identifying stationary phases. The process of fine-tuning key hyperparameters was systematically optimized through Bayesian optimization, coupled with K-fold cross-validation, to minimize the inherent randomness associated with training neural networks. The refined LSTM network achieved a weighted average accuracy of 84%, demonstrating a high level of agreement between the network's identified stationary phases and those manually determined by researchers. This result suggests that LSTM networks can reliably mimic manual identification processes in tribological data, providing a promising avenue for automating data analysis. The study underscores the potential of neural networks to transcend their traditional role in predictive modeling within tribology, opening up new possibilities for their application across a broader spectrum of tasks within the field.
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.
