Long Short-Term Memory Networks for the Automated Identification of the Stationary Phase in Tribological Experiments
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 systematica...
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MDPI AG
2024-11-01
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| Series: | Lubricants |
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| Online Access: | https://www.mdpi.com/2075-4442/12/12/423 |
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| author | Yuxiao Zhao Leyu Lin Alois K. Schlarb |
| author_facet | Yuxiao Zhao Leyu Lin Alois K. Schlarb |
| author_sort | Yuxiao Zhao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5335b01ba8f546d1a0af01b4500faa5f |
| institution | DOAJ |
| issn | 2075-4442 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Lubricants |
| spelling | doaj-art-5335b01ba8f546d1a0af01b4500faa5f2025-08-20T02:53:34ZengMDPI AGLubricants2075-44422024-11-01121242310.3390/lubricants12120423Long Short-Term Memory Networks for the Automated Identification of the Stationary Phase in Tribological ExperimentsYuxiao Zhao0Leyu Lin1Alois K. Schlarb2Chair of Composite Engineering (CCe), Rheinland-Pfälzische Technische Universität (RPTU) Kaiserslautern-Landau, 67663 Kaiserslautern, GermanyChair of Composite Engineering (CCe), Rheinland-Pfälzische Technische Universität (RPTU) Kaiserslautern-Landau, 67663 Kaiserslautern, GermanyChair of Composite Engineering (CCe), Rheinland-Pfälzische Technische Universität (RPTU) Kaiserslautern-Landau, 67663 Kaiserslautern, GermanyThis 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.https://www.mdpi.com/2075-4442/12/12/423artificial neural network (ANN)long short-term memory (LSTM)tribologypolymer-based composites |
| spellingShingle | Yuxiao Zhao Leyu Lin Alois K. Schlarb Long Short-Term Memory Networks for the Automated Identification of the Stationary Phase in Tribological Experiments Lubricants artificial neural network (ANN) long short-term memory (LSTM) tribology polymer-based composites |
| title | Long Short-Term Memory Networks for the Automated Identification of the Stationary Phase in Tribological Experiments |
| title_full | Long Short-Term Memory Networks for the Automated Identification of the Stationary Phase in Tribological Experiments |
| title_fullStr | Long Short-Term Memory Networks for the Automated Identification of the Stationary Phase in Tribological Experiments |
| title_full_unstemmed | Long Short-Term Memory Networks for the Automated Identification of the Stationary Phase in Tribological Experiments |
| title_short | Long Short-Term Memory Networks for the Automated Identification of the Stationary Phase in Tribological Experiments |
| title_sort | long short term memory networks for the automated identification of the stationary phase in tribological experiments |
| topic | artificial neural network (ANN) long short-term memory (LSTM) tribology polymer-based composites |
| url | https://www.mdpi.com/2075-4442/12/12/423 |
| work_keys_str_mv | AT yuxiaozhao longshorttermmemorynetworksfortheautomatedidentificationofthestationaryphaseintribologicalexperiments AT leyulin longshorttermmemorynetworksfortheautomatedidentificationofthestationaryphaseintribologicalexperiments AT aloiskschlarb longshorttermmemorynetworksfortheautomatedidentificationofthestationaryphaseintribologicalexperiments |