A Hybrid Predictive Maintenance Solution for Fault Classification and Remaining Useful Life Estimation of Bearings Using Low-Cost Sensor Hardware
- In recent years, predictive maintenance tasks, especially for bearings, have become increasingly important. Solutions for these use cases concentrate on the classification of faults and the estimation of the Remaining Useful Life (RUL). As of today, these solutions suffer from a lack of training samples. In addition, these solutions often require high-frequency accelerometers, incurringIn recent years, predictive maintenance tasks, especially for bearings, have become increasingly important. Solutions for these use cases concentrate on the classification of faults and the estimation of the Remaining Useful Life (RUL). As of today, these solutions suffer from a lack of training samples. In addition, these solutions often require high-frequency accelerometers, incurring significant costs. To overcome these challenges, this research proposes a combined classification and RUL estimation solution based on a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. This solution relies on a hybrid feature extraction approach, making it especially appropriate for low-cost accelerometers with low sampling frequencies. In addition, it uses transfer learning to be suitable for applications with only a few training samples.…
Document Type: | Conference Proceeding |
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Conference Type: | Konferenzartikel |
Zitierlink: | https://opus.hs-offenburg.de/8303 | Bibliografische Angaben |
Title (English): | A Hybrid Predictive Maintenance Solution for Fault Classification and Remaining Useful Life Estimation of Bearings Using Low-Cost Sensor Hardware |
Conference: | International Conference on Industry 4.0 and Smart Manufacturing (5. : 22-24 November 2023 : Lisbon, Portugal) |
Author: | Sebastian Schwendemann![]() |
Year of Publication: | 2024 |
Date of first Publication: | 2024/03/20 |
Publisher: | Elsevier |
First Page: | 128 |
Last Page: | 138 |
Parent Title (English): | Procedia Computer Science |
Volume: | 232 |
ISSN: | 1877-0509 |
DOI: | https://doi.org/10.1016/j.procs.2024.01.013 |
Language: | English | Inhaltliche Informationen |
Institutes: | Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) |
Collections of the Offenburg University: | Bibliografie |
Research: | ivESK - Institut für verlässliche Embedded Systems und Kommunikationselektronik |
Tag: | Bearings; Fault Classification; Predictive Maintenance | Formale Angaben |
Relevance for "Jahresbericht über Forschungsleistungen": | Konferenzbeitrag: h5-Index > 30 |
Open Access: | Open Access |
Gold | |
Licence (German): | ![]() |