Transfer-Learning-Based Estimation of the Remaining Useful Life of Heterogeneous Bearing Types Using Low-Frequency Accelerometers
- Deep learning approaches are becoming increasingly important for the estimation of the Remaining Useful Life (RUL) of mechanical elements such as bearings. This paper proposes and evaluates a novel transfer learning-based approach for RUL estimations of different bearing types with small datasets and low sampling rates. The approach is based on an intermediate domain that abstracts features of theDeep learning approaches are becoming increasingly important for the estimation of the Remaining Useful Life (RUL) of mechanical elements such as bearings. This paper proposes and evaluates a novel transfer learning-based approach for RUL estimations of different bearing types with small datasets and low sampling rates. The approach is based on an intermediate domain that abstracts features of the bearings based on their fault frequencies. The features are processed by convolutional layers. Finally, the RUL estimation is performed using a Long Short-Term Memory (LSTM) network. The transfer learning relies on a fixed-feature extraction. This novel deep learning approach successfully uses data of a low-frequency range, which is a precondition to use low-cost sensors. It is validated against the IEEE PHM 2012 Data Challenge, where it outperforms the winning approach. The results show its suitability for low-frequency sensor data and for efficient and effective transfer learning between different bearing types.…
Document Type: | Article (reviewed) |
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Zitierlink: | https://opus.hs-offenburg.de/6931 | Bibliografische Angaben |
Title (English): | Transfer-Learning-Based Estimation of the Remaining Useful Life of Heterogeneous Bearing Types Using Low-Frequency Accelerometers |
Author: | Sebastian Schwendemann, Axel SikoraStaff MemberORCiDGND |
Year of Publication: | 2023 |
Date of first Publication: | 2023/02/04 |
Place of publication: | Basel |
Publisher: | MDPI |
First Page: | 1 |
Last Page: | 23 |
Article Number: | 34 |
Parent Title (English): | Journal of Imaging |
Editor: | Paolo Rota, Miguel Angel Guevara Lopez, Francesco Setti |
Volume: | 9 |
Issue: | 2 |
ISSN: | 2313-433X |
DOI: | https://doi.org/10.3390/jimaging9020034 |
URN: | https://urn:nbn:de:bsz:ofb1-opus4-69314 |
Language: | English | Inhaltliche Informationen |
Institutes: | Forschung / ivESK - Institut für verlässliche Embedded Systems und Kommunikationselektronik |
Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) | |
Institutes: | Bibliografie |
Tag: | intermediate domain; predictive maintenance; remaining useful life; transfer learning | Formale Angaben |
Relevance: | Wiss. Zeitschriftenartikel reviewed: Listung in Master Journal List |
Open Access: | Open Access |
Gold | |
Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |