Detailed Study of Different Degradation Stages of Bearings in a Practical Reference Dataset
- The often-occurring short-term orders of manufactured products require a high machine availability. This requirement increases the importance of predictive maintenance solutions for bearings used in machines. There are, among others, hybrid solutions that rely on a physical model. For their usage, knowing the different degradation stages of bearings is essential. This research analyzes theThe often-occurring short-term orders of manufactured products require a high machine availability. This requirement increases the importance of predictive maintenance solutions for bearings used in machines. There are, among others, hybrid solutions that rely on a physical model. For their usage, knowing the different degradation stages of bearings is essential. This research analyzes the underlying failure mechanisms of these stages theoretically and in a practical example of the well-known FEMTO dataset used for the IEEE PHM 2012 Data Challenge to provide this knowledge. In addition, it shows for which use cases the usage of low-frequency accelerometers is sufficient. The analysis provides that the degradation stages toward the end of the bearing life can also be detected with low-frequency accelerometers. Further, the importance of high-frequency accelerometers to detect bearing faults in early degradation stages is pointed out. These aspects have not been paid attention to by industry and research until now, despite providing a considerable cost-saving potential.…
Document Type: | Conference Proceeding |
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Conference Type: | Konferenzartikel |
Zitierlink: | https://opus.hs-offenburg.de/8305 | Bibliografische Angaben |
Title (English): | Detailed Study of Different Degradation Stages of Bearings in a Practical Reference Dataset |
Conference: | IEEE International Conference on Emerging Technologies and Factory Automation (28. : 12-15 September 2023 : Sinaia, Romania) |
Author: | Sebastian Schwendemann, Andreas Rausch, Axel SikoraStaff MemberORCiDGND |
Year of Publication: | 2023 |
Date of first Publication: | 2023/10/12 |
Publisher: | IEEE |
First Page: | 1 |
Last Page: | 8 |
Parent Title (English): | 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA) |
ISBN: | 979-8-3503-3991-8 (Elektronisch) |
ISBN: | 979-8-3503-3990-1 (USB) |
ISBN: | 979-8-3503-3992-5 (Print on Demand) |
ISSN: | 1946-0759 (Elektronisch) |
ISSN: | 1946-0740 (Print on Demand) |
DOI: | https://doi.org/10.1109/ETFA54631.2023.10275478 |
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: | Failure analysis; Manufacturing automation; RUL; accelerometer; bearing; degradation stages; predictive maintenance | Formale Angaben |
Relevance: | Konferenzbeitrag: h5-Index > 30 |
Open Access: | Closed |
Licence (German): | Urheberrechtlich geschützt |