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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.show moreshow less

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Metadaten
Document Type:Conference Proceeding
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 SchwendemannStaff Member, Andreas Rausch, Axel SikoraStaff MemberORCiDGND
Year of Publication:2023
Date of first Publication:2023/11/22
URL:https://www.researchgate.net/publication/375516084
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:Bearings; Fault Classification; Predictive Maintenance
Formale Angaben
Relevance:Konferenzbeitrag: h5-Index < 30
Open Access: Closed 
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International