TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Schwendemann, Sebastian
A1  - Rausch, Andreas
A1  - Sikora, Axel
T1  - A Hybrid Predictive Maintenance Solution for Fault Classification and Remaining Useful Life Estimation of Bearings Using Low-Cost Sensor Hardware
N2  - 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, 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.
KW  - Predictive Maintenance
KW  - Fault Classification
KW  - Bearings
Y1  - 2023
UR  - https://www.researchgate.net/publication/375516084
ER  -