Abstract
The reliable and cost-effective condition monitoring of the bearings installed in water pumps is a real challenge in the industry. This paper presents a novel strong feature selection and extraction algorithm (SFSEA) to extract fault-related features from the instantaneous power spectrum (IPS). The three features extracted from the IPS using the SFSEA are fed to an extreme gradient boosting (XBG) classifier to reliably detect and classify the minor bearing faults. The experiments performed on a lab-scale test setup demonstrated classification accuracy up to 100%, which is better than the previously reported fault classification accuracies and indicates the effectiveness of the proposed method.
Original language | English |
---|---|
Article number | e4225 |
Journal | Sensors |
Volume | 21 |
Issue number | 12 |
Early online date | 20 Jun 2021 |
DOIs | |
Publication status | E-pub ahead of print - 20 Jun 2021 |
Keywords
- induction motors
- stator current sensing
- voltage measurement
- instantaneous power measurement
- vibration measurement
- feature selection