IoT-Driven Robust Bearing Fault Diagnosis for Induction Motors Under Operating-Condition Shift
Şükrü Mustafa Kaya, Alireza Esmaeili Jobani · 2026
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Description
Şükrü Mustafa Kaya tested bearing fault finders for motors when working conditions change in a 2026 study. Models that shine on random data splits can fail badly once speed or load shifts away from training settings.
Raw signal networks, image-based networks and a fused vibration plus current method were compared on Paderborn bearing data under one shared protocol. Simple early fusion gave the best balance of robustness and size in reported results, with only 73,891 parameters in the chosen model. Harder tests hiding whole bearing types still proved tough, marking the next frontier.
Catalog record
| CREATOR | Şükrü Mustafa Kaya, Alireza Esmaeili Jobani |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 108055 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |