Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Comparative analysis of feature selection and classification techniques for robust broken rotor bar diagnosis in induction motors using current and vibration signals
Saved in:
| Published in: | Autonomous Intelligent Systems |
|---|---|
| Format: | Online Article RSS Article |
| Published: |
2025
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867301673281519621 |
|---|---|
| collection | WordPress RSS FRELIP Feed Integration |
| container_title | Autonomous Intelligent Systems |
| description | |
| discipline_display | Artificial Intelligence |
| discipline_facet | Artificial Intelligence |
| format | Online Article RSS Article |
| genre | Journal Article |
| id | rss_article:83246 |
| institution | FRELIP |
| journal_source_facet | Autonomous Intelligent Systems |
| publishDate | 2025 |
| publishDateSort | 2025 |
| record_format | rss_article |
| spellingShingle | Comparative analysis of feature selection and classification techniques for robust broken rotor bar diagnosis in induction motors using current and vibration signals Artificial Intelligence General Artificial Intelligence |
| sub_discipline_display | General |
| sub_discipline_facet | General |
| subject_display | Artificial Intelligence General Artificial Intelligence Artificial Intelligence General Artificial Intelligence |
| subject_facet | Artificial Intelligence General Artificial Intelligence |
| title | Comparative analysis of feature selection and classification techniques for robust broken rotor bar diagnosis in induction motors using current and vibration signals |
| title_auth | Comparative analysis of feature selection and classification techniques for robust broken rotor bar diagnosis in induction motors using current and vibration signals |
| title_full | Comparative analysis of feature selection and classification techniques for robust broken rotor bar diagnosis in induction motors using current and vibration signals |
| title_fullStr | Comparative analysis of feature selection and classification techniques for robust broken rotor bar diagnosis in induction motors using current and vibration signals |
| title_full_unstemmed | Comparative analysis of feature selection and classification techniques for robust broken rotor bar diagnosis in induction motors using current and vibration signals |
| title_short | Comparative analysis of feature selection and classification techniques for robust broken rotor bar diagnosis in induction motors using current and vibration signals |
| title_sort | comparative analysis of feature selection and classification techniques for robust broken rotor bar diagnosis in induction motors using current and vibration signals |
| topic | Artificial Intelligence General Artificial Intelligence |
| url | https://link.springer.com/article/10.1007/s43684-025-00113-0 |