Comprehensive dataset for fault detection and diagnosis in inverter-driven permanent magnet synchronous motor systemszenodo
This work introduces a new, comprehensive dataset for Fault Detection and Diagnosis (FDD) in inverter-driven Permanent Magnet Synchronous Motor (PMSM) systems. Despite the increasing significance of AI-driven FDD techniques, the domain suffers from a lack of publicly accessible, real-world datasets...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
|
| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925000186 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This work introduces a new, comprehensive dataset for Fault Detection and Diagnosis (FDD) in inverter-driven Permanent Magnet Synchronous Motor (PMSM) systems. Despite the increasing significance of AI-driven FDD techniques, the domain suffers from a lack of publicly accessible, real-world datasets for algorithm development and evaluation. Our contribution fills this gap by offering a comprehensive, multi-sensor dataset obtained from a bespoke experimental apparatus. The dataset includes different fault cases, such as open-circuit faults, short-circuit faults, and overheating conditions in the inverter switches. The dataset incorporates 8 raw sensor measurements and 15 derived features, recorded at 10 Hz, amounting to 10,892 samples across 9 operational conditions (one normal, eight fault types). By keeping this dataset publicly accessible, we seek to accelerate research in AI-driven fault identification and diagnosis for electric drive systems. |
|---|---|
| ISSN: | 2352-3409 |