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...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925000186 |
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| author | Abdelkabir Bacha Ramzi El Idrissi Khalid Janati Idrissi Fatima Lmai |
| author_facet | Abdelkabir Bacha Ramzi El Idrissi Khalid Janati Idrissi Fatima Lmai |
| author_sort | Abdelkabir Bacha |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b4a4a61810ad40538638a7ca6fac6ce2 |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-b4a4a61810ad40538638a7ca6fac6ce22025-01-31T05:11:49ZengElsevierData in Brief2352-34092025-02-0158111286Comprehensive dataset for fault detection and diagnosis in inverter-driven permanent magnet synchronous motor systemszenodoAbdelkabir Bacha0Ramzi El Idrissi1Khalid Janati Idrissi2Fatima Lmai3École Nationale Supérieure d’Électricité et de Mécanique, Hassan II university of Casablanca, Morocco; Institut Supérieur d'Etudes Maritimes, Casablanca, Morocco; Corresponding author at: École Nationale Supérieure d’Électricité et de Mécanique, Hassan II university of Casablanca, Morocco.Faculty of Sciences, Hassan II University of Casablanca, MoroccoLaboratory of Engineering, Industrial Management and Innovation, Hassan First University, MoroccoFaculty of Sciences, Hassan II University of Casablanca, MoroccoThis 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.http://www.sciencedirect.com/science/article/pii/S2352340925000186DatasetFault detectionDiagnosisInverter-drivenPMSMExperimental setup |
| spellingShingle | Abdelkabir Bacha Ramzi El Idrissi Khalid Janati Idrissi Fatima Lmai Comprehensive dataset for fault detection and diagnosis in inverter-driven permanent magnet synchronous motor systemszenodo Data in Brief Dataset Fault detection Diagnosis Inverter-driven PMSM Experimental setup |
| title | Comprehensive dataset for fault detection and diagnosis in inverter-driven permanent magnet synchronous motor systemszenodo |
| title_full | Comprehensive dataset for fault detection and diagnosis in inverter-driven permanent magnet synchronous motor systemszenodo |
| title_fullStr | Comprehensive dataset for fault detection and diagnosis in inverter-driven permanent magnet synchronous motor systemszenodo |
| title_full_unstemmed | Comprehensive dataset for fault detection and diagnosis in inverter-driven permanent magnet synchronous motor systemszenodo |
| title_short | Comprehensive dataset for fault detection and diagnosis in inverter-driven permanent magnet synchronous motor systemszenodo |
| title_sort | comprehensive dataset for fault detection and diagnosis in inverter driven permanent magnet synchronous motor systemszenodo |
| topic | Dataset Fault detection Diagnosis Inverter-driven PMSM Experimental setup |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925000186 |
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