An Analysis of Semi-Supervised Machine Learning in Electrical Machines
This research outlines the significance of semi-supervised machine learning (SSML) in dealing with the intricate characteristics of electrical machines. SSML provides a key benefit in enhancing the effectiveness and precision of predictive models for optimizing electrical machine performance, reliab...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10988593/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849322709182316544 |
|---|---|
| author | V. Raju Arvind S. Shyamsharan Poorvajaa Gurunathan Krishna Kumba Nawin Ra |
| author_facet | V. Raju Arvind S. Shyamsharan Poorvajaa Gurunathan Krishna Kumba Nawin Ra |
| author_sort | V. Raju Arvind |
| collection | DOAJ |
| description | This research outlines the significance of semi-supervised machine learning (SSML) in dealing with the intricate characteristics of electrical machines. SSML provides a key benefit in enhancing the effectiveness and precision of predictive models for optimizing electrical machine performance, reliability, and maintenance by leveraging labeled and unlabeled data. The research investigates important SSML algorithms such as self-training, co-training, generative models, and graph-based methods, highlighting their particular uses in fault diagnosis, condition monitoring, and predictive maintenance of electrical machines. Moreover, the document discusses the specific difficulties associated with this merger and offers remedies to improve the utilization of SSML in this significant area. A detailed table summarizes various methods and emphasizes their role in furthering machine learning within the realm of electrical machines. |
| format | Article |
| id | doaj-art-6a21a9b085b845b0a29fc99ca292bcba |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6a21a9b085b845b0a29fc99ca292bcba2025-08-20T03:49:17ZengIEEEIEEE Access2169-35362025-01-0113829278295910.1109/ACCESS.2025.356704710988593An Analysis of Semi-Supervised Machine Learning in Electrical MachinesV. Raju Arvind0https://orcid.org/0009-0008-5778-4244S. Shyamsharan1Poorvajaa Gurunathan2Krishna Kumba3https://orcid.org/0000-0003-1577-2516Nawin Ra4https://orcid.org/0000-0003-0702-0277School of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, IndiaSchool of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, IndiaSchool of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, IndiaSchool of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, IndiaSchool of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, IndiaThis research outlines the significance of semi-supervised machine learning (SSML) in dealing with the intricate characteristics of electrical machines. SSML provides a key benefit in enhancing the effectiveness and precision of predictive models for optimizing electrical machine performance, reliability, and maintenance by leveraging labeled and unlabeled data. The research investigates important SSML algorithms such as self-training, co-training, generative models, and graph-based methods, highlighting their particular uses in fault diagnosis, condition monitoring, and predictive maintenance of electrical machines. Moreover, the document discusses the specific difficulties associated with this merger and offers remedies to improve the utilization of SSML in this significant area. A detailed table summarizes various methods and emphasizes their role in furthering machine learning within the realm of electrical machines.https://ieeexplore.ieee.org/document/10988593/Electrical machinesmachine learninggenerative modelsgraph based methodsself trainingco-training |
| spellingShingle | V. Raju Arvind S. Shyamsharan Poorvajaa Gurunathan Krishna Kumba Nawin Ra An Analysis of Semi-Supervised Machine Learning in Electrical Machines IEEE Access Electrical machines machine learning generative models graph based methods self training co-training |
| title | An Analysis of Semi-Supervised Machine Learning in Electrical Machines |
| title_full | An Analysis of Semi-Supervised Machine Learning in Electrical Machines |
| title_fullStr | An Analysis of Semi-Supervised Machine Learning in Electrical Machines |
| title_full_unstemmed | An Analysis of Semi-Supervised Machine Learning in Electrical Machines |
| title_short | An Analysis of Semi-Supervised Machine Learning in Electrical Machines |
| title_sort | analysis of semi supervised machine learning in electrical machines |
| topic | Electrical machines machine learning generative models graph based methods self training co-training |
| url | https://ieeexplore.ieee.org/document/10988593/ |
| work_keys_str_mv | AT vrajuarvind ananalysisofsemisupervisedmachinelearninginelectricalmachines AT sshyamsharan ananalysisofsemisupervisedmachinelearninginelectricalmachines AT poorvajaagurunathan ananalysisofsemisupervisedmachinelearninginelectricalmachines AT krishnakumba ananalysisofsemisupervisedmachinelearninginelectricalmachines AT nawinra ananalysisofsemisupervisedmachinelearninginelectricalmachines AT vrajuarvind analysisofsemisupervisedmachinelearninginelectricalmachines AT sshyamsharan analysisofsemisupervisedmachinelearninginelectricalmachines AT poorvajaagurunathan analysisofsemisupervisedmachinelearninginelectricalmachines AT krishnakumba analysisofsemisupervisedmachinelearninginelectricalmachines AT nawinra analysisofsemisupervisedmachinelearninginelectricalmachines |