Optimal Robust Time-Domain Feature-Based Bearing Fault and Stator Fault Diagnosis
In machine learning, the extraction of features is necessary for intelligent motor fault diagnosis. In industrial applications, it is necessary to identify the optimal number of features to differentiate various types of fault characteristics with less computational complexity and cost. However, mot...
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of the Industrial Electronics Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10568251/ |
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| author | G. Geetha P. Geethanjali |
| author_facet | G. Geetha P. Geethanjali |
| author_sort | G. Geetha |
| collection | DOAJ |
| description | In machine learning, the extraction of features is necessary for intelligent motor fault diagnosis. In industrial applications, it is necessary to identify the optimal number of features to differentiate various types of fault characteristics with less computational complexity and cost. However, motor fault diagnosis for real-time applications has challenges in capturing characteristics due to variations in speed, load, force, run-to-failure state as well as the type of the motor and its parts. The deep learning techniques that automatically extract features and perform classification have algorithmic complexity. In this work, the authors address these challenges by: 1) selecting and ensembling optimal time-domain features that are capable of identifying motor faults using current signals of the permanent magnet synchronous motor (PMSM) in bearing; and 2) investigating the feature ensemble constituting optimal features for robust fault diagnosis in the PMSM bearing as well as the stator and bearing of squirrel cage induction motor (SCIM) for various conditions. The optimal features mean absolute value, simple sign integral, and waveform length yields 99.8% and 100% for bearing fault and stator fault diagnosis, respectively, in PMSM. These features show 100% accuracy for identification of fault in SCIM and 98.2% accuracy in the run-to-failure state. |
| format | Article |
| id | doaj-art-eaa8a6a6be16416c90cca2e5737f55b6 |
| institution | Kabale University |
| issn | 2644-1284 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Industrial Electronics Society |
| spelling | doaj-art-eaa8a6a6be16416c90cca2e5737f55b62025-01-17T00:01:23ZengIEEEIEEE Open Journal of the Industrial Electronics Society2644-12842024-01-01556257410.1109/OJIES.2024.341740110568251Optimal Robust Time-Domain Feature-Based Bearing Fault and Stator Fault DiagnosisG. Geetha0https://orcid.org/0009-0003-2699-9000P. Geethanjali1https://orcid.org/0000-0002-6659-7052School of Electrical Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Electrical Engineering, Vellore Institute of Technology, Vellore, IndiaIn machine learning, the extraction of features is necessary for intelligent motor fault diagnosis. In industrial applications, it is necessary to identify the optimal number of features to differentiate various types of fault characteristics with less computational complexity and cost. However, motor fault diagnosis for real-time applications has challenges in capturing characteristics due to variations in speed, load, force, run-to-failure state as well as the type of the motor and its parts. The deep learning techniques that automatically extract features and perform classification have algorithmic complexity. In this work, the authors address these challenges by: 1) selecting and ensembling optimal time-domain features that are capable of identifying motor faults using current signals of the permanent magnet synchronous motor (PMSM) in bearing; and 2) investigating the feature ensemble constituting optimal features for robust fault diagnosis in the PMSM bearing as well as the stator and bearing of squirrel cage induction motor (SCIM) for various conditions. The optimal features mean absolute value, simple sign integral, and waveform length yields 99.8% and 100% for bearing fault and stator fault diagnosis, respectively, in PMSM. These features show 100% accuracy for identification of fault in SCIM and 98.2% accuracy in the run-to-failure state.https://ieeexplore.ieee.org/document/10568251/Induction motorpermanent magnet synchronous motor (PMSM)rolling bearingsstatordeep learning (DL)time-domain features |
| spellingShingle | G. Geetha P. Geethanjali Optimal Robust Time-Domain Feature-Based Bearing Fault and Stator Fault Diagnosis IEEE Open Journal of the Industrial Electronics Society Induction motor permanent magnet synchronous motor (PMSM) rolling bearings stator deep learning (DL) time-domain features |
| title | Optimal Robust Time-Domain Feature-Based Bearing Fault and Stator Fault Diagnosis |
| title_full | Optimal Robust Time-Domain Feature-Based Bearing Fault and Stator Fault Diagnosis |
| title_fullStr | Optimal Robust Time-Domain Feature-Based Bearing Fault and Stator Fault Diagnosis |
| title_full_unstemmed | Optimal Robust Time-Domain Feature-Based Bearing Fault and Stator Fault Diagnosis |
| title_short | Optimal Robust Time-Domain Feature-Based Bearing Fault and Stator Fault Diagnosis |
| title_sort | optimal robust time domain feature based bearing fault and stator fault diagnosis |
| topic | Induction motor permanent magnet synchronous motor (PMSM) rolling bearings stator deep learning (DL) time-domain features |
| url | https://ieeexplore.ieee.org/document/10568251/ |
| work_keys_str_mv | AT ggeetha optimalrobusttimedomainfeaturebasedbearingfaultandstatorfaultdiagnosis AT pgeethanjali optimalrobusttimedomainfeaturebasedbearingfaultandstatorfaultdiagnosis |