Dynamic mode decomposition based fault diagnosis in three-phase electrical machines
Three-phase electrical machines are widely used in various industrial applications, and the mechanical fault in those machines leads to an oscillation in the load torque, which introduces an amplitude and/or phase modulation in the stator current. This work proposes a methodology to detect mechanica...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024020048 |
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| author | Saravanakumar Rajendran Rhethika Sreejesh V.S. Kirthika Devi Debashisha Jena David Banjerdpongchai |
| author_facet | Saravanakumar Rajendran Rhethika Sreejesh V.S. Kirthika Devi Debashisha Jena David Banjerdpongchai |
| author_sort | Saravanakumar Rajendran |
| collection | DOAJ |
| description | Three-phase electrical machines are widely used in various industrial applications, and the mechanical fault in those machines leads to an oscillation in the load torque, which introduces an amplitude and/or phase modulation in the stator current. This work proposes a methodology to detect mechanical faults in rotating machines using dynamic mode decomposition (DMD). The DMD technique utilises singular value decomposition (SVD) to extract the dominant modulation frequencies and indexes by constructing a shifted-stack matrix of the three-phase current signal. Further, to validate the proposed methodology, an unbalance in the three-phase signal is examined, which includes both amplitude and phase unbalance with additive white Gaussian noise. The results demonstrated that the proposed DMD extracts the modulation frequencies and indexes with a maximum error of 2%. Additionally, different fault severities are considered to establish a real-time scenario, and the results show that the proposed DMD effectively identifies the faults with a maximum error of 1.3%. |
| format | Article |
| id | doaj-art-22f8aafc788e4179a5b052a22d98a7c1 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-22f8aafc788e4179a5b052a22d98a7c12024-12-29T04:48:01ZengElsevierResults in Engineering2590-12302025-03-0125103761Dynamic mode decomposition based fault diagnosis in three-phase electrical machinesSaravanakumar Rajendran0Rhethika Sreejesh1V.S. Kirthika Devi2Debashisha Jena3David Banjerdpongchai4Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, IndiaDepartment of Electrical and Electronics Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, IndiaDepartment of Electrical and Electronics Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, IndiaDepartment of Electrical and Electronics Engineering, National Institute of Technology Karnataka, Surahtkal, Surathkal, IndiaCenter of Excellence in Intelligent Control Automation of Process Systems, Dept. of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand; Corresponding author.Three-phase electrical machines are widely used in various industrial applications, and the mechanical fault in those machines leads to an oscillation in the load torque, which introduces an amplitude and/or phase modulation in the stator current. This work proposes a methodology to detect mechanical faults in rotating machines using dynamic mode decomposition (DMD). The DMD technique utilises singular value decomposition (SVD) to extract the dominant modulation frequencies and indexes by constructing a shifted-stack matrix of the three-phase current signal. Further, to validate the proposed methodology, an unbalance in the three-phase signal is examined, which includes both amplitude and phase unbalance with additive white Gaussian noise. The results demonstrated that the proposed DMD extracts the modulation frequencies and indexes with a maximum error of 2%. Additionally, different fault severities are considered to establish a real-time scenario, and the results show that the proposed DMD effectively identifies the faults with a maximum error of 1.3%.http://www.sciencedirect.com/science/article/pii/S2590123024020048Mechanical faultsThree-phase stator currentAmplitude modulationPhase modulationDynamic mode decompositionModulation frequency |
| spellingShingle | Saravanakumar Rajendran Rhethika Sreejesh V.S. Kirthika Devi Debashisha Jena David Banjerdpongchai Dynamic mode decomposition based fault diagnosis in three-phase electrical machines Results in Engineering Mechanical faults Three-phase stator current Amplitude modulation Phase modulation Dynamic mode decomposition Modulation frequency |
| title | Dynamic mode decomposition based fault diagnosis in three-phase electrical machines |
| title_full | Dynamic mode decomposition based fault diagnosis in three-phase electrical machines |
| title_fullStr | Dynamic mode decomposition based fault diagnosis in three-phase electrical machines |
| title_full_unstemmed | Dynamic mode decomposition based fault diagnosis in three-phase electrical machines |
| title_short | Dynamic mode decomposition based fault diagnosis in three-phase electrical machines |
| title_sort | dynamic mode decomposition based fault diagnosis in three phase electrical machines |
| topic | Mechanical faults Three-phase stator current Amplitude modulation Phase modulation Dynamic mode decomposition Modulation frequency |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024020048 |
| work_keys_str_mv | AT saravanakumarrajendran dynamicmodedecompositionbasedfaultdiagnosisinthreephaseelectricalmachines AT rhethikasreejesh dynamicmodedecompositionbasedfaultdiagnosisinthreephaseelectricalmachines AT vskirthikadevi dynamicmodedecompositionbasedfaultdiagnosisinthreephaseelectricalmachines AT debashishajena dynamicmodedecompositionbasedfaultdiagnosisinthreephaseelectricalmachines AT davidbanjerdpongchai dynamicmodedecompositionbasedfaultdiagnosisinthreephaseelectricalmachines |