Learning to Hear Broken Motors: Signature-Guided Data Augmentation for Induction-Motor Diagnostics
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913041128357888 |
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| author | Ali, Saraa Khizhik, Aleksandr Svirin, Stepan Ryzhikov, Artem Derkach, Denis |
| author_facet | Ali, Saraa Khizhik, Aleksandr Svirin, Stepan Ryzhikov, Artem Derkach, Denis |
| contents | The application of machine learning (ML) algorithms in the intelligent diagnosis of three-phase engines has the potential to significantly enhance diagnostic performance and accuracy. Traditional methods largely rely on signature analysis, which, despite being a standard practice, can benefit from the integration of advanced ML techniques. In our study, we innovate by combining ML algorithms with a novel unsupervised anomaly generation methodology that takes into account the engine physics model. We propose Signature-Guided Data Augmentation (SGDA), an unsupervised framework that synthesizes physically plausible faults directly in the frequency domain of healthy current signals. Guided by Motor Current Signature Analysis, SGDA creates diverse and realistic anomalies without resorting to computationally intensive simulations. This hybrid approach leverages the strengths of both supervised ML and unsupervised signature analysis, achieving superior diagnostic accuracy and reliability along with wide industrial application. The findings highlight the potential of our approach to contribute significantly to the field of engine diagnostics, offering a robust and efficient solution for real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08412 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Hear Broken Motors: Signature-Guided Data Augmentation for Induction-Motor Diagnostics Ali, Saraa Khizhik, Aleksandr Svirin, Stepan Ryzhikov, Artem Derkach, Denis Machine Learning The application of machine learning (ML) algorithms in the intelligent diagnosis of three-phase engines has the potential to significantly enhance diagnostic performance and accuracy. Traditional methods largely rely on signature analysis, which, despite being a standard practice, can benefit from the integration of advanced ML techniques. In our study, we innovate by combining ML algorithms with a novel unsupervised anomaly generation methodology that takes into account the engine physics model. We propose Signature-Guided Data Augmentation (SGDA), an unsupervised framework that synthesizes physically plausible faults directly in the frequency domain of healthy current signals. Guided by Motor Current Signature Analysis, SGDA creates diverse and realistic anomalies without resorting to computationally intensive simulations. This hybrid approach leverages the strengths of both supervised ML and unsupervised signature analysis, achieving superior diagnostic accuracy and reliability along with wide industrial application. The findings highlight the potential of our approach to contribute significantly to the field of engine diagnostics, offering a robust and efficient solution for real-world applications. |
| title | Learning to Hear Broken Motors: Signature-Guided Data Augmentation for Induction-Motor Diagnostics |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.08412 |