A Frequency-Aware Transformer for Multiscale Fault Diagnosis in Electrical Machines
Motor fault diagnosis is a critical technology for ensuring the reliability of industrial equipment and enabling predictive maintenance. However, conventional fault diagnosis methods struggle to effectively capture complex time-frequency patterns, limiting their ability to perform early fault detect...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11119526/ |
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| author | Yurim Choi Inwhee Joe |
| author_facet | Yurim Choi Inwhee Joe |
| author_sort | Yurim Choi |
| collection | DOAJ |
| description | Motor fault diagnosis is a critical technology for ensuring the reliability of industrial equipment and enabling predictive maintenance. However, conventional fault diagnosis methods struggle to effectively capture complex time-frequency patterns, limiting their ability to perform early fault detection and accurate classification. To address these challenges, this study proposes the Frequency-Aware Motor Fault Transformer (FAMFT), which integrates a self-attention mechanism with multi-scale feature analysis to comprehensively analyze the time-frequency characteristics of multidimensional power quality data, including voltage, current, and harmonics. FAMFT overcomes the limitations of conventional CNN- and RNN-based models through three key innovations: 1) Multi-scale feature extraction via parallel analysis of fine-, intermediate-, and long-term temporal scales, 2) Selective feature enhancement through a frequency gating mechanism, and 3) An interpretable fault diagnosis framework based on SHAP (SHapley Additive Explanations). Experimental results demonstrate that the proposed model achieves 99.9% diagnostic accuracy by maintaining an exceptionally low false alarm rate and missed detection rate, thereby ensuring high reliability. Notably, FAMFT exhibits consistent performance across various load conditions, demonstrating a level of robustness suitable for direct implementation of real-time predictive maintenance systems in industrial environments. This study introduces a novel transformer-based fault diagnosis paradigm, contributing to the stable operation of power systems and improving maintenance efficiency. |
| format | Article |
| id | doaj-art-26ba433739e247d0bc1a997b1013c2ba |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-26ba433739e247d0bc1a997b1013c2ba2025-08-20T03:41:32ZengIEEEIEEE Access2169-35362025-01-011313983113985210.1109/ACCESS.2025.359685911119526A Frequency-Aware Transformer for Multiscale Fault Diagnosis in Electrical MachinesYurim Choi0https://orcid.org/0009-0001-6763-4476Inwhee Joe1https://orcid.org/0000-0002-8435-0395Department of Computer Science, Hanyang University, Seoul, South KoreaDepartment of Computer Science, Hanyang University, Seoul, South KoreaMotor fault diagnosis is a critical technology for ensuring the reliability of industrial equipment and enabling predictive maintenance. However, conventional fault diagnosis methods struggle to effectively capture complex time-frequency patterns, limiting their ability to perform early fault detection and accurate classification. To address these challenges, this study proposes the Frequency-Aware Motor Fault Transformer (FAMFT), which integrates a self-attention mechanism with multi-scale feature analysis to comprehensively analyze the time-frequency characteristics of multidimensional power quality data, including voltage, current, and harmonics. FAMFT overcomes the limitations of conventional CNN- and RNN-based models through three key innovations: 1) Multi-scale feature extraction via parallel analysis of fine-, intermediate-, and long-term temporal scales, 2) Selective feature enhancement through a frequency gating mechanism, and 3) An interpretable fault diagnosis framework based on SHAP (SHapley Additive Explanations). Experimental results demonstrate that the proposed model achieves 99.9% diagnostic accuracy by maintaining an exceptionally low false alarm rate and missed detection rate, thereby ensuring high reliability. Notably, FAMFT exhibits consistent performance across various load conditions, demonstrating a level of robustness suitable for direct implementation of real-time predictive maintenance systems in industrial environments. This study introduces a novel transformer-based fault diagnosis paradigm, contributing to the stable operation of power systems and improving maintenance efficiency.https://ieeexplore.ieee.org/document/11119526/Fault diagnosisfrequency-aware transformerpower quality analysismulti-scale analysispredictive maintenance |
| spellingShingle | Yurim Choi Inwhee Joe A Frequency-Aware Transformer for Multiscale Fault Diagnosis in Electrical Machines IEEE Access Fault diagnosis frequency-aware transformer power quality analysis multi-scale analysis predictive maintenance |
| title | A Frequency-Aware Transformer for Multiscale Fault Diagnosis in Electrical Machines |
| title_full | A Frequency-Aware Transformer for Multiscale Fault Diagnosis in Electrical Machines |
| title_fullStr | A Frequency-Aware Transformer for Multiscale Fault Diagnosis in Electrical Machines |
| title_full_unstemmed | A Frequency-Aware Transformer for Multiscale Fault Diagnosis in Electrical Machines |
| title_short | A Frequency-Aware Transformer for Multiscale Fault Diagnosis in Electrical Machines |
| title_sort | frequency aware transformer for multiscale fault diagnosis in electrical machines |
| topic | Fault diagnosis frequency-aware transformer power quality analysis multi-scale analysis predictive maintenance |
| url | https://ieeexplore.ieee.org/document/11119526/ |
| work_keys_str_mv | AT yurimchoi afrequencyawaretransformerformultiscalefaultdiagnosisinelectricalmachines AT inwheejoe afrequencyawaretransformerformultiscalefaultdiagnosisinelectricalmachines AT yurimchoi frequencyawaretransformerformultiscalefaultdiagnosisinelectricalmachines AT inwheejoe frequencyawaretransformerformultiscalefaultdiagnosisinelectricalmachines |