FreqMGCN-Net: An IoT-Integrated Multi-Parallel Graph Convolutional Network with frequency attention for motor bearing fault diagnosis
Motor bearing fault diagnosis is crucial for predictive maintenance in industrial systems. Traditional methods struggle with complex vibration signals, especially under noisy conditions and with limited labeled data. The advent of Internet of Things (IoT) technology enables real-time data collection...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825006398 |
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| author | Jicai Wang Shahrum Abdullah Chen Gao Azli Arifin Salvinder Singh Karam Sing |
| author_facet | Jicai Wang Shahrum Abdullah Chen Gao Azli Arifin Salvinder Singh Karam Sing |
| author_sort | Jicai Wang |
| collection | DOAJ |
| description | Motor bearing fault diagnosis is crucial for predictive maintenance in industrial systems. Traditional methods struggle with complex vibration signals, especially under noisy conditions and with limited labeled data. The advent of Internet of Things (IoT) technology enables real-time data collection through IoT-enabled sensor networks, which enhances fault detection accuracy. This paper proposes a novel fault diagnosis method, FreqMGCN-Net, which integrates IoT-enabled sensor networks for continuous data acquisition. The method utilizes a frequency attention mechanism-enhanced CNN to extract key frequency-domain features from raw vibration signals, improving model accuracy and robustness. Additionally, the model integrates Multi-parallel Graph Convolutional Networks (MGCN) with Semi-supervised Meta-learning, enabling it to handle multi-sensor data from IoT devices and address challenges like few-shot learning and cross-domain generalization. Experimental results show that FreqMGCN-Net achieves 95.7% accuracy on the CWRU dataset and 93.4% on the Paderborn dataset, outperforming existing models in accuracy, F1-Score, and recall. These results demonstrate the effectiveness of FreqMGCN-Net for real-time motor bearing fault diagnosis in IoT-enabled industrial environments, particularly in noisy and data-scarce conditions. |
| format | Article |
| id | doaj-art-eb204f011a784fc29cee796cb9aeabdb |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-eb204f011a784fc29cee796cb9aeabdb2025-08-20T03:48:14ZengElsevierAlexandria Engineering Journal1110-01682025-09-0112817518510.1016/j.aej.2025.05.019FreqMGCN-Net: An IoT-Integrated Multi-Parallel Graph Convolutional Network with frequency attention for motor bearing fault diagnosisJicai Wang0Shahrum Abdullah1Chen Gao2Azli Arifin3Salvinder Singh Karam Sing4School of Mechatronics and Transportation, Jiaxing Nanyang Polytechnic Institute, Jiaxing, 314031, China; Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Selangor, 43600, Malaysia; Corresponding author at: Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Selangor, 43600, Malaysia.Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Selangor, 43600, MalaysiaSchool of Mechatronics and Transportation, Jiaxing Nanyang Polytechnic Institute, Jiaxing, 314031, China; Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Selangor, 43600, MalaysiaFaculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Selangor, 43600, MalaysiaFaculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Selangor, 43600, MalaysiaMotor bearing fault diagnosis is crucial for predictive maintenance in industrial systems. Traditional methods struggle with complex vibration signals, especially under noisy conditions and with limited labeled data. The advent of Internet of Things (IoT) technology enables real-time data collection through IoT-enabled sensor networks, which enhances fault detection accuracy. This paper proposes a novel fault diagnosis method, FreqMGCN-Net, which integrates IoT-enabled sensor networks for continuous data acquisition. The method utilizes a frequency attention mechanism-enhanced CNN to extract key frequency-domain features from raw vibration signals, improving model accuracy and robustness. Additionally, the model integrates Multi-parallel Graph Convolutional Networks (MGCN) with Semi-supervised Meta-learning, enabling it to handle multi-sensor data from IoT devices and address challenges like few-shot learning and cross-domain generalization. Experimental results show that FreqMGCN-Net achieves 95.7% accuracy on the CWRU dataset and 93.4% on the Paderborn dataset, outperforming existing models in accuracy, F1-Score, and recall. These results demonstrate the effectiveness of FreqMGCN-Net for real-time motor bearing fault diagnosis in IoT-enabled industrial environments, particularly in noisy and data-scarce conditions.http://www.sciencedirect.com/science/article/pii/S1110016825006398Motor bearing fault diagnosisFrequency attention mechanismMulti-parallel graph convolutional networksSemi-supervised meta-learningFew-shot learningIOT |
| spellingShingle | Jicai Wang Shahrum Abdullah Chen Gao Azli Arifin Salvinder Singh Karam Sing FreqMGCN-Net: An IoT-Integrated Multi-Parallel Graph Convolutional Network with frequency attention for motor bearing fault diagnosis Alexandria Engineering Journal Motor bearing fault diagnosis Frequency attention mechanism Multi-parallel graph convolutional networks Semi-supervised meta-learning Few-shot learning IOT |
| title | FreqMGCN-Net: An IoT-Integrated Multi-Parallel Graph Convolutional Network with frequency attention for motor bearing fault diagnosis |
| title_full | FreqMGCN-Net: An IoT-Integrated Multi-Parallel Graph Convolutional Network with frequency attention for motor bearing fault diagnosis |
| title_fullStr | FreqMGCN-Net: An IoT-Integrated Multi-Parallel Graph Convolutional Network with frequency attention for motor bearing fault diagnosis |
| title_full_unstemmed | FreqMGCN-Net: An IoT-Integrated Multi-Parallel Graph Convolutional Network with frequency attention for motor bearing fault diagnosis |
| title_short | FreqMGCN-Net: An IoT-Integrated Multi-Parallel Graph Convolutional Network with frequency attention for motor bearing fault diagnosis |
| title_sort | freqmgcn net an iot integrated multi parallel graph convolutional network with frequency attention for motor bearing fault diagnosis |
| topic | Motor bearing fault diagnosis Frequency attention mechanism Multi-parallel graph convolutional networks Semi-supervised meta-learning Few-shot learning IOT |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825006398 |
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