Advancing machine fault diagnosis: A detailed examination of convolutional neural networks
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913689111625728 |
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| author | Vashishtha, Govind Chauhan, Sumika Sehri, Mert Hebda-Sobkowicz, Justyna Zimroz, Radoslaw Dumond, Patrick Kumar, Rajesh |
| author_facet | Vashishtha, Govind Chauhan, Sumika Sehri, Mert Hebda-Sobkowicz, Justyna Zimroz, Radoslaw Dumond, Patrick Kumar, Rajesh |
| contents | The growing complexity of machinery and the increasing demand for operational efficiency and safety have driven the development of advanced fault diagnosis techniques. Among these, convolutional neural networks (CNNs) have emerged as a powerful tool, offering robust and accurate fault detection and classification capabilities. This comprehensive review delves into the application of CNNs in machine fault diagnosis, covering its theoretical foundation, architectural variations, and practical implementations. The strengths and limitations of CNNs are analyzed in this domain, discussing their effectiveness in handling various fault types, data complexities, and operational environments. Furthermore, we explore the evolving landscape of CNN-based fault diagnosis, examining recent advancements in data augmentation, transfer learning, and hybrid architectures. Finally, we highlight future research directions and potential challenges to further enhance the application of CNNs for reliable and proactive machine fault diagnosis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08689 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advancing machine fault diagnosis: A detailed examination of convolutional neural networks Vashishtha, Govind Chauhan, Sumika Sehri, Mert Hebda-Sobkowicz, Justyna Zimroz, Radoslaw Dumond, Patrick Kumar, Rajesh Machine Learning Artificial Intelligence The growing complexity of machinery and the increasing demand for operational efficiency and safety have driven the development of advanced fault diagnosis techniques. Among these, convolutional neural networks (CNNs) have emerged as a powerful tool, offering robust and accurate fault detection and classification capabilities. This comprehensive review delves into the application of CNNs in machine fault diagnosis, covering its theoretical foundation, architectural variations, and practical implementations. The strengths and limitations of CNNs are analyzed in this domain, discussing their effectiveness in handling various fault types, data complexities, and operational environments. Furthermore, we explore the evolving landscape of CNN-based fault diagnosis, examining recent advancements in data augmentation, transfer learning, and hybrid architectures. Finally, we highlight future research directions and potential challenges to further enhance the application of CNNs for reliable and proactive machine fault diagnosis. |
| title | Advancing machine fault diagnosis: A detailed examination of convolutional neural networks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.08689 |