Efficient Gearbox Fault Diagnosis Based on Improved Multi-Scale CNN with Lightweight Convolutional Attention Article Swipe
Bin Yuan
,
Yong-Chao Li
,
Suifan Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25092636
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25092636
As a core transmission component of modern industrial equipment, the operation status of the gearbox has a significant impact on the reliability and service life of major machinery. In this paper, we propose an intelligent diagnosis framework based on Empirical Mode Decomposition and multimodal feature co-optimization and innovatively construct a fault diagnosis model by fusing a multi-scale convolutional neural network and a lightweight convolutional attention model. The framework extracts the multi-band features of vibration signals through the improved multi-scale convolutional neural network, which significantly enhances adaptability to complex working conditions (variable rotational speed, strong noise); at the same time, the lightweight convolutional attention mechanism is used to replace the multi-attention of the traditional Transformer, which greatly reduces computational complexity while guaranteeing accuracy and realizes highly efficient, lightweight local–global feature modeling. The lightweight convolutional attention is adaptively captured by the dynamic convolutional kernel generation strategy to adaptively capture local features in the time domain, and combined with grouped convolution to enhance the computational efficiency further; in addition, parameterized revised linear units are introduced to retain fault-sensitive negative information, which enhances the model’s ability to detect weak faults. The experimental findings demonstrate that the proposed model achieves an accuracy greater than 98.9%, highlighting its exceptional diagnostic accuracy and robustness. Moreover, compared to other fault diagnosis methods, the model exhibits superior performance under complex working conditions.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s25092636
- https://www.mdpi.com/1424-8220/25/9/2636/pdf?version=1745309444
- OA Status
- gold
- Cited By
- 3
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409684197
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Raw OpenAlex JSON
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https://openalex.org/W4409684197Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s25092636Digital Object Identifier
- Title
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Efficient Gearbox Fault Diagnosis Based on Improved Multi-Scale CNN with Lightweight Convolutional AttentionWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-04-22Full publication date if available
- Authors
-
Bin Yuan, Yong-Chao Li, Suifan ChenList of authors in order
- Landing page
-
https://doi.org/10.3390/s25092636Publisher landing page
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https://www.mdpi.com/1424-8220/25/9/2636/pdf?version=1745309444Direct link to full text PDF
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- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/25/9/2636/pdf?version=1745309444Direct OA link when available
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Convolutional neural network, Computer science, Artificial intelligence, Robustness (evolution), Feature extraction, Pattern recognition (psychology), Kernel (algebra), Mathematics, Biochemistry, Gene, Combinatorics, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W6854592782, https://openalex.org/W4288068833, https://openalex.org/W3176041833, https://openalex.org/W4398244166, https://openalex.org/W3080532600, https://openalex.org/W4403731340, https://openalex.org/W4400386246, https://openalex.org/W4366609190, https://openalex.org/W3116250684, https://openalex.org/W2978144367, https://openalex.org/W4400823921, https://openalex.org/W3015173390, https://openalex.org/W4386028682, https://openalex.org/W4316654916, https://openalex.org/W2007221293, https://openalex.org/W3011995351, https://openalex.org/W2978219032, https://openalex.org/W2003205626, https://openalex.org/W3199798098, https://openalex.org/W2884585870, https://openalex.org/W4392016375, https://openalex.org/W4362732946, https://openalex.org/W4387611015, https://openalex.org/W4381572229, https://openalex.org/W3004916191, https://openalex.org/W6806504485, https://openalex.org/W4383315415, https://openalex.org/W4293704625, https://openalex.org/W3025177399, https://openalex.org/W4321327604, https://openalex.org/W4296252903, https://openalex.org/W3096932637, https://openalex.org/W4387095353, https://openalex.org/W4213018270, https://openalex.org/W3190262421, https://openalex.org/W4205886591, https://openalex.org/W4383618501 |
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