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Wave-ConvNeXt :
Wave-ConvNeXt : An Efficient and Precise Fault Diagnosis Method for IIoT Leveraging Tailored ConvNeXt and Wavelet Transform
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- Zhang, Liangwei (author)
- Department of Industrial Engineering, Dongguan University of Technology, Dongguan, China
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- Lin, Jing (author)
- Mälardalens universitet,Innovation och produktrealisering,Division of Operation and Maintenance, University of Technology, Luleå, Sweden
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- Yang, Zhe (author)
- Department of Industrial Engineering, Dongguan University of Technology, Dongguan, China
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- Shao, Haidong (author)
- College of Mechanical and Vehicle Engineering, State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China
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- Liu, Biyu (author)
- School of Economics and Management, Fuzhou University, Fuzhou, China
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- Li, Chuan (author)
- Department of Industrial Engineering, Dongguan University of Technology, Dongguan, China
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(creator_code:org_t)
- 2024
- 2024
- English.
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In: IEEE Internet of Things Journal. - 2327-4662. ; , s. 1-1
- Related links:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- The burgeoning field of the Industrial Internet of Things (IIoT) necessitates advanced fault diagnosis methods capable of navigating the dual challenges of high predictive accuracy and the constraints of edge computing environments. Our study introduces Wave-ConvNeXt, a novel fault diagnosis model that seamlessly integrates the state-of-the-art ConvNeXt architecture with Wavelet Transform. This innovative model stands out for its lightweight design yet delivers exceptional accuracy in fault diagnosis. In Wave-ConvNeXt, we re-engineer the ConvNeXt model for IIoT applications by adopting onedimensional convolution, tailored for processing high-frequency, non-periodic inputs. This adaptation is complemented by replacing the traditional “patchify” layer with a Wavelet transform layer, which simplifies input signals into sub-signals, thereby easing learning complexities and diminishing the dependence on elaborate deep architectures. Further enhancing this model, we incorporate a squeeze-and-excitation module, enriching its ability to prioritize channel-wise feature relevance, akin to self-attention mechanisms. This integration is rigorously validated through an ablation study. Wave-ConvNeXt epitomizes a holistic approach, enabling an end-to-end optimization of feature learning and fault classification. Our empirical analysis on two real-world IIoT datasets demonstrates Wave-ConvNeXt’s superiority over existing models. It not only elevates prediction accuracy but also significantly curtails computational complexity. Additionally, our exploration into the impact of various mother wavelets reveals the effectiveness of using wavelet basis functions with smaller support, bolstering diagnostic precision. The source code of Wave-ConvNeXt is available at https://github.com/leviszhang/waveConvNeXt.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Engineering (hsv//eng)
Keyword
- Industrial Internet of Things (IIoT)
- Fault Diagnosis
- Wavelet Transform
- ConvNeXt Architecture
- Computational Efficiency
Publication and Content Type
- ref (subject category)
- art (subject category)
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