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DEFR-net: A decompose-enhance fourier residual network for fault diagnosis of rotating machine with high noise immunity.
- Source :
-
Journal of Intelligent & Fuzzy Systems . Mar2024, p1-22. 22p. - Publication Year :
- 2024
-
Abstract
- The actual operating environment of rotating mechanical device contains a large number of noisy interference sources, leading to complex components, strong coupling, and low signal to noise ratio for vibration. It becomes a big challenge for intelligent fault diagnosis from high-noise vibration signals. Thus, this paper proposes a new deep learning approach, namely decomposition-enhance Fourier residual network (DEFR-net), to achieve high noise immunity for vibration signal and learn effective features to discriminate between different types of rotational machine faults. In the proposed DEFR-net, a novel algorithm is proposed to explicitly model high-noise signals for noisy data filtering and effective feature enhancement based on a hard threshold decomposition function and muti-channel self-attention mechanism. Furthermore, it deeply integrates complementary analysis based on fast Fourier transform in the time-frequency domain and extends the breadth of network. The performance of the proposed model is verified by comparison with five state-of-the-art algorithms on two public datasets. Moreover, the noise experimental results show that the fault diagnosis accuracy is still 85.91% when the signal-to-noise-ratio reaches extreme noise of –8 dB. The results demonstrate that the proposed method is a valuable study for intelligent fault diagnosis of rotating machines in high-noise environments. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10641246
- Database :
- Academic Search Index
- Journal :
- Journal of Intelligent & Fuzzy Systems
- Publication Type :
- Academic Journal
- Accession number :
- 176165163
- Full Text :
- https://doi.org/10.3233/jifs-233190