A fault diagnosis method of rotating machinery based on improved multiscale attention entropy and random forests Article Swipe
Fei Chen
,
Liyao Zhang
,
Wen‐Shen Liu
,
Tingting Zhang
,
Zhigao Zhao
,
Weiyu Wang
,
Diyi Chen
,
Bin Wang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2466822/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2466822/v1
In order to precisely diagnose the fault type of rotating machinery, a fault diagnosis method for rotating machinery based on improved multiscale attention entropy and random forests is proposed in this study. Firstly, a nonlinear dynamics technique without hyperparameters namely multiscale attention entropy is proposed for measuring signal complexity by extending attention entropy to multiple time scales. Secondly, aiming at the insufficient coarse graining of multiscale attention entropy, composite multiscale attention entropy is exploited to extraction the features of rotating machinery faults. Then, t-distributed stochastic neighbor embedding is used to overcome the feature redundancy problem by reducing the dimension of the extracted features. Finally, the reduced-dimensional features are inputted into the random forests model to complete fault pattern recognition of rotating machinery. The results of the experiment indicate that the proposed method achieves 98.216%and 98.506% diagnosis rates on two different fault datasets respectively, showing an extremely competitive advantage in comparison with conventional diagnosis models. Meanwhile, the proposed method is adopted to the actual hydropower unit without misjudgment, which verifies its strong adaptability. The research proposes a novel method for detecting faults in rotating machinery such as hydropower units.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2466822/v1
- https://www.researchsquare.com/article/rs-2466822/latest.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4318455198
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4318455198Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-2466822/v1Digital Object Identifier
- Title
-
A fault diagnosis method of rotating machinery based on improved multiscale attention entropy and random forestsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-30Full publication date if available
- Authors
-
Fei Chen, Liyao Zhang, Wen‐Shen Liu, Tingting Zhang, Zhigao Zhao, Weiyu Wang, Diyi Chen, Bin WangList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-2466822/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-2466822/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-2466822/latest.pdfDirect OA link when available
- Concepts
-
Random forest, Statistical physics, Entropy (arrow of time), Fault (geology), Computer science, Artificial intelligence, Pattern recognition (psychology), Physics, Geology, Seismology, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
50Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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