Matching Linear Chirplet Strategy-Based Synchroextracting Transform and Its Application to Rotating Machinery Fault Diagnosis Article Swipe
YOU?
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· 2020
· Open Access
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· DOI: https://doi.org/10.1109/access.2020.3027067
Various time-frequency analysis methods have been employed for the vibration signal processing of rotating machinery under time-varying speeds. However, most methods suffer from time-frequency blurriness, particularly for signals experiencing fast changes of instantaneous frequencies. Synchroextracting Transform is a powerful post-processing tool of time-frequency analysis; its results, nevertheless, greatly depend on the original time-frequency representation. This paper proposes a matching linear chirplet based synchroextracting transform to address the problem. Chirp-rate matching strategy is firstly developed to alleviate smearing problems of time-frequency representations, where the chirp-rates adaptively match true ones of signals with the guidance of kurtosis. The matching strategy is then integrated with synchroextracting transform to further sharpen the time-frequency representation. With enhanced energy concentration level and sharpened instantaneous frequency ridges, the readability of time-frequency representation can be improved, which is also echoed by more accurate extracted instantaneous frequency ridges. Rotating machinery fault diagnosis can then be realized based on the extracted time-frequency ridges.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2020.3027067
- https://ieeexplore.ieee.org/ielx7/6287639/8948470/09206589.pdf
- OA Status
- gold
- Cited By
- 9
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3091698330
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3091698330Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2020.3027067Digital Object Identifier
- Title
-
Matching Linear Chirplet Strategy-Based Synchroextracting Transform and Its Application to Rotating Machinery Fault DiagnosisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Zehui Hua, Juanjuan Shi, Zhongkui ZhuList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2020.3027067Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8948470/09206589.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://ieeexplore.ieee.org/ielx7/6287639/8948470/09206589.pdfDirect OA link when available
- Concepts
-
Time–frequency analysis, Instantaneous phase, Chirp, Computer science, Time–frequency representation, Signal processing, Matching (statistics), Matching pursuit, Representation (politics), Spectrogram, Artificial intelligence, Pattern recognition (psychology), Speech recognition, Computer vision, Mathematics, Telecommunications, Physics, Optics, Statistics, Compressed sensing, Filter (signal processing), Politics, Radar, Law, Political science, LaserTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1, 2023: 2, 2022: 3, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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28Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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