Gearbox Fault Diagnosis Using Multiscale Sparse Frequency-Frequency Distributions Article Swipe
Luke Zhang
,
Yi Li
,
Lincheng Dong
,
Xiaoqing Yang
,
Xiaoxi Ding
,
Qiang Zeng
,
Liming Wang
,
Yimin Shao
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3104281
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3104281
Gear fault related information is distributed over a broad frequency band, indicating a complex modulation mechanism. It is difficult to detect early-stage gear faults accurately by detecting fault frequencies in a limited frequency band. This paper proposes a novel method for achieving fault frequency detection more effectively. A short-frequency Fourier transform with a series of frequency-window functions is initially used to obtain the overall frequency information of a vibration signal. Subsequently, based on sparse decomposition and orthogonal matching pursuit, harmonic atoms are applied to refine modulation components from multiscale pseudo mono-components. A multiscale-sparse frequency-frequency distribution is eventually applied to augment existing fault-related harmonic components. In addition, a synthesized sparse spectrum is acquired by determining the frequency-frequency ridge from the multiscale sparse frequency-frequency distribution. Compared with empirical-mode-decomposition and fast-kurtogram analyses, the effectiveness and superiority of the proposed method for gear fault detection have been verified via experiments.
Related Topics
Concepts
Time–frequency analysis
Computer science
Hilbert–Huang transform
Frequency modulation
Matching pursuit
Frequency band
Fault (geology)
Harmonic
Fault detection and isolation
Instantaneous phase
SIGNAL (programming language)
Modulation (music)
Window function
Pattern recognition (psychology)
Algorithm
Electronic engineering
Artificial intelligence
Acoustics
Radio frequency
Telecommunications
Spectral density
Compressed sensing
Engineering
Physics
Computer vision
Filter (signal processing)
Actuator
Bandwidth (computing)
Programming language
Geology
Seismology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2021.3104281
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/09512066.pdf
- OA Status
- gold
- Cited By
- 24
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3195621661
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3195621661Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2021.3104281Digital Object Identifier
- Title
-
Gearbox Fault Diagnosis Using Multiscale Sparse Frequency-Frequency DistributionsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Luke Zhang, Yi Li, Lincheng Dong, Xiaoqing Yang, Xiaoxi Ding, Qiang Zeng, Liming Wang, Yimin ShaoList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2021.3104281Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/09512066.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/ielx7/6287639/6514899/09512066.pdfDirect OA link when available
- Concepts
-
Time–frequency analysis, Computer science, Hilbert–Huang transform, Frequency modulation, Matching pursuit, Frequency band, Fault (geology), Harmonic, Fault detection and isolation, Instantaneous phase, SIGNAL (programming language), Modulation (music), Window function, Pattern recognition (psychology), Algorithm, Electronic engineering, Artificial intelligence, Acoustics, Radio frequency, Telecommunications, Spectral density, Compressed sensing, Engineering, Physics, Computer vision, Filter (signal processing), Actuator, Bandwidth (computing), Programming language, Geology, SeismologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
24Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 7, 2023: 6, 2022: 6Per-year citation counts (last 5 years)
- References (count)
-
31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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