Robust Fractional Low-Order Multiple Window STFT for Infinite Variance Process Environment
Mechanical fault vibration signal is a typical non-Gaussian process, they can be characterized by the infinite variance process, and the noise within these signals may also be the process in complex environments. The performance of the traditional cross-term reduction algorithm is compromised, somet...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | IET Signal Processing |
| Online Access: | http://dx.doi.org/10.1049/2024/7605121 |
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| _version_ | 1832569199674785792 |
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| author | Haibin Wang Changshou Deng Junbo Long Youxue Zhou |
| author_facet | Haibin Wang Changshou Deng Junbo Long Youxue Zhou |
| author_sort | Haibin Wang |
| collection | DOAJ |
| description | Mechanical fault vibration signal is a typical non-Gaussian process, they can be characterized by the infinite variance process, and the noise within these signals may also be the process in complex environments. The performance of the traditional cross-term reduction algorithm is compromised, sometimes yielding incorrect results under the infinite variance process environment. Several robust fractional lower order time–frequency representation methods are proposed including fractional low-order smoothed pseudo Wigner (FLOSPW), fractional low-order multi-windowed short-time Fourier transform (FLOMWSTFT), and improved fractional low-order multi-windowed short-time Fourier transform (IFLOMWSTFT) utilizing fractional low-order statistics and short-time Fourier transform (STFT) to mitigate cross-terms, enhance time–frequency resolution, and accommodate the infinite variance process environment. When compared to traditional methods, simulation results indicate that they effectively suppress the pulse noise and function effectively in lower mixed signal noise ratio (MSNR) in an infinite variance process environment. The efficacy of the proposed time–frequency algorithm is validated through its application to mechanical bearing outer ring fault vibration signals contaminated with Gaussian noise and subjected to an α infinite variance process. |
| format | Article |
| id | doaj-art-80c4147cdc994d41959df778c23ad151 |
| institution | Kabale University |
| issn | 1751-9683 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-80c4147cdc994d41959df778c23ad1512025-02-02T22:55:25ZengWileyIET Signal Processing1751-96832024-01-01202410.1049/2024/7605121Robust Fractional Low-Order Multiple Window STFT for Infinite Variance Process EnvironmentHaibin Wang0Changshou Deng1Junbo Long2Youxue Zhou3College of Computer and Big Data ScienceCollege of Electronic Information EngineeringCollege of Electronic Information EngineeringCollege of Computer and Big Data ScienceMechanical fault vibration signal is a typical non-Gaussian process, they can be characterized by the infinite variance process, and the noise within these signals may also be the process in complex environments. The performance of the traditional cross-term reduction algorithm is compromised, sometimes yielding incorrect results under the infinite variance process environment. Several robust fractional lower order time–frequency representation methods are proposed including fractional low-order smoothed pseudo Wigner (FLOSPW), fractional low-order multi-windowed short-time Fourier transform (FLOMWSTFT), and improved fractional low-order multi-windowed short-time Fourier transform (IFLOMWSTFT) utilizing fractional low-order statistics and short-time Fourier transform (STFT) to mitigate cross-terms, enhance time–frequency resolution, and accommodate the infinite variance process environment. When compared to traditional methods, simulation results indicate that they effectively suppress the pulse noise and function effectively in lower mixed signal noise ratio (MSNR) in an infinite variance process environment. The efficacy of the proposed time–frequency algorithm is validated through its application to mechanical bearing outer ring fault vibration signals contaminated with Gaussian noise and subjected to an α infinite variance process.http://dx.doi.org/10.1049/2024/7605121 |
| spellingShingle | Haibin Wang Changshou Deng Junbo Long Youxue Zhou Robust Fractional Low-Order Multiple Window STFT for Infinite Variance Process Environment IET Signal Processing |
| title | Robust Fractional Low-Order Multiple Window STFT for Infinite Variance Process Environment |
| title_full | Robust Fractional Low-Order Multiple Window STFT for Infinite Variance Process Environment |
| title_fullStr | Robust Fractional Low-Order Multiple Window STFT for Infinite Variance Process Environment |
| title_full_unstemmed | Robust Fractional Low-Order Multiple Window STFT for Infinite Variance Process Environment |
| title_short | Robust Fractional Low-Order Multiple Window STFT for Infinite Variance Process Environment |
| title_sort | robust fractional low order multiple window stft for infinite variance process environment |
| url | http://dx.doi.org/10.1049/2024/7605121 |
| work_keys_str_mv | AT haibinwang robustfractionallowordermultiplewindowstftforinfinitevarianceprocessenvironment AT changshoudeng robustfractionallowordermultiplewindowstftforinfinitevarianceprocessenvironment AT junbolong robustfractionallowordermultiplewindowstftforinfinitevarianceprocessenvironment AT youxuezhou robustfractionallowordermultiplewindowstftforinfinitevarianceprocessenvironment |