A feature extraction method based on improved resonance sparse decomposition for early faults in rolling bearings
To overcome the difficulty in early fault diagnosis with weak fault characteristics of rolling bearings that are easily drowned out by noise in the complex operation environment, an early fault diagnosis method was proposed by integrating the improved artificial gorilla troops optimizer (IGTO) algor...
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Editorial Office of Journal of Mechanical Strength
2025-06-01
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| Series: | Jixie qiangdu |
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| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2025.06.003 |
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| author | SUN Meng GAO Bingpeng CHENG Jing |
| author_facet | SUN Meng GAO Bingpeng CHENG Jing |
| author_sort | SUN Meng |
| collection | DOAJ |
| description | To overcome the difficulty in early fault diagnosis with weak fault characteristics of rolling bearings that are easily drowned out by noise in the complex operation environment, an early fault diagnosis method was proposed by integrating the improved artificial gorilla troops optimizer (IGTO) algorithm, the optimized resonance-based sparse signal decomposition (RSSD), multi-parameter and sparse maximum harmonics-to-noise-ratio deconvolution (SMHD) method. Firstly, taking the squared envelope spectrum correlated kurtosis (SE-SCK) negative value of the low resonance component as the objective function, IGTO was used to simultaneously optimize the quality factor <inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mi>Q</mi></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/C5FF0B59-9E2C-4918-8B97-9DFF64EDE211-M004.jpg"><?fx-imagestate width="2.37066650" height="2.87866688"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/C5FF0B59-9E2C-4918-8B97-9DFF64EDE211-M004c.jpg"><?fx-imagestate width="2.37066650" height="2.87866688"?></graphic></alternatives></inline-formula>, weight coefficient <inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mi>λ</mi></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/C5FF0B59-9E2C-4918-8B97-9DFF64EDE211-M005.jpg"><?fx-imagestate width="1.52400005" height="2.28600001"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/C5FF0B59-9E2C-4918-8B97-9DFF64EDE211-M005c.jpg"><?fx-imagestate width="1.52400005" height="2.28600001"?></graphic></alternatives></inline-formula> and Lagrange multiplier <inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M6"><mi>μ</mi></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/C5FF0B59-9E2C-4918-8B97-9DFF64EDE211-M006.jpg"><?fx-imagestate width="1.77800000" height="2.96333337"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/C5FF0B59-9E2C-4918-8B97-9DFF64EDE211-M006c.jpg"><?fx-imagestate width="1.77800000" height="2.96333337"?></graphic></alternatives></inline-formula> of RSSD, for the achievement of the optimal matching of wavelet basis function and dissipation function. Secondly, the obtained optimal low resonance component was inputed into SMHD for filtering processing. Finally, the fault features were extracted by the perform envelope spectrum analysis. The algorithm comparison experiments show that the proposed IGTO algorithm has significantly improved optimization performance. The results of simulation and XJTU-SY bearing full life cycle fault signal test show that the proposed method is more useful in extracting early weak fault characteristics of bearings. |
| format | Article |
| id | doaj-art-da5f2fe3b28849639ebc8c4467fded32 |
| institution | OA Journals |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-da5f2fe3b28849639ebc8c4467fded322025-08-20T02:06:28ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692025-06-01471726109626103A feature extraction method based on improved resonance sparse decomposition for early faults in rolling bearingsSUN MengGAO BingpengCHENG JingTo overcome the difficulty in early fault diagnosis with weak fault characteristics of rolling bearings that are easily drowned out by noise in the complex operation environment, an early fault diagnosis method was proposed by integrating the improved artificial gorilla troops optimizer (IGTO) algorithm, the optimized resonance-based sparse signal decomposition (RSSD), multi-parameter and sparse maximum harmonics-to-noise-ratio deconvolution (SMHD) method. Firstly, taking the squared envelope spectrum correlated kurtosis (SE-SCK) negative value of the low resonance component as the objective function, IGTO was used to simultaneously optimize the quality factor <inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mi>Q</mi></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/C5FF0B59-9E2C-4918-8B97-9DFF64EDE211-M004.jpg"><?fx-imagestate width="2.37066650" height="2.87866688"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/C5FF0B59-9E2C-4918-8B97-9DFF64EDE211-M004c.jpg"><?fx-imagestate width="2.37066650" height="2.87866688"?></graphic></alternatives></inline-formula>, weight coefficient <inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mi>λ</mi></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/C5FF0B59-9E2C-4918-8B97-9DFF64EDE211-M005.jpg"><?fx-imagestate width="1.52400005" height="2.28600001"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/C5FF0B59-9E2C-4918-8B97-9DFF64EDE211-M005c.jpg"><?fx-imagestate width="1.52400005" height="2.28600001"?></graphic></alternatives></inline-formula> and Lagrange multiplier <inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M6"><mi>μ</mi></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/C5FF0B59-9E2C-4918-8B97-9DFF64EDE211-M006.jpg"><?fx-imagestate width="1.77800000" height="2.96333337"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/C5FF0B59-9E2C-4918-8B97-9DFF64EDE211-M006c.jpg"><?fx-imagestate width="1.77800000" height="2.96333337"?></graphic></alternatives></inline-formula> of RSSD, for the achievement of the optimal matching of wavelet basis function and dissipation function. Secondly, the obtained optimal low resonance component was inputed into SMHD for filtering processing. Finally, the fault features were extracted by the perform envelope spectrum analysis. The algorithm comparison experiments show that the proposed IGTO algorithm has significantly improved optimization performance. The results of simulation and XJTU-SY bearing full life cycle fault signal test show that the proposed method is more useful in extracting early weak fault characteristics of bearings.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2025.06.003Improved artificial gorilla troops algorithmResonance sparse decompositionSquare envelope spectrum correlation kurtosisSparse maximum harmonics-to-noise-ratio deconvolutionEarly fault diagnosis |
| spellingShingle | SUN Meng GAO Bingpeng CHENG Jing A feature extraction method based on improved resonance sparse decomposition for early faults in rolling bearings Jixie qiangdu Improved artificial gorilla troops algorithm Resonance sparse decomposition Square envelope spectrum correlation kurtosis Sparse maximum harmonics-to-noise-ratio deconvolution Early fault diagnosis |
| title | A feature extraction method based on improved resonance sparse decomposition for early faults in rolling bearings |
| title_full | A feature extraction method based on improved resonance sparse decomposition for early faults in rolling bearings |
| title_fullStr | A feature extraction method based on improved resonance sparse decomposition for early faults in rolling bearings |
| title_full_unstemmed | A feature extraction method based on improved resonance sparse decomposition for early faults in rolling bearings |
| title_short | A feature extraction method based on improved resonance sparse decomposition for early faults in rolling bearings |
| title_sort | feature extraction method based on improved resonance sparse decomposition for early faults in rolling bearings |
| topic | Improved artificial gorilla troops algorithm Resonance sparse decomposition Square envelope spectrum correlation kurtosis Sparse maximum harmonics-to-noise-ratio deconvolution Early fault diagnosis |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2025.06.003 |
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