Bearing Performance Degradation Assessment Based on SC-RMI and Student’s t-HMM
Bearing performance degradation assessment (PDA), as an important part of prognostics and health management (PHM), is significant to prevent major accidents and economic losses in industry. For the data-driven PDA, the extraction and selection of features is quite important. To better integrate the...
Ausführliche Beschreibung
Autor*in: |
Huiming Jiang [verfasserIn] Jinhai Luo [verfasserIn] Bohua Zhou [verfasserIn] Chao Li [verfasserIn] Zhongwei Lv [verfasserIn] Zhibo Yang [verfasserIn] Jin Chen [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2021 |
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Übergeordnetes Werk: |
In: Materials - MDPI AG, 2009, 14(2021), 20, p 6077 |
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Übergeordnetes Werk: |
volume:14 ; year:2021 ; number:20, p 6077 |
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DOI / URN: |
10.3390/ma14206077 |
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Katalog-ID: |
DOAJ008491372 |
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10.3390/ma14206077 doi (DE-627)DOAJ008491372 (DE-599)DOAJ3c8da053bbb3490cb07e036c3c54a53e DE-627 ger DE-627 rakwb eng TK1-9971 TA1-2040 QH201-278.5 QC120-168.85 Huiming Jiang verfasserin aut Bearing Performance Degradation Assessment Based on SC-RMI and Student’s t-HMM 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bearing performance degradation assessment (PDA), as an important part of prognostics and health management (PHM), is significant to prevent major accidents and economic losses in industry. For the data-driven PDA, the extraction and selection of features is quite important. To better integrate the degradation information, the bearing performance degradation assessment based on SC-RMI and Student’s t-HMM is proposed in this article. Firstly, spectral clustering was used as a preprocessing step to cluster features with similar degradation curves. Then, rank mutual information, which is more suitable for trendability estimation of long time series, was utilized to select the optimal feature from each cluster. The feature selection method based on these two steps is called SC-RMI for short. With the selected features, Student’s t-HMM, which is more robust to outliers, was utilized for performance degradation modeling and assessment. The verifications based on an accelerated life test and the public XJTU-SY dataset showed the superiority of the proposed method. performance degradation assessment feature selection spectral clustering rank mutual information Student’s t-HMM Technology T Electrical engineering. Electronics. Nuclear engineering Engineering (General). Civil engineering (General) Microscopy Descriptive and experimental mechanics Jinhai Luo verfasserin aut Bohua Zhou verfasserin aut Chao Li verfasserin aut Zhongwei Lv verfasserin aut Zhibo Yang verfasserin aut Jin Chen verfasserin aut In Materials MDPI AG, 2009 14(2021), 20, p 6077 (DE-627)595712649 (DE-600)2487261-1 19961944 nnns volume:14 year:2021 number:20, p 6077 https://doi.org/10.3390/ma14206077 kostenfrei https://doaj.org/article/3c8da053bbb3490cb07e036c3c54a53e kostenfrei https://www.mdpi.com/1996-1944/14/20/6077 kostenfrei https://doaj.org/toc/1996-1944 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 20, p 6077 |
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10.3390/ma14206077 doi (DE-627)DOAJ008491372 (DE-599)DOAJ3c8da053bbb3490cb07e036c3c54a53e DE-627 ger DE-627 rakwb eng TK1-9971 TA1-2040 QH201-278.5 QC120-168.85 Huiming Jiang verfasserin aut Bearing Performance Degradation Assessment Based on SC-RMI and Student’s t-HMM 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bearing performance degradation assessment (PDA), as an important part of prognostics and health management (PHM), is significant to prevent major accidents and economic losses in industry. For the data-driven PDA, the extraction and selection of features is quite important. To better integrate the degradation information, the bearing performance degradation assessment based on SC-RMI and Student’s t-HMM is proposed in this article. Firstly, spectral clustering was used as a preprocessing step to cluster features with similar degradation curves. Then, rank mutual information, which is more suitable for trendability estimation of long time series, was utilized to select the optimal feature from each cluster. The feature selection method based on these two steps is called SC-RMI for short. With the selected features, Student’s t-HMM, which is more robust to outliers, was utilized for performance degradation modeling and assessment. The verifications based on an accelerated life test and the public XJTU-SY dataset showed the superiority of the proposed method. performance degradation assessment feature selection spectral clustering rank mutual information Student’s t-HMM Technology T Electrical engineering. Electronics. Nuclear engineering Engineering (General). Civil engineering (General) Microscopy Descriptive and experimental mechanics Jinhai Luo verfasserin aut Bohua Zhou verfasserin aut Chao Li verfasserin aut Zhongwei Lv verfasserin aut Zhibo Yang verfasserin aut Jin Chen verfasserin aut In Materials MDPI AG, 2009 14(2021), 20, p 6077 (DE-627)595712649 (DE-600)2487261-1 19961944 nnns volume:14 year:2021 number:20, p 6077 https://doi.org/10.3390/ma14206077 kostenfrei https://doaj.org/article/3c8da053bbb3490cb07e036c3c54a53e kostenfrei https://www.mdpi.com/1996-1944/14/20/6077 kostenfrei https://doaj.org/toc/1996-1944 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 20, p 6077 |
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10.3390/ma14206077 doi (DE-627)DOAJ008491372 (DE-599)DOAJ3c8da053bbb3490cb07e036c3c54a53e DE-627 ger DE-627 rakwb eng TK1-9971 TA1-2040 QH201-278.5 QC120-168.85 Huiming Jiang verfasserin aut Bearing Performance Degradation Assessment Based on SC-RMI and Student’s t-HMM 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bearing performance degradation assessment (PDA), as an important part of prognostics and health management (PHM), is significant to prevent major accidents and economic losses in industry. For the data-driven PDA, the extraction and selection of features is quite important. To better integrate the degradation information, the bearing performance degradation assessment based on SC-RMI and Student’s t-HMM is proposed in this article. Firstly, spectral clustering was used as a preprocessing step to cluster features with similar degradation curves. Then, rank mutual information, which is more suitable for trendability estimation of long time series, was utilized to select the optimal feature from each cluster. The feature selection method based on these two steps is called SC-RMI for short. With the selected features, Student’s t-HMM, which is more robust to outliers, was utilized for performance degradation modeling and assessment. The verifications based on an accelerated life test and the public XJTU-SY dataset showed the superiority of the proposed method. performance degradation assessment feature selection spectral clustering rank mutual information Student’s t-HMM Technology T Electrical engineering. Electronics. Nuclear engineering Engineering (General). Civil engineering (General) Microscopy Descriptive and experimental mechanics Jinhai Luo verfasserin aut Bohua Zhou verfasserin aut Chao Li verfasserin aut Zhongwei Lv verfasserin aut Zhibo Yang verfasserin aut Jin Chen verfasserin aut In Materials MDPI AG, 2009 14(2021), 20, p 6077 (DE-627)595712649 (DE-600)2487261-1 19961944 nnns volume:14 year:2021 number:20, p 6077 https://doi.org/10.3390/ma14206077 kostenfrei https://doaj.org/article/3c8da053bbb3490cb07e036c3c54a53e kostenfrei https://www.mdpi.com/1996-1944/14/20/6077 kostenfrei https://doaj.org/toc/1996-1944 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 20, p 6077 |
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10.3390/ma14206077 doi (DE-627)DOAJ008491372 (DE-599)DOAJ3c8da053bbb3490cb07e036c3c54a53e DE-627 ger DE-627 rakwb eng TK1-9971 TA1-2040 QH201-278.5 QC120-168.85 Huiming Jiang verfasserin aut Bearing Performance Degradation Assessment Based on SC-RMI and Student’s t-HMM 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bearing performance degradation assessment (PDA), as an important part of prognostics and health management (PHM), is significant to prevent major accidents and economic losses in industry. For the data-driven PDA, the extraction and selection of features is quite important. To better integrate the degradation information, the bearing performance degradation assessment based on SC-RMI and Student’s t-HMM is proposed in this article. Firstly, spectral clustering was used as a preprocessing step to cluster features with similar degradation curves. Then, rank mutual information, which is more suitable for trendability estimation of long time series, was utilized to select the optimal feature from each cluster. The feature selection method based on these two steps is called SC-RMI for short. With the selected features, Student’s t-HMM, which is more robust to outliers, was utilized for performance degradation modeling and assessment. The verifications based on an accelerated life test and the public XJTU-SY dataset showed the superiority of the proposed method. performance degradation assessment feature selection spectral clustering rank mutual information Student’s t-HMM Technology T Electrical engineering. Electronics. Nuclear engineering Engineering (General). Civil engineering (General) Microscopy Descriptive and experimental mechanics Jinhai Luo verfasserin aut Bohua Zhou verfasserin aut Chao Li verfasserin aut Zhongwei Lv verfasserin aut Zhibo Yang verfasserin aut Jin Chen verfasserin aut In Materials MDPI AG, 2009 14(2021), 20, p 6077 (DE-627)595712649 (DE-600)2487261-1 19961944 nnns volume:14 year:2021 number:20, p 6077 https://doi.org/10.3390/ma14206077 kostenfrei https://doaj.org/article/3c8da053bbb3490cb07e036c3c54a53e kostenfrei https://www.mdpi.com/1996-1944/14/20/6077 kostenfrei https://doaj.org/toc/1996-1944 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 20, p 6077 |
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Bearing Performance Degradation Assessment Based on SC-RMI and Student’s t-HMM |
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Bearing performance degradation assessment (PDA), as an important part of prognostics and health management (PHM), is significant to prevent major accidents and economic losses in industry. For the data-driven PDA, the extraction and selection of features is quite important. To better integrate the degradation information, the bearing performance degradation assessment based on SC-RMI and Student’s t-HMM is proposed in this article. Firstly, spectral clustering was used as a preprocessing step to cluster features with similar degradation curves. Then, rank mutual information, which is more suitable for trendability estimation of long time series, was utilized to select the optimal feature from each cluster. The feature selection method based on these two steps is called SC-RMI for short. With the selected features, Student’s t-HMM, which is more robust to outliers, was utilized for performance degradation modeling and assessment. The verifications based on an accelerated life test and the public XJTU-SY dataset showed the superiority of the proposed method. |
abstractGer |
Bearing performance degradation assessment (PDA), as an important part of prognostics and health management (PHM), is significant to prevent major accidents and economic losses in industry. For the data-driven PDA, the extraction and selection of features is quite important. To better integrate the degradation information, the bearing performance degradation assessment based on SC-RMI and Student’s t-HMM is proposed in this article. Firstly, spectral clustering was used as a preprocessing step to cluster features with similar degradation curves. Then, rank mutual information, which is more suitable for trendability estimation of long time series, was utilized to select the optimal feature from each cluster. The feature selection method based on these two steps is called SC-RMI for short. With the selected features, Student’s t-HMM, which is more robust to outliers, was utilized for performance degradation modeling and assessment. The verifications based on an accelerated life test and the public XJTU-SY dataset showed the superiority of the proposed method. |
abstract_unstemmed |
Bearing performance degradation assessment (PDA), as an important part of prognostics and health management (PHM), is significant to prevent major accidents and economic losses in industry. For the data-driven PDA, the extraction and selection of features is quite important. To better integrate the degradation information, the bearing performance degradation assessment based on SC-RMI and Student’s t-HMM is proposed in this article. Firstly, spectral clustering was used as a preprocessing step to cluster features with similar degradation curves. Then, rank mutual information, which is more suitable for trendability estimation of long time series, was utilized to select the optimal feature from each cluster. The feature selection method based on these two steps is called SC-RMI for short. With the selected features, Student’s t-HMM, which is more robust to outliers, was utilized for performance degradation modeling and assessment. The verifications based on an accelerated life test and the public XJTU-SY dataset showed the superiority of the proposed method. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ008491372</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240412135117.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/ma14206077</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ008491372</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ3c8da053bbb3490cb07e036c3c54a53e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TA1-2040</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QH201-278.5</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QC120-168.85</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Huiming Jiang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bearing Performance Degradation Assessment Based on SC-RMI and Student’s t-HMM</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Bearing performance degradation assessment (PDA), as an important part of prognostics and health management (PHM), is significant to prevent major accidents and economic losses in industry. For the data-driven PDA, the extraction and selection of features is quite important. To better integrate the degradation information, the bearing performance degradation assessment based on SC-RMI and Student’s t-HMM is proposed in this article. Firstly, spectral clustering was used as a preprocessing step to cluster features with similar degradation curves. Then, rank mutual information, which is more suitable for trendability estimation of long time series, was utilized to select the optimal feature from each cluster. The feature selection method based on these two steps is called SC-RMI for short. With the selected features, Student’s t-HMM, which is more robust to outliers, was utilized for performance degradation modeling and assessment. The verifications based on an accelerated life test and the public XJTU-SY dataset showed the superiority of the proposed method.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">performance degradation assessment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">feature selection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">spectral clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">rank mutual information</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Student’s t-HMM</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. 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