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...
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Autor*in:	Huiming Jiang [verfasserIn] Jinhai Luo [verfasserIn] Bohua Zhou [verfasserIn] Chao Li [verfasserIn] Zhongwei Lv [verfasserIn] Zhibo Yang [verfasserIn] Jin Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	performance degradation assessment feature selection spectral clustering rank mutual information Student’s t-HMM

Übergeordnetes Werk:	In: Materials - MDPI AG, 2009, 14(2021), 20, p 6077
Übergeordnetes Werk:	volume:14 ; year:2021 ; number:20, p 6077

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/ma14206077

Katalog-ID:	DOAJ008491372

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520			\|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.
650		4	\|a performance degradation assessment
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allfields	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
spelling	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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allfieldsGer	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
allfieldsSound	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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callnumber-first	T - Technology
author	Huiming Jiang
spellingShingle	Huiming Jiang misc TK1-9971 misc TA1-2040 misc QH201-278.5 misc QC120-168.85 misc performance degradation assessment misc feature selection misc spectral clustering misc rank mutual information misc Student’s t-HMM misc Technology misc T misc Electrical engineering. Electronics. Nuclear engineering misc Engineering (General). Civil engineering (General) misc Microscopy misc Descriptive and experimental mechanics Bearing Performance Degradation Assessment Based on SC-RMI and Student’s t-HMM
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topic_title	TK1-9971 TA1-2040 QH201-278.5 QC120-168.85 Bearing Performance Degradation Assessment Based on SC-RMI and Student’s t-HMM performance degradation assessment feature selection spectral clustering rank mutual information Student’s t-HMM
topic	misc TK1-9971 misc TA1-2040 misc QH201-278.5 misc QC120-168.85 misc performance degradation assessment misc feature selection misc spectral clustering misc rank mutual information misc Student’s t-HMM misc Technology misc T misc Electrical engineering. Electronics. Nuclear engineering misc Engineering (General). Civil engineering (General) misc Microscopy misc Descriptive and experimental mechanics
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title	Bearing Performance Degradation Assessment Based on SC-RMI and Student’s t-HMM
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title_sort	bearing performance degradation assessment based on sc-rmi and student’s t-hmm
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title_auth	Bearing Performance Degradation Assessment Based on SC-RMI and Student’s t-HMM
abstract	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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