A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy

The vibration signals of rolling bearings are complex and changeable, and extracting meaningful features is difficult. Currently, the commonly used empirical mode decomposition (EMD) algorithms have the problem of mode aliasing. In this paper, a new feature extraction method based on the improved co...
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Gespeichert in:

Autor*in:	Xiao, Maohua [verfasserIn] Wang, Zhenyu [verfasserIn] Zhao, Yuanfang [verfasserIn] Geng, Guosheng [verfasserIn] Dustdar, Schahram [verfasserIn] Donta, Praveen Kumar [verfasserIn] Ji, Guojun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Rolling bearings Feature extraction ICEEMDAN Permutation entropy

Übergeordnetes Werk:	Enthalten in: ISA transactions - Instrumentation, Systems, and Automation Society ; ID: gnd/10022359-X, Amsterdam [u.a.] : Elsevier, 1989, 143, Seite 536-547
Übergeordnetes Werk:	volume:143 ; pages:536-547

DOI / URN:	10.1016/j.isatra.2023.09.009

Katalog-ID:	ELV06613143X

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100	1		\|a Xiao, Maohua \|e verfasserin \|0 (orcid)0000-0001-5213-1035 \|4 aut
245	1	0	\|a A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy
264		1	\|c 2023
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a The vibration signals of rolling bearings are complex and changeable, and extracting meaningful features is difficult. Currently, the commonly used empirical mode decomposition (EMD) algorithms have the problem of mode aliasing. In this paper, a new feature extraction method based on the improved complete ensemble empirical mode decomposition with adapted noise (ICEEMDAN) and permutation entropy is proposed. In this method, the ICEEMDAN algorithm is first improved and optimized to enable a self-selection function The vibration signal is then decomposed into several intrinsic modal functions using this algorithm, and the permutation entropy is extracted as the fault feature of rolling bearings, which improves the accuracy of fault classification and realizes the intelligent feature extraction of different fault states. Then, the Case Western Reserve University dataset is used for verification, and the results show that this scheme can effectively separate the vibration signal characteristics of bearings in different states, and can be used to characterize the characteristics of different bearing signals. Finally, based on the mechanical transmission system bearing experimental platform independently developed by our school, the experimental results show that compared with the unimproved ICEEMDAN algorithm, the diagnostic accuracy rate of the proposed method is 99.5%, which is increased by 6.4%, and it can be effectively used for feature extraction of rolling bearings.
650		4	\|a Rolling bearings
650		4	\|a Feature extraction
650		4	\|a ICEEMDAN
650		4	\|a Permutation entropy
700	1		\|a Wang, Zhenyu \|e verfasserin \|4 aut
700	1		\|a Zhao, Yuanfang \|e verfasserin \|4 aut
700	1		\|a Geng, Guosheng \|e verfasserin \|4 aut
700	1		\|a Dustdar, Schahram \|e verfasserin \|4 aut
700	1		\|a Donta, Praveen Kumar \|e verfasserin \|0 (orcid)0000-0002-8233-6071 \|4 aut
700	1		\|a Ji, Guojun \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|a Instrumentation, Systems, and Automation Society ; ID: gnd/10022359-X \|t ISA transactions \|d Amsterdam [u.a.] : Elsevier, 1989 \|g 143, Seite 536-547 \|h Online-Ressource \|w (DE-627)320505243 \|w (DE-600)2012746-7 \|w (DE-576)271360690 \|x 1879-2022 \|7 nnns
773	1	8	\|g volume:143 \|g pages:536-547
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912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
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912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
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912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
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936	b	k	\|a 50.20 \|j Automatisierungstechnik \|q VZ
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952			\|d 143 \|h 536-547

Indexfelder

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allfields	10.1016/j.isatra.2023.09.009 doi (DE-627)ELV06613143X (ELSEVIER)S0019-0578(23)00416-0 DE-627 ger DE-627 rda eng 530 VZ 50.21 bkl 50.20 bkl Xiao, Maohua verfasserin (orcid)0000-0001-5213-1035 aut A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The vibration signals of rolling bearings are complex and changeable, and extracting meaningful features is difficult. Currently, the commonly used empirical mode decomposition (EMD) algorithms have the problem of mode aliasing. In this paper, a new feature extraction method based on the improved complete ensemble empirical mode decomposition with adapted noise (ICEEMDAN) and permutation entropy is proposed. In this method, the ICEEMDAN algorithm is first improved and optimized to enable a self-selection function The vibration signal is then decomposed into several intrinsic modal functions using this algorithm, and the permutation entropy is extracted as the fault feature of rolling bearings, which improves the accuracy of fault classification and realizes the intelligent feature extraction of different fault states. Then, the Case Western Reserve University dataset is used for verification, and the results show that this scheme can effectively separate the vibration signal characteristics of bearings in different states, and can be used to characterize the characteristics of different bearing signals. Finally, based on the mechanical transmission system bearing experimental platform independently developed by our school, the experimental results show that compared with the unimproved ICEEMDAN algorithm, the diagnostic accuracy rate of the proposed method is 99.5%, which is increased by 6.4%, and it can be effectively used for feature extraction of rolling bearings. Rolling bearings Feature extraction ICEEMDAN Permutation entropy Wang, Zhenyu verfasserin aut Zhao, Yuanfang verfasserin aut Geng, Guosheng verfasserin aut Dustdar, Schahram verfasserin aut Donta, Praveen Kumar verfasserin (orcid)0000-0002-8233-6071 aut Ji, Guojun verfasserin aut Enthalten in Instrumentation, Systems, and Automation Society ; ID: gnd/10022359-X ISA transactions Amsterdam [u.a.] : Elsevier, 1989 143, Seite 536-547 Online-Ressource (DE-627)320505243 (DE-600)2012746-7 (DE-576)271360690 1879-2022 nnns volume:143 pages:536-547 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.21 Messtechnik VZ 50.20 Automatisierungstechnik VZ AR 143 536-547
spelling	10.1016/j.isatra.2023.09.009 doi (DE-627)ELV06613143X (ELSEVIER)S0019-0578(23)00416-0 DE-627 ger DE-627 rda eng 530 VZ 50.21 bkl 50.20 bkl Xiao, Maohua verfasserin (orcid)0000-0001-5213-1035 aut A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The vibration signals of rolling bearings are complex and changeable, and extracting meaningful features is difficult. Currently, the commonly used empirical mode decomposition (EMD) algorithms have the problem of mode aliasing. In this paper, a new feature extraction method based on the improved complete ensemble empirical mode decomposition with adapted noise (ICEEMDAN) and permutation entropy is proposed. In this method, the ICEEMDAN algorithm is first improved and optimized to enable a self-selection function The vibration signal is then decomposed into several intrinsic modal functions using this algorithm, and the permutation entropy is extracted as the fault feature of rolling bearings, which improves the accuracy of fault classification and realizes the intelligent feature extraction of different fault states. Then, the Case Western Reserve University dataset is used for verification, and the results show that this scheme can effectively separate the vibration signal characteristics of bearings in different states, and can be used to characterize the characteristics of different bearing signals. Finally, based on the mechanical transmission system bearing experimental platform independently developed by our school, the experimental results show that compared with the unimproved ICEEMDAN algorithm, the diagnostic accuracy rate of the proposed method is 99.5%, which is increased by 6.4%, and it can be effectively used for feature extraction of rolling bearings. Rolling bearings Feature extraction ICEEMDAN Permutation entropy Wang, Zhenyu verfasserin aut Zhao, Yuanfang verfasserin aut Geng, Guosheng verfasserin aut Dustdar, Schahram verfasserin aut Donta, Praveen Kumar verfasserin (orcid)0000-0002-8233-6071 aut Ji, Guojun verfasserin aut Enthalten in Instrumentation, Systems, and Automation Society ; ID: gnd/10022359-X ISA transactions Amsterdam [u.a.] : Elsevier, 1989 143, Seite 536-547 Online-Ressource (DE-627)320505243 (DE-600)2012746-7 (DE-576)271360690 1879-2022 nnns volume:143 pages:536-547 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.21 Messtechnik VZ 50.20 Automatisierungstechnik VZ AR 143 536-547
allfields_unstemmed	10.1016/j.isatra.2023.09.009 doi (DE-627)ELV06613143X (ELSEVIER)S0019-0578(23)00416-0 DE-627 ger DE-627 rda eng 530 VZ 50.21 bkl 50.20 bkl Xiao, Maohua verfasserin (orcid)0000-0001-5213-1035 aut A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The vibration signals of rolling bearings are complex and changeable, and extracting meaningful features is difficult. Currently, the commonly used empirical mode decomposition (EMD) algorithms have the problem of mode aliasing. In this paper, a new feature extraction method based on the improved complete ensemble empirical mode decomposition with adapted noise (ICEEMDAN) and permutation entropy is proposed. In this method, the ICEEMDAN algorithm is first improved and optimized to enable a self-selection function The vibration signal is then decomposed into several intrinsic modal functions using this algorithm, and the permutation entropy is extracted as the fault feature of rolling bearings, which improves the accuracy of fault classification and realizes the intelligent feature extraction of different fault states. Then, the Case Western Reserve University dataset is used for verification, and the results show that this scheme can effectively separate the vibration signal characteristics of bearings in different states, and can be used to characterize the characteristics of different bearing signals. Finally, based on the mechanical transmission system bearing experimental platform independently developed by our school, the experimental results show that compared with the unimproved ICEEMDAN algorithm, the diagnostic accuracy rate of the proposed method is 99.5%, which is increased by 6.4%, and it can be effectively used for feature extraction of rolling bearings. Rolling bearings Feature extraction ICEEMDAN Permutation entropy Wang, Zhenyu verfasserin aut Zhao, Yuanfang verfasserin aut Geng, Guosheng verfasserin aut Dustdar, Schahram verfasserin aut Donta, Praveen Kumar verfasserin (orcid)0000-0002-8233-6071 aut Ji, Guojun verfasserin aut Enthalten in Instrumentation, Systems, and Automation Society ; ID: gnd/10022359-X ISA transactions Amsterdam [u.a.] : Elsevier, 1989 143, Seite 536-547 Online-Ressource (DE-627)320505243 (DE-600)2012746-7 (DE-576)271360690 1879-2022 nnns volume:143 pages:536-547 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.21 Messtechnik VZ 50.20 Automatisierungstechnik VZ AR 143 536-547
allfieldsGer	10.1016/j.isatra.2023.09.009 doi (DE-627)ELV06613143X (ELSEVIER)S0019-0578(23)00416-0 DE-627 ger DE-627 rda eng 530 VZ 50.21 bkl 50.20 bkl Xiao, Maohua verfasserin (orcid)0000-0001-5213-1035 aut A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The vibration signals of rolling bearings are complex and changeable, and extracting meaningful features is difficult. Currently, the commonly used empirical mode decomposition (EMD) algorithms have the problem of mode aliasing. In this paper, a new feature extraction method based on the improved complete ensemble empirical mode decomposition with adapted noise (ICEEMDAN) and permutation entropy is proposed. In this method, the ICEEMDAN algorithm is first improved and optimized to enable a self-selection function The vibration signal is then decomposed into several intrinsic modal functions using this algorithm, and the permutation entropy is extracted as the fault feature of rolling bearings, which improves the accuracy of fault classification and realizes the intelligent feature extraction of different fault states. Then, the Case Western Reserve University dataset is used for verification, and the results show that this scheme can effectively separate the vibration signal characteristics of bearings in different states, and can be used to characterize the characteristics of different bearing signals. Finally, based on the mechanical transmission system bearing experimental platform independently developed by our school, the experimental results show that compared with the unimproved ICEEMDAN algorithm, the diagnostic accuracy rate of the proposed method is 99.5%, which is increased by 6.4%, and it can be effectively used for feature extraction of rolling bearings. Rolling bearings Feature extraction ICEEMDAN Permutation entropy Wang, Zhenyu verfasserin aut Zhao, Yuanfang verfasserin aut Geng, Guosheng verfasserin aut Dustdar, Schahram verfasserin aut Donta, Praveen Kumar verfasserin (orcid)0000-0002-8233-6071 aut Ji, Guojun verfasserin aut Enthalten in Instrumentation, Systems, and Automation Society ; ID: gnd/10022359-X ISA transactions Amsterdam [u.a.] : Elsevier, 1989 143, Seite 536-547 Online-Ressource (DE-627)320505243 (DE-600)2012746-7 (DE-576)271360690 1879-2022 nnns volume:143 pages:536-547 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.21 Messtechnik VZ 50.20 Automatisierungstechnik VZ AR 143 536-547
allfieldsSound	10.1016/j.isatra.2023.09.009 doi (DE-627)ELV06613143X (ELSEVIER)S0019-0578(23)00416-0 DE-627 ger DE-627 rda eng 530 VZ 50.21 bkl 50.20 bkl Xiao, Maohua verfasserin (orcid)0000-0001-5213-1035 aut A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The vibration signals of rolling bearings are complex and changeable, and extracting meaningful features is difficult. Currently, the commonly used empirical mode decomposition (EMD) algorithms have the problem of mode aliasing. In this paper, a new feature extraction method based on the improved complete ensemble empirical mode decomposition with adapted noise (ICEEMDAN) and permutation entropy is proposed. In this method, the ICEEMDAN algorithm is first improved and optimized to enable a self-selection function The vibration signal is then decomposed into several intrinsic modal functions using this algorithm, and the permutation entropy is extracted as the fault feature of rolling bearings, which improves the accuracy of fault classification and realizes the intelligent feature extraction of different fault states. Then, the Case Western Reserve University dataset is used for verification, and the results show that this scheme can effectively separate the vibration signal characteristics of bearings in different states, and can be used to characterize the characteristics of different bearing signals. Finally, based on the mechanical transmission system bearing experimental platform independently developed by our school, the experimental results show that compared with the unimproved ICEEMDAN algorithm, the diagnostic accuracy rate of the proposed method is 99.5%, which is increased by 6.4%, and it can be effectively used for feature extraction of rolling bearings. Rolling bearings Feature extraction ICEEMDAN Permutation entropy Wang, Zhenyu verfasserin aut Zhao, Yuanfang verfasserin aut Geng, Guosheng verfasserin aut Dustdar, Schahram verfasserin aut Donta, Praveen Kumar verfasserin (orcid)0000-0002-8233-6071 aut Ji, Guojun verfasserin aut Enthalten in Instrumentation, Systems, and Automation Society ; ID: gnd/10022359-X ISA transactions Amsterdam [u.a.] : Elsevier, 1989 143, Seite 536-547 Online-Ressource (DE-627)320505243 (DE-600)2012746-7 (DE-576)271360690 1879-2022 nnns volume:143 pages:536-547 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.21 Messtechnik VZ 50.20 Automatisierungstechnik VZ AR 143 536-547
language	English
source	Enthalten in ISA transactions 143, Seite 536-547 volume:143 pages:536-547
sourceStr	Enthalten in ISA transactions 143, Seite 536-547 volume:143 pages:536-547
format_phy_str_mv	Article
bklname	Messtechnik Automatisierungstechnik
institution	findex.gbv.de
topic_facet	Rolling bearings Feature extraction ICEEMDAN Permutation entropy
dewey-raw	530
isfreeaccess_bool	false
container_title	ISA transactions
authorswithroles_txt_mv	Xiao, Maohua @@aut@@ Wang, Zhenyu @@aut@@ Zhao, Yuanfang @@aut@@ Geng, Guosheng @@aut@@ Dustdar, Schahram @@aut@@ Donta, Praveen Kumar @@aut@@ Ji, Guojun @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320505243
dewey-sort	3530
id	ELV06613143X
language_de	englisch
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author	Xiao, Maohua
spellingShingle	Xiao, Maohua ddc 530 bkl 50.21 bkl 50.20 misc Rolling bearings misc Feature extraction misc ICEEMDAN misc Permutation entropy A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy
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topic_title	530 VZ 50.21 bkl 50.20 bkl A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy Rolling bearings Feature extraction ICEEMDAN Permutation entropy
topic	ddc 530 bkl 50.21 bkl 50.20 misc Rolling bearings misc Feature extraction misc ICEEMDAN misc Permutation entropy
topic_unstemmed	ddc 530 bkl 50.21 bkl 50.20 misc Rolling bearings misc Feature extraction misc ICEEMDAN misc Permutation entropy
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title	A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy
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title_full	A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy
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author_browse	Xiao, Maohua Wang, Zhenyu Zhao, Yuanfang Geng, Guosheng Dustdar, Schahram Donta, Praveen Kumar Ji, Guojun
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title_sort	a new fault feature extraction method of rolling bearings based on the improved self-selection iceemdan-permutation entropy
title_auth	A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy
abstract	The vibration signals of rolling bearings are complex and changeable, and extracting meaningful features is difficult. Currently, the commonly used empirical mode decomposition (EMD) algorithms have the problem of mode aliasing. In this paper, a new feature extraction method based on the improved complete ensemble empirical mode decomposition with adapted noise (ICEEMDAN) and permutation entropy is proposed. In this method, the ICEEMDAN algorithm is first improved and optimized to enable a self-selection function The vibration signal is then decomposed into several intrinsic modal functions using this algorithm, and the permutation entropy is extracted as the fault feature of rolling bearings, which improves the accuracy of fault classification and realizes the intelligent feature extraction of different fault states. Then, the Case Western Reserve University dataset is used for verification, and the results show that this scheme can effectively separate the vibration signal characteristics of bearings in different states, and can be used to characterize the characteristics of different bearing signals. Finally, based on the mechanical transmission system bearing experimental platform independently developed by our school, the experimental results show that compared with the unimproved ICEEMDAN algorithm, the diagnostic accuracy rate of the proposed method is 99.5%, which is increased by 6.4%, and it can be effectively used for feature extraction of rolling bearings.
abstractGer	The vibration signals of rolling bearings are complex and changeable, and extracting meaningful features is difficult. Currently, the commonly used empirical mode decomposition (EMD) algorithms have the problem of mode aliasing. In this paper, a new feature extraction method based on the improved complete ensemble empirical mode decomposition with adapted noise (ICEEMDAN) and permutation entropy is proposed. In this method, the ICEEMDAN algorithm is first improved and optimized to enable a self-selection function The vibration signal is then decomposed into several intrinsic modal functions using this algorithm, and the permutation entropy is extracted as the fault feature of rolling bearings, which improves the accuracy of fault classification and realizes the intelligent feature extraction of different fault states. Then, the Case Western Reserve University dataset is used for verification, and the results show that this scheme can effectively separate the vibration signal characteristics of bearings in different states, and can be used to characterize the characteristics of different bearing signals. Finally, based on the mechanical transmission system bearing experimental platform independently developed by our school, the experimental results show that compared with the unimproved ICEEMDAN algorithm, the diagnostic accuracy rate of the proposed method is 99.5%, which is increased by 6.4%, and it can be effectively used for feature extraction of rolling bearings.
abstract_unstemmed	The vibration signals of rolling bearings are complex and changeable, and extracting meaningful features is difficult. Currently, the commonly used empirical mode decomposition (EMD) algorithms have the problem of mode aliasing. In this paper, a new feature extraction method based on the improved complete ensemble empirical mode decomposition with adapted noise (ICEEMDAN) and permutation entropy is proposed. In this method, the ICEEMDAN algorithm is first improved and optimized to enable a self-selection function The vibration signal is then decomposed into several intrinsic modal functions using this algorithm, and the permutation entropy is extracted as the fault feature of rolling bearings, which improves the accuracy of fault classification and realizes the intelligent feature extraction of different fault states. Then, the Case Western Reserve University dataset is used for verification, and the results show that this scheme can effectively separate the vibration signal characteristics of bearings in different states, and can be used to characterize the characteristics of different bearing signals. Finally, based on the mechanical transmission system bearing experimental platform independently developed by our school, the experimental results show that compared with the unimproved ICEEMDAN algorithm, the diagnostic accuracy rate of the proposed method is 99.5%, which is increased by 6.4%, and it can be effectively used for feature extraction of rolling bearings.
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title_short	A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy
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author2	Wang, Zhenyu Zhao, Yuanfang Geng, Guosheng Dustdar, Schahram Donta, Praveen Kumar Ji, Guojun
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up_date	2024-07-06T16:41:22.749Z
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A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy

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