An Intelligent Fault Diagnosis Method for Bogie Bearings of Metro Vehicles Based on Weighted Improved D-S Evidence Theory

Bogie bearings are very important for the safe and normal operation of metro vehicles. The prevailing fault diagnosis methods for bogie bearings generally utilize a single information source, such as vibration, temperature or acoustics. There are some shortcomings in these methods, including low acc...
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Autor*in:	Jianqiang Liu [verfasserIn] Aifeng Chen [verfasserIn] Nan Zhao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	fault diagnosis bogie bearing multi-source information D-S evidence theory fusion algorithm

Übergeordnetes Werk:	In: Energies - MDPI AG, 2008, 11(2018), 1, p 232
Übergeordnetes Werk:	volume:11 ; year:2018 ; number:1, p 232

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/en11010232

Katalog-ID:	DOAJ031571166

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allfields	10.3390/en11010232 doi (DE-627)DOAJ031571166 (DE-599)DOAJ556d281c3ba3420cb5c1a924f872e619 DE-627 ger DE-627 rakwb eng Jianqiang Liu verfasserin aut An Intelligent Fault Diagnosis Method for Bogie Bearings of Metro Vehicles Based on Weighted Improved D-S Evidence Theory 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bogie bearings are very important for the safe and normal operation of metro vehicles. The prevailing fault diagnosis methods for bogie bearings generally utilize a single information source, such as vibration, temperature or acoustics. There are some shortcomings in these methods, including low accuracy and poor reliability. To address these shortcomings, this paper proposes an intelligent fault diagnosis method. Based on improved D-S (Dempster-Shafer) evidence theory, this method comprehensively analyzes vibration and temperature signals to diagnose bearing faults. In order to verify the feasibility and effectiveness of the proposed method, this study designed the hardware device and constructed a test platform. Bogie bearings with faults occurring on the outer ring, inner ring and rolling elements were tested on this platform. The diagnosis accuracy rate of the proposed fusion algorithm reached 91%, and the misdiagnosis rate was only 2%. The test results showed that the proposed method can accurately and reliably realize fault diagnosis with a high accuracy rate and a low misdiagnosis rate compared to previous methods. Thus, the proposed fault diagnosis method can accurately and effectively identify the faults of metro vehicle bogie bearings. fault diagnosis bogie bearing multi-source information D-S evidence theory fusion algorithm Technology T Aifeng Chen verfasserin aut Nan Zhao verfasserin aut In Energies MDPI AG, 2008 11(2018), 1, p 232 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:11 year:2018 number:1, p 232 https://doi.org/10.3390/en11010232 kostenfrei https://doaj.org/article/556d281c3ba3420cb5c1a924f872e619 kostenfrei http://www.mdpi.com/1996-1073/11/1/232 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 11 2018 1, p 232
spelling	10.3390/en11010232 doi (DE-627)DOAJ031571166 (DE-599)DOAJ556d281c3ba3420cb5c1a924f872e619 DE-627 ger DE-627 rakwb eng Jianqiang Liu verfasserin aut An Intelligent Fault Diagnosis Method for Bogie Bearings of Metro Vehicles Based on Weighted Improved D-S Evidence Theory 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bogie bearings are very important for the safe and normal operation of metro vehicles. The prevailing fault diagnosis methods for bogie bearings generally utilize a single information source, such as vibration, temperature or acoustics. There are some shortcomings in these methods, including low accuracy and poor reliability. To address these shortcomings, this paper proposes an intelligent fault diagnosis method. Based on improved D-S (Dempster-Shafer) evidence theory, this method comprehensively analyzes vibration and temperature signals to diagnose bearing faults. In order to verify the feasibility and effectiveness of the proposed method, this study designed the hardware device and constructed a test platform. Bogie bearings with faults occurring on the outer ring, inner ring and rolling elements were tested on this platform. The diagnosis accuracy rate of the proposed fusion algorithm reached 91%, and the misdiagnosis rate was only 2%. The test results showed that the proposed method can accurately and reliably realize fault diagnosis with a high accuracy rate and a low misdiagnosis rate compared to previous methods. Thus, the proposed fault diagnosis method can accurately and effectively identify the faults of metro vehicle bogie bearings. fault diagnosis bogie bearing multi-source information D-S evidence theory fusion algorithm Technology T Aifeng Chen verfasserin aut Nan Zhao verfasserin aut In Energies MDPI AG, 2008 11(2018), 1, p 232 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:11 year:2018 number:1, p 232 https://doi.org/10.3390/en11010232 kostenfrei https://doaj.org/article/556d281c3ba3420cb5c1a924f872e619 kostenfrei http://www.mdpi.com/1996-1073/11/1/232 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 11 2018 1, p 232
allfields_unstemmed	10.3390/en11010232 doi (DE-627)DOAJ031571166 (DE-599)DOAJ556d281c3ba3420cb5c1a924f872e619 DE-627 ger DE-627 rakwb eng Jianqiang Liu verfasserin aut An Intelligent Fault Diagnosis Method for Bogie Bearings of Metro Vehicles Based on Weighted Improved D-S Evidence Theory 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bogie bearings are very important for the safe and normal operation of metro vehicles. The prevailing fault diagnosis methods for bogie bearings generally utilize a single information source, such as vibration, temperature or acoustics. There are some shortcomings in these methods, including low accuracy and poor reliability. To address these shortcomings, this paper proposes an intelligent fault diagnosis method. Based on improved D-S (Dempster-Shafer) evidence theory, this method comprehensively analyzes vibration and temperature signals to diagnose bearing faults. In order to verify the feasibility and effectiveness of the proposed method, this study designed the hardware device and constructed a test platform. Bogie bearings with faults occurring on the outer ring, inner ring and rolling elements were tested on this platform. The diagnosis accuracy rate of the proposed fusion algorithm reached 91%, and the misdiagnosis rate was only 2%. The test results showed that the proposed method can accurately and reliably realize fault diagnosis with a high accuracy rate and a low misdiagnosis rate compared to previous methods. Thus, the proposed fault diagnosis method can accurately and effectively identify the faults of metro vehicle bogie bearings. fault diagnosis bogie bearing multi-source information D-S evidence theory fusion algorithm Technology T Aifeng Chen verfasserin aut Nan Zhao verfasserin aut In Energies MDPI AG, 2008 11(2018), 1, p 232 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:11 year:2018 number:1, p 232 https://doi.org/10.3390/en11010232 kostenfrei https://doaj.org/article/556d281c3ba3420cb5c1a924f872e619 kostenfrei http://www.mdpi.com/1996-1073/11/1/232 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 11 2018 1, p 232
allfieldsGer	10.3390/en11010232 doi (DE-627)DOAJ031571166 (DE-599)DOAJ556d281c3ba3420cb5c1a924f872e619 DE-627 ger DE-627 rakwb eng Jianqiang Liu verfasserin aut An Intelligent Fault Diagnosis Method for Bogie Bearings of Metro Vehicles Based on Weighted Improved D-S Evidence Theory 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bogie bearings are very important for the safe and normal operation of metro vehicles. The prevailing fault diagnosis methods for bogie bearings generally utilize a single information source, such as vibration, temperature or acoustics. There are some shortcomings in these methods, including low accuracy and poor reliability. To address these shortcomings, this paper proposes an intelligent fault diagnosis method. Based on improved D-S (Dempster-Shafer) evidence theory, this method comprehensively analyzes vibration and temperature signals to diagnose bearing faults. In order to verify the feasibility and effectiveness of the proposed method, this study designed the hardware device and constructed a test platform. Bogie bearings with faults occurring on the outer ring, inner ring and rolling elements were tested on this platform. The diagnosis accuracy rate of the proposed fusion algorithm reached 91%, and the misdiagnosis rate was only 2%. The test results showed that the proposed method can accurately and reliably realize fault diagnosis with a high accuracy rate and a low misdiagnosis rate compared to previous methods. Thus, the proposed fault diagnosis method can accurately and effectively identify the faults of metro vehicle bogie bearings. fault diagnosis bogie bearing multi-source information D-S evidence theory fusion algorithm Technology T Aifeng Chen verfasserin aut Nan Zhao verfasserin aut In Energies MDPI AG, 2008 11(2018), 1, p 232 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:11 year:2018 number:1, p 232 https://doi.org/10.3390/en11010232 kostenfrei https://doaj.org/article/556d281c3ba3420cb5c1a924f872e619 kostenfrei http://www.mdpi.com/1996-1073/11/1/232 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 11 2018 1, p 232
allfieldsSound	10.3390/en11010232 doi (DE-627)DOAJ031571166 (DE-599)DOAJ556d281c3ba3420cb5c1a924f872e619 DE-627 ger DE-627 rakwb eng Jianqiang Liu verfasserin aut An Intelligent Fault Diagnosis Method for Bogie Bearings of Metro Vehicles Based on Weighted Improved D-S Evidence Theory 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bogie bearings are very important for the safe and normal operation of metro vehicles. The prevailing fault diagnosis methods for bogie bearings generally utilize a single information source, such as vibration, temperature or acoustics. There are some shortcomings in these methods, including low accuracy and poor reliability. To address these shortcomings, this paper proposes an intelligent fault diagnosis method. Based on improved D-S (Dempster-Shafer) evidence theory, this method comprehensively analyzes vibration and temperature signals to diagnose bearing faults. In order to verify the feasibility and effectiveness of the proposed method, this study designed the hardware device and constructed a test platform. Bogie bearings with faults occurring on the outer ring, inner ring and rolling elements were tested on this platform. The diagnosis accuracy rate of the proposed fusion algorithm reached 91%, and the misdiagnosis rate was only 2%. The test results showed that the proposed method can accurately and reliably realize fault diagnosis with a high accuracy rate and a low misdiagnosis rate compared to previous methods. Thus, the proposed fault diagnosis method can accurately and effectively identify the faults of metro vehicle bogie bearings. fault diagnosis bogie bearing multi-source information D-S evidence theory fusion algorithm Technology T Aifeng Chen verfasserin aut Nan Zhao verfasserin aut In Energies MDPI AG, 2008 11(2018), 1, p 232 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:11 year:2018 number:1, p 232 https://doi.org/10.3390/en11010232 kostenfrei https://doaj.org/article/556d281c3ba3420cb5c1a924f872e619 kostenfrei http://www.mdpi.com/1996-1073/11/1/232 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 11 2018 1, p 232
language	English
source	In Energies 11(2018), 1, p 232 volume:11 year:2018 number:1, p 232
sourceStr	In Energies 11(2018), 1, p 232 volume:11 year:2018 number:1, p 232
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topic_facet	fault diagnosis bogie bearing multi-source information D-S evidence theory fusion algorithm Technology T
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authorswithroles_txt_mv	Jianqiang Liu @@aut@@ Aifeng Chen @@aut@@ Nan Zhao @@aut@@
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The prevailing fault diagnosis methods for bogie bearings generally utilize a single information source, such as vibration, temperature or acoustics. There are some shortcomings in these methods, including low accuracy and poor reliability. To address these shortcomings, this paper proposes an intelligent fault diagnosis method. Based on improved D-S (Dempster-Shafer) evidence theory, this method comprehensively analyzes vibration and temperature signals to diagnose bearing faults. In order to verify the feasibility and effectiveness of the proposed method, this study designed the hardware device and constructed a test platform. Bogie bearings with faults occurring on the outer ring, inner ring and rolling elements were tested on this platform. The diagnosis accuracy rate of the proposed fusion algorithm reached 91%, and the misdiagnosis rate was only 2%. The test results showed that the proposed method can accurately and reliably realize fault diagnosis with a high accuracy rate and a low misdiagnosis rate compared to previous methods. 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author	Jianqiang Liu
spellingShingle	Jianqiang Liu misc fault diagnosis misc bogie bearing misc multi-source information misc D-S evidence theory misc fusion algorithm misc Technology misc T An Intelligent Fault Diagnosis Method for Bogie Bearings of Metro Vehicles Based on Weighted Improved D-S Evidence Theory
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topic_title	An Intelligent Fault Diagnosis Method for Bogie Bearings of Metro Vehicles Based on Weighted Improved D-S Evidence Theory fault diagnosis bogie bearing multi-source information D-S evidence theory fusion algorithm
topic	misc fault diagnosis misc bogie bearing misc multi-source information misc D-S evidence theory misc fusion algorithm misc Technology misc T
topic_unstemmed	misc fault diagnosis misc bogie bearing misc multi-source information misc D-S evidence theory misc fusion algorithm misc Technology misc T
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title	An Intelligent Fault Diagnosis Method for Bogie Bearings of Metro Vehicles Based on Weighted Improved D-S Evidence Theory
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title_sort	intelligent fault diagnosis method for bogie bearings of metro vehicles based on weighted improved d-s evidence theory
title_auth	An Intelligent Fault Diagnosis Method for Bogie Bearings of Metro Vehicles Based on Weighted Improved D-S Evidence Theory
abstract	Bogie bearings are very important for the safe and normal operation of metro vehicles. The prevailing fault diagnosis methods for bogie bearings generally utilize a single information source, such as vibration, temperature or acoustics. There are some shortcomings in these methods, including low accuracy and poor reliability. To address these shortcomings, this paper proposes an intelligent fault diagnosis method. Based on improved D-S (Dempster-Shafer) evidence theory, this method comprehensively analyzes vibration and temperature signals to diagnose bearing faults. In order to verify the feasibility and effectiveness of the proposed method, this study designed the hardware device and constructed a test platform. Bogie bearings with faults occurring on the outer ring, inner ring and rolling elements were tested on this platform. The diagnosis accuracy rate of the proposed fusion algorithm reached 91%, and the misdiagnosis rate was only 2%. The test results showed that the proposed method can accurately and reliably realize fault diagnosis with a high accuracy rate and a low misdiagnosis rate compared to previous methods. Thus, the proposed fault diagnosis method can accurately and effectively identify the faults of metro vehicle bogie bearings.
abstractGer	Bogie bearings are very important for the safe and normal operation of metro vehicles. The prevailing fault diagnosis methods for bogie bearings generally utilize a single information source, such as vibration, temperature or acoustics. There are some shortcomings in these methods, including low accuracy and poor reliability. To address these shortcomings, this paper proposes an intelligent fault diagnosis method. Based on improved D-S (Dempster-Shafer) evidence theory, this method comprehensively analyzes vibration and temperature signals to diagnose bearing faults. In order to verify the feasibility and effectiveness of the proposed method, this study designed the hardware device and constructed a test platform. Bogie bearings with faults occurring on the outer ring, inner ring and rolling elements were tested on this platform. The diagnosis accuracy rate of the proposed fusion algorithm reached 91%, and the misdiagnosis rate was only 2%. The test results showed that the proposed method can accurately and reliably realize fault diagnosis with a high accuracy rate and a low misdiagnosis rate compared to previous methods. Thus, the proposed fault diagnosis method can accurately and effectively identify the faults of metro vehicle bogie bearings.
abstract_unstemmed	Bogie bearings are very important for the safe and normal operation of metro vehicles. The prevailing fault diagnosis methods for bogie bearings generally utilize a single information source, such as vibration, temperature or acoustics. There are some shortcomings in these methods, including low accuracy and poor reliability. To address these shortcomings, this paper proposes an intelligent fault diagnosis method. Based on improved D-S (Dempster-Shafer) evidence theory, this method comprehensively analyzes vibration and temperature signals to diagnose bearing faults. In order to verify the feasibility and effectiveness of the proposed method, this study designed the hardware device and constructed a test platform. Bogie bearings with faults occurring on the outer ring, inner ring and rolling elements were tested on this platform. The diagnosis accuracy rate of the proposed fusion algorithm reached 91%, and the misdiagnosis rate was only 2%. The test results showed that the proposed method can accurately and reliably realize fault diagnosis with a high accuracy rate and a low misdiagnosis rate compared to previous methods. Thus, the proposed fault diagnosis method can accurately and effectively identify the faults of metro vehicle bogie bearings.
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title_short	An Intelligent Fault Diagnosis Method for Bogie Bearings of Metro Vehicles Based on Weighted Improved D-S Evidence Theory
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