Modified Singular Spectrum Decomposition and Its Application to Composite Fault Diagnosis of Gearboxes

Under the strong noise environment, the composite fault signal of gearbox is weak, which makes it difficult to extract fault features. For this problem, based on noise-assisted method, we propose a novel method called Modified Singular Spectrum Decomposition (MSSD). Singular Spectrum Decomposition (...
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Gespeichert in:

Autor*in:	Junyuan Wang [verfasserIn] Xiaofeng Han [verfasserIn] Zhijian Wang [verfasserIn] Wenhua Du [verfasserIn] Jie Zhou [verfasserIn] Jiping Zhang [verfasserIn] Huihui He [verfasserIn] Xiaoming Guo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Gearbox composite fault algorithm fault diagnosis modified singular spectrum decomposition

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 19(2018), 1, p 62
Übergeordnetes Werk:	volume:19 ; year:2018 ; number:1, p 62

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s19010062

Katalog-ID:	DOAJ085361348

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520			\|a Under the strong noise environment, the composite fault signal of gearbox is weak, which makes it difficult to extract fault features. For this problem, based on noise-assisted method, we propose a novel method called Modified Singular Spectrum Decomposition (MSSD). Singular Spectrum Decomposition (SSD) has many advantages such as high decomposition precision and strong ability to restrain mode mixing, etc. However, the ability of SSD to extract a weak signal is not ideal, the decomposition results usually contain a lot of redundant noise and mode mixing caused by intermittency, which is also a troubling problem. In order to improve the decomposition efficiency and make up for the defects of SSD, the new method MSSD adds an adaptive and particular noise in every SSD decomposition stage for each trial, and in addition, whenever the input signal is decomposed to obtain an intrinsic module function (IMF), a unique residual is obtained. After multiple decomposition, the average value of the residual is used as input to the next stage, until the residual cannot continue to decompose, which means that the residual component has, at most, one extreme value. Finally, analyzing simulated signals to explain the advantages of MSSD compared to ensemble empirical mode decomposition (EEMD) and complete ensemble local mean decomposition with adaptive noise (CEEMDAN). In order to further prove the effectiveness of MSSD, this new method, MSSD, is applied to the fault diagnosis of an engineering gearbox test stand in an actual engineer project case. The final results show that MSSD can extract more fault feature information, and mode mixing has been improved and suffers less interference compared to SSD.
650		4	\|a Gearbox composite fault
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spelling	10.3390/s19010062 doi (DE-627)DOAJ085361348 (DE-599)DOAJc4a9974b3e0849f5915685cc8bb1d5a0 DE-627 ger DE-627 rakwb eng TP1-1185 Junyuan Wang verfasserin aut Modified Singular Spectrum Decomposition and Its Application to Composite Fault Diagnosis of Gearboxes 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Under the strong noise environment, the composite fault signal of gearbox is weak, which makes it difficult to extract fault features. For this problem, based on noise-assisted method, we propose a novel method called Modified Singular Spectrum Decomposition (MSSD). Singular Spectrum Decomposition (SSD) has many advantages such as high decomposition precision and strong ability to restrain mode mixing, etc. However, the ability of SSD to extract a weak signal is not ideal, the decomposition results usually contain a lot of redundant noise and mode mixing caused by intermittency, which is also a troubling problem. In order to improve the decomposition efficiency and make up for the defects of SSD, the new method MSSD adds an adaptive and particular noise in every SSD decomposition stage for each trial, and in addition, whenever the input signal is decomposed to obtain an intrinsic module function (IMF), a unique residual is obtained. After multiple decomposition, the average value of the residual is used as input to the next stage, until the residual cannot continue to decompose, which means that the residual component has, at most, one extreme value. Finally, analyzing simulated signals to explain the advantages of MSSD compared to ensemble empirical mode decomposition (EEMD) and complete ensemble local mean decomposition with adaptive noise (CEEMDAN). In order to further prove the effectiveness of MSSD, this new method, MSSD, is applied to the fault diagnosis of an engineering gearbox test stand in an actual engineer project case. The final results show that MSSD can extract more fault feature information, and mode mixing has been improved and suffers less interference compared to SSD. Gearbox composite fault algorithm fault diagnosis modified singular spectrum decomposition Chemical technology Xiaofeng Han verfasserin aut Zhijian Wang verfasserin aut Wenhua Du verfasserin aut Jie Zhou verfasserin aut Jiping Zhang verfasserin aut Huihui He verfasserin aut Xiaoming Guo verfasserin aut In Sensors MDPI AG, 2003 19(2018), 1, p 62 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2018 number:1, p 62 https://doi.org/10.3390/s19010062 kostenfrei https://doaj.org/article/c4a9974b3e0849f5915685cc8bb1d5a0 kostenfrei http://www.mdpi.com/1424-8220/19/1/62 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_2111 GBV_ILN_2507 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 19 2018 1, p 62
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allfieldsGer	10.3390/s19010062 doi (DE-627)DOAJ085361348 (DE-599)DOAJc4a9974b3e0849f5915685cc8bb1d5a0 DE-627 ger DE-627 rakwb eng TP1-1185 Junyuan Wang verfasserin aut Modified Singular Spectrum Decomposition and Its Application to Composite Fault Diagnosis of Gearboxes 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Under the strong noise environment, the composite fault signal of gearbox is weak, which makes it difficult to extract fault features. For this problem, based on noise-assisted method, we propose a novel method called Modified Singular Spectrum Decomposition (MSSD). Singular Spectrum Decomposition (SSD) has many advantages such as high decomposition precision and strong ability to restrain mode mixing, etc. However, the ability of SSD to extract a weak signal is not ideal, the decomposition results usually contain a lot of redundant noise and mode mixing caused by intermittency, which is also a troubling problem. In order to improve the decomposition efficiency and make up for the defects of SSD, the new method MSSD adds an adaptive and particular noise in every SSD decomposition stage for each trial, and in addition, whenever the input signal is decomposed to obtain an intrinsic module function (IMF), a unique residual is obtained. After multiple decomposition, the average value of the residual is used as input to the next stage, until the residual cannot continue to decompose, which means that the residual component has, at most, one extreme value. Finally, analyzing simulated signals to explain the advantages of MSSD compared to ensemble empirical mode decomposition (EEMD) and complete ensemble local mean decomposition with adaptive noise (CEEMDAN). In order to further prove the effectiveness of MSSD, this new method, MSSD, is applied to the fault diagnosis of an engineering gearbox test stand in an actual engineer project case. The final results show that MSSD can extract more fault feature information, and mode mixing has been improved and suffers less interference compared to SSD. Gearbox composite fault algorithm fault diagnosis modified singular spectrum decomposition Chemical technology Xiaofeng Han verfasserin aut Zhijian Wang verfasserin aut Wenhua Du verfasserin aut Jie Zhou verfasserin aut Jiping Zhang verfasserin aut Huihui He verfasserin aut Xiaoming Guo verfasserin aut In Sensors MDPI AG, 2003 19(2018), 1, p 62 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2018 number:1, p 62 https://doi.org/10.3390/s19010062 kostenfrei https://doaj.org/article/c4a9974b3e0849f5915685cc8bb1d5a0 kostenfrei http://www.mdpi.com/1424-8220/19/1/62 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_2111 GBV_ILN_2507 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 19 2018 1, p 62
allfieldsSound	10.3390/s19010062 doi (DE-627)DOAJ085361348 (DE-599)DOAJc4a9974b3e0849f5915685cc8bb1d5a0 DE-627 ger DE-627 rakwb eng TP1-1185 Junyuan Wang verfasserin aut Modified Singular Spectrum Decomposition and Its Application to Composite Fault Diagnosis of Gearboxes 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Under the strong noise environment, the composite fault signal of gearbox is weak, which makes it difficult to extract fault features. For this problem, based on noise-assisted method, we propose a novel method called Modified Singular Spectrum Decomposition (MSSD). Singular Spectrum Decomposition (SSD) has many advantages such as high decomposition precision and strong ability to restrain mode mixing, etc. However, the ability of SSD to extract a weak signal is not ideal, the decomposition results usually contain a lot of redundant noise and mode mixing caused by intermittency, which is also a troubling problem. In order to improve the decomposition efficiency and make up for the defects of SSD, the new method MSSD adds an adaptive and particular noise in every SSD decomposition stage for each trial, and in addition, whenever the input signal is decomposed to obtain an intrinsic module function (IMF), a unique residual is obtained. After multiple decomposition, the average value of the residual is used as input to the next stage, until the residual cannot continue to decompose, which means that the residual component has, at most, one extreme value. Finally, analyzing simulated signals to explain the advantages of MSSD compared to ensemble empirical mode decomposition (EEMD) and complete ensemble local mean decomposition with adaptive noise (CEEMDAN). In order to further prove the effectiveness of MSSD, this new method, MSSD, is applied to the fault diagnosis of an engineering gearbox test stand in an actual engineer project case. The final results show that MSSD can extract more fault feature information, and mode mixing has been improved and suffers less interference compared to SSD. Gearbox composite fault algorithm fault diagnosis modified singular spectrum decomposition Chemical technology Xiaofeng Han verfasserin aut Zhijian Wang verfasserin aut Wenhua Du verfasserin aut Jie Zhou verfasserin aut Jiping Zhang verfasserin aut Huihui He verfasserin aut Xiaoming Guo verfasserin aut In Sensors MDPI AG, 2003 19(2018), 1, p 62 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2018 number:1, p 62 https://doi.org/10.3390/s19010062 kostenfrei https://doaj.org/article/c4a9974b3e0849f5915685cc8bb1d5a0 kostenfrei http://www.mdpi.com/1424-8220/19/1/62 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_2111 GBV_ILN_2507 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 19 2018 1, p 62
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authorswithroles_txt_mv	Junyuan Wang @@aut@@ Xiaofeng Han @@aut@@ Zhijian Wang @@aut@@ Wenhua Du @@aut@@ Jie Zhou @@aut@@ Jiping Zhang @@aut@@ Huihui He @@aut@@ Xiaoming Guo @@aut@@
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callnumber-first	T - Technology
author	Junyuan Wang
spellingShingle	Junyuan Wang misc TP1-1185 misc Gearbox composite fault misc algorithm misc fault diagnosis misc modified singular spectrum decomposition misc Chemical technology Modified Singular Spectrum Decomposition and Its Application to Composite Fault Diagnosis of Gearboxes
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topic_title	TP1-1185 Modified Singular Spectrum Decomposition and Its Application to Composite Fault Diagnosis of Gearboxes Gearbox composite fault algorithm fault diagnosis modified singular spectrum decomposition
topic	misc TP1-1185 misc Gearbox composite fault misc algorithm misc fault diagnosis misc modified singular spectrum decomposition misc Chemical technology
topic_unstemmed	misc TP1-1185 misc Gearbox composite fault misc algorithm misc fault diagnosis misc modified singular spectrum decomposition misc Chemical technology
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title	Modified Singular Spectrum Decomposition and Its Application to Composite Fault Diagnosis of Gearboxes
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title_sort	modified singular spectrum decomposition and its application to composite fault diagnosis of gearboxes
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title_auth	Modified Singular Spectrum Decomposition and Its Application to Composite Fault Diagnosis of Gearboxes
abstract	Under the strong noise environment, the composite fault signal of gearbox is weak, which makes it difficult to extract fault features. For this problem, based on noise-assisted method, we propose a novel method called Modified Singular Spectrum Decomposition (MSSD). Singular Spectrum Decomposition (SSD) has many advantages such as high decomposition precision and strong ability to restrain mode mixing, etc. However, the ability of SSD to extract a weak signal is not ideal, the decomposition results usually contain a lot of redundant noise and mode mixing caused by intermittency, which is also a troubling problem. In order to improve the decomposition efficiency and make up for the defects of SSD, the new method MSSD adds an adaptive and particular noise in every SSD decomposition stage for each trial, and in addition, whenever the input signal is decomposed to obtain an intrinsic module function (IMF), a unique residual is obtained. After multiple decomposition, the average value of the residual is used as input to the next stage, until the residual cannot continue to decompose, which means that the residual component has, at most, one extreme value. Finally, analyzing simulated signals to explain the advantages of MSSD compared to ensemble empirical mode decomposition (EEMD) and complete ensemble local mean decomposition with adaptive noise (CEEMDAN). In order to further prove the effectiveness of MSSD, this new method, MSSD, is applied to the fault diagnosis of an engineering gearbox test stand in an actual engineer project case. The final results show that MSSD can extract more fault feature information, and mode mixing has been improved and suffers less interference compared to SSD.
abstractGer	Under the strong noise environment, the composite fault signal of gearbox is weak, which makes it difficult to extract fault features. For this problem, based on noise-assisted method, we propose a novel method called Modified Singular Spectrum Decomposition (MSSD). Singular Spectrum Decomposition (SSD) has many advantages such as high decomposition precision and strong ability to restrain mode mixing, etc. However, the ability of SSD to extract a weak signal is not ideal, the decomposition results usually contain a lot of redundant noise and mode mixing caused by intermittency, which is also a troubling problem. In order to improve the decomposition efficiency and make up for the defects of SSD, the new method MSSD adds an adaptive and particular noise in every SSD decomposition stage for each trial, and in addition, whenever the input signal is decomposed to obtain an intrinsic module function (IMF), a unique residual is obtained. After multiple decomposition, the average value of the residual is used as input to the next stage, until the residual cannot continue to decompose, which means that the residual component has, at most, one extreme value. Finally, analyzing simulated signals to explain the advantages of MSSD compared to ensemble empirical mode decomposition (EEMD) and complete ensemble local mean decomposition with adaptive noise (CEEMDAN). In order to further prove the effectiveness of MSSD, this new method, MSSD, is applied to the fault diagnosis of an engineering gearbox test stand in an actual engineer project case. The final results show that MSSD can extract more fault feature information, and mode mixing has been improved and suffers less interference compared to SSD.
abstract_unstemmed	Under the strong noise environment, the composite fault signal of gearbox is weak, which makes it difficult to extract fault features. For this problem, based on noise-assisted method, we propose a novel method called Modified Singular Spectrum Decomposition (MSSD). Singular Spectrum Decomposition (SSD) has many advantages such as high decomposition precision and strong ability to restrain mode mixing, etc. However, the ability of SSD to extract a weak signal is not ideal, the decomposition results usually contain a lot of redundant noise and mode mixing caused by intermittency, which is also a troubling problem. In order to improve the decomposition efficiency and make up for the defects of SSD, the new method MSSD adds an adaptive and particular noise in every SSD decomposition stage for each trial, and in addition, whenever the input signal is decomposed to obtain an intrinsic module function (IMF), a unique residual is obtained. After multiple decomposition, the average value of the residual is used as input to the next stage, until the residual cannot continue to decompose, which means that the residual component has, at most, one extreme value. Finally, analyzing simulated signals to explain the advantages of MSSD compared to ensemble empirical mode decomposition (EEMD) and complete ensemble local mean decomposition with adaptive noise (CEEMDAN). In order to further prove the effectiveness of MSSD, this new method, MSSD, is applied to the fault diagnosis of an engineering gearbox test stand in an actual engineer project case. The final results show that MSSD can extract more fault feature information, and mode mixing has been improved and suffers less interference compared to SSD.
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container_issue	1, p 62
title_short	Modified Singular Spectrum Decomposition and Its Application to Composite Fault Diagnosis of Gearboxes
url	https://doi.org/10.3390/s19010062 https://doaj.org/article/c4a9974b3e0849f5915685cc8bb1d5a0 http://www.mdpi.com/1424-8220/19/1/62 https://doaj.org/toc/1424-8220
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up_date	2024-07-03T14:17:18.592Z
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After multiple decomposition, the average value of the residual is used as input to the next stage, until the residual cannot continue to decompose, which means that the residual component has, at most, one extreme value. Finally, analyzing simulated signals to explain the advantages of MSSD compared to ensemble empirical mode decomposition (EEMD) and complete ensemble local mean decomposition with adaptive noise (CEEMDAN). In order to further prove the effectiveness of MSSD, this new method, MSSD, is applied to the fault diagnosis of an engineering gearbox test stand in an actual engineer project case. 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Modified Singular Spectrum Decomposition and Its Application to Composite Fault Diagnosis of Gearboxes

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