Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis

Pipeline monitoring provides operators with invaluable information regarding the potential risks that may pose threats to the integrity of the entire line. Pipeline leakage results in serious environmental and financial costs that can be avoided through leak detection systems. This study introduces...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yang, Jaehyun [verfasserIn] Mostaghimi, Hamid [verfasserIn] Hugo, Ron [verfasserIn] Park, Simon S. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Leak detection Leak localization Leak Volume Rate Estimation Artificial intelligence Leak induced vibration

Übergeordnetes Werk:	Enthalten in: Measurement - Amsterdam [u.a.] : Elsevier Science, 1983, 187
Übergeordnetes Werk:	volume:187

DOI / URN:	10.1016/j.measurement.2021.110368

Katalog-ID:	ELV007080239

Internformat


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024	7		\|a 10.1016/j.measurement.2021.110368 \|2 doi
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100	1		\|a Yang, Jaehyun \|e verfasserin \|4 aut
245	1	0	\|a Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis
264		1	\|c 2021
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337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Pipeline monitoring provides operators with invaluable information regarding the potential risks that may pose threats to the integrity of the entire line. Pipeline leakage results in serious environmental and financial costs that can be avoided through leak detection systems. This study introduces a comprehensive leak monitoring system that allows leak detection, localization, and volume rate estimation in liquid pipelines installed above ground, simultaneously. To minimize the leak interpretation errors an artificial intelligence (AI)-based leak detection algorithm is developed. Pressure sensors are utilized to capture real-time variations of fluid pressure to localize pipeline leakage through the application of the pressure gradient intersection method. Vibrations of the pipeline are also acquired in real-time through accelerometers, the signals of which are then used to estimate the leak forces through the inverse dynamics of the pipeline between the leak location and the location of the accelerometers. This is achieved by developing a leak-induced vibration (LIV) model that simulates the dynamics of the pipe through a finite element (FE) vibration model. The transfer function of the pipe assembly is then used to design a Kalman filter. The Kalman filter predicts the leak forces and is used to estimate the fluid release through correlation analysis of the leak forces and leak volume rate, experimentally. A lab-scale experimental setup is manufactured to verify the dynamic LIV model and to test the proposed methodology. The performance of the proposed methodology shows 97 %, 96 %, and 92 % of accuracy on average for leak detection, localization, and leak volume rate estimation, respectively.
650		4	\|a Leak detection
650		4	\|a Leak localization
650		4	\|a Leak Volume Rate Estimation
650		4	\|a Artificial intelligence
650		4	\|a Leak induced vibration
700	1		\|a Mostaghimi, Hamid \|e verfasserin \|4 aut
700	1		\|a Hugo, Ron \|e verfasserin \|4 aut
700	1		\|a Park, Simon S. \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Measurement \|d Amsterdam [u.a.] : Elsevier Science, 1983 \|g 187 \|h Online-Ressource \|w (DE-627)320404927 \|w (DE-600)2000550-7 \|w (DE-576)259484342 \|7 nnns
773	1	8	\|g volume:187
912			\|a GBV_USEFLAG_U
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912			\|a GBV_ILN_22
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912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 50.21 \|j Messtechnik
951			\|a AR
952			\|d 187

Indexfelder

author_variant	j y jy h m hm r h rh s s p ss ssp
matchkey_str	yangjaehyunmostaghimihamidhugoronparksim:2021----:ieieeknvlmrtdtcintruhriiilnelgn
hierarchy_sort_str	2021
bklnumber	50.21
publishDate	2021
allfields	10.1016/j.measurement.2021.110368 doi (DE-627)ELV007080239 (ELSEVIER)S0263-2241(21)01262-8 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Yang, Jaehyun verfasserin aut Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pipeline monitoring provides operators with invaluable information regarding the potential risks that may pose threats to the integrity of the entire line. Pipeline leakage results in serious environmental and financial costs that can be avoided through leak detection systems. This study introduces a comprehensive leak monitoring system that allows leak detection, localization, and volume rate estimation in liquid pipelines installed above ground, simultaneously. To minimize the leak interpretation errors an artificial intelligence (AI)-based leak detection algorithm is developed. Pressure sensors are utilized to capture real-time variations of fluid pressure to localize pipeline leakage through the application of the pressure gradient intersection method. Vibrations of the pipeline are also acquired in real-time through accelerometers, the signals of which are then used to estimate the leak forces through the inverse dynamics of the pipeline between the leak location and the location of the accelerometers. This is achieved by developing a leak-induced vibration (LIV) model that simulates the dynamics of the pipe through a finite element (FE) vibration model. The transfer function of the pipe assembly is then used to design a Kalman filter. The Kalman filter predicts the leak forces and is used to estimate the fluid release through correlation analysis of the leak forces and leak volume rate, experimentally. A lab-scale experimental setup is manufactured to verify the dynamic LIV model and to test the proposed methodology. The performance of the proposed methodology shows 97 %, 96 %, and 92 % of accuracy on average for leak detection, localization, and leak volume rate estimation, respectively. Leak detection Leak localization Leak Volume Rate Estimation Artificial intelligence Leak induced vibration Mostaghimi, Hamid verfasserin aut Hugo, Ron verfasserin aut Park, Simon S. verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 187 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:187 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik AR 187
spelling	10.1016/j.measurement.2021.110368 doi (DE-627)ELV007080239 (ELSEVIER)S0263-2241(21)01262-8 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Yang, Jaehyun verfasserin aut Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pipeline monitoring provides operators with invaluable information regarding the potential risks that may pose threats to the integrity of the entire line. Pipeline leakage results in serious environmental and financial costs that can be avoided through leak detection systems. This study introduces a comprehensive leak monitoring system that allows leak detection, localization, and volume rate estimation in liquid pipelines installed above ground, simultaneously. To minimize the leak interpretation errors an artificial intelligence (AI)-based leak detection algorithm is developed. Pressure sensors are utilized to capture real-time variations of fluid pressure to localize pipeline leakage through the application of the pressure gradient intersection method. Vibrations of the pipeline are also acquired in real-time through accelerometers, the signals of which are then used to estimate the leak forces through the inverse dynamics of the pipeline between the leak location and the location of the accelerometers. This is achieved by developing a leak-induced vibration (LIV) model that simulates the dynamics of the pipe through a finite element (FE) vibration model. The transfer function of the pipe assembly is then used to design a Kalman filter. The Kalman filter predicts the leak forces and is used to estimate the fluid release through correlation analysis of the leak forces and leak volume rate, experimentally. A lab-scale experimental setup is manufactured to verify the dynamic LIV model and to test the proposed methodology. The performance of the proposed methodology shows 97 %, 96 %, and 92 % of accuracy on average for leak detection, localization, and leak volume rate estimation, respectively. Leak detection Leak localization Leak Volume Rate Estimation Artificial intelligence Leak induced vibration Mostaghimi, Hamid verfasserin aut Hugo, Ron verfasserin aut Park, Simon S. verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 187 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:187 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik AR 187
allfields_unstemmed	10.1016/j.measurement.2021.110368 doi (DE-627)ELV007080239 (ELSEVIER)S0263-2241(21)01262-8 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Yang, Jaehyun verfasserin aut Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pipeline monitoring provides operators with invaluable information regarding the potential risks that may pose threats to the integrity of the entire line. Pipeline leakage results in serious environmental and financial costs that can be avoided through leak detection systems. This study introduces a comprehensive leak monitoring system that allows leak detection, localization, and volume rate estimation in liquid pipelines installed above ground, simultaneously. To minimize the leak interpretation errors an artificial intelligence (AI)-based leak detection algorithm is developed. Pressure sensors are utilized to capture real-time variations of fluid pressure to localize pipeline leakage through the application of the pressure gradient intersection method. Vibrations of the pipeline are also acquired in real-time through accelerometers, the signals of which are then used to estimate the leak forces through the inverse dynamics of the pipeline between the leak location and the location of the accelerometers. This is achieved by developing a leak-induced vibration (LIV) model that simulates the dynamics of the pipe through a finite element (FE) vibration model. The transfer function of the pipe assembly is then used to design a Kalman filter. The Kalman filter predicts the leak forces and is used to estimate the fluid release through correlation analysis of the leak forces and leak volume rate, experimentally. A lab-scale experimental setup is manufactured to verify the dynamic LIV model and to test the proposed methodology. The performance of the proposed methodology shows 97 %, 96 %, and 92 % of accuracy on average for leak detection, localization, and leak volume rate estimation, respectively. Leak detection Leak localization Leak Volume Rate Estimation Artificial intelligence Leak induced vibration Mostaghimi, Hamid verfasserin aut Hugo, Ron verfasserin aut Park, Simon S. verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 187 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:187 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik AR 187
allfieldsGer	10.1016/j.measurement.2021.110368 doi (DE-627)ELV007080239 (ELSEVIER)S0263-2241(21)01262-8 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Yang, Jaehyun verfasserin aut Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pipeline monitoring provides operators with invaluable information regarding the potential risks that may pose threats to the integrity of the entire line. Pipeline leakage results in serious environmental and financial costs that can be avoided through leak detection systems. This study introduces a comprehensive leak monitoring system that allows leak detection, localization, and volume rate estimation in liquid pipelines installed above ground, simultaneously. To minimize the leak interpretation errors an artificial intelligence (AI)-based leak detection algorithm is developed. Pressure sensors are utilized to capture real-time variations of fluid pressure to localize pipeline leakage through the application of the pressure gradient intersection method. Vibrations of the pipeline are also acquired in real-time through accelerometers, the signals of which are then used to estimate the leak forces through the inverse dynamics of the pipeline between the leak location and the location of the accelerometers. This is achieved by developing a leak-induced vibration (LIV) model that simulates the dynamics of the pipe through a finite element (FE) vibration model. The transfer function of the pipe assembly is then used to design a Kalman filter. The Kalman filter predicts the leak forces and is used to estimate the fluid release through correlation analysis of the leak forces and leak volume rate, experimentally. A lab-scale experimental setup is manufactured to verify the dynamic LIV model and to test the proposed methodology. The performance of the proposed methodology shows 97 %, 96 %, and 92 % of accuracy on average for leak detection, localization, and leak volume rate estimation, respectively. Leak detection Leak localization Leak Volume Rate Estimation Artificial intelligence Leak induced vibration Mostaghimi, Hamid verfasserin aut Hugo, Ron verfasserin aut Park, Simon S. verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 187 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:187 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik AR 187
allfieldsSound	10.1016/j.measurement.2021.110368 doi (DE-627)ELV007080239 (ELSEVIER)S0263-2241(21)01262-8 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Yang, Jaehyun verfasserin aut Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pipeline monitoring provides operators with invaluable information regarding the potential risks that may pose threats to the integrity of the entire line. Pipeline leakage results in serious environmental and financial costs that can be avoided through leak detection systems. This study introduces a comprehensive leak monitoring system that allows leak detection, localization, and volume rate estimation in liquid pipelines installed above ground, simultaneously. To minimize the leak interpretation errors an artificial intelligence (AI)-based leak detection algorithm is developed. Pressure sensors are utilized to capture real-time variations of fluid pressure to localize pipeline leakage through the application of the pressure gradient intersection method. Vibrations of the pipeline are also acquired in real-time through accelerometers, the signals of which are then used to estimate the leak forces through the inverse dynamics of the pipeline between the leak location and the location of the accelerometers. This is achieved by developing a leak-induced vibration (LIV) model that simulates the dynamics of the pipe through a finite element (FE) vibration model. The transfer function of the pipe assembly is then used to design a Kalman filter. The Kalman filter predicts the leak forces and is used to estimate the fluid release through correlation analysis of the leak forces and leak volume rate, experimentally. A lab-scale experimental setup is manufactured to verify the dynamic LIV model and to test the proposed methodology. The performance of the proposed methodology shows 97 %, 96 %, and 92 % of accuracy on average for leak detection, localization, and leak volume rate estimation, respectively. Leak detection Leak localization Leak Volume Rate Estimation Artificial intelligence Leak induced vibration Mostaghimi, Hamid verfasserin aut Hugo, Ron verfasserin aut Park, Simon S. verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 187 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:187 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik AR 187
language	English
source	Enthalten in Measurement 187 volume:187
sourceStr	Enthalten in Measurement 187 volume:187
format_phy_str_mv	Article
bklname	Messtechnik
institution	findex.gbv.de
topic_facet	Leak detection Leak localization Leak Volume Rate Estimation Artificial intelligence Leak induced vibration
dewey-raw	660
isfreeaccess_bool	false
container_title	Measurement
authorswithroles_txt_mv	Yang, Jaehyun @@aut@@ Mostaghimi, Hamid @@aut@@ Hugo, Ron @@aut@@ Park, Simon S. @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	320404927
dewey-sort	3660
id	ELV007080239
language_de	englisch
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author	Yang, Jaehyun
spellingShingle	Yang, Jaehyun ddc 660 bkl 50.21 misc Leak detection misc Leak localization misc Leak Volume Rate Estimation misc Artificial intelligence misc Leak induced vibration Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis
authorStr	Yang, Jaehyun
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topic_title	660 DE-600 50.21 bkl Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis Leak detection Leak localization Leak Volume Rate Estimation Artificial intelligence Leak induced vibration
topic	ddc 660 bkl 50.21 misc Leak detection misc Leak localization misc Leak Volume Rate Estimation misc Artificial intelligence misc Leak induced vibration
topic_unstemmed	ddc 660 bkl 50.21 misc Leak detection misc Leak localization misc Leak Volume Rate Estimation misc Artificial intelligence misc Leak induced vibration
topic_browse	ddc 660 bkl 50.21 misc Leak detection misc Leak localization misc Leak Volume Rate Estimation misc Artificial intelligence misc Leak induced vibration
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title	Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis
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title_full	Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis
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author_browse	Yang, Jaehyun Mostaghimi, Hamid Hugo, Ron Park, Simon S.
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title_sort	pipeline leak and volume rate detections through artificial intelligence and vibration analysis
title_auth	Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis
abstract	Pipeline monitoring provides operators with invaluable information regarding the potential risks that may pose threats to the integrity of the entire line. Pipeline leakage results in serious environmental and financial costs that can be avoided through leak detection systems. This study introduces a comprehensive leak monitoring system that allows leak detection, localization, and volume rate estimation in liquid pipelines installed above ground, simultaneously. To minimize the leak interpretation errors an artificial intelligence (AI)-based leak detection algorithm is developed. Pressure sensors are utilized to capture real-time variations of fluid pressure to localize pipeline leakage through the application of the pressure gradient intersection method. Vibrations of the pipeline are also acquired in real-time through accelerometers, the signals of which are then used to estimate the leak forces through the inverse dynamics of the pipeline between the leak location and the location of the accelerometers. This is achieved by developing a leak-induced vibration (LIV) model that simulates the dynamics of the pipe through a finite element (FE) vibration model. The transfer function of the pipe assembly is then used to design a Kalman filter. The Kalman filter predicts the leak forces and is used to estimate the fluid release through correlation analysis of the leak forces and leak volume rate, experimentally. A lab-scale experimental setup is manufactured to verify the dynamic LIV model and to test the proposed methodology. The performance of the proposed methodology shows 97 %, 96 %, and 92 % of accuracy on average for leak detection, localization, and leak volume rate estimation, respectively.
abstractGer	Pipeline monitoring provides operators with invaluable information regarding the potential risks that may pose threats to the integrity of the entire line. Pipeline leakage results in serious environmental and financial costs that can be avoided through leak detection systems. This study introduces a comprehensive leak monitoring system that allows leak detection, localization, and volume rate estimation in liquid pipelines installed above ground, simultaneously. To minimize the leak interpretation errors an artificial intelligence (AI)-based leak detection algorithm is developed. Pressure sensors are utilized to capture real-time variations of fluid pressure to localize pipeline leakage through the application of the pressure gradient intersection method. Vibrations of the pipeline are also acquired in real-time through accelerometers, the signals of which are then used to estimate the leak forces through the inverse dynamics of the pipeline between the leak location and the location of the accelerometers. This is achieved by developing a leak-induced vibration (LIV) model that simulates the dynamics of the pipe through a finite element (FE) vibration model. The transfer function of the pipe assembly is then used to design a Kalman filter. The Kalman filter predicts the leak forces and is used to estimate the fluid release through correlation analysis of the leak forces and leak volume rate, experimentally. A lab-scale experimental setup is manufactured to verify the dynamic LIV model and to test the proposed methodology. The performance of the proposed methodology shows 97 %, 96 %, and 92 % of accuracy on average for leak detection, localization, and leak volume rate estimation, respectively.
abstract_unstemmed	Pipeline monitoring provides operators with invaluable information regarding the potential risks that may pose threats to the integrity of the entire line. Pipeline leakage results in serious environmental and financial costs that can be avoided through leak detection systems. This study introduces a comprehensive leak monitoring system that allows leak detection, localization, and volume rate estimation in liquid pipelines installed above ground, simultaneously. To minimize the leak interpretation errors an artificial intelligence (AI)-based leak detection algorithm is developed. Pressure sensors are utilized to capture real-time variations of fluid pressure to localize pipeline leakage through the application of the pressure gradient intersection method. Vibrations of the pipeline are also acquired in real-time through accelerometers, the signals of which are then used to estimate the leak forces through the inverse dynamics of the pipeline between the leak location and the location of the accelerometers. This is achieved by developing a leak-induced vibration (LIV) model that simulates the dynamics of the pipe through a finite element (FE) vibration model. The transfer function of the pipe assembly is then used to design a Kalman filter. The Kalman filter predicts the leak forces and is used to estimate the fluid release through correlation analysis of the leak forces and leak volume rate, experimentally. A lab-scale experimental setup is manufactured to verify the dynamic LIV model and to test the proposed methodology. The performance of the proposed methodology shows 97 %, 96 %, and 92 % of accuracy on average for leak detection, localization, and leak volume rate estimation, respectively.
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title_short	Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis
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author2	Mostaghimi, Hamid Hugo, Ron Park, Simon S.
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doi_str	10.1016/j.measurement.2021.110368
up_date	2024-07-06T23:33:03.013Z
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Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis

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