Anomaly detection in a hyper-compressor in low-density polyethylene manufacturing processes using WPCA-based principal component control limit
Abstract Low-Density Polyethylene (LDPE) was synthesized from ethylene at high-temperature and pressure condition. Hyper-compressor used to increase pressure up to 3,500 atm should be monitored and controlled delicately or it cannot guarantee stable operation of the process causing process shutdown...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Park, Byeong Eon [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© The Korean Institute of Chemical Engineers 2020 |
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| Übergeordnetes Werk: |
Enthalten in: The Korean journal of chemical engineering - Seoul : Inst., 1984, 37(2020), 1 vom: Jan., Seite 11-18 |
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| Übergeordnetes Werk: |
volume:37 ; year:2020 ; number:1 ; month:01 ; pages:11-18 |
| Links: |
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| DOI / URN: |
10.1007/s11814-019-0403-y |
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SPR02252729X |
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| 245 | 1 | 0 | |a Anomaly detection in a hyper-compressor in low-density polyethylene manufacturing processes using WPCA-based principal component control limit |
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| 520 | |a Abstract Low-Density Polyethylene (LDPE) was synthesized from ethylene at high-temperature and pressure condition. Hyper-compressor used to increase pressure up to 3,500 atm should be monitored and controlled delicately or it cannot guarantee stable operation of the process causing process shutdown (SD), which is directly related to product yield and process safety. This paper presents a data-based multivariate statistical monitoring method to detect anomalies in the hyper-compressor of a LDPE manufacturing process with weighted principal component analysis model (WPCA), which can consider both time-varying and time-invariant characteristic of data combining principal component analysis (PCA) and slow feature analysis (SFA). Operation data of the LDPE manufacturing process was gathered hourly for four years. WPCA-based principal component control limit (PCCL) was used as an index to determine anomaly and applied to five emergency shutdown (ESD) cases, respectively. As a result, all the five anomalies were detected by a PCCL, respectively, as a sign of SD. Moreover, it shows a better anomaly detection performance than the monitoring method using $ T^{2} $ and squared prediction error (SPE) based on PCA, SFA, or WPCA. | ||
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| 650 | 4 | |a Fault Isolation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Slow Feature Analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Principal Component Analysis |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Kim, Ji Seon |4 aut | |
| 700 | 1 | |a Lee, Jeong-Keun |4 aut | |
| 700 | 1 | |a Lee, In-Beum |4 aut | |
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10.1007/s11814-019-0403-y doi (DE-627)SPR02252729X (SPR)s11814-019-0403-y-e DE-627 ger DE-627 rakwb eng Park, Byeong Eon verfasserin aut Anomaly detection in a hyper-compressor in low-density polyethylene manufacturing processes using WPCA-based principal component control limit 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Institute of Chemical Engineers 2020 Abstract Low-Density Polyethylene (LDPE) was synthesized from ethylene at high-temperature and pressure condition. Hyper-compressor used to increase pressure up to 3,500 atm should be monitored and controlled delicately or it cannot guarantee stable operation of the process causing process shutdown (SD), which is directly related to product yield and process safety. This paper presents a data-based multivariate statistical monitoring method to detect anomalies in the hyper-compressor of a LDPE manufacturing process with weighted principal component analysis model (WPCA), which can consider both time-varying and time-invariant characteristic of data combining principal component analysis (PCA) and slow feature analysis (SFA). Operation data of the LDPE manufacturing process was gathered hourly for four years. WPCA-based principal component control limit (PCCL) was used as an index to determine anomaly and applied to five emergency shutdown (ESD) cases, respectively. As a result, all the five anomalies were detected by a PCCL, respectively, as a sign of SD. Moreover, it shows a better anomaly detection performance than the monitoring method using $ T^{2} $ and squared prediction error (SPE) based on PCA, SFA, or WPCA. Fault Detection (dpeaa)DE-He213 Fault Isolation (dpeaa)DE-He213 Slow Feature Analysis (dpeaa)DE-He213 Principal Component Analysis (dpeaa)DE-He213 Kim, Ji Seon aut Lee, Jeong-Keun aut Lee, In-Beum aut Enthalten in The Korean journal of chemical engineering Seoul : Inst., 1984 37(2020), 1 vom: Jan., Seite 11-18 (DE-627)391337246 (DE-600)2152566-3 1975-7220 nnns volume:37 year:2020 number:1 month:01 pages:11-18 https://dx.doi.org/10.1007/s11814-019-0403-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2020 1 01 11-18 |
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10.1007/s11814-019-0403-y doi (DE-627)SPR02252729X (SPR)s11814-019-0403-y-e DE-627 ger DE-627 rakwb eng Park, Byeong Eon verfasserin aut Anomaly detection in a hyper-compressor in low-density polyethylene manufacturing processes using WPCA-based principal component control limit 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Institute of Chemical Engineers 2020 Abstract Low-Density Polyethylene (LDPE) was synthesized from ethylene at high-temperature and pressure condition. Hyper-compressor used to increase pressure up to 3,500 atm should be monitored and controlled delicately or it cannot guarantee stable operation of the process causing process shutdown (SD), which is directly related to product yield and process safety. This paper presents a data-based multivariate statistical monitoring method to detect anomalies in the hyper-compressor of a LDPE manufacturing process with weighted principal component analysis model (WPCA), which can consider both time-varying and time-invariant characteristic of data combining principal component analysis (PCA) and slow feature analysis (SFA). Operation data of the LDPE manufacturing process was gathered hourly for four years. WPCA-based principal component control limit (PCCL) was used as an index to determine anomaly and applied to five emergency shutdown (ESD) cases, respectively. As a result, all the five anomalies were detected by a PCCL, respectively, as a sign of SD. Moreover, it shows a better anomaly detection performance than the monitoring method using $ T^{2} $ and squared prediction error (SPE) based on PCA, SFA, or WPCA. Fault Detection (dpeaa)DE-He213 Fault Isolation (dpeaa)DE-He213 Slow Feature Analysis (dpeaa)DE-He213 Principal Component Analysis (dpeaa)DE-He213 Kim, Ji Seon aut Lee, Jeong-Keun aut Lee, In-Beum aut Enthalten in The Korean journal of chemical engineering Seoul : Inst., 1984 37(2020), 1 vom: Jan., Seite 11-18 (DE-627)391337246 (DE-600)2152566-3 1975-7220 nnns volume:37 year:2020 number:1 month:01 pages:11-18 https://dx.doi.org/10.1007/s11814-019-0403-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2020 1 01 11-18 |
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10.1007/s11814-019-0403-y doi (DE-627)SPR02252729X (SPR)s11814-019-0403-y-e DE-627 ger DE-627 rakwb eng Park, Byeong Eon verfasserin aut Anomaly detection in a hyper-compressor in low-density polyethylene manufacturing processes using WPCA-based principal component control limit 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Institute of Chemical Engineers 2020 Abstract Low-Density Polyethylene (LDPE) was synthesized from ethylene at high-temperature and pressure condition. Hyper-compressor used to increase pressure up to 3,500 atm should be monitored and controlled delicately or it cannot guarantee stable operation of the process causing process shutdown (SD), which is directly related to product yield and process safety. This paper presents a data-based multivariate statistical monitoring method to detect anomalies in the hyper-compressor of a LDPE manufacturing process with weighted principal component analysis model (WPCA), which can consider both time-varying and time-invariant characteristic of data combining principal component analysis (PCA) and slow feature analysis (SFA). Operation data of the LDPE manufacturing process was gathered hourly for four years. WPCA-based principal component control limit (PCCL) was used as an index to determine anomaly and applied to five emergency shutdown (ESD) cases, respectively. As a result, all the five anomalies were detected by a PCCL, respectively, as a sign of SD. Moreover, it shows a better anomaly detection performance than the monitoring method using $ T^{2} $ and squared prediction error (SPE) based on PCA, SFA, or WPCA. Fault Detection (dpeaa)DE-He213 Fault Isolation (dpeaa)DE-He213 Slow Feature Analysis (dpeaa)DE-He213 Principal Component Analysis (dpeaa)DE-He213 Kim, Ji Seon aut Lee, Jeong-Keun aut Lee, In-Beum aut Enthalten in The Korean journal of chemical engineering Seoul : Inst., 1984 37(2020), 1 vom: Jan., Seite 11-18 (DE-627)391337246 (DE-600)2152566-3 1975-7220 nnns volume:37 year:2020 number:1 month:01 pages:11-18 https://dx.doi.org/10.1007/s11814-019-0403-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2020 1 01 11-18 |
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10.1007/s11814-019-0403-y doi (DE-627)SPR02252729X (SPR)s11814-019-0403-y-e DE-627 ger DE-627 rakwb eng Park, Byeong Eon verfasserin aut Anomaly detection in a hyper-compressor in low-density polyethylene manufacturing processes using WPCA-based principal component control limit 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Institute of Chemical Engineers 2020 Abstract Low-Density Polyethylene (LDPE) was synthesized from ethylene at high-temperature and pressure condition. Hyper-compressor used to increase pressure up to 3,500 atm should be monitored and controlled delicately or it cannot guarantee stable operation of the process causing process shutdown (SD), which is directly related to product yield and process safety. This paper presents a data-based multivariate statistical monitoring method to detect anomalies in the hyper-compressor of a LDPE manufacturing process with weighted principal component analysis model (WPCA), which can consider both time-varying and time-invariant characteristic of data combining principal component analysis (PCA) and slow feature analysis (SFA). Operation data of the LDPE manufacturing process was gathered hourly for four years. WPCA-based principal component control limit (PCCL) was used as an index to determine anomaly and applied to five emergency shutdown (ESD) cases, respectively. As a result, all the five anomalies were detected by a PCCL, respectively, as a sign of SD. Moreover, it shows a better anomaly detection performance than the monitoring method using $ T^{2} $ and squared prediction error (SPE) based on PCA, SFA, or WPCA. Fault Detection (dpeaa)DE-He213 Fault Isolation (dpeaa)DE-He213 Slow Feature Analysis (dpeaa)DE-He213 Principal Component Analysis (dpeaa)DE-He213 Kim, Ji Seon aut Lee, Jeong-Keun aut Lee, In-Beum aut Enthalten in The Korean journal of chemical engineering Seoul : Inst., 1984 37(2020), 1 vom: Jan., Seite 11-18 (DE-627)391337246 (DE-600)2152566-3 1975-7220 nnns volume:37 year:2020 number:1 month:01 pages:11-18 https://dx.doi.org/10.1007/s11814-019-0403-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2020 1 01 11-18 |
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10.1007/s11814-019-0403-y doi (DE-627)SPR02252729X (SPR)s11814-019-0403-y-e DE-627 ger DE-627 rakwb eng Park, Byeong Eon verfasserin aut Anomaly detection in a hyper-compressor in low-density polyethylene manufacturing processes using WPCA-based principal component control limit 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Institute of Chemical Engineers 2020 Abstract Low-Density Polyethylene (LDPE) was synthesized from ethylene at high-temperature and pressure condition. Hyper-compressor used to increase pressure up to 3,500 atm should be monitored and controlled delicately or it cannot guarantee stable operation of the process causing process shutdown (SD), which is directly related to product yield and process safety. This paper presents a data-based multivariate statistical monitoring method to detect anomalies in the hyper-compressor of a LDPE manufacturing process with weighted principal component analysis model (WPCA), which can consider both time-varying and time-invariant characteristic of data combining principal component analysis (PCA) and slow feature analysis (SFA). Operation data of the LDPE manufacturing process was gathered hourly for four years. WPCA-based principal component control limit (PCCL) was used as an index to determine anomaly and applied to five emergency shutdown (ESD) cases, respectively. As a result, all the five anomalies were detected by a PCCL, respectively, as a sign of SD. Moreover, it shows a better anomaly detection performance than the monitoring method using $ T^{2} $ and squared prediction error (SPE) based on PCA, SFA, or WPCA. Fault Detection (dpeaa)DE-He213 Fault Isolation (dpeaa)DE-He213 Slow Feature Analysis (dpeaa)DE-He213 Principal Component Analysis (dpeaa)DE-He213 Kim, Ji Seon aut Lee, Jeong-Keun aut Lee, In-Beum aut Enthalten in The Korean journal of chemical engineering Seoul : Inst., 1984 37(2020), 1 vom: Jan., Seite 11-18 (DE-627)391337246 (DE-600)2152566-3 1975-7220 nnns volume:37 year:2020 number:1 month:01 pages:11-18 https://dx.doi.org/10.1007/s11814-019-0403-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2020 1 01 11-18 |
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Hyper-compressor used to increase pressure up to 3,500 atm should be monitored and controlled delicately or it cannot guarantee stable operation of the process causing process shutdown (SD), which is directly related to product yield and process safety. This paper presents a data-based multivariate statistical monitoring method to detect anomalies in the hyper-compressor of a LDPE manufacturing process with weighted principal component analysis model (WPCA), which can consider both time-varying and time-invariant characteristic of data combining principal component analysis (PCA) and slow feature analysis (SFA). Operation data of the LDPE manufacturing process was gathered hourly for four years. WPCA-based principal component control limit (PCCL) was used as an index to determine anomaly and applied to five emergency shutdown (ESD) cases, respectively. As a result, all the five anomalies were detected by a PCCL, respectively, as a sign of SD. 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Park, Byeong Eon |
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Park, Byeong Eon misc Fault Detection misc Fault Isolation misc Slow Feature Analysis misc Principal Component Analysis Anomaly detection in a hyper-compressor in low-density polyethylene manufacturing processes using WPCA-based principal component control limit |
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Anomaly detection in a hyper-compressor in low-density polyethylene manufacturing processes using WPCA-based principal component control limit Fault Detection (dpeaa)DE-He213 Fault Isolation (dpeaa)DE-He213 Slow Feature Analysis (dpeaa)DE-He213 Principal Component Analysis (dpeaa)DE-He213 |
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Anomaly detection in a hyper-compressor in low-density polyethylene manufacturing processes using WPCA-based principal component control limit |
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Anomaly detection in a hyper-compressor in low-density polyethylene manufacturing processes using WPCA-based principal component control limit |
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anomaly detection in a hyper-compressor in low-density polyethylene manufacturing processes using wpca-based principal component control limit |
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Anomaly detection in a hyper-compressor in low-density polyethylene manufacturing processes using WPCA-based principal component control limit |
| abstract |
Abstract Low-Density Polyethylene (LDPE) was synthesized from ethylene at high-temperature and pressure condition. Hyper-compressor used to increase pressure up to 3,500 atm should be monitored and controlled delicately or it cannot guarantee stable operation of the process causing process shutdown (SD), which is directly related to product yield and process safety. This paper presents a data-based multivariate statistical monitoring method to detect anomalies in the hyper-compressor of a LDPE manufacturing process with weighted principal component analysis model (WPCA), which can consider both time-varying and time-invariant characteristic of data combining principal component analysis (PCA) and slow feature analysis (SFA). Operation data of the LDPE manufacturing process was gathered hourly for four years. WPCA-based principal component control limit (PCCL) was used as an index to determine anomaly and applied to five emergency shutdown (ESD) cases, respectively. As a result, all the five anomalies were detected by a PCCL, respectively, as a sign of SD. Moreover, it shows a better anomaly detection performance than the monitoring method using $ T^{2} $ and squared prediction error (SPE) based on PCA, SFA, or WPCA. © The Korean Institute of Chemical Engineers 2020 |
| abstractGer |
Abstract Low-Density Polyethylene (LDPE) was synthesized from ethylene at high-temperature and pressure condition. Hyper-compressor used to increase pressure up to 3,500 atm should be monitored and controlled delicately or it cannot guarantee stable operation of the process causing process shutdown (SD), which is directly related to product yield and process safety. This paper presents a data-based multivariate statistical monitoring method to detect anomalies in the hyper-compressor of a LDPE manufacturing process with weighted principal component analysis model (WPCA), which can consider both time-varying and time-invariant characteristic of data combining principal component analysis (PCA) and slow feature analysis (SFA). Operation data of the LDPE manufacturing process was gathered hourly for four years. WPCA-based principal component control limit (PCCL) was used as an index to determine anomaly and applied to five emergency shutdown (ESD) cases, respectively. As a result, all the five anomalies were detected by a PCCL, respectively, as a sign of SD. Moreover, it shows a better anomaly detection performance than the monitoring method using $ T^{2} $ and squared prediction error (SPE) based on PCA, SFA, or WPCA. © The Korean Institute of Chemical Engineers 2020 |
| abstract_unstemmed |
Abstract Low-Density Polyethylene (LDPE) was synthesized from ethylene at high-temperature and pressure condition. Hyper-compressor used to increase pressure up to 3,500 atm should be monitored and controlled delicately or it cannot guarantee stable operation of the process causing process shutdown (SD), which is directly related to product yield and process safety. This paper presents a data-based multivariate statistical monitoring method to detect anomalies in the hyper-compressor of a LDPE manufacturing process with weighted principal component analysis model (WPCA), which can consider both time-varying and time-invariant characteristic of data combining principal component analysis (PCA) and slow feature analysis (SFA). Operation data of the LDPE manufacturing process was gathered hourly for four years. WPCA-based principal component control limit (PCCL) was used as an index to determine anomaly and applied to five emergency shutdown (ESD) cases, respectively. As a result, all the five anomalies were detected by a PCCL, respectively, as a sign of SD. Moreover, it shows a better anomaly detection performance than the monitoring method using $ T^{2} $ and squared prediction error (SPE) based on PCA, SFA, or WPCA. © The Korean Institute of Chemical Engineers 2020 |
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Anomaly detection in a hyper-compressor in low-density polyethylene manufacturing processes using WPCA-based principal component control limit |
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Kim, Ji Seon Lee, Jeong-Keun Lee, In-Beum |
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