Two-stage time-varying hidden conditional random fields with variable selection for process operating mode diagnosis
In industrial processes, the availability of a large amount of process variables provides flexibility for process monitoring; however, too many process variables with possible redundant information can also contribute to high false positives. In order to make good use of the relevant information inc...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Fang, Mengqi [verfasserIn] Huang, Biao [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
Process operating mode diagnosis |
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| Übergeordnetes Werk: |
Enthalten in: Chemometrics and intelligent laboratory systems - Amsterdam [u.a.] : Elsevier Science, 1986, 214 |
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| Übergeordnetes Werk: |
volume:214 |
| DOI / URN: |
10.1016/j.chemolab.2021.104330 |
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| Katalog-ID: |
ELV006143644 |
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| 100 | 1 | |a Fang, Mengqi |e verfasserin |0 (orcid)0000-0002-6601-6510 |4 aut | |
| 245 | 1 | 0 | |a Two-stage time-varying hidden conditional random fields with variable selection for process operating mode diagnosis |
| 264 | 1 | |c 2021 | |
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| 520 | |a In industrial processes, the availability of a large amount of process variables provides flexibility for process monitoring; however, too many process variables with possible redundant information can also contribute to high false positives. In order to make good use of the relevant information included in the process variables, a novel two-stage hidden conditional random field (HCRF) algorithm is developed in this paper to perform real-time process operating mode diagnosis. In the first-stage HCRF, the max-margin training strategy is employed to discriminate multiple operating modes, and by recursively eliminating the fault-irrelevant variables, the most relevant variables can be selected during the first-stage training process. On the basis of the first-stage HCRF outputs, the second-stage HCRF is proposed to adapt the dynamic changes of the process with time-varying model structure. Therefore, switchings among process operating modes can be captured to make timely diagnosis. To demonstrate the performance of the proposed algorithm, two case studies are conducted with comparisons to the conventional algorithms. Superior performance is observed through the examples. | ||
| 650 | 4 | |a Process operating mode diagnosis | |
| 650 | 4 | |a Variable selection | |
| 650 | 4 | |a Hidden conditional random fields | |
| 650 | 4 | |a Variational Bayesian approach | |
| 700 | 1 | |a Huang, Biao |e verfasserin |0 (orcid)0000-0001-9082-2216 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Chemometrics and intelligent laboratory systems |d Amsterdam [u.a.] : Elsevier Science, 1986 |g 214 |h Online-Ressource |w (DE-627)320603512 |w (DE-600)2020467-X |w (DE-576)255554133 |x 0169-7439 |7 nnns |
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| 936 | b | k | |a 35.07 |j Chemisches Labor |j chemische Methoden |
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10.1016/j.chemolab.2021.104330 doi (DE-627)ELV006143644 (ELSEVIER)S0169-7439(21)00098-8 DE-627 ger DE-627 rda eng 540 DE-600 35.07 bkl 35.05 bkl Fang, Mengqi verfasserin (orcid)0000-0002-6601-6510 aut Two-stage time-varying hidden conditional random fields with variable selection for process operating mode diagnosis 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In industrial processes, the availability of a large amount of process variables provides flexibility for process monitoring; however, too many process variables with possible redundant information can also contribute to high false positives. In order to make good use of the relevant information included in the process variables, a novel two-stage hidden conditional random field (HCRF) algorithm is developed in this paper to perform real-time process operating mode diagnosis. In the first-stage HCRF, the max-margin training strategy is employed to discriminate multiple operating modes, and by recursively eliminating the fault-irrelevant variables, the most relevant variables can be selected during the first-stage training process. On the basis of the first-stage HCRF outputs, the second-stage HCRF is proposed to adapt the dynamic changes of the process with time-varying model structure. Therefore, switchings among process operating modes can be captured to make timely diagnosis. To demonstrate the performance of the proposed algorithm, two case studies are conducted with comparisons to the conventional algorithms. Superior performance is observed through the examples. Process operating mode diagnosis Variable selection Hidden conditional random fields Variational Bayesian approach Huang, Biao verfasserin (orcid)0000-0001-9082-2216 aut Enthalten in Chemometrics and intelligent laboratory systems Amsterdam [u.a.] : Elsevier Science, 1986 214 Online-Ressource (DE-627)320603512 (DE-600)2020467-X (DE-576)255554133 0169-7439 nnns volume:214 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_101 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_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_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_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 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 35.07 Chemisches Labor chemische Methoden 35.05 Mathematische Chemie chemische Statistik AR 214 |
| spelling |
10.1016/j.chemolab.2021.104330 doi (DE-627)ELV006143644 (ELSEVIER)S0169-7439(21)00098-8 DE-627 ger DE-627 rda eng 540 DE-600 35.07 bkl 35.05 bkl Fang, Mengqi verfasserin (orcid)0000-0002-6601-6510 aut Two-stage time-varying hidden conditional random fields with variable selection for process operating mode diagnosis 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In industrial processes, the availability of a large amount of process variables provides flexibility for process monitoring; however, too many process variables with possible redundant information can also contribute to high false positives. In order to make good use of the relevant information included in the process variables, a novel two-stage hidden conditional random field (HCRF) algorithm is developed in this paper to perform real-time process operating mode diagnosis. In the first-stage HCRF, the max-margin training strategy is employed to discriminate multiple operating modes, and by recursively eliminating the fault-irrelevant variables, the most relevant variables can be selected during the first-stage training process. On the basis of the first-stage HCRF outputs, the second-stage HCRF is proposed to adapt the dynamic changes of the process with time-varying model structure. Therefore, switchings among process operating modes can be captured to make timely diagnosis. To demonstrate the performance of the proposed algorithm, two case studies are conducted with comparisons to the conventional algorithms. Superior performance is observed through the examples. Process operating mode diagnosis Variable selection Hidden conditional random fields Variational Bayesian approach Huang, Biao verfasserin (orcid)0000-0001-9082-2216 aut Enthalten in Chemometrics and intelligent laboratory systems Amsterdam [u.a.] : Elsevier Science, 1986 214 Online-Ressource (DE-627)320603512 (DE-600)2020467-X (DE-576)255554133 0169-7439 nnns volume:214 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_101 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_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_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_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 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 35.07 Chemisches Labor chemische Methoden 35.05 Mathematische Chemie chemische Statistik AR 214 |
| allfields_unstemmed |
10.1016/j.chemolab.2021.104330 doi (DE-627)ELV006143644 (ELSEVIER)S0169-7439(21)00098-8 DE-627 ger DE-627 rda eng 540 DE-600 35.07 bkl 35.05 bkl Fang, Mengqi verfasserin (orcid)0000-0002-6601-6510 aut Two-stage time-varying hidden conditional random fields with variable selection for process operating mode diagnosis 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In industrial processes, the availability of a large amount of process variables provides flexibility for process monitoring; however, too many process variables with possible redundant information can also contribute to high false positives. In order to make good use of the relevant information included in the process variables, a novel two-stage hidden conditional random field (HCRF) algorithm is developed in this paper to perform real-time process operating mode diagnosis. In the first-stage HCRF, the max-margin training strategy is employed to discriminate multiple operating modes, and by recursively eliminating the fault-irrelevant variables, the most relevant variables can be selected during the first-stage training process. On the basis of the first-stage HCRF outputs, the second-stage HCRF is proposed to adapt the dynamic changes of the process with time-varying model structure. Therefore, switchings among process operating modes can be captured to make timely diagnosis. To demonstrate the performance of the proposed algorithm, two case studies are conducted with comparisons to the conventional algorithms. Superior performance is observed through the examples. Process operating mode diagnosis Variable selection Hidden conditional random fields Variational Bayesian approach Huang, Biao verfasserin (orcid)0000-0001-9082-2216 aut Enthalten in Chemometrics and intelligent laboratory systems Amsterdam [u.a.] : Elsevier Science, 1986 214 Online-Ressource (DE-627)320603512 (DE-600)2020467-X (DE-576)255554133 0169-7439 nnns volume:214 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_101 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_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_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_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 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 35.07 Chemisches Labor chemische Methoden 35.05 Mathematische Chemie chemische Statistik AR 214 |
| allfieldsGer |
10.1016/j.chemolab.2021.104330 doi (DE-627)ELV006143644 (ELSEVIER)S0169-7439(21)00098-8 DE-627 ger DE-627 rda eng 540 DE-600 35.07 bkl 35.05 bkl Fang, Mengqi verfasserin (orcid)0000-0002-6601-6510 aut Two-stage time-varying hidden conditional random fields with variable selection for process operating mode diagnosis 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In industrial processes, the availability of a large amount of process variables provides flexibility for process monitoring; however, too many process variables with possible redundant information can also contribute to high false positives. In order to make good use of the relevant information included in the process variables, a novel two-stage hidden conditional random field (HCRF) algorithm is developed in this paper to perform real-time process operating mode diagnosis. In the first-stage HCRF, the max-margin training strategy is employed to discriminate multiple operating modes, and by recursively eliminating the fault-irrelevant variables, the most relevant variables can be selected during the first-stage training process. On the basis of the first-stage HCRF outputs, the second-stage HCRF is proposed to adapt the dynamic changes of the process with time-varying model structure. Therefore, switchings among process operating modes can be captured to make timely diagnosis. To demonstrate the performance of the proposed algorithm, two case studies are conducted with comparisons to the conventional algorithms. Superior performance is observed through the examples. Process operating mode diagnosis Variable selection Hidden conditional random fields Variational Bayesian approach Huang, Biao verfasserin (orcid)0000-0001-9082-2216 aut Enthalten in Chemometrics and intelligent laboratory systems Amsterdam [u.a.] : Elsevier Science, 1986 214 Online-Ressource (DE-627)320603512 (DE-600)2020467-X (DE-576)255554133 0169-7439 nnns volume:214 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_101 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_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_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_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 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 35.07 Chemisches Labor chemische Methoden 35.05 Mathematische Chemie chemische Statistik AR 214 |
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Fang, Mengqi |
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two-stage time-varying hidden conditional random fields with variable selection for process operating mode diagnosis |
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Two-stage time-varying hidden conditional random fields with variable selection for process operating mode diagnosis |
| abstract |
In industrial processes, the availability of a large amount of process variables provides flexibility for process monitoring; however, too many process variables with possible redundant information can also contribute to high false positives. In order to make good use of the relevant information included in the process variables, a novel two-stage hidden conditional random field (HCRF) algorithm is developed in this paper to perform real-time process operating mode diagnosis. In the first-stage HCRF, the max-margin training strategy is employed to discriminate multiple operating modes, and by recursively eliminating the fault-irrelevant variables, the most relevant variables can be selected during the first-stage training process. On the basis of the first-stage HCRF outputs, the second-stage HCRF is proposed to adapt the dynamic changes of the process with time-varying model structure. Therefore, switchings among process operating modes can be captured to make timely diagnosis. To demonstrate the performance of the proposed algorithm, two case studies are conducted with comparisons to the conventional algorithms. Superior performance is observed through the examples. |
| abstractGer |
In industrial processes, the availability of a large amount of process variables provides flexibility for process monitoring; however, too many process variables with possible redundant information can also contribute to high false positives. In order to make good use of the relevant information included in the process variables, a novel two-stage hidden conditional random field (HCRF) algorithm is developed in this paper to perform real-time process operating mode diagnosis. In the first-stage HCRF, the max-margin training strategy is employed to discriminate multiple operating modes, and by recursively eliminating the fault-irrelevant variables, the most relevant variables can be selected during the first-stage training process. On the basis of the first-stage HCRF outputs, the second-stage HCRF is proposed to adapt the dynamic changes of the process with time-varying model structure. Therefore, switchings among process operating modes can be captured to make timely diagnosis. To demonstrate the performance of the proposed algorithm, two case studies are conducted with comparisons to the conventional algorithms. Superior performance is observed through the examples. |
| abstract_unstemmed |
In industrial processes, the availability of a large amount of process variables provides flexibility for process monitoring; however, too many process variables with possible redundant information can also contribute to high false positives. In order to make good use of the relevant information included in the process variables, a novel two-stage hidden conditional random field (HCRF) algorithm is developed in this paper to perform real-time process operating mode diagnosis. In the first-stage HCRF, the max-margin training strategy is employed to discriminate multiple operating modes, and by recursively eliminating the fault-irrelevant variables, the most relevant variables can be selected during the first-stage training process. On the basis of the first-stage HCRF outputs, the second-stage HCRF is proposed to adapt the dynamic changes of the process with time-varying model structure. Therefore, switchings among process operating modes can be captured to make timely diagnosis. To demonstrate the performance of the proposed algorithm, two case studies are conducted with comparisons to the conventional algorithms. Superior performance is observed through the examples. |
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| title_short |
Two-stage time-varying hidden conditional random fields with variable selection for process operating mode diagnosis |
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