메뉴 건너뛰기




Volumn 59, Issue 7, 2013, Pages 2339-2347

Classification of the Degradation of Soft Sensor Models and Discussion on Adaptive Models

Author keywords

Adaptive model; Degradation; Predictive ability; Process control; Soft sensor

Indexed keywords

ADAPTIVE MODELS; ADAPTIVE SOFT-SENSOR; CLASSIFICATION RESULTS; EXPLANATORY VARIABLES; PREDICTIVE ABILITIES; PREDICTIVE ACCURACY; SIMULATED DATASETS; SOFT SENSORS;

EID: 84879309312     PISSN: 00011541     EISSN: 15475905     Source Type: Journal    
DOI: 10.1002/aic.14006     Document Type: Article
Times cited : (69)

References (12)
  • 1
    • 35548968908 scopus 로고    scopus 로고
    • Data-based process monitoring, process control, and quality improvement: recent developments and applications in steel industry
    • Kano M, Nakagawa Y. Data-based process monitoring, process control, and quality improvement: recent developments and applications in steel industry. Comput Chem Eng. 2008;32:12-24.
    • (2008) Comput Chem Eng. , vol.32 , pp. 12-24
    • Kano, M.1    Nakagawa, Y.2
  • 2
    • 67349089877 scopus 로고    scopus 로고
    • Data-driven soft sensors in the process industry
    • Kadlec P, Gabrys B, Strandt S. Data-driven soft sensors in the process industry. Comput Chem Eng. 2009;33:795-814.
    • (2009) Comput Chem Eng. , vol.33 , pp. 795-814
    • Kadlec, P.1    Gabrys, B.2    Strandt, S.3
  • 3
    • 58449118276 scopus 로고    scopus 로고
    • Development of a new soft sensor method using independent component analysis and partial least squares
    • Kaneko H, Arakawa M, Funatsu K. Development of a new soft sensor method using independent component analysis and partial least squares. AIChE J. 2009;55:87-98.
    • (2009) AIChE J. , vol.55 , pp. 87-98
    • Kaneko, H.1    Arakawa, M.2    Funatsu, K.3
  • 4
    • 79955611348 scopus 로고    scopus 로고
    • Applicability domains and accuracy of prediction of soft sensor models
    • Kaneko H, Arakawa M, Funatsu K. Applicability domains and accuracy of prediction of soft sensor models. AIChE J. 2011;57:1506-1513.
    • (2011) AIChE J. , vol.57 , pp. 1506-1513
    • Kaneko, H.1    Arakawa, M.2    Funatsu, K.3
  • 5
    • 79954599740 scopus 로고    scopus 로고
    • Local learning-based adaptive soft sensor for catalyst activation prediction
    • Kadlec P, Gabrys B. Local learning-based adaptive soft sensor for catalyst activation prediction, AIChE J. 2010;57:1288-1301.
    • (2010) AIChE J. , vol.57 , pp. 1288-1301
    • Kadlec, P.1    Gabrys, B.2
  • 6
    • 0032044750 scopus 로고    scopus 로고
    • Recursive PLS algorithms for adaptive data modeling
    • Qin SJ. Recursive PLS algorithms for adaptive data modeling. Comput Chem Eng. 1998;22:503-514.
    • (1998) Comput Chem Eng. , vol.22 , pp. 503-514
    • Qin, S.J.1
  • 7
    • 2942558590 scopus 로고    scopus 로고
    • A new data-based methodology for nonlinear process modeling
    • Cheng C, Chiu MS. A new data-based methodology for nonlinear process modeling. Chem Eng Sci. 2004;59:2801-2810.
    • (2004) Chem Eng Sci. , vol.59 , pp. 2801-2810
    • Cheng, C.1    Chiu, M.S.2
  • 8
    • 68049143320 scopus 로고    scopus 로고
    • Soft-sensor development using correlation-based just-in-time modeling
    • Fujiwara K, Kano M, Hasebe S, Takinami A. Soft-sensor development using correlation-based just-in-time modeling. AIChE J. 2009;55:1754-1765.
    • (2009) AIChE J. , vol.55 , pp. 1754-1765
    • Fujiwara, K.1    Kano, M.2    Hasebe, S.3    Takinami, A.4
  • 9
    • 79959784751 scopus 로고    scopus 로고
    • Maintenance-free soft sensor models with time difference of process variables
    • Kaneko H, Funatsu K. Maintenance-free soft sensor models with time difference of process variables. Chemom Intell Lab Syst. 2011;107:312-317.
    • (2011) Chemom Intell Lab Syst. , vol.107 , pp. 312-317
    • Kaneko, H.1    Funatsu, K.2
  • 10
    • 80055094175 scopus 로고    scopus 로고
    • A soft sensor method based on values predicted from multiple intervals of time difference for improvement and estimation of prediction accuracy
    • Kaneko H, Funatsu K. A soft sensor method based on values predicted from multiple intervals of time difference for improvement and estimation of prediction accuracy. Chemom Intell Lab Syst. 2011;109:197-206.
    • (2011) Chemom Intell Lab Syst. , vol.109 , pp. 197-206
    • Kaneko, H.1    Funatsu, K.2
  • 11
    • 0030269512 scopus 로고    scopus 로고
    • Identification of faulty sensors using principal component analysis
    • Dunia R, Qin SJ, Edgar TF, McAvoy TJ. Identification of faulty sensors using principal component analysis. AIChE J. 1996;42:2797-2812.
    • (1996) AIChE J. , vol.42 , pp. 2797-2812
    • Dunia, R.1    Qin, S.J.2    Edgar, T.F.3    McAvoy, T.J.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.