Estudio comparativo de técnicas de detección de fallos basadas en el Análisis de Componentes Principales (PCA)

  1. D. Garcia-Alvarez 1
  2. M.J. Fuente 1
  1. 1 Universidad de Valladolid
    info

    Universidad de Valladolid

    Valladolid, España

    ROR https://ror.org/01fvbaw18

Revista:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Ano de publicación: 2011

Volume: 8

Número: 3

Páxinas: 182-195

Tipo: Artigo

DOI: 10.1016/J.RIAI.2011.06.006 DIALNET GOOGLE SCHOLAR

Outras publicacións en: Revista iberoamericana de automática e informática industrial ( RIAI )

Resumo

This paper describes and compares different variations of fault detection using principal components analysis (PCA). PCA is a multivariate statistical technique. The paper describes how to design a fault detection system based on PCA, also it describes different statistics, these statistics are calculated to monitor the process state. The different methods compared in this paper are: adaptive PCA (APCA), multi-scale PCA (MSPCA), exponentially weighted PCA (EWPCA), PCA using linear and non-linear external analysis (PCAEA and PCANLEA) and nonlinear PCA (NLPCA). The comparative study is based on several quantitative and qualitative parameters.

Referencias bibliográficas

  • Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M., 2003. Diagnosis and Fault Tolerant Control. Springer.
  • Chiang, L., Russell, E., Braatz, R., 2000. Fault Detection and Diagnosis in Industrial Systems. Springer, Nueva York.
  • Dong, D., McAvoy, T., 1996. Nonlinear principal component analysis based on principal curves and neural networks. Computers & Chemical Engineering 20, 65–78.
  • Fourie, S., de Vaal, P., 2000. Advanced process monitoring using an on-line non-linear multiscale principal component analysis methodology. Computers & Chemical Engineering 24, 755–760.
  • Fuente, M., Garcia, G., Sainz, G., 2008. Fault diagnosis in a plant using fisher discriminant analysis. Proceding of the 16th Mediterranean Conference on Control and Automation Congress Centre, Ajaccio, France, 53–58.
  • Garcia-Alvarez, D., Fuente, M., 2008. Analisis comparativo de tecnicas de deteccion de fallos utilizando analisis de componentes principales (pca). Proceding of the 29th Spanish Conference on Automation, Tarragona, Spain.
  • Jackson, J., 1991. A user’s guide to principal components. Wiley.
  • Jackson, J., Mudholkar, G., 1979. Control procedures for residuals associated with principal component analysis. Technometrics 21, 341–349.
  • Kano, M., Hasebe, S., Hashimoto, I., Ohno, H., 2004. Evolution of multivariable statistical process control: aplication or independent component analysis and external analysis. Computers & Chemical Engineering 28, 1157– 1166.
  • Kourti, T., 2005. Application of latent variable methods to process control and multivariable statistical process control in industry. International journal of adaptative control and signal processing 19, 213–246.
  • Kourti, T., MacGregor, J., 1996. Multivariate spc methods for process and product monitoring. Journal of Quality Technology 28, 409–428.
  • Kramer, M., 1991. Nonlinear principal component analysis using autoassociative neural network. The American Institute of Chemical Engineers Journal 37, 233–243.
  • Kramer, M., 1992. Autoassociative neural networks. Computers & Chemical Engineering 16 (4), 313–328.
  • Lane, S., Martin, E., Morris, A., Gower, P., 2003. Application of exponentially weighted principal component analysis for the monitoring of a polymer film manufacturing process. Transactions of the Institute of Measurement and Control 25, 17–35.
  • MacGregor, J., Kourti, T., 1995. Statistical process control of multivariate processes. Control Engineering Practice 3 (3), 403–414.
  • Misra, M., Yue, H., Qin, S., Ling, C., Septiembre 2002. Multivariable process monitoring and fault diagnosis by multi-scale pca. Computers & Chemical Engineering 26, 1281–1293.
  • Peña, D., 2002. Análisis multivariante de datos. McGraw-Hill.
  • Puigjaner, L., Ollero, P., Prada, C., Jiménez, L., 2006. Estrategias de modelado, simulación y optimización de procesos químicos. Editorial Síntesis.
  • Shlens, J., 2005. A tutorial on principal component analysis. La Jolla, CA 92037: Salk Institute for Biological Studies.
  • Tan, S., Mavrovouniotis, M., 1995. Reducing data dimensionality through optimizing neural network inputs. The American Institute of Chemical Engineers Journal 41, 1471–1480.
  • Tien, D., Lim, K., Jun, L., November 2-6 2004. Compartive study of pca approaches in process monitoring and fault detection. The 30th annual conference of the IEEE industrial electronics society, 2594–2599.
  • Venkatasubramanian, V., Rengaswamy, R., Kavuri, S., Yin, K., 2003a. A review of process fault detection and diagnosis. part i: Quantitative model-based methods. Computers & Chemical Engineering 27, 291–311.
  • Venkatasubramanian, V., Rengaswamy, R., Kavuri, S., Yin, K., 2003b. A review of process fault detection and diagnosis. part ii: Qualitative models and search strategies. Computers & Chemical Engineering 27, 313–326.
  • Venkatasubramanian, V., Rengaswamy, R., Kavuri, S., Yin, K., 2003c. A review of process fault detection and diagnosis. part iii: Process history based methods. Computers & Chemical Engineering 27, 327–346.
  • Weighell, M., Martin, E., Morris, A., 2001. The statistical monitoring of a complex manufacturing process. Journal of Applied Statistics 28, 409–425.
  • Wold, S., 1987. Principal component analysis. Chemometrics and intelligent laboratory systems 2, 37–52.
  • Zarzo, M., 2004. Aplicación de técnicas estadísticas multivariantes al control de la calidad de procesos por lotes. Ph.D. thesis, Universidad Politécnica de Valencia.
  • Zumoffen, D., Basualdo, M., 2007. From large chemical plant data to fault diagnosis integrated to decentralized fault tolerant control: pulp mill process application. Industrial & Engineering Chemistry Research 47, 1201–1220.
Fonte de datos: Dialnet