zbMATH — the first resource for mathematics

Examples
Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

Operators
a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
Fields
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
Improved principal component monitoring using the local approach. (English) Zbl 1126.62122
Summary: This paper shows that current multivariate statistical monitoring technology may not detect incipient changes in the variable covariance structure nor changes in the geometry of the underlying variable decomposition. To overcome these deficiencies, the local approach is incorporated into the multivariate statistical monitoring framework to define two new univariate statistics for fault detection. Fault isolation is achieved by constructing a fault diagnosis chart which reveals changes in the covariance structure resulting from the presence of a fault. A theoretical analysis is presented and the proposed monitoring approach is exemplified using application studies involving recorded data from two complex industrial processes.

MSC:
62P30Applications of statistics in engineering and industry
62H25Factor analysis and principal components; correspondence analysis
WorldCat.org
Full Text: DOI
References:
[1] Anderson, T. W.: An introduction to multivariate statistical analysis. (1958) · Zbl 0083.14601
[2] Basseville, M.: On-board component fault detection and isolation using the statistical local approach. Automatica 34, No. 11, 1391-1415 (1998) · Zbl 0945.93608
[3] Chen, J.; Patton, R.: Robust model based fault diagnosis for dynamic systems. (1999) · Zbl 0920.93001
[4] Jackson, J. E.: A users guide to principal components. Wiley series in probability and mathematical statistics. (1991)
[5] Jackson, J. E.; Mudholkar, G. S.: Control procedures for residuals associated with principal component analysis. Technometrics 21, 341-349 (1979) · Zbl 0439.62039
[6] Kano, M.; Hasebe, S.; Hashimoto, I.; Ohno, H.: A new multivariate statistical process monitoring method using principal component analysis. Computers & chemical engineering 25, No. 7-8, 1103-1113 (2001)
[7] Kourti, T.: Application of latent variable methods to process control and multivariate statistical process control in industry. International journal of adaptive control and signal processing 19, No. 4, 213-246 (2005) · Zbl 1113.62147
[8] Kruger, U.; Chen, Q.; Sandoz, D. J.; Mcfarlane, R. C.: Extended PLS approach for enhanced condition monitoring of industrial processes. Aiche journal 47, No. 9, 2076-2091 (2001)
[9] Ku, W.; Storer, R. H.; Georgakis, C.: Disturbance rejection and isolation by dynamic principal component analysis. Chemometrics & intelligent laboratory systems 30, 179-196 (1995)
[10] Lieftucht, D.; Kruger, U.; Irwin, G. W.; Treasure, R. J.: Fault reconstruction in linear dynamic systems using multivariate statistics. IEE Proceedings on control theory and applications 153, No. 4, 437-446 (2006)
[11] Li, P., Treasure, R., & Kruger, U. (2005). Dynamic principal component analysis using subspace model identification (pp. 727-736), Lecture notes in computer science. Berlin: Springer.
[12] Macgregor, J. F.; Kourti, T.: Statistical process control of multivariate processes. Control engineering practice 3, No. 3, 403-414 (1995)
[13] Negiz, A.; Çinar, A.: Statistical monitoring of multivariable continuous processes with state-space models. Aiche journal 43, No. 8, 2002-2020 (1997)
[14] Nomikos, P.; Macgregor, J. F.: Multivariate SPC charts for monitoring batch processes. Technometrics 37, No. 1, 41-59 (1995) · Zbl 0825.62740
[15] Patton, R.; Frank, P.; Clark, R.: Fault diagnosis in dynamic systems--theory and applications. (1989)
[16] Russel, E. L.; Chiang, L. H.; Braatz, R. D.: Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis. Chemometrics & intelligent laboratory systems 51, No. 1, 81-93 (2000)
[17] Simoglou, A.; Martin, E. B.; Morris, A. J.: Statistical performance monitoring of dynamic multivariate processes using state space modeling. Computers & chemical engineering 26, No. 6, 909-920 (2002)
[18] Treasure, R. J.; Kruger, U.; Cooper, J. E.: Dynamic multivariate statistical process control using subspace identification. Journal of process control 14, 279-292 (2004)
[19] Van Overschee, P.; De Moor, B.: Subspace identification for linear systems. (1996) · Zbl 0888.93001
[20] Wang, X.; Kruger, U.; Irwin, G. W.: Process monitoring approach using fast moving window PCA. Industrial & engineering chemistry research 44, No. 15, 5691-5702 (2005)
[21] Wise, B. M.; Gallagher, N. B.: The process chemometrics approach to process monitoring and fault detection. Journal of process control 6, No. 6, 329-348 (1996)
[22] Zhang, Q.; Basseville, M.; Benveniste, A.: Early warning of slight changes in systems and plants with application to condition based maintenance. Automatica 30, No. 1, 95-114 (1994) · Zbl 0800.93239