Abstract
This paper proposes a new online principal component analysis (PCA) index-based parameter estimation approach to detect a sensor fault. The proposed index is based on PCA technique and Mahalanobis distance and it is entitled principal component Mahalanobis distance (PCMD). The principle of the proposed PCMD is to detect a disagreement between the reference PCA model parameter that represent a normal system function and the PCA model parameter that estimated online to represent current system behavior. Indeed, the PCMD index evaluate the Mahalanobis distance between the principal components (PCs) of the reference PCA model and the new PCs that represent the current function of the system. These PCs are determined online using a Moving Window PCA technique (MWPCA). To evaluate performances of the proposed index, PCMD is applied on a numerical example and a chemical reactor (CSTR), and the results are satisfactory compared to other index such as S PCA and S λpca
Similar content being viewed by others
References
Hongli D (2012) Gao. Hui-Jun Fault detection for Markovian jump systems with sensor saturations and randomly varying nonlinearities. IEEE Trans Circ Syst I: Regular Paper 59(10):2354–2362
Frank PM (1990) Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy—a survey and some new results. Automatica 26(3):459–474
Tharraut Y, Mourot G, Ragot J, Maquin D (2008) Fault detection and isolation with robust principal component analysis. Int J Appl Math Comput Sci 18(4):429–442
Zhang Y, Ma C (2011) Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS. Chem Eng Sci 66:64–72
Zhao C, Wang F, Mao Z, Lu N, Jia M (2008) Adaptive monitoring based on independent component analysis for multiphase batch processes with limited modeling data. Am Chem Soc 47:3104–3113
Voegtlin T (2004) Recursive PCA and the structure of time series. the 2004 I.E. International Joint Conference on Neural Networks, Berlin, pp 1893–1897
W Xun, K Uwe, W George (2005) Process monitoring approach using fast moving window PCA. Ind Eng Chem Res
Ding. S, Zhang. P (2010) On the application of PCA technique to fault diagnosis. Tsinghua Sci Technol 15
Harkat MF, Mourot G; Ragot J (2001) Sensor failure detection and Isolation of air quality monitoring network. 4th International Conference on Acoustical and Vibratory Surveillance Methods and Diagnostic Techniques, Compiègne, France
Harakat MF, Gilles M, Jose R (2006) An improved PCA scheme for sensor FDI: application to an air quality monitoring network. J Process Control 16:625–634
Krzanowski W (1979) Between-groups comparison of principal components. J Am Stat Assoc 74:703–707
Singhal A, Seborg DE (2006) Clustering multivariate time-series data. J Chemom 19:427–438
Weihua LH, Henry Y, Valle-Cervantes S, Joe Qin S (2000) Recursive PCA for adaptive process monitoring. J Process Control 10:471–486
Krzanowski, WJ, 1979. Between-groups comparison of principal components. J Amer 208 Stat Assoc, v 74, n 367, 703–707
Guerfel. M (2012) Diagnostic des systèmes par analyse des données et sans modèle de comportement a priori, PHD Thesis of National School of Engineers of Tunis
Johannesmeyer MC, Singhal A, Seborg DE (2002) Pattern matching in historical data. AIChE J 48:2022–2038
Franke D (1994) Application of extended Gershgorin theorems to certain distributed-parameter control problems. IEEE Conf Decis Control Lauderdale USA 1151–1156
Janos G, Weihua L, Yunbing H and Thomas M (1999) Isolation enhanced principal component analysis. AIchE J 95, No. 2
Taouali O, Elaissi I, Messaoud H (2012) Online identification of nonlinear system using reduced kernel principal component analysis. Neural Comput Appl 161–169
Jaffel I, Taouali O, Elaissi I, Messaoud H ((2013) A new online fault detection method based on PCA technique. IMA J Math Control Inf 31 (4), pp. 487-499
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jaffel, I., Taouali, O., Harkat, M.F. et al. Online process monitoring using a new PCMD index. Int J Adv Manuf Technol 80, 947–957 (2015). https://doi.org/10.1007/s00170-015-7094-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-015-7094-2