New monitoring method based principal component analysis and fuzzy clustering

This work concerns the principal component analysis applied to the supervision of quality parameters of the flour production line. Our contribution lies in the combined use of the principal component analysis technique and the clustering algorithms in the field of production system diagnosis. This approach allows detecting and locating the system defects, based on the drifts of the product quality parameters. A comparative study between the classification performance by clustering algorithms and the principal component analysis has been proposed. Locating parameters in defect is based on the technique of fault direction in partial least square.   Key words: Fuzzy clustering, fault detection, fault location, principal component analysis (PCA).

[1]  I. Jolliffe.  Principal Component Analysis , 2002 .

[2]  Witold Pedrycz,et al.  Fuzzy C-Means, Gustafson-Kessel FCM, and Kernel-Based FCM: A Comparative Study , 2007, Analysis and Design of Intelligent Systems using Soft Computing Techniques.

[3]  Xiao-Hong Wu,et al.  A Possibilistic C-Means Clustering Algorithm Based on Kernel Methods , 2006, 2006 International Conference on Communications, Circuits and Systems.

[4]  Gin-Shuh Liang,et al.  Computing, Artificial Intelligence and Information Technology Cluster analysis based on fuzzy equivalence relation , 2005 .

[5]  Isak Gath,et al.  Unsupervised Optimal Fuzzy Clustering , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Sung-Bae Cho,et al.  A Fuzzy Clustering Algorithm for Analysis of Gene Expression Profiles , 2004, PRICAI.

[7]  Abdelkrim Moussaoui,et al.  Quality Monitoring Using Principal Component Analysis and Fuzzy Logic Application in Continuous Casting Process , 2007 .

[8]  Timo Ahvenlampi,et al.  Clustering Algorithms in Process Monitoring and Control Application to Continuous Digesters , 2005, Informatica.

[9]  Lotfi Nabli,et al.  ECG signal monitoring using linear PCA , 2011 .

[10]  Y. Fukuyama,et al.  A new method of choosing the number of clusters for the fuzzy c-mean method , 1989 .

[11]  Gilles Mourot,et al.  An improved PCA scheme for sensor FDI: Application to an air quality monitoring network , 2006 .

[12]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[13]  U. Kortela,et al.  DIAGNOSIS SYSTEM FOR CONTINUOUS COOKING PROCESS , 2005 .

[14]  Jongwoo Kim,et al.  A note on the Gustafson-Kessel and adaptive fuzzy clustering algorithms , 1999, IEEE Trans. Fuzzy Syst..

[15]  Xue Z. Wang,et al.  Multidimensional visualisation for process historical data analysis: a comparative study with multivariate statistical process control , 2005 .

[16]  Deborah F. Cook,et al.  Visualization of multivariate data with radial plots using SAS , 2001 .

[17]  James M. Keller,et al.  A possibilistic fuzzy c-means clustering algorithm , 2005, IEEE Transactions on Fuzzy Systems.

[18]  S. Joe Qin,et al.  Reconstruction-Based Fault Identification Using a Combined Index , 2001 .

[19]  Witold Pedrycz,et al.  Advances in Fuzzy Clustering and its Applications , 2007 .

[20]  Xue Z. Wang,et al.  Knowledge discovery from process operational data using PCA and fuzzy clustering , 2001 .

[21]  P Dulyakarn,et al.  FUZZY C-MEANS CLUSTERING USING SPATIAL INFORMATION WITH APPLICATION TO REMOTE SENSING , 2001 .

[22]  Q. Peter He,et al.  A New Fault Diagnosis Method Using Fault Directions in Fisher Discriminant Analysis , 2005 .

[23]  Yingwei Zhang,et al.  Process Monitoring, Fault Diagnosis and Quality Prediction Methods Based on the Multivariate Statistical Techniques , 2010 .

[24]  S. Qin,et al.  Selection of the Number of Principal Components: The Variance of the Reconstruction Error Criterion with a Comparison to Other Methods† , 1999 .

[25]  Pasi Luukka.  PCA for fuzzy data and similarity classifier in building recognition system for post-operative patient data , 2009, Expert Syst. Appl..

[26]  S. Joe Qin,et al.  Subspace approach to multidimensional fault identification and reconstruction , 1998 .

[27]  Mikko Vermasvuori,et al.  Methodology for utilising prior knowledge in constructing data-based process monitoring systems with an application to a dearomatisation process , 2008 .

[28]  V. Faber.  Partial least squares, conjugate gradient and the fisher discriminant , 1996 .

[29]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Miin-Shen Yang,et al.  Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation , 2006, Pattern Recognit. Lett..

[31]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[32]  Boudewijn P. F. Lelieveldt,et al.  A new cluster validity index for the fuzzy c-mean , 1998, Pattern Recognit. Lett..

[33]  H. Dhouibi,et al.  Monitoring approach using Nonlinear Principal Component Analysis , 2011, 2011 International Conference on Communications, Computing and Control Applications (CCCA).

[34]  Thomas E. Marlin,et al.  Multivariate statistical monitoring of process operating performance , 1991 .

[35]  Witold Pedrycz,et al.  Fuzzy clustering with partial supervision , 1997, IEEE Trans. Syst. Man Cybern. Part B.