Online process monitoring using a new PCMD index

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 SPCA and Spcaλ

[1]  Chi Ma,et al.  Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS , 2011 .

[2]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[3]  W. Krzanowski.  Between-Groups Comparison of Principal Components , 1979 .

[4]  D. Franke.  Application of extended Gershgorin theorems to certain distributed-parameter control problems , 1985, 1985 24th IEEE Conference on Decision and Control.

[5]  Hassani Messaoud,et al.  A new online fault detection method based on PCA technique , 2013, IMA J. Math. Control. Inf..

[6]  Hassani Messaoud,et al.  Online identification of nonlinear system using reduced kernel principal component analysis , 2010, Neural Computing and Applications.

[7]  D. Seborg,et al.  Pattern Matching in Historical Data , 2002 .

[8]  Huijun Gao,et al.  Fault Detection for Markovian Jump Systems With Sensor Saturations and Randomly Varying Nonlinearities , 2012, IEEE Transactions on Circuits and Systems I: Regular Papers.

[9]  T. Voegtlin,et al.  Recursive PCA and the structure of time series , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[10]  G. Irwin,et al.  Process monitoring approach using fast moving window PCA , 2005 .

[11]  Weihua Li,et al.  Recursive PCA for adaptive process monitoring , 1999 .

[12]  Didier Maquin,et al.  Fault Detection and Isolation with Robust Principal Component Analysis , 2008, 2008 16th Mediterranean Conference on Control and Automation.

[13]  D. Seborg,et al.  Clustering multivariate time‐series data , 2005 .

[14]  Fuli Wang,et al.  Adaptive Monitoring Based on Independent Component Analysis for Multiphase Batch Processes with Limited Modeling Data , 2008 .

[15]  Weihua Li,et al.  Isolation enhanced principal component analysis , 1999 .

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

[17]  Ping Zhang,et al.  On the application of PCA technique to fault diagnosis , 2010 .