An adaptive regression adjusted monitoring and fault isolation scheme

A new method is proposed in this work to detect and isolate faults in a system consisting of multivariate normal data. The proposed method reduces the variable dimension via a T2 decomposition and combines multivariate monitoring and diagnosis in a unified approach. An adaptive regression adjusted (ARA) chart is introduced to utilize the diagnosis result in order to improve the monitoring scheme. Once an out‐of‐control alarm is detected, the fault isolation can be done directly. Copyright © 2007 John Wiley & Sons, Ltd.

[1]  A. C. Rencher.  The contribution of individual variables to Hotelling's T2, Wilks' lambda, and R2. , 1993, Biometrics.

[2]  Theodora Kourti,et al.  Statistical Process Control of Multivariate Processes , 1994 .

[3]  H. Hotelling,et al.  Multivariate Quality Control , 1947 .

[4]  Richard S.H. Mah,et al.  Generalized likelihood ratios for gross error identification in dynamic processes , 1988 .

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

[6]  D. Hawkins.  Multivariate quality control based on regression-adjusted variables , 1991 .

[7]  Thomas F. Edgar,et al.  Identification of faulty sensors using principal component analysis , 1996 .

[8]  Bernd Freisleben,et al.  Greedy and Local Search Heuristics for Unconstrained Binary Quadratic Programming , 2002, J. Heuristics.

[9]  Martin Pelikan,et al.  Hierarchical Bayesian optimization algorithm: toward a new generation of evolutionary algorithms , 2010, SICE 2003 Annual Conference (IEEE Cat. No.03TH8734).

[10]  Thomas R. Cundari,et al.  Robust Fuzzy Principal Component Analysis (FPCA). A Comparative Study Concerning Interaction of Carbon-Hydrogen Bonds with Molybdenum-Oxo Bonds , 2002, J. Chem. Inf. Comput. Sci..

[11]  Barry M. Wise,et al.  The process chemometrics approach to process monitoring and fault detection , 1995 .

[12]  J. Edward Jackson,et al.  A User's Guide to Principal Components: Jackson/User's Guide to Principal Components , 2004 .

[13]  John C. Young,et al.  A Practical Approach for Interpreting Multivariate T2 Control Chart Signals , 1997 .

[14]  Seongkyu Yoon,et al.  Statistical and causal model‐based approaches to fault detection and isolation , 2000 .

[15]  George C. Runger,et al.  Projections and the U(2) Multivariate Control Chart , 1996 .

[16]  John C. Young,et al.  IMPROVING THE SENSITIVITY OF THE T2 STATISTIC IN MULTIVARIATE PROCESS CONTROL , 1999 .

[17]  J. E. Jackson,et al.  Control Procedures for Residuals Associated With Principal Component Analysis , 1979 .

[18]  Nola D. Tracy,et al.  Decomposition of T2 for Multivariate Control Chart Interpretation , 1995 .

[19]  J. E. Jackson.  A User's Guide to Principal Components , 1991 .

[20]  J. Edward Jackson,et al.  Principal Components and Factor Analysis: Part I - Principal Components , 1980 .

[21]  Theodora Kourti,et al.  Multivariate SPC Methods for Process and Product Monitoring , 1996 .