document: A new subspace identification approach based on principal component analysis Principal component analysis -LRB- PCA -RRB- has been widely used for monitoring complex industrial processes with multiple variables and diagnosing process and sensor faults . The objective of this paper is to develop a new subspace identification algorithm that gives consistent model estimates under the errors-in-variables -LRB- EIV -RRB- situation . In this paper , we propose a new subspace identification approach using principal component analysis . PCA naturally falls into the category of EIV formulation , which resembles total least squares and allows for errors in both process input and output . We propose to use PCA to determine the system observability subspace , the matrices and the system order for an EIV formulation . Standard PCA is modified with instrumental variables in order to achieve consistent estimates of the system matrices . The proposed subspace identification method is demonstrated using a simulated process and a real industrial process for model identification and order determination . For comparison the MOESP algorithm and N4SID algorithm are used as benchmarks to demonstrate the advantages of the proposed PCA based subspace model identification -LRB- SMI -RRB- algorithm
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