Monitoring of Nonlinear Time-Delay Processes Based on Adaptive Method and Moving Window

A new adaptive kernel principal component analysis (KPCA) algorithm for monitoring nonlinear time-delay process is proposed. The main contribution of the proposed algorithm is to combine adaptive KPCA with moving window principal component analysis (MWPCA) algorithm, and exponentially weighted princ...

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Bibliographic Details
Published in:Mathematical problems in engineering Vol. 2014; no. 1
Main Authors: Fan, Yunpeng, Zhang, Wei, Zhang, Yingwei
Format: Journal Article
Language:English
Published: New York Hindawi Publishing Corporation 01.01.2014
John Wiley & Sons, Inc
Subjects:
Adaptive algorithms
Algorithms
Annealing
Fault diagnosis
Finite element analysis
Kernels
Mathematical analysis
Mathematical models
Mathematical problems
Monitoring
Nonlinearity
Principal component analysis
Principal components analysis
Statistical analysis
Statistical methods
ISSN:1024-123X, 1563-5147
Online Access:Get full text
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Summary:A new adaptive kernel principal component analysis (KPCA) algorithm for monitoring nonlinear time-delay process is proposed. The main contribution of the proposed algorithm is to combine adaptive KPCA with moving window principal component analysis (MWPCA) algorithm, and exponentially weighted principal component analysis (EWPCA) algorithm respectively. The new algorithm prejudges the new available sample with MKPCA method to decide whether the model is updated. Then update the KPCA model using EWKPCA method. And also extend MPCA and EWPCA from linear data space to nonlinear data space effectively. Monitoring experiment is performed using the proposed algorithm. The simulation results show that the proposed method is effective.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2014/546138