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Noise-resistant joint diagonalization independent component analysis based process fault detection

Noise-resistant joint diagonalization independent component analysis based process fault detection
Noise-resistant joint diagonalization independent component analysis based process fault detection
Fast independent component analysis (FastICA) is an efficient feature extraction tool widely used for process fault detection. However, the conventional FastICA-based fault detection method does not consider the ubiquitous measurement noise and may exhibit unsatisfactory performance under the adverse effects of the measurement noise. To solve this problem, we propose a new process fault detection method based on noise-resistant joint diagonalization independent component analysis (NRJDICA), which explicitly takes the measurement noise into consideration. Specifically, the NRJDICA algorithm is developed to estimate the mixing matrix and the independent components (ICs) by whitening the measured variables and performing the joint diagonalization of the whitened variables' time-delayed covariance matrices. The relationships between the kurtosis statistics of the ICs and the fourth-order cross cumulant statistics of the measured variables are then derived based on the estimated mixing matrix to help sorting the estimated ICs and selecting the dominant ICs. The serial correlation information of each dominant IC is next estimated by using a moving window technique, based on which a monitoring statistic is constructed to conduct fault detection. The simulation studies using a three-variable system and a continuous stirred tank reactor show that the proposed method has superior fault detection performance over the traditional FastICA-based fault detection.
0925-2312
652-666
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Cai, Lianfang
b696f3db-590f-4a8f-b31c-b9a6eb9fe0ae
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Cai, Lianfang
b696f3db-590f-4a8f-b31c-b9a6eb9fe0ae
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Tian, Xuemin, Cai, Lianfang and Chen, Sheng (2015) Noise-resistant joint diagonalization independent component analysis based process fault detection. Neurocomputing, 149 (B), 652-666. (doi:10.1016/j.neucom.2014.08.009).

Record type: Article

Abstract

Fast independent component analysis (FastICA) is an efficient feature extraction tool widely used for process fault detection. However, the conventional FastICA-based fault detection method does not consider the ubiquitous measurement noise and may exhibit unsatisfactory performance under the adverse effects of the measurement noise. To solve this problem, we propose a new process fault detection method based on noise-resistant joint diagonalization independent component analysis (NRJDICA), which explicitly takes the measurement noise into consideration. Specifically, the NRJDICA algorithm is developed to estimate the mixing matrix and the independent components (ICs) by whitening the measured variables and performing the joint diagonalization of the whitened variables' time-delayed covariance matrices. The relationships between the kurtosis statistics of the ICs and the fourth-order cross cumulant statistics of the measured variables are then derived based on the estimated mixing matrix to help sorting the estimated ICs and selecting the dominant ICs. The serial correlation information of each dominant IC is next estimated by using a moving window technique, based on which a monitoring statistic is constructed to conduct fault detection. The simulation studies using a three-variable system and a continuous stirred tank reactor show that the proposed method has superior fault detection performance over the traditional FastICA-based fault detection.

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Published date: 3 February 2015
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 370835
URI: https://eprints.soton.ac.uk/id/eprint/370835
ISSN: 0925-2312
PURE UUID: 625e1fcd-f140-4e26-a740-4db5f1ec665e

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Date deposited: 10 Nov 2014 12:24
Last modified: 17 Jul 2017 21:48

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