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A process monitoring method based on noisy independent component analysis

A process monitoring method based on noisy independent component analysis
A process monitoring method based on noisy independent component analysis
Independent component analysis (ICA) is an effective feature extraction tool for process monitoring. However, the conventional ICA-based process monitoring methods usually adopt noise-free ICA models and thus may perform unsatisfactorily under the adverse effects of the measurement noise. In this paper, a process monitoring method using a new noisy independent component analysis, referred to as NoisyICAn, is proposed. Using the noisy ICA model which considers the measurement noise explicitly, a NoisyICAn algorithm is developed to estimate the mixing matrix by setting up a series of the fourthorder cumulant matrices of the measured data and performing the joint diagonalization of these matrices. The kurtosis relationships of the independent components and measured variables are subsequently obtained based on the estimated mixing matrix, for recursively estimating the kurtosis of independent components. Two monitoring statistics are then built to detect process faults using the obtained recursive estimate of the independent components' kurtosis. The simulation studies are carried out on a simple three-variable system and a continuous stirred tank reactor system, and the results obtained demonstrate that the proposed NoisyICAn-based monitoring method outperforms the conventional noise-free ICA-based monitoring methods as well as the benchmark monitoring methods based on the existing noisy ICA schemes adopted from blind source separation, in terms of the fault detection time and local fault detection rate.
0925-2312
231-246
Cai, Lianfang
b696f3db-590f-4a8f-b31c-b9a6eb9fe0ae
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Cai, Lianfang
b696f3db-590f-4a8f-b31c-b9a6eb9fe0ae
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Cai, Lianfang, Tian, Xuemin and Chen, Sheng (2014) A process monitoring method based on noisy independent component analysis. Neurocomputing, 127, 231-246. (doi:10.1016/j.neucom.2013.07.029).

Record type: Article

Abstract

Independent component analysis (ICA) is an effective feature extraction tool for process monitoring. However, the conventional ICA-based process monitoring methods usually adopt noise-free ICA models and thus may perform unsatisfactorily under the adverse effects of the measurement noise. In this paper, a process monitoring method using a new noisy independent component analysis, referred to as NoisyICAn, is proposed. Using the noisy ICA model which considers the measurement noise explicitly, a NoisyICAn algorithm is developed to estimate the mixing matrix by setting up a series of the fourthorder cumulant matrices of the measured data and performing the joint diagonalization of these matrices. The kurtosis relationships of the independent components and measured variables are subsequently obtained based on the estimated mixing matrix, for recursively estimating the kurtosis of independent components. Two monitoring statistics are then built to detect process faults using the obtained recursive estimate of the independent components' kurtosis. The simulation studies are carried out on a simple three-variable system and a continuous stirred tank reactor system, and the results obtained demonstrate that the proposed NoisyICAn-based monitoring method outperforms the conventional noise-free ICA-based monitoring methods as well as the benchmark monitoring methods based on the existing noisy ICA schemes adopted from blind source separation, in terms of the fault detection time and local fault detection rate.

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e-pub ahead of print date: 15 March 2014
Additional Information: Advances in Intelligent Systems — Selected papers from the 2012 Brazilian Symposium on Neural Networks (SBRN 2012)
Organisations: Southampton Wireless Group

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Local EPrints ID: 360192
URI: http://eprints.soton.ac.uk/id/eprint/360192
ISSN: 0925-2312
PURE UUID: 8ee5da00-3b2a-4547-b2a4-a652231b43c4

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Date deposited: 28 Nov 2013 13:43
Last modified: 25 Nov 2019 20:45

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