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Statistics local fisher discriminant analysis for industrial process fault classification

Statistics local fisher discriminant analysis for industrial process fault classification
Statistics local fisher discriminant analysis for industrial process fault classification
In order to effectively identify industrial process faults, an improved Fisher discriminant analysis (FDA) method, referred to as the statistics local Fisher discriminant analysis (SLFDA), is proposed for fault classification. For mining statistics information hidden in process data, statistics pattern analysis is firstly applied to transform the original measured variables into the corresponding statistics, including second-order and higher-order ones. Furthermore, considering the local structure characteristics of fault data, local FDA (LFDA) is performed which computes the discriminant vectors by modifying the optimization objective with local weighting factor. Simulation results on the benchmark Tennessee Eastman process show that the proposed SLFDA has a better fault classification performance than the FDA and LFDA methods.
IEEE
Deng, Xiaogang
c95b981b-d71c-4058-9e29-03cecca6003f
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Christopher
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Deng, Xiaogang
c95b981b-d71c-4058-9e29-03cecca6003f
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Christopher
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Deng, Xiaogang, Tian, Xuemin, Chen, Sheng and Harris, Christopher (2016) Statistics local fisher discriminant analysis for industrial process fault classification. In Control (CONTROL), 2016 UKACC 11th International Conference on. IEEE. 6 pp . (doi:10.1109/CONTROL.2016.7737588).

Record type: Conference or Workshop Item (Paper)

Abstract

In order to effectively identify industrial process faults, an improved Fisher discriminant analysis (FDA) method, referred to as the statistics local Fisher discriminant analysis (SLFDA), is proposed for fault classification. For mining statistics information hidden in process data, statistics pattern analysis is firstly applied to transform the original measured variables into the corresponding statistics, including second-order and higher-order ones. Furthermore, considering the local structure characteristics of fault data, local FDA (LFDA) is performed which computes the discriminant vectors by modifying the optimization objective with local weighting factor. Simulation results on the benchmark Tennessee Eastman process show that the proposed SLFDA has a better fault classification performance than the FDA and LFDA methods.

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More information

Accepted/In Press date: 16 May 2016
e-pub ahead of print date: 31 August 2016
Venue - Dates: UKACC Control 2016, Belfast, United Kingdom, 2016-08-30 - 2016-09-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 399978
URI: http://eprints.soton.ac.uk/id/eprint/399978
PURE UUID: 99c91675-edde-4114-998d-d5559c0ed6a0

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Date deposited: 06 Sep 2016 09:36
Last modified: 06 Oct 2020 16:56

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Contributors

Author: Xiaogang Deng
Author: Xuemin Tian
Author: Sheng Chen
Author: Christopher Harris

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