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Nonlinear process fault diagnosis based on serial principle component analysis

Nonlinear process fault diagnosis based on serial principle component analysis
Nonlinear process fault diagnosis based on serial principle component analysis
Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear–nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built
for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process’s structure
to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and
identification.
2162-237X
560-572
Deng, Xiaogang
0810518c-134f-46a7-82dd-6f9cd954bf19
Tian, Xuemin
27c9111b-caf2-4a10-a096-007d84f8be44
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Christopher
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Deng, Xiaogang
0810518c-134f-46a7-82dd-6f9cd954bf19
Tian, Xuemin
27c9111b-caf2-4a10-a096-007d84f8be44
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Christopher
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Deng, Xiaogang, Tian, Xuemin, Chen, Sheng and Harris, Christopher (2018) Nonlinear process fault diagnosis based on serial principle component analysis. IEEE Transactions on Neural Networks and Learning Systems, 29 (3), 560-572. (doi:10.1109/TNNLS.2016.2635111).

Record type: Article

Abstract

Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear–nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built
for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process’s structure
to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and
identification.

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Accepted/In Press date: 25 August 2016
e-pub ahead of print date: 22 December 2016
Published date: March 2018

Identifiers

Local EPrints ID: 418443
URI: http://eprints.soton.ac.uk/id/eprint/418443
ISSN: 2162-237X
PURE UUID: 5655dfee-87d8-499d-bc69-e1fefbf09729

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Date deposited: 08 Mar 2018 17:30
Last modified: 06 Oct 2020 19:02

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Contributors

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

University divisions

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