A Novel Feature Selection Method for Process Fault Diagnosis

Chun Mei Yu; Sheng Bo Yang

doi:10.4028/www.scientific.net/AMM.427-429.2045

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 427-429A Novel Feature Selection Method for Process Fault...

A Novel Feature Selection Method for Process Fault Diagnosis

Abstract:

To increase fault classification performance and reduce computational complexity,the feature selection process has been used for fault diagnosis.In this paper, we proposed a sparse representation based feature selection method and gave detailed procedure of the algorithm. Traditional selecting methods based on wavelet package decomposition and Bhattacharyya distance methods,and sparse methods, including sparse representation classifier, sparsity preserving projection and sparse principal component analysis,were compared to the proposed method.Simulations showed the proposed selecting method gave better performance on fault diagnosis with Tennessee Eastman Process data.

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Periodical:

Applied Mechanics and Materials (Volumes 427-429)

Pages:

2045-2049

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.427-429.2045

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Online since:

September 2013

Authors:

Chun Mei Yu, Sheng Bo Yang

Keywords:

Fault Diagnosis, Feature Selection, Process Industry, Sparse Representation

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