Multi-class heteroscedastic linear dimensionality reduction scheme for diagnosing process faults

Document Type

Conference Proceeding

Publication Date

6-12-2017

Publication Title

Canadian Conference on Electrical and Computer Engineering

Abstract

Dimensionality reduction is an important factor in fault diagnosis, when dealing with a high-dimensional feature space, since it decreases the computational burden and the model complexity. This paper focuses on the development and comparison of several state-of-the-art linear dimensionality reduction techniques to provide discriminant features for the process fault diagnosis. These techniques, including heteroscedastic discriminant analysis, Fisher's discriminant analysis, Chernoff discriminant analysis and principal component analysis, can handle multi-class feature sets. The attained results show that the heteroscedastic variant outperforms other techniques both in terms of performance measures and speed.

DOI

10.1109/CCECE.2017.7946716

ISSN

08407789

ISBN

9781509055388

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