Title
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
Recommended Citation
Chakrabarti, Shiladitya; Razavi-Far, Roozbeh; Saif, Mehrdad; and Rueda, Luis. (2017). Multi-class heteroscedastic linear dimensionality reduction scheme for diagnosing process faults. Canadian Conference on Electrical and Computer Engineering.
https://scholar.uwindsor.ca/electricalengpub/151