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Fault diagnosis based on multi-scale classification using kernel fisher discriminant analysis and gaussian mixture model and k-nearest neighbor method
Norazwan Md Nor1, Mohd Azlan Hussain2, Che Rosmani Che Hassan3.
Effective fault monitoring, detection and diagnosis of chemical processes is important
to ensure the consistency and high product quality, as well as the safety of the
processes. Fault diagnosis problems can be considered as classification problems as
these techniques have been proposed and greatly improved over the past few years.
However, a chemical process is often characterized by large scale and non-linear
behavior. When linear discriminant analysis is used for fault diagnosis in the system, a lot
of incorrect diagnosis will occur. As solution, this paper presents a novel approach for
feature extraction and classification framework in chemical process systems based on
wavelet transformation and discriminant analysis. The proposed multi-scale kernel Fisher
discriminant analysis (MSKFDA) method used the combination of kernel Fisher
discriminant analysis (KFDA) and discrete wavelet transform (DWT) to improve the
classification performance as compared to conventional approaches. A DWT is applied
to extract the process dynamics at different scales by decomposed the data into
multiple scales, analyzed by the KFDA and only dynamical characteristics with
important information was reconstructed by inverse discrete wavelet transform (IDWT).
Then, Gaussian mixture model (GMM) and K-nearest neighbor (KNN) method were
individually applied for the fault classification using the output from the MSKFDA
approach. These two classifiers are evaluated and compared based on their
performance on the Tennessee Eastman process database. The proposed framework
for GMM and KNN classifiers had achieved average classification accuracies of 84.72%
and 82.00%, respectively, with the results sh
Affiliation:
- University of Malaya, Malaysia
- University of Malaya, Malaysia
- University of Malaya, Malaysia
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Indexation |
Indexed by |
MyJurnal (2021) |
H-Index
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6 |
Immediacy Index
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0.000 |
Rank |
0 |
Indexed by |
Scopus 2020 |
Impact Factor
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CiteScore (1.4) |
Rank |
Q3 (Engineering (all)) |
Additional Information |
SJR (0.191) |
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