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Kernelized relative entropy for direct fault detection in industrial rotary kilns
Author(s) -
Hamadouche Anis,
Kouadri Abdelmalek,
Bensmail Abderazak
Publication year - 2018
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2879
Subject(s) - kernel density estimation , kullback–leibler divergence , principal component analysis , basis (linear algebra) , divergence (linguistics) , linear subspace , mathematics , probability density function , entropy (arrow of time) , fault detection and isolation , density estimation , computer science , algorithm , statistics , data mining , artificial intelligence , estimator , linguistics , philosophy , geometry , physics , quantum mechanics , actuator
Summary The objective of this work is to use a 1‐dimensional signal that reflects the dissimilarity between multidimensional probability densities for detection. With the modified Kullback‐Leibler divergence, faults can be directly detected without any normality assumption or joint monitoring of related test statistics in different subspaces such as the T 2 and S P E in principal component analysis–based methods. To relieve the difficulty associated with asymptotic high‐dimensional density estimates, we have estimated the density ratio rather than the densities themselves. This can be done by approximating the density ratio with kernel basis functions and learn the weights from the available data. The developed algorithm is generic and can be applied to any industrial system as long as process historical data is available. As a case study, we apply this algorithm to a real rotary kiln in operation, which is an integral part of the cement manufacturing plant of Ain El Kebira, Algeria.