Optimized Relative Transformation Matrix Using Bacterial Foraging Algorithm for Process Fault Detection

Fault diagnosis of an aluminum electrolysis cell has long been a challenging industrial issue due to its inherent difficulty in extracting meaningful features from numerous nonlinear and highly coupled parameters. To solve this problem, this paper presents optimized relative transformation matrix (R...

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Veröffentlicht in:	IEEE transactions on industrial electronics (1982) 2016-04, Vol.63 (4), p.2595-2605
Hauptverfasser:	Yi, Jun, Huang, Di, Fu, Siyao, He, Haibo, Li, Taifu
Format:	Artikel
Sprache:	eng
Schlagworte:	Aluminum Aluminum electrolytic production bacterial foraging algorithm Covariance matrices Eigenvalues and eigenfunctions fault detection Fault diagnosis Feature extraction Kernel kernel principal component analysis Optimization relative transformation
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