Detecting abnormal situations using the Kullback-Leibler divergence

DOI10.1016/j.automatica.2014.09.005zbMath1300.93029OpenAlexW2080008918MaRDI QIDQ473296

Lei Xie, Jiusun Zeng, Uwe Kruger, Xun Wang, J. L. Geluk

Publication date: 24 November 2014

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2014.09.005

zbMATH Keywords

fault detectionKullback-Leibler divergenceincipient fault conditionincreased sensitivitymultivariate probability density function

Mathematics Subject Classification ID

Stochastic systems in control theory (general) (93E03) Large-scale systems (93A15)

Related Items (4)

Detection of intermittent faults based on an optimally weighted moving average \(T^2\) control chart with stationary observations ⋮ Assessment of \(T^2\)- and \(Q\)-statistics for detecting additive and multiplicative faults in multivariate statistical process monitoring ⋮ Multiple kernel boosting framework based on information measure for classification ⋮ Intrinsic Mode Function Selection and Statistical Information Analysis for Bearing Ball Fault Detection

Cites Work

• Asymptotic methods in statistical decision theory
• Semiparametric density estimation under a two-sample density ratio model
• Improved principal component monitoring using the local approach
• Distributions of the Kullback-Leibler divergence with applications
• Application of latent variable methods to process control and multivariate statistical process control in industry
• On Information and Sufficiency
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