Nov 26, 2010 · In this paper, a method is presented which allows abnormal or unexpected operating conditions to be identified from measured response data.

Aug 31, 2024 · In this paper, a method is presented which allows abnormal or unexpected operating conditions to be identified from measured response data.

In this paper, a method is presented which allows abnormal or unexpected operating conditions to be identified from measured response data.

Based on kernel density estimation (KDE) and Kullback-Leibler divergence (KLID), a new data-driven fault diagnosis method is proposed from a statistical ...

Jan 22, 2015 · Based on kernel density estimation (KDE) and Kullback-Leibler divergence (KLID), a new data-driven fault diagnosis method is proposed from a ...

An integrated Kullback-Leibler divergence, which aggregates the KLID of all the selected features, is introduced to discriminate various faultmodes/damage ...

Supplementary Data. Fault detection in rotating machinery using kernel-based probability density estimation. Authors: Desforges, M. J.; Jacob, P. J.; Ball, ...

Apr 25, 2024 · M. J. Desforges, P. J. Jacob, Andrew D. Ball: Fault detection in rotating machinery using kernel-based probability density estimation.

Fault diagnosis of rotating machinery based on kernel density estimation and Kullback-Leibler divergence. https://doi.org/10.1007/s12206-014-1012-7.

May 31, 2017 · By using the KDE and KLID jointly, an integrated Kullback-Leibler divergence can be developed to identify faults modes/health status of rotating ...