Electric Motor Fault Detection and Diagnosis by Kernel Density Estimation and Kullback–Leibler Divergence Based on Stator Current Measurements

Electric Motor ◽

Kernel Density ◽

Stator Current ◽

Leibler Divergence ◽

Detection And Diagnosis

Electric motor defects diagnosis based on kernel density estimation and Kullback–Leibler divergence in quality control scenario

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2015.05.004 ◽

2015 ◽

Vol 44 ◽

pp. 25-32 ◽

Cited By ~ 21

Author(s):

Francesco Ferracuti ◽

Andrea Giantomassi ◽

Sabrina Iarlori ◽

Gianluca Ippoliti ◽

Sauro Longhi

Keyword(s):

Quality Control ◽

Density Estimation ◽

Electric Motor ◽

Kernel Density ◽

Leibler Divergence

Incipient fault detection and diagnosis based on Kullback–Leibler divergence using Principal Component Analysis: Part I

Signal Processing ◽

10.1016/j.sigpro.2013.05.018 ◽

2014 ◽

Vol 94 ◽

pp. 278-287 ◽

Cited By ~ 98

Author(s):

Jinane Harmouche ◽

Claude Delpha ◽

Demba Diallo

Keyword(s):

Principal Component Analysis ◽

Fault Detection ◽

Principal Component ◽

Component Analysis ◽

Leibler Divergence ◽

Incipient Fault ◽

Detection And Diagnosis ◽

Incipient Fault Detection

A H-infinity Fault Detection and Diagnosis Scheme for Discrete Nonlinear System Using Output Probability Density Estimation

10.1063/1.3106516 ◽

2009 ◽

Cited By ~ 1

Author(s):

Yumin Zhang ◽

Qing-Guo Wang ◽

Kai-Yew Lum ◽

Lotfi Beji ◽

Samir Otmane ◽

...

Keyword(s):

Nonlinear System ◽

Fault Detection ◽

Probability Density ◽

Density Estimation ◽

Probability Density Estimation ◽

H Infinity ◽

Detection And Diagnosis ◽

Output Probability

Incipient fault detection and diagnosis based on Kullback–Leibler divergence using principal component analysis: Part II

Signal Processing ◽

10.1016/j.sigpro.2014.06.023 ◽

2015 ◽

Vol 109 ◽

pp. 334-344 ◽

Cited By ~ 55

Author(s):

Jinane Harmouche ◽

Claude Delpha ◽

Demba Diallo

Keyword(s):

Principal Component Analysis ◽

Fault Detection ◽

Principal Component ◽

Component Analysis ◽

Leibler Divergence ◽

Incipient Fault ◽

Detection And Diagnosis ◽

Incipient Fault Detection

Incipient offset current sensor fault detection and diagnosis using statistical analysis and the Kullback Leibler divergence for AC drive

IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society ◽

10.1109/iecon.2017.8217416 ◽

2017 ◽

Cited By ~ 1

Author(s):

D. Diallo ◽

C. Delpha

Keyword(s):

Statistical Analysis ◽

Fault Detection ◽

Current Sensor ◽

Sensor Fault ◽

Ac Drive ◽

Leibler Divergence ◽

Sensor Fault Detection ◽

Detection And Diagnosis

Wireless sensor network fault detection via semi-supervised local kernel density estimation

2015 IEEE International Conference on Industrial Technology (ICIT) ◽

10.1109/icit.2015.7125308 ◽

2015 ◽

Author(s):

Mingbo Zhao ◽

Tommy W. S. Chow

Keyword(s):

Wireless Sensor Network ◽

Fault Detection ◽

Sensor Network ◽

Density Estimation ◽

Kernel Density ◽

Wireless Sensor ◽

Network Fault

Distributionally Robust Fault Detection by using Kernel Density Estimation

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.810 ◽

2020 ◽

Vol 53 (2) ◽

pp. 652-657

Author(s):

Ting Xue ◽

Maiying Zhong ◽

Lijia Luo ◽

Linlin Li ◽

Steven X. Ding

Keyword(s):

Fault Detection ◽

Density Estimation ◽

Kernel Density ◽

Robust Fault Detection ◽

Distributionally Robust

Fault diagnosis of rotating machinery based on kernel density estimation and Kullback-Leibler divergence

Journal of Mechanical Science and Technology ◽

10.1007/s12206-014-1012-7 ◽

2014 ◽

Vol 28 (11) ◽

pp. 4441-4454 ◽

Cited By ~ 26

Author(s):

Fan Zhang ◽

Yu Liu ◽

Chujie Chen ◽

Yan-Feng Li ◽

Hong-Zhong Huang

Keyword(s):

Fault Diagnosis ◽

Density Estimation ◽

Kernel Density ◽

Rotating Machinery ◽

Leibler Divergence

Fault detection and diagnosis for general discrete-time stochastic systems using output probability density estimation

IEEE Conference on Decision and Control and European Control Conference ◽

10.1109/cdc.2011.6160202 ◽

2011 ◽

Cited By ~ 4

Author(s):

Zakwan Skaf ◽

Ahmad AI-Bayati ◽

Hong Wang

Keyword(s):

Fault Detection ◽

Probability Density ◽

Density Estimation ◽

Discrete Time ◽

Stochastic Systems ◽

Probability Density Estimation ◽

Detection And Diagnosis ◽

Output Probability

Nonlinear process fault detection and identification using kernel PCA and kernel density estimation

Systems Science & Control Engineering ◽

10.1080/21642583.2016.1198940 ◽

2016 ◽

Vol 4 (1) ◽

pp. 165-174 ◽

Cited By ~ 33

Author(s):

Raphael Tari Samuel ◽

Yi Cao

Keyword(s):

Fault Detection ◽

Density Estimation ◽

Fault Detection And Identification

Kernel Density ◽

Nonlinear Process ◽

Kernel Pca ◽

Detection And Identification ◽