ICONS 2019 Paper Abstract


Paper ThB1SP.1

Morales-Menendez, Ruben (Tecnologico de Monterrey), Ruiz Quinde, Israel Benjamin (Tecnológico de Monterrey), Vallejo, Antonio (Tecnologico de Monterrey), Chuya Sumba, Jorge (Tecnológico de Monterrey), Escajeda Ochoa, Luis Enrique (Tecnológico de Monterrey)

Bearing Fault Diagnosis Based on Optimal Time-Frequency Representation Method

Scheduled for presentation during the Regular Session "Fault Detection, Diagnosis and Fault-tolerant Control II" (ThB1SP), Thursday, August 22, 2019, 16:00−16:20,

5th IFAC International Conference on Intelligent Control and Automation Sciences, August 21-23, 2019, Queen’s University Belfast, Northern Ireland

This information is tentative and subject to change. Compiled on November 29, 2021

Keywords Computer-aided design tools, Diagnosis, fault detection and fault tolerant control, Signal processing


Wigner-Ville Distribution (WVD) is probably the most used non-linear time-frequency distribution for signal processing in fault diagnosis, due to the advantages of excellent resolution and localization in time-frequency domain. However, the presence of interference cross terms when they are applied to multicomponent signals can give misleading interpretations. A methodology based on Local Mean Decomposition (LMD) and WVD is proposed to get more reliable bearing fault diagnosis from Time-Frequency Representations (TFR) of the vibration signals. Kullback-Leibler Divergence (KLD) guides the selection of the optimal frequency band with the most relevant information about the fault. Early results based on experimental data show successful diagnosis.


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