Bearing Fault Diagnosis Based on Optimal Time-Frequency Representation Method⁎. Issue 11 (2019)

• Record Type: Journal Article
• Authors: Quinde, Israel Ruiz; Sumba, Jorge Chuya; Ochoa, Luis Escajeda; Guevara, Antonio Jr. Vallejo; Morales-Menendez, Ruben
• Is Part Of: IFAC-PapersOnLine. Volume 52:Issue 11(2019)
• Page Start: 194
• Page End: 199
• Publication Date: 2019
• Subjects: Wigner-Ville Distribution -- Fault Diagnosis -- Bearing Spindles; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2019.09.140
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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 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 based on 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.