A Modified EEMD Decomposition for the Detection of Rolling Bearing Faults

Abstract

The vibration signals of rolling element bearings are non-linear and non-stationary and the corresponding fault features are difficult to be extracted. EEMD (Ensemble empirical mode decomposition) is effective to detect bearing faults. In the present investigation, MEEMD (Modified EEMD) is presented to diagnose the outer and inner race faults of bearings. The original vibration signals are analyzed using IMFs (intrinsic mode functions) extracted by MEEMD decomposition and Hilbert spectrum in the proposed method. The numerical and experimental results of the comparison between MEEMD and EEMD indicate that the proposed method is more effective to extract the fault features of outer and inner race of bearings than EEMD.

Related Papers

• → R peak detection in electrocardiogram signal based on a combination between empirical mode decomposition and Hilbert transform(2014)19 cited
• → Instantaneous frequency and wave mode identification in a magnetosheath using few spatial points(2013)4 cited
• → Application and Improvement of Hilbert-Huang Transform in Subsynchronous Oscillation Mode Parameter Identification(2021)3 cited
• New Process Method for End Effects of HILBERT-HUANG Transform(2008)
• End effect restraint of Hilbert-Huang transform and its application in power quality monitoring(2008)