Ensemble empirical mode decomposition and Hilbert-Huang transform applied to bearing fault diagnosis

• Published in: 2010 3rd International Congress on Image and Signal Processing Vol. 7; pp. 3413 - 3417
• Main Authors: Hui Li, Yucai Wang, Yanfang Ma
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2010
• Subjects: bearing; ensemble empirical mode decomposition; Fault detection; Fault diagnosis; Frequency modulation; hilbert-huang transform; Time frequency analysis; Transforms; Vibrations; White noise
• ISBN: 1424465133; 9781424465132
• DOI: 10.1109/CISP.2010.5647389

A signal analysis technique for bearing fault diagnosis based on ensemble empirical mode decomposition (EEMD) and Hilbert-Huang transform (HHT) is presented. EEMD can adaptively decompose vibration signal into a series of zero mean Amplitude Modulation-Frequency Modulation (AM-FM) Intrinsic Mode Functions (IMFs) without mode mixing. Hilbert transform tracks the modulation energy of the interesting Intrinsic Mode Functions (IMFs) and estimates the instantaneous amplitude and instantaneous frequency at any time instant. In the end, the Hilbert-Huang transform spectrum is applied to the vibration signal. Therefore, the character of the bearing fault can be recognized according to the Hilbert-Huang transform spectrum. The experimental results show that Hilbert-Huang transform spectrum analysis based on EEMD and HHT provide a viable signal analysis tool for bearing fault detection.