Application-of-frequency-family-separation-method-based-upon-EMD-and-local-Hilbert-energy-spectrum-method-to-gear-fault-diagnosis_2008_Mechanism-and-Machine-Theory.pdf

Available online at hanism
and
Machine Theory
Mechanism and Machine Theory 43 (2008) 712–723
ate/mechmt
Application of frequency family separation method based upon
EMD and local Hilbert energy spectrum method to gear
fault diagnosis
Junsheng Cheng a,*, Dejie Yu a, Jiashi Tang b, Yu Yang a
a College of Mechanical and Automotive Engineering, Hunan University, Changsha 410082, China
b College of Mechanics and Aerospace, Hunan University, Changsha 410082, China
Received 3 December 2006; received in revised form 23 March 2007; accepted 23 May 2007
Available online 16 July 2007
Abstract
Targeting the advantages of Hilbert–Huang transform (HHT) and the characteristics of gear fault vibration signals,
HHT is introduced into gear fault diagnosis. The concept of local Hilbert energy spectrum is proposed and two gear fault
diagnosis approaches, namely, frequency family separation method based on EMD (empirical mode position) and
local Hilbert energy spectrum method, are put forward, which are applied to gear fault diagnosis. Considering that the gear
fault vibration signal is a ponent amplitude-demodulated and frequency-demodulated (AM–FM) signal and
EMD could exactly pose the AM–FM signal into a number of intrinsic mode functions (IMFs), each of which
can be amplitude-demodulated or frequency-ponent, the frequency families could be separated effectively
from the gear vibration signal by applying EMD to the gear vibration signal. Furthermore, when faults occur in gear, the
energy of the gear vibration signal would change correspondingly, whilst the local Hilbert energy spectrum can exactly
provide the energy distribution of the signal in certain frequency with the change of the time and frequency. Thus, the fault
information of the gear vibration signal can be extracted effectively from the local Hilbert energy spectrum. The analysis
results from the experimental signals show that both frequency family separation method based on EMD and loca