Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring

2006 ◽  
Vol 55 (6) ◽  
pp. 2320-2329 ◽  
Author(s):  
Ruqiang Yan ◽  
Robert X. Gao
Keyword(s):  
Health Monitoring ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Hilbert Huang Transform ◽  
Machine Health Monitoring ◽  
Machine Health ◽  
Vibration Signal Analysis

Application of Hilbert–Huang transform for vibration signal analysis in end-milling

2018 ◽  
Vol 53 ◽  
pp. 263-277 ◽  
Author(s):  
Agus Susanto ◽  
Chia-Hung Liu ◽  
Keiji Yamada ◽  
Yean-Ren Hwang ◽  
Ryutaro Tanaka ◽  
...  
Keyword(s):  
Signal Analysis ◽  
End Milling ◽  
Vibration Signal ◽  
Hilbert Huang Transform ◽  
Vibration Signal Analysis

Health Monitoring of a Gear Box Using Vibration Signal Analysis

2015 ◽  
Vol 813-814 ◽  
pp. 1012-1017 ◽  
Author(s):  
M.R. Praveen ◽  
M. Saimurugan
Keyword(s):  
Frequency Domain ◽  
Health Monitoring ◽  
Crucial Role ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Vibration Signals ◽  
Nearest Neighbours ◽  
Gear Box ◽  
Vibration Signal Analysis

A gear plays a crucial role in the performance of a gear box. The faults in a gear reduces the gear life and if problem arises in shaft it affects bearing. Gear box is finally affected due to these faults. Vibration signals carries information about condition of a gear box which are captured using piezoelectric accelerometer. In this paper, features are extracted and classified using K nearest neighbours (KNN) algorithms for both time and frequency domain. The effectiveness of KNN in classification of gear faults for both time and frequency domain is discussed and compared.

Application of Hilbert-Huang Transform to Vibration Signal Analysis of Coal and Gangue

2010 ◽  
Vol 40-41 ◽  
pp. 995-999 ◽  
Author(s):  
Wei Liu
Keyword(s):  
Signal Analysis ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Frequency Interval ◽  
Hilbert Huang Transform ◽  
Mode Function ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
The Difference ◽  
Vibration Signal Analysis

In this paper, a new method of vibration signal analysis of coal and gangue based on Hilbert-Huang transform is presented. Empirical mode decomposition algorithm was used to decompose the original vibration signal of coal and gangue into the intrinsic modes for further extract useful information contained in response signals under complicated environment. By analyzing local Hilbert marginal spectrum and local energy spectrum of the first four intrinsic mode function components, we found the difference of coal and rock in specific frequency interval that the amplitude and energy mainly distributed at frequency interval between 100Hz and 600Hz when coal was drawn, while the amplitude and energy were more concentrated at 1000Hz or so when gangue was drawn. Furthermore, the further analysis result from marginal spectrum of each intrinsic mode function component agreed well with the conclusion above. So the extracted features with the propose approach can be served as coal and gangue interface recognition.

Ensemble Empirical Mode Decomposition for Machine Health Diagnosis

2009 ◽  
Vol 413-414 ◽  
pp. 167-174 ◽  
Author(s):  
Jian Zhang ◽  
Ru Qiang Yan ◽  
Robert X. Gao
Keyword(s):  
Empirical Mode Decomposition ◽  
Health Monitoring ◽  
Ensemble Empirical Mode Decomposition ◽  
Signal Processing Technique ◽  
Mode Decomposition ◽  
Machine Health Monitoring ◽  
Defect Diagnosis ◽  
Machine Health ◽  
Vibration Signal Analysis ◽  
Mode Mixing

Ensemble Empirical Mode Decomposition (EEMD) is a new signal processing technique aimed at solving the problem of mode mixing present in the original Empirical Mode Decomposition (EMD) algorithm. This paper investigates its utility for machine health monitoring and defect diagnosis. The mechanism of EEMD is first introduced. Parameters that affect effectiveness of the EEMD are then discussed with the assistance of a simulated signal in which the mode mixing exists. Experimental study on bearing vibration signal analysis verified its effectiveness of EEMD for machine health monitoring and defect diagnosis.

Sign in / Sign up

Export Citation Format

Share Document