An optimized time varying filtering based empirical mode decomposition method with grey wolf optimizer for machinery fault diagnosis

2018 ◽  
Vol 418 ◽  
pp. 55-78 ◽  
Author(s):  
Xin Zhang ◽  
Zhiwen Liu ◽  
Qiang Miao ◽  
Lei Wang
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Grey Wolf Optimizer ◽  
Time Varying ◽  
Grey Wolf ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Machinery Fault Diagnosis

Identification of Linear Time-Varying Dynamical Systems Using Hilbert Transform and Empirical Mode Decomposition Method

2006 ◽  
Vol 74 (2) ◽  
pp. 223-230 ◽  
Author(s):  
Z. Y. Shi ◽  
S. S. Law
Keyword(s):  
Dynamical Systems ◽  
Empirical Mode Decomposition ◽  
Hilbert Transform ◽  
Decomposition Method ◽  
Degrees Of Freedom ◽  
Linear Time ◽  
Time Varying ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
The Hilbert Transform

This paper addresses the identification of linear time-varying multi-degrees-of-freedom systems. The identification approach is based on the Hilbert transform and the empirical mode decomposition method with free vibration response signals. Three-different types of time-varying systems, i.e., smoothly varying, periodically varying, and abruptly varying stiffness and damping of a linear time-varying system, are studied. Numerical simulations demonstrate the effectiveness and accuracy of the proposed method with single- and multi-degrees-of-freedom dynamical systems.

Fault Diagnosis of Transmission Based on EMD and Fractal Technology

2014 ◽  
Vol 926-930 ◽  
pp. 1712-1715
Author(s):  
Zhen Shu Ma ◽  
Chao Liu ◽  
Hua Gang Sun ◽  
Zhi Chuan Liu
Keyword(s):  
Fractal Dimension ◽  
Fault Diagnosis ◽  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Great Influence ◽  
Vibration Signal ◽  
Fault Type ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

As a result of the presence of noise in the measured vibration signal has a great influence on the results of calculation of fractal dimension, Therefore the empirical mode decomposition method for noise reduction of gear vibration signal is used, calculation fractal dimension, extraction fault feature of Gear in different conditions. The measured results show that: Different fault states have different fractal dimension, we can judge the fault type of gear effectively by the fractal dimension.

Sign in / Sign up

Export Citation Format

Share Document