An order tracking technique for the gear fault diagnosis using local mean decomposition method

2012 ◽  
Vol 55 ◽  
pp. 67-76 ◽  
Author(s):  
Junsheng Cheng ◽  
Kang Zhang ◽  
Yu Yang
Keyword(s):  
Fault Diagnosis ◽  
Decomposition Method ◽  
Local Mean Decomposition ◽  
Tracking Technique ◽  
Order Tracking ◽  
Gear Fault ◽  
Local Mean ◽  
Gear Fault Diagnosis

The Application of Local Mean Decomposition and Variable Predictive Model-Based Class Discriminate in Gear Fault Diagnosis

2014 ◽  
Vol 1014 ◽  
pp. 510-515 ◽  
Author(s):  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Shu Guo ◽  
Min Gou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Predictive Model ◽  
Feature Vector ◽  
Local Mean Decomposition ◽  
Outer Race ◽  
Model Based ◽  
Gear Fault ◽  
Local Mean ◽  
Gear Fault Diagnosis ◽  
Non Stationary Signal

The time-domain energy message conveyed by vibration signals of different gear fault are different, so a method based on local mean decomposition (LMD) and variable predictive model-based class discriminate (VPMCD) is proposed to diagnose gear fault model. The vibration signal of gear which is the research object in this paper is decomposed into a series of product functions (PF) by LMD method. Then a further analysis is to select the PF components which contain main fault information of gear, the energy feature parameters of the selected PF components are used to form a fault feature vector. The variable predictive model-based class discriminate is a new multivariate classification approach for pattern recognition, through taking fully advantages of the fault feature vector. Finally, gear fault diagnosis is distinguished into normal state, inner race fault and outer race fault. The results show that LMD method can decompose a complex non-stationary signal into a number of PF components whose frequency is from high to low. And the method based on LMD and VPMCD has a high fault recognition function by analyzing the fault feature vector of PF.

A Demodulating Approach Based on Local Mean Decomposition and Combined Morlet Wavelets and its Application in Mechanical Fault Diagnosis

2012 ◽  
Vol 239-240 ◽  
pp. 1039-1044
Author(s):  
Hui Mei Li ◽  
Zhao Zhong Cai ◽  
Gang An
Keyword(s):  
Numerical Simulation ◽  
Fault Diagnosis ◽  
Adaptive Filtering ◽  
Local Mean Decomposition ◽  
Gear Fault ◽  
Local Mean ◽  
Gear Fault Diagnosis

Local mean decomposition(LMD) as a new demodulating approach has some problems needing study and solution.This paper analyzed the overlapping phenomenon of product functions(PFs) generated when LMD decomposes signals through studying the adaptive filtering characteristic of LMD and numerical simulation. The results show that the overlapping phenomenon of PFs is general. Then in order to improve the demodulating effect of LMD, a new demodulating method based on LMD and combined Morlet wavelets was proposed and applied in gear fault diagnosis. The results show that this method is efficient.

A Study on Fault Diagnosis of Gears Based on Local Mean Decomposition Method

2013 ◽  
Vol 683 ◽  
pp. 899-902
Author(s):  
Qiang Pan ◽  
Deng Hong Xiao ◽  
Tian He
Keyword(s):  
Fourier Transform ◽  
Fault Diagnosis ◽  
Hilbert Transform ◽  
Decomposition Method ◽  
Local Mean Decomposition ◽  
Local Mean ◽  
Component Amplitude

In present paper, the effectiveness of local mean decomposition (LMD) method to signals of fault gears, which are multi-component amplitude modulated and frequency modulated, is demonstrated. A series of tests on wearing and broken tooth of gears are conducted. And the fault characteristics extracted by Fourier transform, Hilbert transform and LMD are compared. The results validate that LMD method is an effective way to extract the characteristics of fault gears and improve the accuracy of fault diagnosis of gears since it is able to reduce effect of false components.

Gear Pitting Corrosion Fault Diagnosis Based on Local Mean Decomposition

2012 ◽  
Vol 562-564 ◽  
pp. 812-815 ◽  
Author(s):  
Ya Nong Chen ◽  
Tian He ◽  
Deng Hong Xiao ◽  
Hai Tao Cui
Keyword(s):  
Fault Diagnosis ◽  
Pitting Corrosion ◽  
Simulation Analysis ◽  
Analysis Method ◽  
Local Mean Decomposition ◽  
Time Frequency ◽  
Gear Fault ◽  
Modulation Signal ◽  
Adaptive Time ◽  
Local Mean

The local mean decomposition (LMD), a new adaptive time-frequency analysis method, is the research focus in the fault diagnosis field in recent years. In this paper, the LMD’s characteristics are obtained by processing multi-component frequency and amplitude modulation signal, which are usually used to describe the gear pitting corrosion fault signals. Base on the simulation analysis, LMD is presented to deal with the vibration signals of gear pitting corrosion fault, comparing with traditional method. The results show that the gear pitting corrosion defect can be diagnosed by LMD effectively, and LMD can eliminate the false composition effect, thus improving the accuracy of gear fault diagnosis.

Sign in / Sign up

Export Citation Format

Share Document