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.

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.

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.

Fault Diagnosis Method of Gear Based on Local Mean Decomposition and Least Squares Support Vector Machine

2013 ◽  
Vol 415 ◽  
pp. 548-554
Author(s):  
Zhou Wan ◽  
Xing Zhi Liao ◽  
Xin Xiong ◽  
Zhi Rong Li
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Vibration Signal ◽  
Support Vector ◽  
Vibrational Signal ◽  
Local Mean Decomposition ◽  
Gear Fault ◽  
Input Sample ◽  
Local Mean

For differences of time-domain energy distribution of different gear fault vibration signal, an analytical method based on local mean decomposition (LMD) and least squares support vector machine (LS-SVM) is proposed to apply to gear fault diagnosis. First vibrational signal of gear is decomposed into a series of product functions (PF) by LMD method. Then extracting energy characteristic parameters of PF components which contain main fault information to constitute a fault feature vectors, which is considered as input sample of well-trained LS-SVM, and then identifying working state and fault type of different gear can be identified accurately and effectively by diagnostic method based on LMD and LS-SVM.

Improved Local Mean Decomposition and its Application to Fault Diagnosis of Train Bearing

2013 ◽  
Vol 819 ◽  
pp. 155-159
Author(s):  
Peng Wang ◽  
Huai Xiang Ma
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Processing Method ◽  
Nonstationary Signal ◽  
Practical Application ◽  
Local Mean Decomposition ◽  
Signal Processing Method ◽  
Signal Demodulation ◽  
Improvement Method ◽  
Local Mean

Fault diagnosis of train bearing is an important method to ensure the security of railway. The key to the fault diagnosis is the method of vibration signal demodulation. The local mean decomposition (LMD) is a self-adapted signal processing method which has a good performance in nonlinear nonstationary signal demodulation. The improved LMD method based on kurtosis criterion can prevent errors in the process of calculating the product functions. With the verification of simulation and wheel set experiment, the improvement method has been certified usefully in practical application.

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