Fault Diagnosis of Gear Wear Based on Local Mean Decomposition

2012 ◽  
Vol 459 ◽  
pp. 298-302 ◽  
Author(s):  
Hui Li
Keyword(s):  
Fault Diagnosis ◽  
Strong Evidence ◽  
Vibration Signal ◽  
Experimental Results ◽  
New Approach ◽  
Local Mean Decomposition ◽  
Local Mean ◽  
Envelope Signal

A new approach to fault diagnosis of gear wear based on Local mean decomposition (LMD) is proposed. Local mean decomposition can adaptively decomposes the vibration signal into a series of product functions (PFs), which is the product of an envelope signal and a frequency modulated signal. LMD is capable of revealing interesting feature embedded in the signal. The experimental examples are conducted to evaluate the effectiveness of the proposed approach. The experimental results provide strong evidence that the performance of the approach based on local mean decomposition is better to extract the fault characteristics of the faulty gear and can effectively diagnose the gear wear fault.

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.

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.

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 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.

Processing Method for End Effect of Local Mean Decomposition Based on Extreme Point and Distant

2016 ◽  
Vol 851 ◽  
pp. 574-581
Author(s):  
Qi Cai Chi ◽  
Min Zhou ◽  
Shi Jian Zhou ◽  
Feng Wei Wang
Keyword(s):  
Extreme Point ◽  
Vibration Signal ◽  
Original Data ◽  
Processing Method ◽  
Support Vector ◽  
End Effect ◽  
Local Mean Decomposition ◽  
Local Mean ◽  
Simulation Signal ◽  
Factor Of Influence

The end effect of the local Mean Decomposition (LMD) causes serious distortion of the LMD decomposition results. And the most important factor of influence end effect is the extreme point and its distance, so the paper extracted the several factors, and composed of different sequences, using support vector machine (SVM) method respectively on the sets of data to predict, makes the original data can be extended. The research on the simulation signal and vibration signal shows that the method can effectively restrain the end effect of the decomposition.

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