Rolling Element Defect Diagnosis Based on Local Mean Decomposition

2011 ◽  
Vol 117-119 ◽  
pp. 33-37 ◽  
Author(s):  
Tian He ◽  
Xian Dong Liu ◽  
Ying Chun Shan ◽  
Qiang Pan
Keyword(s):  
Fourier Transform ◽  
Amplitude Modulation ◽  
Simulation Analysis ◽  
Vibration Signals ◽  
Local Mean Decomposition ◽  
Spectrum Method ◽  
Rolling Element ◽  
Modulation Signal ◽  
Local Mean ◽  
Envelope Spectrum

A method to extract rolling element fault characteristics from fault signal, based on local mean decomposition (LMD) and Fourier transform (FT), is introduced in this study. The LMD’s characteristics are obtained by processing multi-component frequency and amplitude modulation signal, which are usually used to describe the bearing fault signals. Base on the simulation analysis, the envelope spectrum method called LMD-FT is presented to deal with the vibration signals of rolling balling bearing with element fault. The results show that the rolling element defect can be diagnosed by LMD-FT effectively

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.

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.

Fault Diagnosis Method of Gearbox Based on Local Mean Decomposition

2013 ◽  
Vol 376 ◽  
pp. 441-445 ◽  
Author(s):  
Jian Zhang ◽  
Hui Mei Li ◽  
Yan Feng Tang ◽  
Qin Qin Wang
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Linear Combination ◽  
Frequency Method ◽  
Local Mean Decomposition ◽  
Time Frequency ◽  
Finite Set ◽  
Diagnosis Method ◽  
Modulation Signal ◽  
Local Mean

Local mean decomposition(LMD),which is a new time-frequency method, can decompose a complex multicomponent modulation signal into a linear combination of a finite set of mono-component modulation signals. LMD integrates two signal processing procedures: decomposition and demodulation, and it can extract modulation feature efficiently. The basic theory and algorithm of LMD is introduced, and the effection of LMD is verified trough simulation. LMD is applied in gearbox fault diagnosis and successfully extracts modulation feature.

Local Mean Decomposition Based Bearing Fault Detection

2012 ◽  
Vol 490-495 ◽  
pp. 360-364 ◽  
Author(s):  
Hui Li
Keyword(s):  
Vibration Signal ◽  
Noise Condition ◽  
Local Mean Decomposition ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Product Function ◽  
Local Mean ◽  
Novel Method ◽  
Envelope Spectrum

A novel method of bearing fault diagnosis based on local mean decomposition (LMD) is proposed. LMD method is self-adaptive to non-stationary and non-linear signal. LMD can adaptively decompose the vibration signal into a series of product functions (PFs), which is the product of an envelope signal and a frequency modulated signal. Then the envelope spectrum is applied to the selected product function that stands for the bearing faults. Therefore, the character of the bearing fault can be recognized according to the envelope spectrum of product function. The experimental results show that local mean decomposition based envelope spectrum can effectively detect and diagnose bearing inner and outer race fault under strong background noise condition.

scholarly journals Genetic algorithms to determine the optimal parameters of an ensemble local mean decomposition

2021 ◽  
Vol 61 (3) ◽  
pp. 465-475
Author(s):  
Willian T. F. D. Silva ◽  
Filipe D. D. M. Borges
Keyword(s):  
Genetic Algorithms ◽  
Processing Time ◽  
Optimization Method ◽  
Correct Choice ◽  
Optimal Parameters ◽  
Vibration Signals ◽  
Local Mean Decomposition ◽  
Parameters Selection ◽  
Local Mean

An optimization method for an ensemble local mean decomposition (ELMD) parameters selection using genetic algorithms is proposed. The execution of this technique depends heavily on the correct choice of the parameters of its model as pointed out in previous works. The effectiveness of the proposed method was evaluated using synthetic signals, discussed by several authors. The resulting algorithm obtained similar results to OELMD, but with an 82% reduction in processing time. Actual vibration signals were also analysed, presenting satisfactory results.

Gas pipeline small leakage feature extraction based on LMD envelope spectrum entropy and PCA–RWSVM

2016 ◽  
Vol 38 (12) ◽  
pp. 1460-1470 ◽  
Author(s):  
Lina Wang ◽  
Xianwen Gao ◽  
Tan Liu
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Principal Component ◽  
Support Vector ◽  
Local Mean Decomposition ◽  
Natural Gas Pipelines ◽  
Product Function ◽  
Vector Machines ◽  
Local Mean ◽  
Envelope Spectrum

This paper presents a novel intelligent method based on local mean decomposition and multi-class reproducing wavelet support vector machines (RWSVMs), which are applied to detect leakage in natural gas pipelines. First, local mean decomposition is used to construct product function components to decompose the leakage signals. Then, we select the leakage signals which contain the most leakage information, according to the kurtosis features of these signals, through principal component analysis. Next, we reconstruct the principal product function components in order to acquire the envelope spectrum. Finally, we confirm the leak aperture by inputting envelope spectrum entropy features, as feature vectors, into the RWSVMs. Through analysing the pipeline leakage signals, the experiments show that this method can effectively identify different leak categories.

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