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 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.

scholarly journals A Hybrid Time-Frequency Analysis Method for Railway Rolling-Element Bearing Fault Diagnosis

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Yao Cheng ◽  
Dong Zou ◽  
Weihua Zhang ◽  
Zhiwei Wang
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Rolling Element Bearings ◽  
Analysis Method ◽  
Local Mean Decomposition ◽  
Time Frequency ◽  
Fm Signals ◽  
Rolling Element ◽  
Local Mean ◽  
Fault Information

The health condition of rolling-element bearings is important for machine performance and operating safety. Due to external interferences, the impulse-related fault information is always buried in the raw vibration signal. To solve this problem, a hybrid time-frequency analysis method combining ensemble local mean decomposition (ELMD) and the Teager-Kaiser energy operator (TKEO) is proposed for the fault diagnosis of high-speed train bearings. The ELMD method is a significant improvement over local mean decomposition (LMD) for addressing the mode-mixing problem. The TKEO method is effective for separating amplitude-modulated (AM) and frequency-modulated (FM) signals from a raw signal. But it is only valid for monocomponent AM-FM signals. The proposed time-frequency method integrates the advantages of ELMD and TKEO to detect localized defects in rolling-element bearings. First, a raw signal is decomposed into an ensemble of PFs and a residual component using ELMD. A novel sensitive parameter (SP) is introduced to select the sensitive PF that contains the most fault-related information. Subsequently, the TKEO is applied to extract both the amplitude and frequency modulations from the selected PF. The experimental results of rolling element and outer race fault signals confirmed that the proposed method could effectively recover fault information from raw signals contaminated by strong noise and other interferences.

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.

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

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.

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.

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