Fault Diagnosis of Offshore Platforms Using the Local Mean Decomposition Method

2011 ◽  
Vol 365 ◽  
pp. 94-97
Author(s):  
Jin Shan Lin
Keyword(s):  
Fault Diagnosis ◽  
Signal Analysis ◽  
Offshore Platforms ◽  
Local Mean Decomposition ◽  
Signal Processing Method ◽  
Mode Decomposition ◽  
Local Mean ◽  
Nonlinear Signal ◽  
Nonlinear Signal Analysis ◽  
Emd Method

Traditional techniques are not suitable for exploring non-stationary and nonlinear signals. Although empirical mode decomposition (EMD) is a powerful tool for the non-stationary and nonlinear signal analysis, yet it still has some shortcomings. Local mean decomposition (LMD), a novel signal processing method, seemingly overcomes many deficiencies of the EMD method and can take place of the EMD method for analyzing non-stationary and nonlinear signals. In this paper, the LMD method is employed to examine the signal captured from the decks of the WZ12-1 platform and succeeds in displaying the reasons causing the excessive vibration of the WZ12-1 platform. The results suggest that the LMD method seems to be a feasible method for fault diagnosis of offshore platforms.

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.

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.

scholarly journals Protein Aggregation/Folding: The Role of Deterministic Singularities of Sequence Hydrophobicity as Determined by Nonlinear Signal Analysis of Acylphosphatase and Aβ(1–40)

2003 ◽  
Vol 85 (6) ◽  
pp. 3544-3557 ◽  
Author(s):  
Joseph P. Zbilut ◽  
Alfredo Colosimo ◽  
Filippo Conti ◽  
Mauro Colafranceschi ◽  
Cesare Manetti ◽  
...  
Keyword(s):  
Protein Aggregation ◽  
Signal Analysis ◽  
Nonlinear Signal ◽  
Nonlinear Signal Analysis

Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis

2018 ◽  
Vol 155 ◽  
pp. 11-17 ◽  
Author(s):  
Maie Bachmann ◽  
Laura Päeske ◽  
Kaia Kalev ◽  
Katrin Aarma ◽  
Andres Lehtmets ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Single Channel ◽  
Linear And Nonlinear ◽  
Nonlinear Signal ◽  
Nonlinear Signal Analysis

The Research of Fault Diagnosis Method of Roller Bearing Based on EMD and VPMCD

2014 ◽  
Vol 1014 ◽  
pp. 505-509 ◽  
Author(s):  
Ran Tao ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Shu Guo ◽  
Kun Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Roller Bearing ◽  
Classification Performance ◽  
Fault Models ◽  
Time Frequency ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Nonlinear Signal ◽  
Stable Classification

Empirical mode decomposition (EMD) can extract real time-frequency characteristics from the non-stationary and nonlinear signal. Variable prediction model based class discriminate (VPMCD) is introduced into roller bearing fault diagnosis in this paper. Therefore, a fault diagnosis method based on EMD and VPMCD is put forward in the paper. Firstly, the different feature vectors in the signal are extracted by EMD. Then, different fault models of roller bearing are distinguished by using VPMCD. Finally, an simulation example based on EMD and VPMCD is shown in this paper. The results show that this method can gain very stable classification performance and good computational efficiency.

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.

Sign in / Sign up

Export Citation Format

Share Document