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.

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.

Local Mean Decomposition Combined with SVD and Application in Telemetry Vibration Signal Processing

2013 ◽  
Vol 347-350 ◽  
pp. 854-858
Author(s):  
Zhen Xing Li ◽  
Wei Xiao Dai
Keyword(s):  
Signal Processing ◽  
Vibration Signal ◽  
Singular Value ◽  
Noise Effect ◽  
Local Mean Decomposition ◽  
Time Frequency ◽  
Local Mean ◽  
Simulation Signal ◽  
Value Decomposition ◽  
Series Product

To suppress the noise effect on the performance of local mean decomposition (LMD) in signal processing, a new method combined with singular value decomposition (SVD) denosing was proposed in this work. SVD is applied to denoise the observed signal, and then the signal decompose to series product functions (PF) by LMD, meanwhile, SVD is applied to denoise the PF and sum the PF to LMD again. This method can suppress the noise effect to attain high precision PF for time-frequency analysis. Simulation signal and telemetry vibration signal processing results show the effectiveness of the method.

Detection of Damaged Tooth by Support Vector Machines

2014 ◽  
Vol 627 ◽  
pp. 79-83
Author(s):  
Qing Rong Fan ◽  
Kiyotaka Ikejo ◽  
Kazuteru Nagamura
Keyword(s):  
Support Vector Machines ◽  
Gear Tooth ◽  
Vibration Signal ◽  
Original Data ◽  
Cyclic Fatigue ◽  
Mean Value ◽  
Support Vector ◽  
Statistical Parameters ◽  
Machine System ◽  
Vector Machines

Gear is one of the most important and commonly used components in machine system. Early detection of gear damage is crucial to prevent the machine system from malfunction. This paper proposes a method for detection of damaged tooth based on support vector machines. Statistical parameters of standard deviation, root mean square value, maximum value and mean value are extracted from the vibration signal as representative features of tooth conditions to be input to the support vector machine classifier. The validity of the presented method is confirmed by the application of detecting early damaged tooth during the cyclic fatigue test. The vibration acceleration on gear box is acquired as original data. Furthermore, the signal of each gear tooth is separately extracted from the signal for a further analysis.The experimental results demonstrate the effectiveness of the proposed method.

scholarly journals Fault Frequency Identification of Rolling Bearing Using Reinforced Ensemble Local Mean Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Bo Qin ◽  
Quanyi Luo ◽  
Juanjuan Zhang ◽  
Zixian Li ◽  
Yan Qin
Keyword(s):  
Vibration Signal ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Local Mean Decomposition ◽  
Spectral Kurtosis ◽  
Multiple Interfaces ◽  
Frequency Components ◽  
Frequency Identification ◽  
Diagnosis Accuracy ◽  
Local Mean

The vibration signal of rolling bearing exhibits the characteristics of energy attenuation and complex time-varying modulation caused by the transmission with multiple interfaces and complex paths. In view of this, strong ambient noise easily masks faulty signs of rolling bearings, resulting in inaccurate identification or even totally missing the real fault frequencies. To overcome this problem, we propose a reinforced ensemble local mean decomposition method to capture and screen the essential faulty frequencies of rolling bearing, further boosting fault diagnosis accuracy. Firstly, the vibration signal is decomposed into a series of preliminary features through ensemble local mean decomposition, and then the frequency components above the average level are energy-enhanced. In this way, principal frequency components related to rolling bearing failure can be identified with the fast spectral kurtosis algorithm. Finally, the efficacy of the proposed approach is verified through both a benchmark case and a practical platform. The results show that the selected fault characteristic components are accurate, and the identification and diagnosis of rolling bearing status are improved. Especially for the signals with strong noise, the proposed method still could accurately diagnose fault frequency.

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