scholarly journals Improving the Accuracy of Fault Frequency by Means of Local Mean Decomposition and Ratio Correction Method for Rolling Bearing Failure

2019 ◽  
Vol 9 (9) ◽  
pp. 1888 ◽  
Author(s):  
Yongqiang Duan ◽  
Chengdong Wang ◽  
Yong Chen ◽  
Peisen Liu
Keyword(s):  
Fault Location ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Correction Method ◽  
Bearing Failure ◽  
Local Mean Decomposition ◽  
Local Mean ◽  
Hilbert Envelope ◽  
Ratio Correction ◽  
Envelope Spectrum

The fault frequencies are as they are and cannot be improved. One can only improve its estimation quality. This paper proposes a fault diagnosis method by combining local mean decomposition (LMD) and the ratio correction method to process the short-time signals. Firstly, the vibration signal of rolling bearing is decomposed into a series of product functions (PFs) by LMD. The PF, which contains the richest fault information, is selected to perform envelope spectrum analysis by the Hilbert transform (HT). Secondly, the Hilbert envelope spectrum of the selected PF is corrected with the ratio correction method. Finally, higher precision fault frequencies are extracted from the corrected Hilbert envelope spectrum, and then the fault location is accurately determined. The proposed method of this paper can be used in online real-time monitoring technology of rolling bearing failure.

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.

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.

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.

Refinement Envelope Analysis for Small Failures of Rolling Bearing

2011 ◽  
Vol 383-390 ◽  
pp. 2622-2627
Author(s):  
Shu Shang Zhao ◽  
Juan Juan Pan
Keyword(s):  
Fault Diagnosis ◽  
Hilbert Transform ◽  
Vibration Spectrum ◽  
Rolling Bearing ◽  
Bearing Failure ◽  
Wavelet Filter ◽  
Bearing Defect ◽  
Small Fault ◽  
Bearing Vibration ◽  
Envelope Spectrum

In the rotating machinery, rolling bearing is used widespread in many places. Due to various reasons, there is great dispersion in the life of bearing. Therefore, it is very important to have fault diagnosis of rolling bearing, especially the small fault diagnosis of rolling bearing. According to the characteristics of rolling bearing defect signals and the features integrated with wavelet transform, Hilbert transform and envelope spectrum detailed analysis, this text proposed a method to judge the bearing failure. At first, bearing vibration signals are reconstructed from wavelet filter and envelope signals are obtained by Hilbert transform and then vibration spectrum is obtained from the refining envelope spectrum. Bearing failure is judged from the refining frequency spectrum. Bearing failure is also estimated by experiment to verify the correctness of theoretical analysis.

Median line-gram and its application in the fault diagnosis of rolling bearing

2022 ◽  
Author(s):  
Xinglong Wang ◽  
Jinde Zheng ◽  
Jun Zhang
Keyword(s):  
Frequency Band ◽  
Fault Location ◽  
Rolling Bearing ◽  
Suggested Method ◽  
Median Line ◽  
Frequency Bands ◽  
Unique Approach ◽  
Envelope Spectrum ◽  
Line Segmentation ◽  
Selection Of

Abstract The level selection of frequency band division structure relies on previous information in most gram approaches that capture the optimal demodulation frequency band (ODFB). When an improper level is specified in these approaches, the fault characteristic information contained in the produced ODFB may be insufficient. This research proposes a unique approach termed median line-gram (MELgram) to tackle the level selection problem. To divide the frequency domain signal into a series of demodulation frequency bands, a spectrum median line segmentation model based on Akima interpolation is first created. The level and boundary of the segmentation model can be adaptively identified by this means. Second, the acquired frequency bands are quantized using the negative entropy index, and the ODFB is defined as the frequency band with the largest value. Third, the envelope spectrum is used to determine the ODFB characteristic frequency to pinpoint the bearing fault location. Finally, both simulation and experimental signal analysis are used to demonstrate the efficiency of the suggested method. Furthermore, the suggested method extracts more defect feature information from the ODFB than existing methods.

Feature Extraction of Rolling Bearing Based on LMD Energy Feature

2017 ◽  
Vol 868 ◽  
pp. 363-368
Author(s):  
Bang Sheng Xing ◽  
Le Xu
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Small Samples ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Fault Features ◽  
Local Mean ◽  
Energy Feature

For the situation that it is difficult to diagnose rolling bearings fault effectively for small samples, so it proposes a feature extraction method of rolling bearing based on local mean decomposition (LMD) energy feature. Due to the frequency domain distribution of vibration signals will change when different faults occur in rolling bearings, so it can use LMD energy feature method to extract the fault features of rolling bearings. The instances analysis and extracted results show that the LMD energy feature can extract the vibration signal fault feature of rolling bearings effectively.

Bearing Fault Detection Based on Multi-Scale Laplace Wavelet Transform Spectrum

2013 ◽  
Vol 739 ◽  
pp. 413-417
Author(s):  
Ya Ning Wang
Keyword(s):  
Wavelet Transform ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Three Dimensions ◽  
Noise Condition ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Integrated Technique ◽  
Envelope Spectrum

Laplace wavelet transform is self-adaptive to non-stationary and non-linear signal, which can detect the singularity characteristic of a signal precisely under strong background noise condition. A new method of bearing fault diagnosis based on multi-scale Laplace wavelet transform spectrum is proposed. The multi scale Laplace wavelet transform spectrum technique combines the advantages of Laplace wavelet transform, envelope spectrum and three dimensions color map into one integrated technique. The bearing fault vibration signal is firstly decomposed using Laplace wavelet transform. In the end, the multi scale Laplace wavelet transform spectrum is obtained and the characteristics of the bearing fault can be recognized according to the multi-scale Laplace wavelet transform spectrum. The proposed method has been verified by vibration signals obtained from rolling bearing with inner race fault.

Feature extraction of rolling bearing fault signal based on local mean decomposition and Teager energy operator

2017 ◽  
Vol 69 (6) ◽  
pp. 872-880 ◽  
Author(s):  
Jianhua Cai
Keyword(s):  
Fourier Method ◽  
Energy Operator ◽  
Rolling Bearing ◽  
Transform Method ◽  
Content Type ◽  
Local Mean Decomposition ◽  
Teager Energy Operator ◽  
Marginal Spectrum ◽  
Local Mean ◽  
Teager Energy

Purpose This paper aims to explore a new way to extract the fault feature of a rolling bearing signal on the basis of a combinatorial method. Design/methodology/approach By combining local mean decomposition (LMD) with Teager energy operator, a new feature-extraction method of a rolling bearing fault signal was proposed, called the LMD–Teager transform method. The principles and steps of method are presented, and the physical meaning of the time–frequency power spectrum and marginal spectrum is discussed. On the basis of comparison with the fast Fourier transform method, a simulated non-stationary signal was processed to verify the effect of the new method. Meanwhile, an analysis was conducted by using the recorded vibration signals which include inner race, out race and bearing ball fault signal. Findings The results show that the proposed method is more suitable for the non-stationary fault signal because the LMD–Teager transform method breaks through the difficulty of the Fourier transform method that can process only the stationary signal. The new method can extract more useful information and can provide better analysis accuracy and resolution compared with the traditional Fourier method. Originality/value Combining the advantage of the local mean decomposition and the Teager energy operator, the LMD–Teager method suits the nature of the fault signal. A marginal spectrum obtained from the LMD–Teager method minimizes the estimation bias brought about by the non-stationarity of the fault signal. So, the LMD–Teager transform has better analysis accuracy and resolution than the traditional Fourier method, which provides a good alternative for fault diagnosis of the rolling bearing.

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.

Sign in / Sign up

Export Citation Format

Share Document