Detecting Damage of Rolling Bearings Using EMD

2005 ◽  
Vol 293-294 ◽  
pp. 753-760 ◽  
Author(s):  
Qiang Gao ◽  
Zheng Jia He ◽  
Xue Feng Chen ◽  
Ke Yu Qi
Keyword(s):  
Mechanical Vibration ◽  
Vibration Signal ◽  
Analysis Method ◽  
Rolling Bearings ◽  
High Frequency Signal ◽  
Outer Race ◽  
Mode Decomposition ◽  
Envelope Signal ◽  
Inner Race ◽  
Emd Method

Empirical mode decomposition (EMD) method is introduced, and a new EMD based approach for damage detection of rolling bearings is presented. In this approach, the characteristic high-frequency signal with amplitude modulation of rolling bearings with local damage is separated from the mechanical vibration signal as an intrinsic mode function (IMF) by using EMD, and an envelope signal can be obtained by using Hilbert transform. Then, the characteristic frequency of damage of rolling bearings is extracted by applying Fourier transform to the envelope signal. The presented approach is used to analyse experimental signals collected from rolling bearings with outer race damage or inner race damage, and the results indicate that the EMD based approach can detect damage of rolling bearings more effectively comparing with traditional envelope analysis method.

The Mechanical Fault Diagnosis Method Based on EMD and Time Series Analysis

2014 ◽  
Vol 602-605 ◽  
pp. 2330-2333 ◽  
Author(s):  
Jun Ma ◽  
Shi Hai Zhang
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Time Series Analysis ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Analysis Method ◽  
Series Analysis ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

It is the precondition of vibration fault diagnosis technology that appropriate signal analysis method is applied to separate mechanical fault character message from vibration monitoring signal. Based on the characteristics of multi-exciting, multi-model, non-stationary, nonlinear of the complex mechanical vibration signal, the EMD (Empirical Mode Decomposition) method is firstly applied to decompose the most refined IMF (Intrinsic Mode Function) components of vibration monitoring signal, and then the time series analysis method is applied to estimate power spectrums of IMF components and separate the fault character messages. The feasibility and advantage of the associated method are proved by analyzing the diesel engine crankshaft vibration monitoring signal in the paper.

Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

2014 ◽  
Vol 1014 ◽  
pp. 501-504 ◽  
Author(s):  
Shu Guo ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Classification Performance ◽  
Outer Race ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

scholarly journals Multiple Fault Detection of Rolling Bearing through Ensemble Empirical Mode Decomposition of Vibration Signal

2019 ◽  
Vol 9 (2) ◽  
pp. 2724-2726
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Driven ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Multiple Faults ◽  
Mode Decomposition

Generally, two or more faults occur simultaneously in the bearings. These Compound Faults (CF) in bearing, are most difficult type of faults to detect, by any data-driven method including machine learning. Hence, it is a primary requirement to decompose the fault vibration signals logically, so that frequencies can be grouped in parts. Empirical Mode Decomposition (EMD) is one of the simplest techniques of decomposition of signals. In this paper we have used Ensemble Empirical Mode Decomposition (EEMD) technique for compound fault detection/identification. Ensembled Empirical Mode Decomposition is found useful, where a white noise helps to detect the bearing frequencies. The graphs show clearly the capability of EEMD to detect the multiple faults in rolling bearings.

The Research on the Methodology of Diagnosing the Fault of Bearing in Warships Based on NS-EMD

2013 ◽  
Vol 711 ◽  
pp. 352-357
Author(s):  
Jin E Huang ◽  
Dong Xu ◽  
Yan Lei Wang ◽  
Yang Zhang ◽  
Shuang Wang
Keyword(s):  
Signal Processing ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Processing Method ◽  
Image Method ◽  
Vibration Source ◽  
Rolling Bearings ◽  
Average Value ◽  
Signal Processing Method ◽  
Emd Method

EMD is now a commonly used nonlinear and instable signal processing method, but it has boundary runaway and modal aliasing. The single disunited IMF cannot well reflect the characteristics of the respective vibration source. Therefore, in order to suppress the boundary runaway that will appear in the process of EMD, the image method is used to extend the length of signal data. To solve the modal aliasing, it is necessary to decompose the extended data by the EMD method, to distinguish the IMF that produces modal aliasing after decomposition, to integrate it according to the integrity of the EMD and then to re-decompose it after adding broadband white noise with the average value of zero. On the basis of that, it is better to improve NS-EMD method and realize the AM-FM demodulation by standardized method. By the spectrum analysis, we extract the fault characteristics of rolling bearings and propose a method to diagnose faults of rolling bearing. The results of analyzing the simulation and the vibration signal of fault rolling bearing shows that the method can effectively extract fault characteristics of rolling bearing.

Statistical and frequency analysis of vibrations signals of roller bearings using empirical mode decomposition

2019 ◽  
Vol 233 (4) ◽  
pp. 856-870 ◽  
Author(s):  
Parbant Singh ◽  
SP Harsha
Keyword(s):  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Effective Parameters ◽  
Statistical Parameters ◽  
Crest Factor ◽  
Outer Race ◽  
Bearing Defects ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

In the present work, defect detection in rolling bearing using empirical mode decomposition of vibration signal data has been done. Higher order statistical parameters viz root mean square, kurtosis, skewness, and crest factor are utilized to diagnose bearing fault. Simulated bearing defects as spall on outer race, on roller, and on inner race are used for the study. For experimental study, four different load and speed combination have been chosen to widen the acceptability of results. The effect of bearing speed on statistical parameters is also studied. Effectiveness of signal decomposition by the empirical mode decomposition method has been established by the results. Kurtosis and crest factor values of decomposed and unprocessed signals have been selected and empirical mode decomposition-based values are shown as effective parameters for defect identification. The crest factor and Kurtosis of outer race defect show greater sensitivity to the load and speed variations, while the skewness of same defect shows its insensitivity to load and speed variations.

scholarly journals A Novel Method Based on Multi-Island Genetic Algorithm Improved Variational Mode Decomposition and Multi-Features for Fault Diagnosis of Rolling Bearing

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 995 ◽  
Author(s):  
Tao Liang ◽  
Hao Lu
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Recognition Rate ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Feature Extraction Method ◽  
Mode Decomposition

Aiming at the problem that it is difficult to extract fault features from the nonlinear and non-stationary vibration signals of wind turbine rolling bearings, which leads to the low diagnosis and recognition rate, a feature extraction method based on multi-island genetic algorithm (MIGA) improved variational mode decomposition (VMD) and multi-features is proposed. The decomposition effect of the VMD method is limited by the number of decompositions and the selection of penalty factors. This paper uses MIGA to optimize the parameters. The improved VMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMF), and a group of components containing the most information is selected through the Holder coefficient. For these components, multi-features based on Renyi entropy feature, singular value feature, and Hjorth parameter feature are extracted as the final feature vector, which is input to the classifier to realize the fault diagnosis of rolling bearing. The experimental results prove that the proposed method can more effectively extract the fault characteristics of rolling bearings. The fault diagnosis model based on this method can accurately identify bearing signals of 16 different fault types, severity, and damage points.

New Algorithm to Identify Inrush Current Based on Improved EMD

2012 ◽  
Vol 433-440 ◽  
pp. 6927-6934
Author(s):  
Da Zhuang Chen ◽  
Jia Dong Huang ◽  
Yang Sun
Keyword(s):  
Data Driven ◽  
Power Transformers ◽  
Inrush Current ◽  
Analysis Method ◽  
Hilbert Huang Transform ◽  
The Core ◽  
Mode Decomposition ◽  
Multicomponent Signals ◽  
Frequency Components ◽  
Emd Method

Empirical mode decomposition (EMD), which is the core mechanic of the Hilbert-Huang transform(HHT), is a local, fully data driven and self-adaptive analysis approach. It is a powerful tool for analyzing multi-component signals. Aiming at the reduction of scale mixing and artificial frequency components, an improved scheme was proposed for analysis and reconstruction of nonstationary and multicomponent signals. The improved EMD method uses the wavelet analysis method and normalized correlation coefficient to deal with the problems. Because the inrush current is a peaked wave with nonstationary component, a new algorithm based on improved EMD is presented for fast discrimination between inrush current and fault current of power transformers. Theoretical analysis and dynamic simulation results show that the method is effective and reliable under various fault conditions and simple to be applied.

Antifriction Bearings Damage Analysis Using Experimental Data Based Models

2013 ◽  
Vol 135 (4) ◽  
Author(s):  
R. G. Desavale ◽  
R. Venkatachalam ◽  
S. P. Chavan
Keyword(s):  
Experimental Data ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Damage Analysis ◽  
Maintenance Strategies ◽  
Outer Race ◽  
Analysis Techniques ◽  
Catastrophic Failures ◽  
Original Contribution ◽  
Inner Race

Diagnosis of antifriction bearings is usually performed by means of vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. The aim is to monitor the integrity of the bearing components, in order to avoid catastrophic failures, or to implement condition based maintenance strategies. In particular, the trend in this field is to combine in a simple theory the different signal-enhancement and signal-analysis techniques. The experimental data based model (EDBM) has been pointed out as a key tool that is able to highlight the effect of possible damage in one of the bearing components within the vibration signal. This paper presents the application of the EDBM technique to signals collected on a test-rig, and be able to test damaged fibrizer roller bearings in different working conditions. The effectiveness of the technique has been tested by comparing the results of one undamaged bearing with three bearings artificially damaged in different locations, namely on the inner race, outer race, and rollers. Since EDBM performances are dependent on the filter length, the most suitable value of this parameter is defined on the basis of both the application and measured signals. This paper represents an original contribution of the paper.

scholarly journals Incipient Fault Diagnosis of Rolling Bearings Based on Impulse-Step Impact Dictionary and Re-Weighted Minimizing Nonconvex Penalty Lq Regular Technique

Entropy ◽  
2017 ◽  
Vol 19 (8) ◽  
pp. 421 ◽  
Author(s):  
Qing Li ◽  
Steven Liang
Keyword(s):  
Fault Diagnosis ◽  
Matching Pursuit ◽  
Vibration Signal ◽  
L1 Norm ◽  
Rolling Bearings ◽  
Mode Decomposition ◽  
Incipient Fault ◽  
Nonconvex Penalty ◽  
Treat All ◽  
Incipient Fault Diagnosis

The periodical transient impulses caused by localized faults are sensitive and important characteristic information for rotating machinery fault diagnosis. However, it is very difficult to accurately extract transient impulses at the incipient fault stage because the fault impulse features are rather weak and always corrupted by heavy background noise. In this paper, a new transient impulse extraction methodology is proposed based on impulse-step dictionary and re-weighted minimizing nonconvex penalty Lq regular (R-WMNPLq, q = 0.5) for the incipient fault diagnosis of rolling bearings. Prior to the sparse representation, the original vibration signal is preprocessed by the variational mode decomposition (VMD) technique. Due to the physical mechanism of periodic double impacts, including step-like and impulse-like impacts, an impulse-step impact dictionary atom could be designed to match the natural waveform structure of vibration signals. On the other hand, the traditional sparse reconstruction approaches such as orthogonal matching pursuit (OMP), L1-norm regularization treat all vibration signal values equally and thus ignore the fact that the vibration peak value may have more useful information about periodical transient impulses and should be preserved at a larger weight value. Therefore, penalty and smoothing parameters are introduced on the reconstructed model to guarantee the reasonable distribution consistence of peak vibration values. Lastly, the proposed technique is applied to accelerated lifetime testing of rolling bearings, where it achieves a more noticeable and higher diagnostic accuracy compared with OMP, L1-norm regularization and traditional spectral Kurtogram (SK) method.

Research of the Transient Signal Recognition Based on Empirical Mode Decomposition

2014 ◽  
Vol 548-549 ◽  
pp. 1173-1178
Author(s):  
Wan Jin Wang ◽  
Kui Feng Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Transient Signal ◽  
Transient Signals ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Time Frequency Localization ◽  
Stationary Vibration ◽  
The Time Domain

This paper introduces the empirical mode decomposition (EMD) method of the basic theory, problems and means to solve. Apply the approach to mechanical vibration signal containing a transient pulse processing and analysis carried out, and the wavelet time-frequency analysis methods are compared, the results show that it can effectively decompose nonlinear and non-stationary vibration signals, and has a self-adaptive, and in the time domain and frequency domain have better resolution capabilities, and the component with a more clear physical meaning. Due to its diversity of showing the results, you can make further precise analysis of a single component, and the transient signals can be effectively recognized, and can locate mutation point in time, describing the time-frequency localization properties. EMD, transient signals, mechanical vibration

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