Signal Processing for Enhancing Impulsiveness Toward Estimating Location of Multiple Roller Defects in a Taper Roller Bearing

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Anil Kumar ◽  
Rajesh Kumar
Keyword(s):  
Signal Processing ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Inverse Filtering ◽  
Processing Scheme ◽  
Adaptive Wavelet ◽  
Rolling Element ◽  
Taper Roller Bearing ◽  
Defect Frequency

Abstract Rolling element defect identification is a difficult task. The reason being that defect on the rolling element has both rotational as well as revolutionary motion. To identify rolling element defect in a taper roller bearing, a novel signal processing scheme is proposed which results in a substantial increase in kurtosis and impulse factor of the vibration signal. The scheme constitutes a series of operations. In the beginning, the raw signal is decomposed by ensemble empirical mode decomposition (EEMD) and inverse filtering (INF). The above two stages of signal processing extract hidden impulses which are suppressed in the noise present in the experimental data. In the third stage of processing, continuous wavelet transform (CWT) using adaptive wavelet is applied to the preprocessed signal to produce a 2D map of the CWT scalogram. This transformation results in a higher coefficient in the region of impulse produced due to the defect. Finally, time marginal integration (TMI) of the CWT scalogram is carried out for defect localization. The defect frequency was evaluated with an accuracy of 97.81% and defect location was identified with an accuracy of 92%.

An Enhanced Extraction Method Based on EEMD for Processing a Bearing Vibration Signal With Multiple Vibration Sources

2014 ◽  
Author(s):  
Wei Guo
Keyword(s):  
Empirical Mode Decomposition ◽  
Early Stage ◽  
Similarity Criterion ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Combination Technique ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Eemd Method ◽  
Effective Signal

Condition monitoring and fault diagnosis for rolling element bearings is an imperative part for preventive maintenance procedures and reliability improvement of rotating machines. When a localized fault occurs at the early stage of real bearing failures, the impulses generated by the defect are relatively weak and usually overwhelmed by large noise and other higher-level macro-structural vibrations generated by adjacent machine components and machines. To indicate the bearing faulty state as early as possible, it is necessary to develop an effective signal processing method for extracting the weak bearing signal from a vibration signal containing multiple vibration sources. The ensemble empirical mode decomposition (EEMD) method inherits the advantage of the popular empirical mode decomposition (EMD) method and can adaptively decompose a multi-component signal into a number of different bands of simple signal components. However, the energy dispersion and many redundant components make the decomposition result obtained by the EEMD losing the physical significance. In this paper, to enhance the decomposition performance of the EEMD method, the similarity criterion and the corresponding combination technique are proposed to determine the similar signal components and then generate the real mono-component signals. To validate the effectiveness of the proposed method, it is applied to analyze raw vibration signals collected from two faulty bearings, each of which involves more than one vibration sources. The results demonstrate that the proposed method can accurately extract the bearing feature signal; meanwhile, it makes the physical meaning of each IMF clear.

The Combination of Empirical Mode Decomposition and Minimum Entropy Deconvolution for Roller Bearing Diagnostics in Non-Stationary Operation

2012 ◽  
Author(s):  
R. Ricci ◽  
P. Borghesani ◽  
S. Chatterton ◽  
P. Pennacchi
Keyword(s):  
Signal Processing ◽  
Empirical Mode Decomposition ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Minimum Entropy ◽  
Bearing Diagnostics ◽  
Mode Decomposition ◽  
Data Adaptive ◽  
The Hilbert Transform ◽  
Processing Techniques

Diagnostics is based on the characterization of mechanical system condition and allows early detection of a possible fault. Signal processing is an approach widely used in diagnostics, since it allows directly characterizing the state of the system. Several types of advanced signal processing techniques have been proposed in the last decades and added to more conventional ones. Seldom, these techniques are able to consider non-stationary operations. Diagnostics of roller bearings is not an exception of this framework. In this paper, a new vibration signal processing tool, able to perform roller bearing diagnostics in whatever working condition and noise level, is developed on the basis of two data-adaptive techniques as Empirical Mode Decomposition (EMD), Minimum Entropy Deconvolution (MED), coupled by means of the mathematics related to the Hilbert transform. The effectiveness of the new signal processing tool is proven by means of experimental data measured in a test-rig that employs high power industrial size components.

Oscillatory behavior-based wavelet decomposition for the monitoring of bearing condition in centrifugal pumps

2017 ◽  
Vol 232 (6) ◽  
pp. 757-772 ◽  
Author(s):  
Anil Kumar ◽  
Rajesh Kumar
Keyword(s):  
Signal Processing ◽  
Wavelet Decomposition ◽  
Simulation Analysis ◽  
Vibration Signal ◽  
Frequency Factor ◽  
Oscillatory Behavior ◽  
Q Factor ◽  
Centrifugal Pumps ◽  
Processing Scheme ◽  
Bearing Defect

Bearing failure is one of the reasons for centrifugal pump breakdown. Existing methods developed for bearing fault diagnosis do not work satisfactorily when the vibration signature of bearing is overlapped by the signature from other defect sources such as an impeller defect. A vibration signal processing scheme making use of ensemble empirical mode decomposition and dual Q-factor wavelet decomposition is proposed to extract information of the bearing defect in a pump. A criterion called as frequency factor is also proposed to find the best decomposition level for the given high and low Q-factor wavelet decomposition parameters. The transient impulses due to bearing defect are effectively extracted separating traces of oscillatory signature of impeller defect and the noise in the signal. The same has been demonstrated using simulation analysis and experimental study. A comparison of the proposed method with existing signal processing methods is also presented.

scholarly journals A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3994 ◽  
Author(s):  
Dong Zhen ◽  
Junchao Guo ◽  
Yuandong Xu ◽  
Hao Zhang ◽  
Fengshou Gu
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Weighted Average ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Fault Features ◽  
Rolling Element ◽  
Vibration Signature ◽  
Stationary Vibration

To realize the accurate fault detection of rolling element bearings, a novel fault detection method based on non-stationary vibration signal analysis using weighted average ensemble empirical mode decomposition (WAEEMD) and modulation signal bispectrum (MSB) is proposed in this paper. Bispectrum is a third-order statistic, which can not only effectively suppress Gaussian noise, but also help identify phase coupling. However, it cannot effectively decompose the modulation components which are inherent in vibration signals. To alleviate this issue, MSB based on the modulation characteristics of the signals is developed for demodulation and noise reduction. Still, the direct application of MSB has some interfering frequency components when extracting fault features from non-stationary signals. Ensemble empirical mode decomposition (EEMD) is an advanced nonlinear and non-stationary signal processing approach that can decompose the signal into a list of stationary intrinsic mode functions (IMFs). The proposed method takes advantage of WAEEMD and MSB for bearing fault diagnosis based on vibration signature analysis. Firstly, the vibration signal is decomposed into IMFs with a different frequency band using EEMD. Then, the IMFs are reconstructed into a new signal by the weighted average method, called WAEEMD, based on Teager energy kurtosis (TEK). Finally, MSB is applied to decompose the modulated components in the reconstructed signal and extract the fault characteristic frequencies for fault detection. Furthermore, the efficiency and performance of the proposed WAEEMD-MSB approach is demonstrated on the fault diagnosis for a motor bearing outer race fault and a gearbox bearing inner race fault. The experimental results verify that the WAEEMD-MSB has superior performance over conventional MSB and EEMD-MSB in extracting fault features and has precise and effective advantages for rolling element bearing fault detection.

Experimental and numerical investigation of defect-size estimation in taper roller bearing

2018 ◽  
Vol 49 (11) ◽  
pp. 345-354 ◽  
Author(s):  
Ahmed Nabhan ◽  
Ahmed Rashed
Keyword(s):  
Numerical Model ◽  
Contact Time ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Close Correlation ◽  
Defect Size ◽  
Size Estimation ◽  
Vibration Signals ◽  
Outer Race ◽  
Taper Roller Bearing

In this article, the estimation of different defect sizes present on the outer race of taper roller bearing confirms the effectiveness of the applied method for different vibration signals. Experiments and numerical model conducted for three primary conditions of bearing setting, which are positive, negative, and zero clearance. The outer race is installed in five different positions so that the defect located at 0°, 45°, 90°, 135°, and 180°. The output of the numerical model finds close correlation with the vibration signal pattern–obtained experiments. From the results, it is clear that defect-size estimation is more precise when the defect is introduced in the unloading area, and the contact time depends directly on the size of the defect, through which it is easy to calculate its value of the defect.

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