Compound faults detection of the rolling element bearing based on the optimal complex Morlet wavelet filter

2017 ◽  
Vol 232 (10) ◽  
pp. 1786-1801 ◽  
Author(s):  
Xiaohui Gu ◽  
Shaopu Yang ◽  
Yongqiang Liu ◽  
Feiyue Deng ◽  
Bin Ren
Keyword(s):  
Particle Filtering ◽  
Morlet Wavelet ◽  
Previous Method ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Wavelet Filter ◽  
Outer Race ◽  
Rolling Element ◽  
Complex Morlet Wavelet ◽  
Envelope Spectrum

Wavelet filter is widely used in extracting fault features embedded in the noisy vibration signal, especially the complex Morlet wavelet. In most occasions, the filter parameters are optimized adaptively with a suitable objective function. And then, with the Hilbert transform demodulation analysis, the single localized fault in rolling element bearings can be detected. To extend it for compound faults detection, a novel index deduced from the different intervals of the prominent bearing fault frequencies and subsequent harmonics in the envelope spectrum is proposed. By maximizing the ratio of correlated kurtosis to kurtosis of the envelope spectrum amplitudes of the filtered signal, the optimal complex Morlet wavelet filters corresponding to the different faults are designed by the particle filtering method, respectively. Two cases of real signals are analyzed to evaluate the performance of the proposed method, which include one case of experiment signal with artificial outer race fault coupled with roller fault, as well as one case of engineering data with outer race fault coupled with inner race fault. Furthermore, some comparisons with a previous method are also conducted. The results demonstrate the effectiveness and robustness of the method in compound faults diagnosis of the rolling element bearings.

New Feature Extraction Method for the Detection of Defects in Rolling Element Bearings

2012 ◽  
Vol 134 (8) ◽  
Author(s):  
Ling Xiang ◽  
Aijun Hu
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Intrinsic Mode Functions ◽  
Feature Extraction Method ◽  
Outer Race ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Envelope Spectrum ◽  
Detection Of Defects

This paper proposes a new method based on ensemble empirical mode decomposition (EEMD) and kurtosis criterion for the detection of defects in rolling element bearings. Some intrinsic mode functions (IMFs) are presented to obtain symptom wave by EEMD. The different kurtosis of the intrinsic mode function is determined to select the envelope spectrum. The fault feature based on the IMF envelope spectrum whose kurtosis is the maximum is extracted, and fault patterns of roller bearings can be effectively differentiated. Practical examples of diagnosis for a rolling element bearing are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race and inner-race, can be effectively identified by the proposed method.

Spalling Size Evaluation of Rolling Element Bearing Using Acoustic Emission

2013 ◽  
Vol 569-570 ◽  
pp. 497-504 ◽  
Author(s):  
An Bo Ming ◽  
Zhao Ye Qin ◽  
Wei Zhang ◽  
Fu Lei Chu
Keyword(s):  
Acoustic Emission ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Outer Race ◽  
Cepstrum Analysis ◽  
Rolling Element ◽  
Rolling Elements ◽  
Element Bearing ◽  
Envelope Spectrum ◽  
Size Evaluation

Spalling of the races or rolling elements is one of the most common faults in rolling element bearings. Exact estimation of the spall size is helpful to the life prediction for rolling element bearings. In this paper, the dual-impulsive phenomenon in the response of a spalled rolling element bearing is investigated experimentally, where the acoustic emission signals are utilized. A new method is proposed to estimate the spall size by extracting the envelope of harmonics of the ball passing frequency on the outer race from the squared envelope spectrum. Compared with the cepstrum analysis, the proposed procedure shows more powerful anti-noise ability in the fault size evaluation.

Multi-Objective Design Optimization of Rolling Element Bearings Using ABC, AIA and PSO Technique

2013 ◽  
Vol 2 (3) ◽  
pp. 102-125
Author(s):  
Vimal Savsani
Keyword(s):  
High Reliability ◽  
Optimization Technique ◽  
Operating Conditions ◽  
Rolling Element Bearing ◽  
Design Parameters ◽  
Rolling Element Bearings ◽  
Automotive Manufacturing ◽  
Outer Race ◽  
Multi Objective ◽  
Rolling Element

Rolling element bearings are widely used as important components in most of the mechanical engineering applications. These bearings find wide applications in automotive, manufacturing and aeronautical industries. The problem associated with rolling element bearings are that the design and selection are based on different operating conditions to reach their excellent performance, long life and high reliability. This leads to the requirement of optimal design of rolling element bearings. Optimization aspects of a rolling element bearing are presented in this paper considering three different objectives namely, dynamic capacity, static capacity and elastohydrodynamic minimum film thickness. The design parameters include mean diameter of rolling, ball diameter, number of balls, and inner and outer race groove curvature radii. Different constants associated with the constraints are given some ranges and are included as design variables. The optimization procedure is carried out using artificial bee colony (ABC) optimization technique, artificial immune algorithm (AIA), and particle swarm optimization (PSO) technique. Both single and multi-objective optimization aspects are considered. The results of the considered techniques are compared with the previously published results. The considered techniques have given much better results in comparison to the previously tried approaches.

Double Impulses Extraction of Faulty Rolling Element Bearing Based on EEMD and Complex Morlet Wavelet

2014 ◽  
Vol 904 ◽  
pp. 437-441 ◽  
Author(s):  
You Bing Kong ◽  
Yu Guo ◽  
Wu Xing
Keyword(s):  
High Frequency ◽  
Random Noise ◽  
Ensemble Empirical Mode Decomposition ◽  
Morlet Wavelet ◽  
Rolling Element Bearing ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Element Bearing ◽  
Continuous Noise ◽  
Complex Morlet Wavelet

A method for the double impulses extraction of faulty rolling element bearing (REB) is proposed in this paper. In the proposed approach, the (Ensemble Empirical Mode Decomposition) EEMD are employed for filtering the random noise and high-frequency continuous noise without any mode mixing. Then, the extracted signal is filtered with complex Morlet wavelet, which enhanced the double impulses greatly. As a result, clear double impulses can be obtained through the envelope. The effectiveness of the approach is demonstrated by the simulation study.

Nonlinear Mechanics of Rolling Contacts With Surface Defects

2010 ◽  
Author(s):  
Mohsen Nakhaeinejad ◽  
Jaewon Choi ◽  
Michael D. Bryant
Keyword(s):  
Surface Defects ◽  
Nonlinear Behavior ◽  
Rolling Contact ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Detailed Model ◽  
Outer Race ◽  
Model Based ◽  
Rolling Contacts ◽  
Rolling Element

Nonlinear behavior of force and displacements in rolling contacts with the presence of surface defects are studied. Model-based fault assessments in rolling element bearings and gears require detailed modeling and dynamics of faults. A detailed model of rolling element bearings with direct correspondence between parameters of the model and physical components is developed. The model incorporates dynamics of faults, nonlinear contacts, slips and surface separations. Mechanics of contacts with inner race faults (IRF), ball faults (BF), and outer race faults (ORF) are studied using the developed model. Contacts force, displacement and impulse signals are studied for different size and types of surface defects. It is shown that impulse signals contain useful information about the severity of surface defects in rolling element bearing. Results provide model-based diagnostics a deep knowledge of rolling contact mechanics with surface defects to be used for fault assessments.

Dynamic Modeling of Rolling Element Bearings With Surface Contact Defects Using Bond Graphs

2010 ◽  
Vol 133 (1) ◽  
Author(s):  
Mohsen Nakhaeinejad ◽  
Michael D. Bryant
Keyword(s):  
Three Dimensional ◽  
Surface Profile ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Bond Graphs ◽  
Outer Race ◽  
Proposed Model ◽  
Rolling Element ◽  
Type Size ◽  
Localized Faults

Multibody dynamics of healthy and faulty rolling element bearings were modeled using vector bond graphs. A 33 degree of freedom (DOF) model was constructed for a bearing with nine balls and two rings (11 elements). The developed model can be extended to a rolling element bearing with n elements and (3×n) DOF in planar and (6×n) DOF in three dimensional motions. The model incorporates the gyroscopic and centrifugal effects, contact elastic deflections and forces, contact slip, contact separations, and localized faults. Dents and pits on inner race and outer race and balls were modeled through surface profile changes. Bearing load zones under various radial loads and clearances were simulated. The effects of type, size, and shape of faults on the vibration response in rolling element bearings and dynamics of contacts in the presence of localized faults were studied. Experiments with healthy and faulty bearings were conducted to validate the model. The proposed model clearly mimics healthy and faulty rolling element bearings.

Rolling element bearing fault detection based on the complex Morlet wavelet transform and performance evaluation using artificial neural network and support vector machine

2019 ◽  
Vol 50 (9-11) ◽  
pp. 313-327 ◽  
Author(s):  
Chandrabhanu Malla ◽  
Ankur Rai ◽  
Vaishali Kaul ◽  
Isham Panigrahi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Morlet Wavelet ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing ◽  
Complex Morlet Wavelet

Condition monitoring and fault diagnosis of rolling element bearings are very important to ensure proper working of different types of machinery. Condition monitoring of rotating machines is mainly based on the analysis of machine vibration. The vibration signals from the mechanical fault generally comprise periodic impulses with specified characteristic frequency corresponds to a particular defect. But due to heavy noise in the industry, the vibration signals have a very low signal-to-noise ratio. Hence, it requires an appropriate technique to extract the impulses from the noisy signal. This article emphasized on the fault diagnosis of rolling element bearings having some specific size of defects on various bearing elements using the complex Morlet wavelet analysis. The phase and amplitude map of the complex Morlet wavelet are utilized for identification and diagnosis of the fault in the rolling element bearing. The amplitude and phase map corresponding to the complex Morlet wavelet are found to show unique informative signatures in the presence of bearing faults. The classification technique based on artificial neural network and support vector machine for rolling element bearing fault detection is presented in this article. The classification results of bearing faults clearly indicate that support vector machine has a more precise bearing fault classification ability than artificial neural network.

Application of Degree of Cyclostationarity in Rolling Element Bearing Diagnosis

2005 ◽  
Vol 293-294 ◽  
pp. 347-354 ◽  
Author(s):  
Guo Bi ◽  
Jin Chen ◽  
Jun He ◽  
Fuchang Zhou ◽  
Gui Cai Zhang
Keyword(s):  
Frequency Domain ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Spectral Correlation ◽  
Outer Race ◽  
Cyclic Frequency ◽  
Bearing Condition Monitoring ◽  
Rolling Element ◽  
Rolling Elements ◽  
Element Bearing

Minor and random slip between rolling elements and races in rolling element bearings makes vibration signals have periodically time-varying ensemble statistics, which is known as cyclostationarity. Two second-order cyclostationary methods, the spectral correlation density (SCD) and the degree of cyclostationarity (DCS), are talked about in this paper based on a statistical model of rolling element bearings. The SCD provides redundant information in bi-frequency plane and cyclic frequency domain embodies the majority of it, which is a series of non-zero discrete cyclic frequencies completely reflecting the fault characters of rolling element bearings. The DCS has virtues of less computation and clearer representation, at the same time keeps the same characters with SCD in cyclic frequency domain. And the DCS is also proved to be resistant to the additive and multiplicative stationary noise. Simulation and experiential results from three rolling element bearing faults: outer race defect, inner race defect and rolling element defect, indicate practicability of the DCS analysis in rolling element bearing condition monitoring and fault diagnosis.

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