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.

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.

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.

Analysis of the conditions for the occurrence of the effect of a minimum of friction in hybrid bearings based on the load separation principle

2018 ◽  
Vol 233 (2) ◽  
pp. 271-280
Author(s):  
Roman Polyakov ◽  
Leonid Savin ◽  
Alex Fetisov
Keyword(s):  
Rolling Contact ◽  
Friction Reduction ◽  
Fluid Film ◽  
Rolling Element Bearing ◽  
Contact Friction ◽  
Rolling Element Bearings ◽  
Operating Characteristics ◽  
Fluid Film Bearings ◽  
Friction Regime ◽  
Rolling Element

Reliability of rotating machinery is determined to a considerable degree by the bearing units. For several applications the requirements in rotational speed, bearing load and maximal vibration level are so extreme that neither rolling-element bearings nor fluid-film bearings could provide necessary operating characteristics during all regimes of operation. Hybrid bearings, which are a combination of rolling-element and fluid-film bearings, can improve performance characteristics and reliability of the rotor-bearing systems. A hybrid bearing, where a rolling-element bearing and a fluid-film bearing are positioned parallel to the vector of external load (PLEX), has the following advantages compared to a single bearing, whether rolling-element or fluid-film one: increase of life expectancy, load capacity increase, friction reduction, thermal regime enhancement, increase of stiffness, and damping properties. The present paper presents the results of theoretical and numerical research of friction characteristics of PLEX in mixed sliding and rolling friction, i.e. combination of viscous and rolling contact friction, regime. The conditions of minimum friction effect occurrence have been substantiated, and rational relations between characteristics of hybrid rolling-element bearings and fluid-film bearings needed for provision of such effect have been experimentally proven. Finally, the paper presents recommendations regarding design of such hybrid bearings for heavily loaded bearing nodes of rolling mills.

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.

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.

Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission

2021 ◽  
pp. 107754632110161
Author(s):  
Aref Aasi ◽  
Ramtin Tabatabaei ◽  
Erfan Aasi ◽  
Seyed Mohammad Jafari
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Time Domain ◽  
Central Moment ◽  
Rolling Element Bearings ◽  
Test Rig ◽  
Acoustic Emission Sensor ◽  
Outer Race ◽  
Common Time ◽  
Rolling Element

Inspired by previous achievements, different time-domain features for diagnosis of rolling element bearings are investigated in this study. An experimental test rig is prepared for condition monitoring of angular contact bearing by using an acoustic emission sensor for this purpose. The acoustic emission signals are acquired from defective bearing, and the sensor takes signals from defects on the inner or outer race of the bearing. By studying the literature works, different domains of features are classified, and the most common time-domain features are selected for condition monitoring. The considered features are calculated for obtained signals with different loadings, speeds, and sizes of defects on the inner and outer race of the bearing. Our results indicate that the clearance, sixth central moment, impulse, kurtosis, and crest factors are appropriate features for diagnosis purposes. Moreover, our results show that the clearance factor for small defects and sixth central moment for large defects are promising for defect diagnosis on rolling element bearings.

Simultaneous Monitoring of Rolling-Element and Journal Bearings Using Analysis of Structure-Born Ultrasound Acoustic Emissions

2010 ◽  
Author(s):  
A. Albers ◽  
M. Dickerhof
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emissions ◽  
Piezoelectric Sensor ◽  
Journal Bearings ◽  
Rolling Element Bearings ◽  
Acoustic Emission Sensor ◽  
Outer Race ◽  
Rolling Element ◽  
Characteristic Values ◽  
Acoustic Emission Technology

The application of Acoustic Emission technology for monitoring rolling element or hydrodynamic plain bearings has been addressed by several authors in former times. Most of these investigations took place under idealized conditions, to allow the concentration on one single source of emission, typically recorded by means of a piezoelectric sensor. This can be achieved by either eliminating other sources in advance or taking measures to shield them out (e. g. by placing the acoustic emission sensor very close to the source of interest), so that in consequence only one source of structure-born sound is present in the signal. With a practical orientation this is often not possible. In point of fact, a multitude of potential sources of emission can be worth considering, unfortunately superimposing one another. The investigations reported in this paper are therefore focused on the simultaneous monitoring of both bearing types mentioned above. Only one piezoelectric acoustic emission sensor is utilized, which is placed rather far away from the monitored bearings. By derivation of characteristic values from the sensor signal, different simulated defects can be detected reliably: seeded defects in the inner and outer race of rolling element bearings as well as the occurrence of mixed friction in the sliding surface bearing due to interrupted lubricant inflow.

Two-step fault diagnosis framework for rolling element bearings with imbalanced data based on GSA-WELM and GSA-ELM

2017 ◽  
Vol 232 (16) ◽  
pp. 2937-2947 ◽  
Author(s):  
Yuan Lan ◽  
Xiaohong Han ◽  
Weiwei Zong ◽  
Xiaojian Ding ◽  
Xiaoyan Xiong ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Gravitational Search Algorithm ◽  
Imbalanced Data ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Misclassification Cost ◽  
Weighted Extreme Learning Machine ◽  
Rolling Element ◽  
Learning Machine

Rolling element bearings constitute the key parts on rotating machinery, and their fault diagnosis is of great importance. In many real bearing fault diagnosis applications, the number of fault data is much less than the number of normal data, i.e. the data are imbalanced. Many traditional diagnosis methods will get low accuracy because they have a natural tendency to favor the majority class by assuming balanced class distribution or equal misclassification cost. To deal with imbalanced data, in this article, a novel two-step fault diagnosis framework is proposed to diagnose the status of rolling element bearings. Our proposed framework consists of two steps for fault diagnosis, where Step 1 makes use of weighted extreme learning machine in an effort to classify the normal or abnormal categories, and Step 2 further diagnoses the underlying anomaly in detail by using preliminary extreme learning machine. In addition, gravitational search algorithm is applied to further extract the significant features and determine the optimal parameters of the weighted extreme learning machine and extreme learning machine classifiers. The effectiveness of our proposed approach is testified on the raw data collected from the rolling element bearing experiments conducted in our Institute, and the empirical results show that our approach is really fast and can achieve the diagnosis accuracies more than 96%.

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