scholarly journals Reconstruction of angular speed variations in the angular domain to diagnose and quantify taper roller bearing outer race fault

2019 ◽  
Vol 120 ◽  
pp. 1-15 ◽  
Author(s):  
Adeline Bourdon ◽  
Simon Chesné ◽  
Hugo André ◽  
Didier Rémond
Keyword(s):  
Roller Bearing ◽  
Angular Speed ◽  
Angular Domain ◽  
Outer Race ◽  
Taper Roller Bearing

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.

Thermal Modeling of a Railroad Tapered-Roller Bearing Using Finite Element Analysis

2012 ◽  
Vol 4 (3) ◽  
Author(s):  
Constantine M. Tarawneh ◽  
Arturo A. Fuentes ◽  
Javier A. Kypuros ◽  
Lariza A. Navarro ◽  
Andrei G. Vaipan ◽  
...  
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Surface Temperature ◽  
Elevated Temperatures ◽  
Roller Bearing ◽  
Fe Model ◽  
Element Analysis ◽  
Tapered Roller Bearing ◽  
Outer Race ◽  
Railroad Industry

In the railroad industry, distressed bearings in service are primarily identified using wayside hot-box detectors (HBDs). Current technology has expanded the role of these detectors to monitor bearings that appear to “warm trend” relative to the average temperatures of the remainder of bearings on the train. Several bearings set-out for trending and classified as nonverified, meaning no discernible damage, revealed that a common feature was discoloration of rollers within a cone (inner race) assembly. Subsequent laboratory experiments were performed to determine a minimum temperature and environment necessary to reproduce these discolorations and concluded that the discoloration is most likely due to roller temperatures greater than 232 °C (450 °F) for periods of at least 4 h. The latter finding sparked several discussions and speculations in the railroad industry as to whether it is possible to have rollers reaching such elevated temperatures without heating the bearing cup (outer race) to a temperature significant enough to trigger the HBDs. With this motivation, and based on previous experimental and analytical work, a thermal finite element analysis (FEA) of a railroad bearing pressed onto an axle was conducted using ALGOR 20.3™. The finite element (FE) model was used to simulate different heating scenarios with the purpose of obtaining the temperatures of internal components of the bearing assembly, as well as the heat generation rates and the bearing cup surface temperature. The results showed that, even though some rollers can reach unsafe operating temperatures, the bearing cup surface temperature does not exhibit levels that would trigger HBD alarms.

scholarly journals A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
HungLinh Ao ◽  
Junsheng Cheng ◽  
Kenli Li ◽  
Tung Khac Truong
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Roller Bearing ◽  
Local Characteristic ◽  
Support Vector ◽  
Svm Classifier ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Energy Entropy

This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

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 Modeling and Dynamic Analysis of Spherical Roller Bearing with Localized Defects: Analytical Formulation to Calculate Defect Depth and Stiffness

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Behnam Ghalamchi ◽  
Jussi Sopanen ◽  
Aki Mikkola
Keyword(s):  
Dynamic Model ◽  
Contact Stiffness ◽  
Roller Bearing ◽  
Measurement Data ◽  
Contact Forces ◽  
Hertzian Contact ◽  
Radial Clearance ◽  
Outer Race ◽  
Rolling Elements ◽  
Localized Defects

Since spherical roller bearings can carry high load in both axial and radial direction, they are increasingly used in industrial machineries and it is becoming important to understand the dynamic behavior of SRBs, especially when they are affected by internal imperfections. This paper introduces a dynamic model for an SRB that includes an inner and outer race surface defect. The proposed model shows the behavior of the bearing as a function of defect location and size. The new dynamic model describes the contact forces between bearing rolling elements and race surfaces as nonlinear Hertzian contact deformations, taking radial clearance into account. Two defect cases were simulated: an elliptical surface on the inner and outer races. In elliptical surface concavity, it is assumed that roller-to-race-surface contact is continuous as each roller passes over the defect. Contact stiffness in the defect area varies as a function of the defect contact geometry. Compared to measurement data, the results obtained using the simulation are highly accurate.

Experimental Investigation of Noise Sources in a Needle Roller Bearing System

1999 ◽  
Author(s):  
Niccolò Baldanzini ◽  
Federico Beraldo ◽  
Monica Carfagni
Keyword(s):  
Experimental Investigation ◽  
Online Monitoring ◽  
Roller Bearing ◽  
Practical Method ◽  
Sound Power ◽  
Noise Emission ◽  
Noise Sources ◽  
Outer Race ◽  
Bearing System ◽  
Race Analysis

Abstract An experimental investigation was undertaken to determine the causes of noise emission scatter in hosiery machines. Following the experimental measurement of the sound power levels, the hosiery machine’s mechanical system was assembled and tested with components of various sizes. The results indicated that the source of the noise emissions was a bearing’s outer race. Analysis of the outer race’s roundness profile in relation to vibrations provided accurate predictions of machine behavior. On the basis of a correlation between noise and vibrations, a practical method of online monitoring was developed.

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