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.

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 Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Achmad Widodo ◽  
Djoeli Satrijo ◽  
Toni Prahasto ◽  
Gang-Min Lim ◽  
Byeong-Keun Choi
Keyword(s):  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Self Organizing Map ◽  
Fault Diagnostics ◽  
Vibration Signals ◽  
Thermal Images ◽  
Outer Race ◽  
Intelligent Machine ◽  
Machine Fault ◽  
The Time Domain

This paper deals with the maintenance technique for industrial machinery using the artificial neural network so-called self-organizing map (SOM). The aim of this work is to develop intelligent maintenance system for machinery based on an alternative way, namely, thermal images instead of vibration signals. SOM is selected due to its simplicity and is categorized as an unsupervised algorithm. Following the SOM training, machine fault diagnostics is performed by using the pattern recognition technique of machine conditions. The data used in this work are thermal images and vibration signals, which were acquired from machine fault simulator (MFS). It is a reliable tool and is able to simulate several conditions of faulty machine such as unbalance, misalignment, looseness, and rolling element bearing faults (outer race, inner race, ball, and cage defects). Data acquisition were conducted simultaneously by infrared thermography camera and vibration sensors installed in the MFS. The experimental data are presented as thermal image and vibration signal in the time domain. Feature extraction was carried out to obtain salient features sensitive to machine conditions from thermal images and vibration signals. These features are then used to train the SOM for intelligent machine diagnostics process. The results show that SOM can perform intelligent fault diagnostics with plausible accuracies.

Wind turbine bearing fault diagnosis based on adaptive local iterative filtering and approximate entropy

2016 ◽  
Vol 231 (17) ◽  
pp. 3228-3237 ◽  
Author(s):  
Xueli An ◽  
Luoping Pan
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Nearest Neighbor ◽  
Roller Bearing ◽  
Approximate Entropy ◽  
Vibration Signals ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Iterative Filtering

For the unsteady characteristics of a fault vibration signal from a wind turbine rolling bearing, a bearing fault diagnosis method based on adaptive local iterative filtering and approximate entropy is proposed. The adaptive local iterative filtering method is used to decompose original vibration signals into a finite number of stationary components. The components which comprise major fault information are selected for further analysis. The approximate entropy of the selected components is calculated as a fault feature value and input to a fault classifier. The classifier is based on the nearest neighbor algorithm. The vibration signals from a spherical roller bearing on a wind turbine in its normal state, with an outer race fault, an inner race fault and a roller fault are analyzed. The results show that the proposed method can accurately and efficiently identify the fault modes present in the rolling bearings of a wind turbine.

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%.

scholarly journals Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery

2009 ◽  
Vol 16 (1) ◽  
pp. 89-98 ◽  
Author(s):  
Junsheng Cheng ◽  
Dejie Yu ◽  
Jiashi Tang ◽  
Yu Yang
Keyword(s):  
Fault Diagnosis ◽  
Feature Vector ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Singular Values ◽  
Rotating Machinery ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD) techniques, the SVD technique based on empirical mode decomposition (EMD) is applied to the fault feature extraction of the rotating machinery vibration signals. The EMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices could be formed automatically. By applying the SVD technique to the initial feature vector matrices, the singular values of matrices could be obtained, which could be used as the fault feature vectors of support vector machines (SVMs) classifier. The analysis results from the gear and roller bearing vibration signals show that the fault diagnosis method based on EMD, SVD and SVM can extract fault features effectively and classify working conditions and fault patterns of gears and roller bearings accurately even when the number of samples is small.

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.

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