Vibration Feature of Locomotive Axle Box with a Localized Defect in its Bearing Outer Race

2020 ◽  
Author(s):  
Yuqing Liu ◽  
Zaigang Chen
Keyword(s):  
Outer Race

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.

scholarly journals Gradual Wear Diagnosis of Outer-Race Rolling Bearing Faults through Artificial Intelligence Methods and Stray Flux Signals

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1486
Author(s):  
Israel Zamudio-Ramirez ◽  
Roque A. Osornio-Rios ◽  
Jose A. Antonino-Daviu ◽  
Jonathan Cureño-Osornio ◽  
Juan-Jose Saucedo-Dorantes
Keyword(s):  
Kinematic Chain ◽  
Industrial Applications ◽  
Electric Motors ◽  
Feed Forward Neural Network ◽  
Mechatronic Systems ◽  
Linear Discriminant ◽  
Bearing Faults ◽  
Outer Race ◽  
Non Invasive ◽  
Operation Conditions

Electric motors have been widely used as fundamental elements for driving kinematic chains on mechatronic systems, which are very important components for the proper operation of several industrial applications. Although electric motors are very robust and efficient machines, they are prone to suffer from different faults. One of the most frequent causes of failure is due to a degradation on the bearings. This fault has commonly been diagnosed at advanced stages by means of vibration and current signals. Since low-amplitude fault-related signals are typically obtained, the diagnosis of faults at incipient stages turns out to be a challenging task. In this context, it is desired to develop non-invasive techniques able to diagnose bearing faults at early stages, enabling to achieve adequate maintenance actions. This paper presents a non-invasive gradual wear diagnosis method for bearing outer-race faults. The proposal relies on the application of a linear discriminant analysis (LDA) to statistical and Katz’s fractal dimension features obtained from stray flux signals, and then an automatic classification is performed by means of a feed-forward neural network (FFNN). The results obtained demonstrates the effectiveness of the proposed method, which is validated on a kinematic chain (composed by a 0.746 KW induction motor, a belt and pulleys transmission system and an alternator as a load) under several operation conditions: healthy condition, 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm hole diameter on the bearing outer race, and 60 Hz, 50 Hz, 15 Hz and 5 Hz power supply frequencies

Analysis of faults in rotor-bearing system using three-level full factorial design and response surface methodology

2021 ◽  
pp. 095745652110307
Author(s):  
Hara P Mishra ◽  
Arun Jalan
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Fault Analysis ◽  
Vibration Response ◽  
Effect Analysis ◽  
Outer Race ◽  
Surface Plot ◽  
Rotor Bearing ◽  
Full Factorial ◽  
Bearing System

This article presents the experimental and statistical methodology for localized fault analysis in the rotor-bearing system. These defects on outer race, on inner race, and on a combination of ball and outer race are considered. In this study speed, load and defects were considered as the essential process variables to understand their significance and effects on vibration response for the rotor-bearing system. Three factors at three levels were considered for experimentation, and the experiment was designed for L27 based on design of experiments (DOE) methodology. From the experiments, the vibration response results are recorded in terms of root mean square value for the analysis. Response surface methodology (RSM) is used for identifying the interaction effect of varying process parameters upon the response of vibrations by response surface plot. The rotor-bearing test setup is used for experimentation and is analyzed by using DOE. This study establishes the prediction of fault in the rotor-bearing system in combined parametric effect analysis and its influence with DOE and RSM.

Roller Skewing Behavior in Roller Bearings

1982 ◽  
Vol 104 (3) ◽  
pp. 311-320
Author(s):  
L. J. Nypan
Keyword(s):  
Diameter Ratio ◽  
Radial Load ◽  
Outer Race ◽  
Roller Bearings ◽  
Length To Diameter Ratio

Measurements of roller skewing of a 1.15 length to diameter ratio roller in 118 mm bore roller bearings of 0.18 and 0.21 mm (0.0073 and 0.0083 in.) clearance operating with a 4450 N (1000 lb) radial load at shaft speeds of 4000, 8000, and 12,000 rpm with outer race misalignment of 0, 0.5, and −0.5 deg are reported.

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.

The Application of Local Mean Decomposition and Variable Predictive Model-Based Class Discriminate in Gear Fault Diagnosis

2014 ◽  
Vol 1014 ◽  
pp. 510-515 ◽  
Author(s):  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Shu Guo ◽  
Min Gou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Predictive Model ◽  
Feature Vector ◽  
Local Mean Decomposition ◽  
Outer Race ◽  
Model Based ◽  
Gear Fault ◽  
Local Mean ◽  
Gear Fault Diagnosis ◽  
Non Stationary Signal

The time-domain energy message conveyed by vibration signals of different gear fault are different, so a method based on local mean decomposition (LMD) and variable predictive model-based class discriminate (VPMCD) is proposed to diagnose gear fault model. The vibration signal of gear which is the research object in this paper is decomposed into a series of product functions (PF) by LMD method. Then a further analysis is to select the PF components which contain main fault information of gear, the energy feature parameters of the selected PF components are used to form a fault feature vector. The variable predictive model-based class discriminate is a new multivariate classification approach for pattern recognition, through taking fully advantages of the fault feature vector. Finally, gear fault diagnosis is distinguished into normal state, inner race fault and outer race fault. The results show that LMD method can decompose a complex non-stationary signal into a number of PF components whose frequency is from high to low. And the method based on LMD and VPMCD has a high fault recognition function by analyzing the fault feature vector of PF.

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.

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.

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