scholarly journals Dynamic Influence of Wheel Flat on Fatigue Life of the Traction Motor Bearing in Vibration Environment of a Locomotive

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5810
Author(s):  
Bingbin Guo ◽  
Zhixiang Luo ◽  
Bo Zhang ◽  
Yuqing Liu ◽  
Zaigang Chen
Keyword(s):  
Fatigue Life ◽  
Prediction Models ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Traction Motor ◽  
Gear Mesh ◽  
Impact Forces ◽  
Bearing Life ◽  
Bearing Loads ◽  
Wheel Flat

Wheel flat can cause a large impact between the wheel and rail and excites a forced vibration in the locomotive and track structure systems. The working conditions and fatigue life of the motor bearings are significantly affected by the intensified wheel–rail interaction via the transmission path of the gear mesh. In this study, a fatigue life prediction method of the traction motor bearings in a locomotive is proposed. Based on the L−P theory or ISO 281 combined with the Miner linear damage theory and vehicle–track coupled dynamics, the irregular loads induced by the track random irregularity and gear mesh are considered in this proposed method. It can greatly increase the accuracy of predictions compared with the traditional prediction models of a rolling bearing life whose bearing loads are assumed to be constant. The results indicate that the periodic impact forces and larger mesh forces caused by the wheel flat will reduce the fatigue life of the motor bearings, especially when the flat length is larger than 30 mm. Using this method, the effects of the flat length and relatively constant velocity of the locomotive are analyzed. The proposed method can provide a theoretical basis to guarantee safe and reliable working for motor bearings.

On the Accuracy of Rolling Bearing Fatigue Life Prediction

1996 ◽  
Vol 118 (2) ◽  
pp. 297-309 ◽  
Author(s):  
T. A. Harris ◽  
J. I. McCool
Keyword(s):  
Fatigue Life ◽  
Life Prediction ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Critical Design ◽  
Bearing Life ◽  
Limit Stress ◽  
Fatigue Endurance

Ball and roller bearings are designed to meet endurance requirements basically determined according to the Standard fatigue life calculation method. This method is based on the Lundberg-Palmgren fatigue life theory as modified by reliability, material, and lubrication factors. As application load and spied requirements have increased, the Lundberg-Palmgren method has resulted in bearings of increased size, adding unnecessarily to the size and weight of mechanisms. This is a critical design situation for weight and size-sensitive components such as aircraft gas turbine engines and helicopter power transmissions. The bearing life prediction method developed by Ioannides and Harris recognizes the existence of a fatigue limit stress. If the stresses an operating bearing experiences do not exceed the limit stress, the bearing can achieve infinite life. In any case, the method tends to predict longer lives than the Lundberg-Palmgren method. This paper evaluates the life prediction accuracies of the Lundberg-Palmgren and Ioannides-Harris methods by comparing lives calculated according to these methods and to those actually experienced in 62 different applications. As a result of the investigation, the Ioannides-Harris method is shown to more accurately predict bearing fatigue endurance.

A2BLT+F2AST: A Rapid Bearing Life and Reliability Evaluation Tool

2005 ◽  
Author(s):  
Xinglin Li
Keyword(s):  
Control System ◽  
Fatigue Life ◽  
Fatigue Failure ◽  
Rolling Bearing ◽  
Reliability Evaluation ◽  
Test Method ◽  
Evaluation Tool ◽  
Life Test ◽  
Bearing Life ◽  
Almost All

The fatigue failure of almost all mechanical parts is caused by the friction and wear of materials. Bearings are the most widely used machine elements and are critical to almost all forms of rotary machinery. This paper focuses on rolling bearing fatigue failure and its test methodologies. If we use normal life test method, under low load and high speed, the test period will be too long and the cost will be too much. But how do accelerate the test to shorten the test period? In this paper, the A2BLT+F2AST—a rapid bearing life and reliability evaluation tool is introduced, it is the integration of hardware, software and standards for solving problems of Accelerated Automatic rolling Bearing fatigue Life and reliability Tester and Fast Failure Analysis System Technology. A2BLT+F2AST—a rapid bearing life and reliability evaluation tool is successfully applied in more than one hundred rolling bearing manufactures and users all over the world. The ABLT series automatic control bearing fatigue life and reliability is successfully developed by our center based on the life tester and life test method of research. The certificate (ZS type, B type, ABLT type) by contrast test: Under the condition of keeping getting in touch with the consistent premise of failure mechanism, accelerating life test can shorten more than 80% test time compared with the normal regulations life test. It can also shorten the new product development period, especially for the automobile hub bearings. The ABLT are composed by test bed, test head, driving system, loading system, lubricating system, electrical control system and monitored control system.

scholarly journals Research on low cycle fatigue life prediction considering average strain

2022 ◽  
Author(s):  
Yan Peng ◽  
Yang Liu ◽  
Haoran Li ◽  
Jiankang Xing
Keyword(s):  
Fatigue Life ◽  
Life Prediction ◽  
Damage Mechanics ◽  
Prediction Models ◽  
Low Cycle Fatigue ◽  
Prediction Method ◽  
Fatigue Life Prediction ◽  
Model Parameters ◽  
Cycle Fatigue ◽  
Average Strain

Abstract To address the difficult problems in the study of the effect of average strain on fatigue life under low-cycle fatigue loads, the effect of average strain on the low-cycle fatigue life of materials under different strain cycle ratios was discussed based on the framework of damage mechanics and its irreversible thermodynamics. By introducing the Ramberg-Osgood cyclic constitutive equation, a new low-cycle fatigue life prediction method based on the intrinsic damage dissipation theory considering average strain was proposed, which revealed the correlation between low-cycle fatigue strain life , material properties, and average strain. Through the analysis of the low-cycle fatigue test data of five different metal materials, the model parameters of the corresponding materials were obtained. The calculation results indicate that the proposed life prediction method is in good agreement with the test, and a reasonable characterization of the low-cycle fatigue life under the influence of average strain is realized. Comparing calculations with three typical low-cycle fatigue life prediction models, the new method is within two times the error band, and the prediction effect is significantly better than the existing models, which is more suitable for low-cycle fatigue life prediction. The low-cycle fatigue life prediction of different cyclic strain ratios based on the critical region intrinsic damage dissipation power method provides a new idea for the research of low-cycle fatigue life prediction of metallic materials.

scholarly journals Dynamic analysis of traction motor in a locomotive considering surface waviness on races of a motor bearing

2021 ◽  
Author(s):  
Yuqing Liu ◽  
Zaigang Chen ◽  
Wei Li ◽  
Kaiyun Wang
Keyword(s):  
Power Transmission ◽  
Contact Stiffness ◽  
Coupled System ◽  
Rolling Bearing ◽  
Surface Waviness ◽  
Traction Motor ◽  
Integer Multiple ◽  
Gear Mesh ◽  
Motor Effects ◽  
Motor Bearing

AbstractThe traction motor is the power source of the locomotive. If the surface waviness occurs on the races of the motor bearing, it will cause abnormal vibration and noise, accelerate fatigue and wear, and seriously affect the stability and safety of the traction power transmission. In this paper, an excitation model coupling the time-varying displacement and contact stiffness excitations is adopted to investigate the effect of the surface waviness of the motor bearing on the traction motor under the excitation from the locomotive-track coupled system. The detailed mechanical power transmission path and the internal/external excitations (e.g., wheel–rail interaction, gear mesh, and internal interactions of the rolling bearing) of the locomotive are comprehensively considered to provide accurate dynamic loads for the traction motor. Effects of the wavenumber and amplitude of the surface waviness on the traction motor and its neighbor components of the locomotive are investigated. The results indicate that controlling the amplitude of the waviness and avoiding the wavenumber being an integer multiple of the number of the rollers are helpful for reducing the abnormal vibration and noise of the traction motor.

Remaining life prognostics of rolling bearing based on relative features and multivariable support vector machine

2013 ◽  
Vol 227 (12) ◽  
pp. 2849-2860 ◽  
Author(s):  
Xuefeng Chen ◽  
Zhongjie Shen ◽  
Zhengjia He ◽  
Chuang Sun ◽  
Zhiwen Liu
Keyword(s):  
Support Vector Machine ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Small Samples ◽  
Maintenance Cost ◽  
Support Vector ◽  
Remaining Life ◽  
Univariate Time Series ◽  
Bearing Life ◽  
The Individual

Life prognostics are an important way to reduce production loss, save maintenance cost and avoid fatal machine breakdowns. Predicting the remaining life of rolling bearing with small samples is a challenge due to lack of enough condition monitoring data. This study proposes a novel prognostics model based on relative features and multivariable support vector machine to meet the challenge. Support vector machine is an effective prediction method for the small samples. However, it only focuses on the univariate time series prognosis and fails to predict the remaining life directly. So multivariable support vector machine is constructed for the life prognostics with many relative features, which are closely linked to the remaining life. Unlike the univariate support vector machine, multivariable support vector machine considers the influences among various variables and excavates the potential information of small samples as much as possible. Besides, relative root mean square with ineffectiveness of the individual difference is used to assess the bearing performance degradation and divided the stages of the whole bearing life. The simulation and run-to-failure experiments are carried out to validate the novel prognostics model. And the results demonstrate that multivariable support vector machine utilizes many kinds of useful information for the precise prediction with practical values.

scholarly journals Fatigue Life Analysis of Rolling Bearings Based on Quasistatic Modeling

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Wei Guo ◽  
Hongrui Cao ◽  
Zhengjia He ◽  
Laihao Yang
Keyword(s):  
Fatigue Life ◽  
Great Influence ◽  
Prediction Method ◽  
Coupling Model ◽  
Accelerated Life Test ◽  
Rolling Bearings ◽  
Accelerated Life ◽  
Bearing Life ◽  
Rolling Elements ◽  
Fatigue Life Analysis

Rolling bearings are widely used in aeroengine, machine tool spindles, locomotive wheelset, and so forth. Rolling bearings are usually the weakest components that influence the remaining life of the whole machine. In this paper, a fatigue life prediction method is proposed based on quasistatic modeling of rolling bearings. With consideration of radial centrifugal expansion and thermal deformations on the geometric displacement in the bearings, the Jones’ bearing model is updated, which can predict the contact angle, deformation, and load between rolling elements and bearing raceways more accurately. Based on Hertz contact theory and contact mechanics, the contact stress field between rolling elements and raceways is calculated. A coupling model of fatigue life and damage for rolling bearings is given and verified through accelerated life test. Afterwards, the variation of bearing life is investigated under different working conditions, that is, axial load, radial load, and rotational speed. The results suggested that the working condition had a great influence on fatigue life of bearing parts and the order in which the damage appears on bearing parts.

Efficiency of application of the phase-chronometric method and neurodiagnostics for monitoring the degradation of rolling bearings during operation

2020 ◽  
pp. 43-50
Author(s):  
A.S. Komshin ◽  
K.G. Potapov ◽  
V.I. Pronyakin ◽  
A.B. Syritskii
Keyword(s):  
Life Cycle ◽  
Wavelet Analysis ◽  
Metrological Support ◽  
Rolling Bearing ◽  
Technical Condition ◽  
Measurement Information ◽  
Rolling Bearings ◽  
Vibration Diagnostics ◽  
Bearing Life ◽  
Alternative Approach

The paper presents an alternative approach to metrological support and assessment of the technical condition of rolling bearings in operation. The analysis of existing approaches, including methods of vibration diagnostics, envelope analysis, wavelet analysis, etc. Considers the possibility of applying a phase-chronometric method for support on the basis of neurodiagnostics bearing life cycle on the basis of the unified format of measurement information. The possibility of diagnosing a rolling bearing when analyzing measurement information from the shaft and separator was evaluated.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

scholarly journals Fatigue Testing of Wearable Sensing Technologies: Issues and Opportunities

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4070
Author(s):  
Andrea Karen Persons ◽  
John E. Ball ◽  
Charles Freeman ◽  
David M. Macias ◽  
Chartrisa LaShan Simpson ◽  
...  
Keyword(s):  
Fatigue Life ◽  
Prediction Models ◽  
Fatigue Testing ◽  
Low Cycle Fatigue ◽  
Wearable Sensors ◽  
Fatigue Tests ◽  
Proof Of Concept ◽  
Cycle Fatigue ◽  
Wearable Sensing ◽  
Failure Data

Standards for the fatigue testing of wearable sensing technologies are lacking. The majority of published fatigue tests for wearable sensors are performed on proof-of-concept stretch sensors fabricated from a variety of materials. Due to their flexibility and stretchability, polymers are often used in the fabrication of wearable sensors. Other materials, including textiles, carbon nanotubes, graphene, and conductive metals or inks, may be used in conjunction with polymers to fabricate wearable sensors. Depending on the combination of the materials used, the fatigue behaviors of wearable sensors can vary. Additionally, fatigue testing methodologies for the sensors also vary, with most tests focusing only on the low-cycle fatigue (LCF) regime, and few sensors are cycled until failure or runout are achieved. Fatigue life predictions of wearable sensors are also lacking. These issues make direct comparisons of wearable sensors difficult. To facilitate direct comparisons of wearable sensors and to move proof-of-concept sensors from “bench to bedside,” fatigue testing standards should be established. Further, both high-cycle fatigue (HCF) and failure data are needed to determine the appropriateness in the use, modification, development, and validation of fatigue life prediction models and to further the understanding of how cracks initiate and propagate in wearable sensing technologies.

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