Race Of Bearing Research Articles

Bearing Fault Evaluation for Structural Health Monitoring, Fault Detection, Failure Prevention and Prognosis

In this work the two disciplines of condition based maintenance (CBM), structural health monitoring (SHM) and prognostics are described fault identification and estimation is an important and necessary step in condition based maintenance. In the present work, an experiment is carried out with a customized test setup where the seeded defects are introduced in the inner race and outer race of a radial ball bearing. The relationship between the acquired vibration data and their relation with the seeded defect is found in this paper. When experiment is performed on the test setup designed for Fault prediction, Analytical Wavelet Transform proved an effective tool for the analysis of vibration signal. In this work, AWT followed by the Power Spectral Density is implemented on vibration signals of a defective Radial Ball Bearing. After finding the fault, its location and its intensity Ball Bearing's remaining useful life is estimated.

A train bearing fault detection and diagnosis using acoustic emission

Article history: Received 6 September, 2015 Accepted 9 December 2015 Available online 9 December 2015 This paper provides a method of acoustic emission (AE) technique to detect a train bearing fault of tapered bearing unit (TBU). An approach is to utilize acoustic emission signals which were captured from piezoelectric transducer and processed using Fourier transform. The transformed signals may contain unique characteristic features relating to the various types of bearing faults. The experiments on different operating conditions were investigated and they corresponded to (a) a normal bearing and (b) outer race defect bearing. The result is promising for faulty bearing identification and discrimination between different bearing conditions. © 2016 Growing Science Ltd. All rights reserved.

Detection of Combined Gear-Bearing Fault in Single Stage Spur Gear Box Using Artificial Neural Network

Gears and bearings are important components of almost every machines used in industrial environment. Hence detection of defect in any of these must be detected in advance to avoid catastrophic failure. This paper aims to address the effect of bearing defect on gear vibration signature and effect gear defect on bearing vibration signature. Also its purpose is to make vibration analysis of single stage spur gear box, when both gear and bearing are defective. A condition monitoring set up is designed for analyzing the defect in outer race of bearing and damaged tooth of gear. MATLAB is used for feature extraction and neural network is used for diagnosis. In the literature, many authors have analyzed defects in bearings and gears separately. But it is found that the real situation may be more complex. The work presents a laboratory investigation carried out through an experimental set-up for the study of combined gear –bearing fault. This paper proposes a novel approach of damage detection in which defects in multiple components are analyzed using vibration signal.

Failure Analysis of Ball-Bearing of Turbo-Pump Used in Liquid Rocket Engine

Rolling contact bearings often referred as antifriction bearings are found in many applications where rotating components are involved. Extreme operating conditions, counterfeit or inferior quality bearings, inadequate lubrication, etc often lead to bearing failures. Each rolling contact bearing failure leaves traces of evidences on what caused its failure. Failure analysis of one such bearing is the subject of this paper.One of the engine ground tests was aborted due to the failure of a self-aligning deep groove ball bearing used in turbo-pump of a liquid rocket engine. Series of engine ground tests are conducted as a part of injector acceptance for a liquid rocket engine. The bearing failure occurred in the seventh engine test of one of such series of ground tests. The bearing mentioned here is made of SAE 52100 steel. Post-test hardware inspection revealed that the bearing and the dynamic seals on either side of the bearing are in failed condition. Inner race of bearing was found blackish in appearance with indentation and corrosion marks. Localized black spots were observed in outer race. Bearing balls were found in deformed condition. The paper highlights the failure analysis of the bearing, root cause of the bearing failure and recommendations to avoid such failures.

Evaluation of the Period of Initiation of Fatigue Microcracks on the Race of an Axle Bearing

We propose a numerical method of evaluation of the period of initiation of a fatigue microcrack at the tip of a surface cavity-like defect in the race of an axle bearing of the locomotive. In this method, we use a model based on the energy approach of fracture mechanics. To find the required mechanical characteristics of the material of the racer of an axle bearing, we perform static and fatigue tests of specimens of ShKh15 steel. It is shown that if the surface cavity-like defect is located in the most loaded place of the race, then a fatigue microcrack can be initiated near its smooth tip for a relatively short period of operation of the bearing.

Влияние осевых деформаций на отклонение от плоскостности торцовой поверхности кольца подшипника при шлифовании

Влияние осевых деформаций на отклонение от плоскостности торцовой поверхности кольца подшипника при шлифовании

SVD principle analysis and fault diagnosis for bearings based on the correlation coefficient

Aiming at solving the existing sharp problems by using singular value decomposition (SVD) in the fault diagnosis of rolling bearings, such as the determination of the delay step k for creating the Hankel matrix and selection of effective singular values, the present study proposes a novel adaptive SVD method for fault feature detection based on the correlation coefficient by analyzing the principles of the SVD method. This proposed method achieves not only the optimal determination of the delay step k by means of the absolute value of the autocorrelation function sequence of the collected vibration signal, but also the adaptive selection of effective singular values using the index corresponding to useful component signals including weak fault information to detect weak fault signals for rolling bearings, especially weak impulse signals. The effectiveness of this method has been verified by contrastive results between the proposed method and traditional SVD, even using the wavelet-based method through simulated experiments. Finally, the proposed method has been applied to fault diagnosis for a deep-groove ball bearing in which a single point fault located on either the inner or outer race of rolling bearings is obtained successfully. Therefore, it can be stated that the proposed method is of great practical value in engineering applications.

Auto-Eliminating Clearance Auxiliary Bearing Device for Active Magnetic Bearing Systems

An auto-eliminating clearance auxiliary bearing device (ACABD) for active magnetic bearings (AMBs) has recently been designed and a prototype system was tested in this study. ACABD mainly consists of a series of connected tilting supports. In this design, a ball bearing is directly mounted on a rotor as the auxiliary bearing in an AMB system. The outer race of this ball bearing is surrounded by these supports. In the open position, there exists an initial protective clearance between the outer race of the ball bearing and the tilting supports. When the bearing-mounted rotor drops on these supports due to an AMB failure, the friction between the outer race of the ball bearing and the tilting support rotates these supports to eliminate the clearance and recenter the rotor. Due to elimination of the clearance, the possibility of backward whirl of the rotor is eliminated. The movement mechanism of this device is analyzed kinematically. The rotation of these supports, elimination of the protective clearance, as well as the rotor rotating stability after the clearance elimination are discussed from the perspective of statics. Furthermore, a preliminary prototype experimental rig was developed to demonstrate the feasibility of the ACABD after AMB system failure. Experimental results show that, after the AMB system failed, the ACABD immediately eliminated the clearance between the outer race of the ball bearing and the supports and pushed the rotor center close to the initial position.

Structural Health Monitoring of Rotating Machines in Manufacturing Processes by Vibration Methods

The paper presents results of the active diagnostics experiments on influence of fatigue metal damage of the inner race of bearing and unbalance of rotating masses on vibration generated by the machine. Analysis of vibration related phenomena is a solution commonly applied in Structural Health Monitoring (SHM) systems. The application of vibroacoustics methods for technical condition monitoring has been developed in the past years in many systems of manufacturing processes. Vibroacoustic methods, based on the analysis of vibration or acoustic signals perceived as residual processes of non-invasive nature, is becoming more and more important in this respect. The scope of its application as well as the applicability of methods in numerous diagnostic systems also results from the capabilities of advanced methods of signal analysis and identification of numerous characteristics of technical condition. One of the most common operation damages are caused by rolling bearings wear. The scope of research contains tests on bearing damage and the unbalance of disc. The wear processes and unbalance are closely related to the vibration levels (arising from the mass loss of plastic deformation, and the fatigue damage). The research was conducted on special research test bench for vibration monitoring for rotating machinery. Structural health monitoring of machinery has to be conducted in different states and working conditions of the manufacturing system. Thus for simulating of different operating conditions the experiments have been conducted during run up of the machine which consist the transient states of working and during work on constant rotational speed of the power generate engine. For the identification of the symptoms of the machinery and equipments health monitoring the vibration signal have been analysed in time domain and frequency transformation as well. The performed signals are not stationary. Thus it is better to observe the signal simultaneously in time and frequency domains. For this purpose the spectrograms were determined. Spectrograms computes the short-time Fourier transform of a signal by default divided into segments. During the transformation the Hamming window and noverlap were used. For the comparison of the vibration of good and damage bearings signals registered for different frequencies have been presented in form of spectrograms and RMS distributions.

2.5MW 풍력발전기 기어박스 동특성 연구

In this study, a gearbox and blade were modeled in the MASTA program, and the housing and carrier components were modeled using a finite element method. Using substructure synthesis, all the components were combined and used to establish a vibration model of a 2.5-MW wind turbine gearbox. In addition, the safety displacement factor was evaluated using an AGMA data sheet about bearing's outer race for the input shaft and output shaft. As a result, the bearing's outer race for the input shaft, and the radial and axial responses were satisfied by the <TEX>$1^{st}$</TEX> and <TEX>$2^{nd}$</TEX> planetary gears and the <TEX>$3^{nd}$</TEX> helical gear transmission error(TE), respectively. However, the output shaft support bearing's outer race responses were not satisfied with the radial response by the <TEX>$2^{nd}$</TEX> TE and axial response by the <TEX>$3^{rd}$</TEX> TE. To reduce the vibration, tooth modification was needed. After profile tooth modification, at the outer race of the output shaft support bearing, the radial response was reduced by approximately <TEX>$20{\mu}m$</TEX>, and the axial response was reduced by approximately <TEX>$6{\mu}m$</TEX>.

A Modified EEMD Decomposition for the Detection of Rolling Bearing Faults

The vibration signals of rolling element bearings are non-linear and non-stationary and the corresponding fault features are difficult to be extracted. EEMD (Ensemble empirical mode decomposition) is effective to detect bearing faults. In the present investigation, MEEMD (Modified EEMD) is presented to diagnose the outer and inner race faults of bearings. The original vibration signals are analyzed using IMFs (intrinsic mode functions) extracted by MEEMD decomposition and Hilbert spectrum in the proposed method. The numerical and experimental results of the comparison between MEEMD and EEMD indicate that the proposed method is more effective to extract the fault features of outer and inner race of bearings than EEMD.

Measurement of Forming Stresses in Plain Spherical Bearings Using Neutron Diffraction

Nosing is a cold metal-forming process used in the manufacture of plain spherical bearings. This process ensures the outer bearing race conforms to the shape of the inner race (a ball), with a composite liner in-between to provide a low frictional moment. These bearings must be precision engineered due to the large forces and demanding environments they operate within in service. The manufacture of these bearings and related process settings is very much an experiential route, although increasingly Finite Element simulations are used to predict and characterise complex material behaviour. It is imperative the numerical nosing models are validated against experimental measurements due to uncertainties in material properties, process variables and part manufacture variations. In this paper, neutron diffraction is used to determine the residual stresses in a large nosed bearing. Measurements were made on the POLDI instrument at PSI, Switzerland. This paper compares the predicted stresses with measurement results, and draws conclusions concerning the validity and usability of the models.

Stochastic Dynamic Model of the Vibration Signals of Rolling Bearing and their Analysis

On the basis of a stochastic dynamical model of a rolling bearing, presented in the form of a system of two nonlinear second-order differential equations, we carry out computer simulation and investigate the vertical and horizontal components of vibration. Using the methods of the statistics of periodically correlated random processes, we show that, in the case of damage appearing on the outer or inner races, vibrations acquire the properties of periodic nonstationarity. We also analyze the time variability of the estimates of expectation, describing the determinate components of vibrations, and the estimates of variances, determining the power of fluctuations. We present the dependences of correlation components, i.e., the Fourier coefficients of correlation functions on time shift. Finally, we substantiate the specific features of the structure of a periodically correlated random process, describing vertical and horizontal vibrations in the presence of defects on the outer and inner races of a rolling bearing.

Fault Size Estimation in the Outer Race of Ball Bearing Using Discrete Wavelet Transform of the Vibration Signal

Vibration signal analysis being one of the widely acceptable tools for bearing condition monitoring; the estimation of fault size from vibration signal is still a challenge and a subject of interest to the researchers. This work presents the decomposition of the vibration signal by using discrete wavelet transform assisted by sym5 wavelet. Symlet wavelet has a linear phase nature which maintains sharpness in the signal even when there is sudden change in the signal. The decomposed signal evidently splits the peak corresponding to the ball entry into and exit from the fault, enabling in an estimation of the defect size present in the bearing. Experiments conducted for different sizes of the defect present on the outer race of deep groove ball bearing affirm the efficacy of the applied technique for different vibration signals. The output of the proposed technique finds close correlation with the actual defect size measured from optical microscope with the maximum deviation in the result of 2.06%.

Wavelet and Scalar Indicator based Fault Assessment Approach for Rolling Element Bearings

A failed rolling element bearing when removed from a machine mostly displays cracks and flaking. Identification of such defects along with the severity of damage may avoid malfunctioning and breakdown of machines. The aim of this paper is to diagnose bearing faults and asses the defect severity using vibration signal processing techniques. For this purpose, application of spline wavelets in finding the existence of flaws and use of scalar indicator in determining the severity of defect from initial stage of degradation has been illustrated. Parameters of spline wavelets have been identified on the basis of visual similarity, correlation coefficient and maximum mutual entropy. Further, a new scalar indicator has been developed to quantify the level of defect in the bearing. Results of analysis yielded adequate fault related information along with defect severity at inner race, outer race and rolling element components of a deep groove ball bearing.

Vibrations Generated by Rolling Element Bearings having Multiple Local Defects on Races

Abstract The vibrations generated by deep groove ball bearings having multiple defects on races have been studied in this paper. The vibrations are analysed in both time and frequency domains. The equations for time delay between two or more successive impulses have been derived and validated with simulated and experimental results. The relationships between amplitudes of frequencies for impulse train, delayed impulse train and combination of two impulse trains have been established. Frequency spectra for single and two defects on either race of deep groove ball bearings are compared. No additional frequencies due to time delay between successive impulses are observed in case of multiple defects. The frequency spectra do not provide any information about number of defects; however this information is found in time domain analysis.

Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms

This study presents a fault detection of roller bearings through signal processing and optimization techniques. After the occurrence of scratch-type defects on the inner race of bearings, variations of kurtosis values are investigated in terms of two different data processing techniques: minimum entropy deconvolution (MED), and the Teager-Kaiser Energy Operator (TKEO). MED and the TKEO are employed to qualitatively enhance the discrimination of defect-induced repeating peaks on bearing vibration data with measurement noise. Given the perspective of the execution sequence of MED and the TKEO, the study found that the kurtosis sensitivity towards a defect on bearings could be highly improved. Also, the vibration signal from both healthy and damaged bearings is decomposed into multiple intrinsic mode functions (IMFs), through empirical mode decomposition (EMD). The weight vectors of IMFs become design variables for a genetic algorithm (GA). The weights of each IMF can be optimized through the genetic algorithm, to enhance the sensitivity of kurtosis on damaged bearing signals. Experimental results show that the EMD-GA approach successfully improved the resolution of detectability between a roller bearing with defect, and an intact system.

Thrust bearing groove race defect measurement by wavelet decomposition of pre-processed vibration signal

Nature of vibration signal from a ball bearing system reveals location of fault and its severity. A proper signal processing technique is required to extract relevant information from a vibration signal. In this work measurement of groove race defect width in thrust bearing is presented by processing vibration signal using wavelet based technique. In the first step, pre-processing of vibration signal is done by multiplying the signal amplitude with its own absolute values. It helps to overcome the problem of detection of weak burst in normal vibration signal retaining sign of the original signal. In the second step, Symlet5 wavelet based decomposition is applied to the pre-processed signal to measure the size of seeded defect in outer groove race of thrust bearing. The decomposition based on Symlet wavelet is preferred because it reduces the ambiguity in detecting the entry point and exit point of the defect as it is having linear phase in nature. Measurement has been done for the groove defect width over the range of 0.4399–1.4854mm. Measurement of defect has been also verified using image examination. Maximum deviation in result has been found in the cases which have minimum axial load (48N) for every size of defect. With increase in axial load to 73N and further to 98N the bearing system gets properly stressed, which increases the accuracy of measurement. With further increase in axial load to 126N and above, it has been observed that the burst disappears due to prevailing over-stressed condition of the loading disk and additional stiffness and damping of the flexible shaft at increased load.

Order Vibration Analysis of High-Speed Spindle System

Before the working speed of high-speed spindle system reaches the critical speed, violent vibration which directly affects the quality of the work piece has been already discovered. To precisely study the speed at which violent vibration happens and the modes of vibration, this paper provided an order vibration approach through studying the model analysis and order of spindle system. The nature frequencies of model analysis were verified and the order of spindle system was confirmed by speed ascend experiment. The result shows that the violent vibration is caused by the manufacturing error of outer ring race of bearing and the mode of vibration mainly located on the grinding wheel which greatly affects the accuracy of spindle system. It is useful for optimizing the structure and operation procedure of high-speed spindle system.

A two-dimensional isotropic spline filter

Abstract The work deals with the development of a two-dimensional isotropic spline filter with application to the separation of components of the surface topography, i.e. roughness, waviness and form. An appropriate variational problem was formulated to define the mapping of primary surface data into filtered surface data with a transfer function of a circularly symmetric low-pass Butterworth filter of a given order. The variational problem was solved by approximating the filtered surface data by means of two-dimensional B-spline functions. The Fourier transform of the filter impulse response was determined to estimate the quality of approximation of the Butterworth filter and to select the appropriate filter cutoff. The paper presents one application of the designed filter to determine 3D roughness of an inner ring race of a ball bearing.