The importance of bearing stiffness and load when estimating the size of a defect in a rolling element bearing

2018 ◽  
Vol 18 (5-6) ◽  
pp. 1527-1542 ◽  
Author(s):  
Francesco Larizza ◽  
Alireza Moazen-Ahmadi ◽  
Carl Q Howard ◽  
Steven Grainger
Keyword(s):  
Large Data ◽  
Rolling Element Bearing ◽  
Static Stiffness ◽  
Data Set ◽  
Rolling Element ◽  
Bearing Stiffness ◽  
Bearing Assembly ◽  
Element Bearing ◽  
Vibration Signatures ◽  
Force Displacement

The change in the static stiffness of a bearing assembly is an important discriminator when determining the size of a defect in a rolling element bearing. In this article, the force–displacement relationships for defective bearings under various static radial loadings at various cage angular positions are analytically estimated and experimentally measured and analyzed. The study shows that the applied load has a significant effect on the static stiffness variations in defective rolling element bearings. The experimental measurements of the effect of the defect size on the varying stiffness of the bearing assembly, which has not been shown previously, provides valuable knowledge for developing methods to distinguish between defective bearings with defects that are smaller or larger than one angular ball spacing. The methods and results presented here contribute to the wider experimental investigation of the effects of loadings on the varying static stiffness of defective bearings and its effects on the measured vibration signatures. A large data set was obtained and has been made publicly available.

scholarly journals Unbalance Response Prediction for Rotors on Ball Bearings Using Speed- and Load-Dependent Nonlinear Bearing Stiffness

2005 ◽  
Vol 2005 (1) ◽  
pp. 53-59 ◽  
Author(s):  
David P. Fleming ◽  
J. V. Poplawski
Keyword(s):  
Response Prediction ◽  
Rolling Element Bearing ◽  
Response Analysis ◽  
Ball Bearings ◽  
Moderate Amount ◽  
Constant Stiffness ◽  
Unbalance Response ◽  
Rolling Element ◽  
Bearing Stiffness ◽  
Element Bearing

Rolling-element bearing forces vary nonlinearly with bearing deflection. Thus, an accurate rotordynamic analysis requires that bearing forces corresponding to the actual bearing deflection be utilized. For this work, bearing forces were calculated by COBRA-AHS, a recently developed rolling-element bearing analysis code. Bearing stiffness was found to be a strong function of bearing deflection, with higher deflection producing markedly higher stiffness. Curves fitted to the bearing data for a range of speeds and loads were supplied to a flexible rotor unbalance response analysis. The rotordynamic analysis showed that vibration response varied nonlinearly with the amount of rotor imbalance. Moreover, the increase in stiffness as critical speeds were approached caused a large increase in rotor and bearing vibration amplitude over part of the speed range compared to the case of constant-stiffness bearings. Regions of bistable operation were possible, in which the amplitude at a given speed was much larger during rotor acceleration than during deceleration. A moderate amount of damping will eliminate the bistable region, but this damping is not inherent in ball bearings.

Investigation of the Gas Flow in the Rolling Element Bearing Assembly of a Centrifugal Compressor

2005 ◽  
Author(s):  
Michael M. Cui
Keyword(s):  
Heat Transfer ◽  
Flow Field ◽  
Centrifugal Compressor ◽  
Gas Flow ◽  
Operating Conditions ◽  
Rolling Element Bearing ◽  
Rolling Element ◽  
Flow Through ◽  
Bearing Assembly ◽  
Element Bearing

Combined with the geometric features, the pressure differential and bearing motion define the gas flow through the rolling-element-bearing assembly of a centrifugal compressor. The gas flow field then affects the oil distribution and heat transfer characteristics of the assembly accordingly. Investigations of the refrigerant gas flow through the rolling element bearing assembly of a centrifugal compressor are presented. A series of cases are studied for different operating conditions. The analyses include the geometric details of the assembly, such as the shaft, races, cages, balls, oil feeding system, and surrounding components. Refrigerant R123 is used as the working fluid. Both detailed three-dimensional flow field features and integrated parameters are calculated. The interactions between bearing motion and the surrounding structures are characterized. The flow patterns inside the bearings are defined. These results help us gain an insight into the basic physics that governs the bearing internal mass and heat transfer. The data and techniques developed can be used to design and optimize bearing and oil supply systems for the improvement of lubrication and cooling efficiency.

scholarly journals Transient Vibration Prediction for Rotors on Ball Bearings Using Load-Dependent Nonlinear Bearing Stiffness

2004 ◽  
Vol 10 (6) ◽  
pp. 489-494 ◽  
Author(s):  
David P. Fleming ◽  
J. V. Poplawski
Keyword(s):  
High Speed ◽  
Roller Bearing ◽  
Transient Behavior ◽  
Rolling Element Bearing ◽  
Ball Bearings ◽  
Transient Vibration ◽  
Vibration Prediction ◽  
Rolling Element ◽  
Bearing Stiffness ◽  
Element Bearing

Rolling-element bearing forces vary nonlinearly with bearing deflection. Thus an accurate rotordynamic transient analysis requires bearing forces to be determined at each step of the transient solution. Analyses have been carried out to show the effect of accurate bearing transient forces (accounting for nonlinear speed and load-dependent bearing stiffness) as compared to conventional use of average rolling-element bearing stiffness. Bearing forces were calculated by COBRA-AHS (Computer Optimized Ball and Roller Bearing Analysis—Advanced High Speed) and supplied to the rotordynamics code ARDS (Analysis of Rotor Dynamic Systems) for accurate simulation of rotor transient behavior. COBRA-AHS is a fast-running five degree-of-freedom computer code able to calculate high speed rolling-element bearing load-displacement data for radial and angular contact ball bearings and also for cylindrical and tapered roller bearings. Results show that use of nonlinear bearing characteristics is essential for accurate prediction of rotordynamic behavior.

scholarly journals NEW APPROACH OF CLASSIFICATION OF ROLLING ELEMENT BEARING FAULT USING ARTIFICIAL NEURAL NETWORK

1970 ◽  
Vol 40 (2) ◽  
pp. 119-130 ◽  
Author(s):  
V. Hariharan ◽  
PSS. Srinivasan
Keyword(s):  
Neural Network ◽  
Time Domain ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Data Set ◽  
New Approach ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing

The paper presents a new approach to the classification of rolling element bearing faults by implementing Artificial Neural Network. Diagnostics of rolling element bearing faults actually represents the problem of pattern classification and recognition, where the key step is feature extraction from the vibration signal. Characterization of each recorded vibration signal is performed by a combination of signal's time-varying statistical parameters and characteristic rolling element bearing fault frequency components obtained through the frequency spectrum analysis method. The experimental data is collected for four bearings at three different speeds. The sensor is located at three different positions for each bearing. Both time domain and frequency domain signals were measured. Thus the data was three time spectrums and three frequency spectrums for each speed for a bearing. The entire data set comprised of 72 (6 x 3 x 4) data. The time domain signal was comprised of 8192 samples and extracting these features from a huge data set was difficult. To overcome this difficulty the 8192 samples were split into 32 bins each containing 256 samples. Two Network RBFN and PNN are used to classify the bearing defects. The entire process of splitting and evaluating the seven features was coded in MATLAB.  From these seven features the most suitable features are for explaining the intensity of the defect is discussed.Key Words: Feature Extraction; Fault Frequencies; Roller Bearing; Bearing fault; Crest Factor; Variant;Radial Basis Function Network (RBFN); Probabilistic Neural Network (PNN)DOI: 10.3329/jme.v40i2.5353Journal of Mechanical Engineering, Vol. ME 40, No. 2, December 2009 119-130

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