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.

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 Rolling Element Bearing Fault Diagnosis Based on Multiscale General Fractal Features

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Weigang Wen ◽  
Zhaoyan Fan ◽  
Donald Karg ◽  
Weidong Cheng
Keyword(s):  
Fault Diagnosis ◽  
Fractal Dimensions ◽  
Feature Space ◽  
Operating Conditions ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Vibration Signals ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Element Bearing

Nonlinear characteristics are ubiquitous in the vibration signals produced by rolling element bearings. Fractal dimensions are effective tools to illustrate nonlinearity. This paper proposes a new approach based on Multiscale General Fractal Dimensions (MGFDs) to realize fault diagnosis of rolling element bearings, which are robust to the effects of variation in operating conditions. The vibration signals of bearing are analyzed to extract the general fractal dimensions in multiscales, which are in turn utilized to construct a feature space to identify fault pattern. Finally, bearing faults are revealed by pattern recognition. Case studies are carried out to evaluate the validity and accuracy of the approach. It is verified that this approach is effective for fault diagnosis of rolling element bearings under various operating conditions via experiment and data analysis.

On-Line Tracking and Monitoring of Rolling Element Bearing Faults

2014 ◽  
Author(s):  
S. Chatterton ◽  
P. Borghesani ◽  
P. Pennacchi ◽  
A. Vania
Keyword(s):  
Real Time ◽  
Damage Index ◽  
Vibration Signal ◽  
Operating Conditions ◽  
Rolling Element Bearing ◽  
Simple Task ◽  
Alarm Signaling ◽  
Rolling Element ◽  
On Line ◽  
Element Bearing

Diagnostics of rolling element bearings is usually performed by the analysis of vibration signal using suitable signal analysis tools, such as the most used and simplest method, Envelope Analysis. This method is based on the identification of bearing damage frequency components in the so-called Square Envelope Spectrum. If the assessment of the bearing health is quite a simple task, the on-line monitoring and the real-time evaluation of the trend of a suitable damage index is a complex task to be performed in an automatic way. The damage index must be robust against variations of system operating conditions and external vibration sources to avoid misleading results. The damage index should be also simple to be evaluated in the case of real-time applications. In the paper, the case of a rolling element bearing in which the defect develops until a permanent failure is described as well as the algorithm implemented for alarm signaling.

scholarly journals Improved Envelope Spectrum via Feature Optimisation-gram (IESFOgram): A novel tool for rolling element bearing diagnostics under non-stationary operating conditions

2020 ◽  
Vol 144 ◽  
pp. 106891 ◽  
Author(s):  
Alexandre Mauricio ◽  
Wade A. Smith ◽  
Robert B. Randall ◽  
Jerome Antoni ◽  
Konstantinos Gryllias
Keyword(s):  
Operating Conditions ◽  
Rolling Element Bearing ◽  
Bearing Diagnostics ◽  
Rolling Element ◽  
Element Bearing ◽  
Envelope Spectrum

An overview of dynamic modeling of rolling-element bearings

2020 ◽  
pp. 095745652094827
Author(s):  
Surajkumar G Kumbhar ◽  
Edwin Sudhagar P ◽  
RG Desavale
Keyword(s):  
Dynamic Behavior ◽  
Dynamic Modeling ◽  
Operating Conditions ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Roller Bearings ◽  
Rolling Element ◽  
Vibration Responses ◽  
Vibration Signature ◽  
Element Bearing

The marvelous uniqueness of vibration responses of faulty roller bearings can be simply observed through its vibration signature. Therefore, vibration analysis has been claimed as an effective tool not only for primitive detection but also for subsequent analysis. The dynamic behavior of roller bearings has been investigated by systematic modeling of system and its validation under diverse operating conditions. This article presents an overview of imperative marks in the development of dynamic modeling of rolling-element bearing, which especially predicted vibration responses of damaged bearings. This study aims to address dimensional analysis; a new and imperative way to model the dynamic behavior of rolling-element bearings and their real-time performance in a rotor-bearing system. The findings are described with influential advantages over earlier research to pinpoint the intention behind its development. A literature summary is trailed by remarkable findings and future directions for research.

Rolling element bearing multi-fault diagnosis using morphological joint time–frequency adaptive kernel–based semi-smart framework

2021 ◽  
pp. 107754632110228
Author(s):  
Sunil Lonare ◽  
Neville Fernandes ◽  
Aditya Abhyankar
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Fault Location ◽  
Operating Conditions ◽  
Experimental Results ◽  
Rolling Element Bearing ◽  
Time Frequency ◽  
Rolling Element ◽  
Adaptive Kernel ◽  
Element Bearing

The wavelet transform is a state of the art time–frequency analysis method for rolling element bearing localized fault detection, using vibration signals. When these localized faults are present at more than one location of bearing, it is called “multi-fault.” Using wavelet transform fault detection with high severity is possible, but this method fails to detect the presence of fault as well as the location of a fault in multi-fault case and when the fault severity is low. The identification of the fault location, in rolling element bearing when more than one location of bearing contains a localized fault, is very useful for further root cause analysis; therefore, multi-fault detection is a challenge today. In the present work, a new morphological joint time–frequency adaptive kernel–based semi-smart framework is developed to address this challenge. In morphological joint time–frequency adaptive kernel, the kernel will adapt itself by analyzing the basic morphology of the bearing under observation and by considering the location of a fault. The simulation and experimental results show that morphological joint time–frequency adaptive kernel–based framework is able to detect low severity single fault as well as the location of the localized fault on rolling element bearing in the multi-fault case. Experimental results also show that the morphological joint time–frequency adaptive kernel framework is independent of bearing dimensions as well as machine operating conditions.

scholarly journals Detection of Rolling-Element Bearing Faults in Non-stationary Quasi-Parallel Machinery Using Residual Analysis Augmented by Neural Networks

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Dustin Helm ◽  
Markus Timusk
Keyword(s):  
Fault Detection ◽  
Operating Conditions ◽  
Rolling Element Bearing ◽  
Detection Scheme ◽  
Bearing Faults ◽  
Feature Vectors ◽  
Gear Mesh ◽  
Rolling Element ◽  
Element Bearing ◽  
The Relationship

This work proposes a methodology for the detection of rolling-element bearing faults in quasi-parallel machinery. In the context of this work, parallel machinery is considered to be any group of identical components of a mechanical system that are linked to operate on the same duty cycle.  Quasi-parallel machinery can further be defined as two components not identical mechanically, but their operating conditions are correlated and they operate in the same environmental conditions. Furthermore, a new fault detection architecture is proposed wherein a feed-forward neural network (FFNN) is utilized to identify the relationship between signals. The proposed technique is based on the analysis of a calculated residual between feature vectors from two separate components. This technique is designed to reduce the effects of changes in the machines operating state on the condition monitoring system. When a fault detection system is monitoring multiple components in a larger system that are mechanically linked, signals and information that can be gleaned from the system can be used to reduce influences from factors that are not related to condition. The FFNN is used to identify the relationship between the feature vectors from two quasi-parallel components and eliminate the difference when no fault is present. The proposed method is tested on vibration data from two gearboxes that are connected in series. The gearboxes contain bearings operating at different speeds and gear mesh frequencies. In these conditions, a variety of rolling-element bearing faults are detected. The results indicate that improvement in fault detection accuracy can be achieved by using the additional information available from the quasi-parallel machine. The proposed method is directly compared to a typical AANN novelty detection scheme.

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