Rolling bearings � Noise testing of rolling bearing greases

2020 ◽  
Keyword(s):  
Rolling Bearing ◽  
Rolling Bearings

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.

The Effect of the EHD Pressure Spike on Rolling Bearing Fatigue

1987 ◽  
Vol 109 (3) ◽  
pp. 444-450 ◽  
Author(s):  
L. Houpert ◽  
E. Ioannides ◽  
J. C. Kuypers ◽  
J. Tripp
Keyword(s):  
Pressure Distribution ◽  
High Stress ◽  
Surface Damage ◽  
Rolling Bearing ◽  
Shear Stresses ◽  
Stress Concentrations ◽  
Surface Layers ◽  
Rolling Bearings ◽  
Life Model ◽  
Pressure Spike

A recently proposed fatigue life model for rolling bearings has been applied to the study of lifetime reduction under conditions conducive to microspalling. The presence of a spike in the EHD pressure distribution produces large shear stresses localized very close to the surface which may account for early failure. This paper describes a parametric study of the effect of such spikes. Accurate stress fields in the volume are calculated for simulated pressure spikes of different height, width and position relative to a Hertzian pressure distribution, as well as for different lubricant traction coefficients and film thicknesses. Despite the high stress concentrations in the surface layers, reductions in life predicted by the model are modest. Typically, the pressure spike may halve the life, with the implication that subsurface fatigue still dominates. In corroboration of this prediction, preliminary experimental work designed to reproduce microspalling conditions shows that microindents due to overrolling particles are a much more common form of surface damage than microspalling.

Contribution to Rolling Bearing Reliability Modeling

2011 ◽  
Vol 110-116 ◽  
pp. 2497-2503 ◽  
Author(s):  
Zdenek Vintr ◽  
Michal Vintr
Keyword(s):  
Operating Time ◽  
Rolling Bearing ◽  
Time To Failure ◽  
Rolling Bearings ◽  
Mean Time To Failure ◽  
Reliability Modeling ◽  
Time Between Failures ◽  
Large Groups ◽  
Mean Time

Rolling bearings are usually considered to be non-repaired items the reliability of which is characterized by mean time to failure, or so called basic rating life. Reliability describes these parameters well in case the bearings are used in operation up to the very time the failure occurs, or during the time corresponding with basic rating life. In case of railway applications the bearings are often used in large groups and are preventively replaced after much shorter operating time as compared with their basic rating life. In the article there is a model which enables us to describe the bearings reliability in this specific case and to specify a number of failures which might be expected from a group of bearings during operating time, or to determine mean operating time between failures of bearings.

Rolling Bearing Parametric Excitation of a Jeffcott Rotor System

2020 ◽  
Author(s):  
Ghasem Ghannad Tehrani ◽  
Chiara Gastaldi ◽  
Teresa Maria Berruti
Keyword(s):  
Parametric Excitation ◽  
Rotational Speed ◽  
Input Parameter ◽  
Equations Of Motion ◽  
Harmonic Balance Method ◽  
Rolling Bearing ◽  
Mean Value ◽  
Hertzian Contact ◽  
Rolling Bearings ◽  
Jeffcott Rotor

Abstract Rolling bearings are still widely used in aeroengines. Whenever rotors are modeled, rolling bearing components are typically modeled using springs. In simpler models, this spring is considered to have a constant mean value. However, the rolling bearing stiffness changes with time due to the positions of the balls with respect to the load on the bearing, thus giving rise to an internal excitation known as Parametric Excitation. Due to this parametric excitation, the rotor-bearings system may become unstable for specific combinations of boundary conditions (e.g. rotational speed) and system characteristics (rotor flexibility etc.). Being able to identify these instability regions at a glance is an important tool for the designer, as it allows to discard since the early design stages those configurations which may lead to catastrophic failures. In this paper, a Jeffcott rotor supported and excited by such rolling bearings is used as a demonstrator. In the first step, the expression for the time–varying stiffness of the bearings is analytically derived by applying the Hertzian Contact Theory. Then, the equations of motion of the complete system are provided. In this study, the Harmonic Balance Method (HBM) is used to as an approximate procedure to draw a stability map, thus dividing the input parameter space, i.e. rotational speed and rotor physical characteristics, into stable and unstable regions.

Analysis of the Behaviour of Rolling Bearings in Contaminated Oil Using Some Condition Monitoring Techniques

2006 ◽  
Vol 220 (5) ◽  
pp. 447-453 ◽  
Author(s):  
T Akagaki ◽  
M Nakamura ◽  
T Monzen ◽  
M Kawabata
Keyword(s):  
Vibration Analysis ◽  
Wear Debris ◽  
Friction And Wear ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Deep Groove ◽  
Deep Groove Ball Bearing ◽  
Observation Results ◽  
Wear Debris Analysis ◽  
Worn Surfaces

Friction and wear behaviours of rolling bearing in contaminated oil containing white-fused alumina particles were studied. The friction and wear processes were monitored using wear debris analysis, such as ferrography and spectrometric oil analysis program, and vibration analysis. Test bearing was a deep groove ball bearing (6002P5); Wear debris and worn surfaces of the bearing components were observed with a scanning electron microscope (SEM). It was found that the friction coefficient in the contaminated oil became lower by about 0.001 than that in the new oil for the large contaminants. The results of wear debris analysis showed that the large contaminants caused the high wear rate in the bearing. Three types of wear debris were commonly observed: thread-like debris, cutting chip debris, and plate-like debris. On the basis of the SEM observation results of the worn surfaces, wear mechanisms of these wear debris were discussed. The results of vibration analysis showed that the probability density function of vibration waveform was normal distribution in both the new and contaminated oils. In the contaminated oil, it changed depending on the contaminant size and the runtime, i.e. the progress of wear in the bearing. The result of wear debris analysis was related to that of vibration analysis and discussed.

scholarly journals Fault Diagnosis of Rolling Bearing Based on a Novel Adaptive High-Order Local Projection Denoising Method

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Rui Yuan ◽  
Yong Lv ◽  
Gangbing Song
Keyword(s):  
Fault Diagnosis ◽  
Numerical Simulations ◽  
Rolling Bearing ◽  
High Order ◽  
Rolling Bearings ◽  
Theoretical Derivation ◽  
Denoising Method ◽  
Vibration Signals ◽  
Local Projection ◽  
Ring Fault

Rolling bearings are vital components in rotary machinery, and their operating condition affects the entire mechanical systems. As one of the most important denoising methods for nonlinear systems, local projection (LP) denoising method can be used to reduce noise effectively. Afterwards, high-order polynomials are utilized to estimate the centroid of the neighborhood to better preserve complete geometry of attractors; thus, high-order local projection (HLP) can improve noise reduction performance. This paper proposed an adaptive high-order local projection (AHLP) denoising method in the field of fault diagnosis of rolling bearings to deal with different kinds of vibration signals of faulty rolling bearings. Optimal orders can be selected corresponding to vibration signals of outer ring fault (ORF) and inner ring fault (IRF) rolling bearings, because they have different nonlinear geometric structures. The vibration signal model of faulty rolling bearing is adopted in numerical simulations, and the characteristic frequencies of simulated signals can be well extracted by the proposed method. Furthermore, two kinds of experimental data have been processed in application researches, and fault frequencies of ORF and IRF rolling bearings can be both clearly extracted by the proposed method. The theoretical derivation, numerical simulations, and application research can indicate that the proposed novel approach is promising in the field of fault diagnosis of rolling bearing.

scholarly journals Research on Rolling Bearing Fault Feature Extraction and Diagnosis Method Based on Image Processing

2021 ◽  
Vol 5 (2) ◽  
pp. 1-7
Author(s):  
Sun Y
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Working Conditions ◽  
Rolling Bearing ◽  
Extraction Methods ◽  
Working Environment ◽  
Ring Rolling ◽  
Rolling Bearings ◽  
Binary Matrix ◽  
The Time Domain

In economic construction, there are many large and important machinery and equipment. Some equipment will continue to work in a harsh working environment, so many and various failures will occur. Rolling bearings are one of the widely used parts in rotating machinery. They are generally composed of inner ring, outer ring, rolling element and holding. The frame is composed of four parts, the failure of the bearing is particularly important, and its safe operation has a vital impact on the entire equipment, Feature extraction is the key link in the subsequent identification of fault types, Although feature extraction in the time domain and frequency domain is effective, it is also necessary to find new feature extraction methods in new areas. On the basis of the snowflake image obtained by using the principle of SDP(Symmetrized Dot Pattern), a method for extracting fault features of rolling bearings based on image processing is proposed, and the snowflake standard map for different working conditions is constructed. The number of snowflake images under different working conditions is different. The binary matrix of the test image is compared with it, and then classified and identified. Finally, the algorithm is validated, and the ideal result is obtained to verify its rationality and effectiveness.

scholarly journals A Denoising Autoencoder-Based Bearing Fault Diagnosis System for Time-Domain Vibration Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yi Gu ◽  
Jiawei Cao ◽  
Xin Song ◽  
Jian Yao
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Bp Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Novel Approach ◽  
Fault Diagnosis System ◽  
Original Time

The condition monitoring of rotating machinery is always a focus of intelligent fault diagnosis. In view of the traditional methods’ excessive dependence on prior knowledge to manually extract features, their limited capacity to learn complex nonlinear relations in fault signals and the mixing of the collected signals with environmental noise in the course of the work of rotating machines, this article proposes a novel approach for detecting the bearing fault, which is based on deep learning. To effectively detect, locate, and identify faults in rolling bearings, a stacked noise reduction autoencoder is utilized for abstracting characteristic from the original vibration of signals, and then, the characteristic is provided as input for backpropagation (BP) network classifier. The results output by this classifier represent different fault categories. Experimental results obtained on rolling bearing datasets show that this method can be used to effectively diagnose bearing faults based on original time-domain signals.

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